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euronews	--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - de - fr - nl license: - cc0-1.0 multilinguality: - multilingual size_categories: - n<1K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition paperswithcode_id: europeana-newspapers pretty_name: Europeana Newspapers dataset_info: - config_name: fr-bnf features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC splits: - name: train num_bytes: 3340299 num_examples: 1 download_size: 1542418 dataset_size: 3340299 - config_name: nl-kb features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC splits: - name: train num_bytes: 3104213 num_examples: 1 download_size: 1502162 dataset_size: 3104213 - config_name: de-sbb features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC splits: - name: train num_bytes: 817295 num_examples: 1 download_size: 407756 dataset_size: 817295 - config_name: de-onb features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC splits: - name: train num_bytes: 502369 num_examples: 1 download_size: 271252 dataset_size: 502369 - config_name: de-lft features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC splits: - name: train num_bytes: 1263429 num_examples: 1 download_size: 677779 dataset_size: 1263429 --- # Dataset Card for Europeana Newspapers ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Github](https://github.com/EuropeanaNewspapers/ner-corpora) - Repository: [Github](https://github.com/EuropeanaNewspapers/ner-corpora) - Paper: [Aclweb](https://www.aclweb.org/anthology/L16-1689/) - Leaderboard: - Point of Contact: ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@jplu](https://github.com/jplu) for adding this dataset.
europa_eac_tm	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - bg - cs - da - de - el - en - es - et - fi - fr - hr - hu - is - it - lt - lv - mt - nl - 'no' - pl - pt - ro - sk - sl - sv - tr license: - cc-by-4.0 multilinguality: - translation size_categories: - 1K<n<10K source_datasets: - original task_categories: - translation task_ids: [] pretty_name: Europa Education and Culture Translation Memory (EAC-TM) dataset_info: - config_name: en2bg features: - name: translation dtype: translation: languages: - en - bg - name: sentence_type dtype: class_label: names: '0': form_data '1': sentence_data splits: - name: train num_bytes: 664252 num_examples: 4061 download_size: 3521416 dataset_size: 664252 - config_name: en2cs features: - name: translation dtype: translation: languages: - en - cs - name: sentence_type dtype: class_label: names: '0': form_data '1': sentence_data splits: - name: train num_bytes: 365983 num_examples: 3351 download_size: 3521416 dataset_size: 365983 - config_name: en2da features: - name: translation dtype: translation: languages: - en - da - name: sentence_type dtype: class_label: names: '0': form_data '1': sentence_data splits: - name: train num_bytes: 422079 num_examples: 3757 download_size: 3521416 dataset_size: 422079 - config_name: en2de features: - name: translation dtype: translation: languages: - en - de - name: sentence_type dtype: class_label: names: '0': form_data '1': sentence_data splits: - name: train num_bytes: 579566 num_examples: 4473 download_size: 3521416 dataset_size: 579566 - config_name: en2el features: - name: translation dtype: translation: languages: - en - el - name: sentence_type dtype: class_label: names: '0': form_data '1': sentence_data splits: - name: train num_bytes: 491346 num_examples: 2818 download_size: 3521416 dataset_size: 491346 - config_name: en2es features: - name: translation dtype: translation: languages: - en - es - name: sentence_type dtype: class_label: names: '0': form_data '1': sentence_data splits: - name: train num_bytes: 555218 num_examples: 4303 download_size: 3521416 dataset_size: 555218 - config_name: en2et features: - name: translation dtype: translation: languages: - en - et - name: sentence_type dtype: class_label: names: '0': form_data '1': sentence_data splits: - name: train num_bytes: 247284 num_examples: 2270 download_size: 3521416 dataset_size: 247284 - config_name: en2fi features: - name: translation dtype: translation: languages: - en - fi - name: sentence_type dtype: class_label: names: '0': form_data '1': sentence_data splits: - name: train num_bytes: 150560 num_examples: 1458 download_size: 3521416 dataset_size: 150560 - config_name: en2fr features: - name: translation dtype: translation: languages: - en - fr - name: sentence_type dtype: class_label: names: '0': form_data '1': sentence_data splits: - name: train num_bytes: 575579 num_examples: 4476 download_size: 3521416 dataset_size: 575579 - config_name: en2hu features: - name: translation dtype: translation: languages: - en - hu - name: sentence_type dtype: class_label: names: '0': form_data '1': sentence_data splits: - name: train num_bytes: 454802 num_examples: 3455 download_size: 3521416 dataset_size: 454802 - config_name: en2is features: - name: translation dtype: translation: languages: - en - is - name: sentence_type dtype: class_label: names: '0': form_data '1': sentence_data splits: - name: train num_bytes: 268194 num_examples: 2206 download_size: 3521416 dataset_size: 268194 - config_name: en2it features: - name: translation dtype: translation: languages: - en - it - name: sentence_type dtype: class_label: names: '0': form_data '1': sentence_data splits: - name: train num_bytes: 270634 num_examples: 2170 download_size: 3521416 dataset_size: 270634 - config_name: en2lt features: - name: translation dtype: translation: languages: - en - lt - name: sentence_type dtype: class_label: names: '0': form_data '1': sentence_data splits: - name: train num_bytes: 358844 num_examples: 3386 download_size: 3521416 dataset_size: 358844 - config_name: en2lv features: - name: translation dtype: translation: languages: - en - lv - name: sentence_type dtype: class_label: names: '0': form_data '1': sentence_data splits: - name: train num_bytes: 437487 num_examples: 3880 download_size: 3521416 dataset_size: 437487 - config_name: en2mt features: - name: translation dtype: translation: languages: - en - mt - name: sentence_type dtype: class_label: names: '0': form_data '1': sentence_data splits: - name: train num_bytes: 178675 num_examples: 1722 download_size: 3521416 dataset_size: 178675 - config_name: en2nb features: - name: translation dtype: translation: languages: - en - nb - name: sentence_type dtype: class_label: names: '0': form_data '1': sentence_data splits: - name: train num_bytes: 85833 num_examples: 642 download_size: 3521416 dataset_size: 85833 - config_name: en2nl features: - name: translation dtype: translation: languages: - en - nl - name: sentence_type dtype: class_label: names: '0': form_data '1': sentence_data splits: - name: train num_bytes: 188531 num_examples: 1805 download_size: 3521416 dataset_size: 188531 - config_name: en2pl features: - name: translation dtype: translation: languages: - en - pl - name: sentence_type dtype: class_label: names: '0': form_data '1': sentence_data splits: - name: train num_bytes: 515976 num_examples: 4027 download_size: 3521416 dataset_size: 515976 - config_name: en2pt features: - name: translation dtype: translation: languages: - en - pt - name: sentence_type dtype: class_label: names: '0': form_data '1': sentence_data splits: - name: train num_bytes: 422125 num_examples: 3501 download_size: 3521416 dataset_size: 422125 - config_name: en2ro features: - name: translation dtype: translation: languages: - en - ro - name: sentence_type dtype: class_label: names: '0': form_data '1': sentence_data splits: - name: train num_bytes: 345468 num_examples: 3159 download_size: 3521416 dataset_size: 345468 - config_name: en2sk features: - name: translation dtype: translation: languages: - en - sk - name: sentence_type dtype: class_label: names: '0': form_data '1': sentence_data splits: - name: train num_bytes: 306049 num_examples: 2972 download_size: 3521416 dataset_size: 306049 - config_name: en2sl features: - name: translation dtype: translation: languages: - en - sl - name: sentence_type dtype: class_label: names: '0': form_data '1': sentence_data splits: - name: train num_bytes: 577524 num_examples: 4644 download_size: 3521416 dataset_size: 577524 - config_name: en2sv features: - name: translation dtype: translation: languages: - en - sv - name: sentence_type dtype: class_label: names: '0': form_data '1': sentence_data splits: - name: train num_bytes: 304954 num_examples: 2909 download_size: 3521416 dataset_size: 304954 - config_name: en2tr features: - name: translation dtype: translation: languages: - en - tr - name: sentence_type dtype: class_label: names: '0': form_data '1': sentence_data splits: - name: train num_bytes: 328267 num_examples: 3198 download_size: 3521416 dataset_size: 328267 --- # Dataset Card for Europa Education and Culture Translation Memory (EAC-TM) ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://ec.europa.eu/jrc/en/language-technologies/eac-translation-memory](https://ec.europa.eu/jrc/en/language-technologies/eac-translation-memory) - Paper: [https://link.springer.com/article/10.1007/s10579-014-9277-0](https://link.springer.com/article/10.1007/s10579-014-9277-0) - Point of Contact: [ralf.steinberg@jrc.ec.europa.eu](mailto:ralf.steinberg@jrc.ec.europa.eu) ### Dataset Summary This dataset is a corpus of manually produced translations from english to up to 25 languages, released in 2012 by the European Union's Directorate General for Education and Culture (EAC). To load a language pair that is not part of the config, just specify the language code as language pair. For example, if you want to translate Czech to Greek: `dataset = load_dataset("europa_eac_tm", language_pair=("cs", "el"))` ### Supported Tasks and Leaderboards - `text2text-generation`: the dataset can be used to train a model for `machine-translation`. Machine translation models are usually evaluated using metrics such as [BLEU](https://huggingface.co/metrics/bleu), [ROUGE](https://huggingface.co/metrics/rouge) or [SacreBLEU](https://huggingface.co/metrics/sacrebleu). You can use the [mBART](https://huggingface.co/facebook/mbart-large-cc25) model for this task. This task has active leaderboards which can be found at [https://paperswithcode.com/task/machine-translation](https://paperswithcode.com/task/machine-translation), which usually rank models based on [BLEU score](https://huggingface.co/metrics/bleu). ### Languages The sentences in this dataset were originally written in English (source language is English) and then translated into the other languages. The sentences are extracted from electroniv forms: application and report forms for decentralised actions of EAC's Life-long Learning Programme (LLP) and the Youth in Action Programme. The contents in the electronic forms are technically split into two types: (a) the labels and contents of drop-down menus (referred to as 'Forms' Data) and (b) checkboxes (referred to as 'Reference Data'). The dataset contains traduction of English sentences or parts of sentences to Bulgarian, Czech, Danish, Dutch, Estonian, German, Greek, Finnish, French, Croatian, Hungarian, Icelandic, Italian, Latvian, Lithuanian, Maltese, Norwegian, Polish, Portuguese, Romanian, Slovak, Slovenian, Spanish, Swedish and Turkish. Language codes: - `bg` - `cs` - `da` - `de` - `el` - `en` - `es` - `et` - `fi` - `fr` - `hr` - `hu` - `is` - `it` - `lt` - `lv` - `mt` - `nl` - `no` - `pl` - `pt` - `ro` - `sk` - `sl` - `sv` - `tr` ## Dataset Structure ### Data Instances ``` { "translation": { "en":"Sentence to translate", "<target_language>": "Phrase à traduire", }, "sentence_type": 0 } ``` ### Data Fields - `translation`: Mapping of sentences to translate (in English) and translated sentences. - `sentence_type`: Integer value, 0 if the sentence is a 'form data' (extracted from the labels and contents of drop-down menus of the source electronic forms) or 1 if the sentence is a 'reference data' (extracted from the electronic forms checkboxes). ### Data Splits The data is not splitted (only the `train` split is available). ## Dataset Creation ### Curation Rationale The EAC-TM is relatively small compared to the JRC-Acquis and to DGT-TM, but it has the advantage that it focuses on a very different domain, namely that of education and culture. Also, it includes translation units for the languages Croatian (HR), Icelandic (IS), Norwegian (Bokmål, NB or Norwegian, NO) and Turkish (TR). ### Source Data #### Initial Data Collection and Normalization EAC-TM was built in the context of translating electronic forms: application and report forms for decentralised actions of EAC's Life-long Learning Programme (LLP) and the Youth in Action Programme. All documents and sentences were originally written in English (source language is English) and then translated into the other languages. The contents in the electronic forms are technically split into two types: (a) the labels and contents of drop-down menus (referred to as 'Forms' Data) and (b) checkboxes (referred to as 'Reference Data'). Due to the different types of data, the two collections are kept separate. For example, labels can be 'Country', 'Please specify your home country' etc., while examples for reference data are 'Germany', 'Basic/general programmes', 'Education and Culture' etc. The data consists of translations carried out between the end of the year 2008 and July 2012. #### Who are the source language producers? The texts were translated by staff of the National Agencies of the Lifelong Learning and Youth in Action programmes. They are typically professionals in the field of education/youth and EU programmes. They are thus not professional translators, but they are normally native speakers of the target language. ### Annotations #### Annotation process Sentences were manually translated by humans. #### Who are the annotators? The texts were translated by staff of the National Agencies of the Lifelong Learning and Youth in Action programmes. They are typically professionals in the field of education/youth and EU programmes. They are thus not professional translators, but they are normally native speakers of the target language. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information © European Union, 1995-2020 The Commission's reuse policy is implemented by the [Commission Decision of 12 December 2011 on the reuse of Commission documents](https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32011D0833). Unless otherwise indicated (e.g. in individual copyright notices), content owned by the EU on this website is licensed under the [Creative Commons Attribution 4.0 International (CC BY 4.0) licence](http://creativecommons.org/licenses/by/4.0/). This means that reuse is allowed, provided appropriate credit is given and changes are indicated. You may be required to clear additional rights if a specific content depicts identifiable private individuals or includes third-party works. To use or reproduce content that is not owned by the EU, you may need to seek permission directly from the rightholders. Software or documents covered by industrial property rights, such as patents, trade marks, registered designs, logos and names, are excluded from the Commission's reuse policy and are not licensed to you. ### Citation Information ``` @Article{Steinberger2014, author={Steinberger, Ralf and Ebrahim, Mohamed and Poulis, Alexandros and Carrasco-Benitez, Manuel and Schl{\"u}ter, Patrick and Przybyszewski, Marek and Gilbro, Signe}, title={An overview of the European Union's highly multilingual parallel corpora}, journal={Language Resources and Evaluation}, year={2014}, month={Dec}, day={01}, volume={48}, number={4}, pages={679-707}, issn={1574-0218}, doi={10.1007/s10579-014-9277-0}, url={https://doi.org/10.1007/s10579-014-9277-0} } ``` ### Contributions Thanks to [@SBrandeis](https://github.com/SBrandeis) for adding this dataset.
europa_ecdc_tm	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - bg - cs - da - de - el - en - es - et - fi - fr - ga - hu - is - it - lt - lv - mt - nl - 'no' - pl - pt - ro - sk - sl - sv license: - cc-by-sa-4.0 multilinguality: - translation size_categories: - 1K<n<10K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: EuropaEcdcTm dataset_info: - config_name: en2bg features: - name: translation dtype: translation: languages: - en - bg splits: - name: train num_bytes: 798444 num_examples: 2567 download_size: 4286636 dataset_size: 798444 - config_name: en2cs features: - name: translation dtype: translation: languages: - en - cs splits: - name: train num_bytes: 585423 num_examples: 2562 download_size: 4286636 dataset_size: 585423 - config_name: en2da features: - name: translation dtype: translation: languages: - en - da splits: - name: train num_bytes: 545106 num_examples: 2577 download_size: 4286636 dataset_size: 545106 - config_name: en2de features: - name: translation dtype: translation: languages: - en - de splits: - name: train num_bytes: 588974 num_examples: 2560 download_size: 4286636 dataset_size: 588974 - config_name: en2el features: - name: translation dtype: translation: languages: - en - el splits: - name: train num_bytes: 849151 num_examples: 2530 download_size: 4286636 dataset_size: 849151 - config_name: en2es features: - name: translation dtype: translation: languages: - en - es splits: - name: train num_bytes: 582798 num_examples: 2564 download_size: 4286636 dataset_size: 582798 - config_name: en2et features: - name: translation dtype: translation: languages: - en - et splits: - name: train num_bytes: 543554 num_examples: 2581 download_size: 4286636 dataset_size: 543554 - config_name: en2fi features: - name: translation dtype: translation: languages: - en - fi splits: - name: train num_bytes: 573069 num_examples: 2617 download_size: 4286636 dataset_size: 573069 - config_name: en2fr features: - name: translation dtype: translation: languages: - en - fr splits: - name: train num_bytes: 595489 num_examples: 2561 download_size: 4286636 dataset_size: 595489 - config_name: en2ga features: - name: translation dtype: translation: languages: - en - ga splits: - name: train num_bytes: 286362 num_examples: 1356 download_size: 4286636 dataset_size: 286362 - config_name: en2hu features: - name: translation dtype: translation: languages: - en - hu splits: - name: train num_bytes: 600536 num_examples: 2571 download_size: 4286636 dataset_size: 600536 - config_name: en2is features: - name: translation dtype: translation: languages: - en - is splits: - name: train num_bytes: 557055 num_examples: 2511 download_size: 4286636 dataset_size: 557055 - config_name: en2it features: - name: translation dtype: translation: languages: - en - it splits: - name: train num_bytes: 576797 num_examples: 2534 download_size: 4286636 dataset_size: 576797 - config_name: en2lt features: - name: translation dtype: translation: languages: - en - lt splits: - name: train num_bytes: 645429 num_examples: 2545 download_size: 4286636 dataset_size: 645429 - config_name: en2lv features: - name: translation dtype: translation: languages: - en - lv splits: - name: train num_bytes: 576217 num_examples: 2542 download_size: 4286636 dataset_size: 576217 - config_name: en2mt features: - name: translation dtype: translation: languages: - en - mt splits: - name: train num_bytes: 608263 num_examples: 2539 download_size: 4286636 dataset_size: 608263 - config_name: en2nl features: - name: translation dtype: translation: languages: - en - nl splits: - name: train num_bytes: 569643 num_examples: 2510 download_size: 4286636 dataset_size: 569643 - config_name: en2no features: - name: translation dtype: translation: languages: - en - 'no' splits: - name: train num_bytes: 536725 num_examples: 2537 download_size: 4286636 dataset_size: 536725 - config_name: en2pl features: - name: translation dtype: translation: languages: - en - pl splits: - name: train num_bytes: 644402 num_examples: 2546 download_size: 4286636 dataset_size: 644402 - config_name: en2pt features: - name: translation dtype: translation: languages: - en - pt splits: - name: train num_bytes: 583638 num_examples: 2531 download_size: 4286636 dataset_size: 583638 - config_name: en2ro features: - name: translation dtype: translation: languages: - en - ro splits: - name: train num_bytes: 585159 num_examples: 2555 download_size: 4286636 dataset_size: 585159 - config_name: en2sk features: - name: translation dtype: translation: languages: - en - sk splits: - name: train num_bytes: 627797 num_examples: 2525 download_size: 4286636 dataset_size: 627797 - config_name: en2sl features: - name: translation dtype: translation: languages: - en - sl splits: - name: train num_bytes: 594027 num_examples: 2545 download_size: 4286636 dataset_size: 594027 - config_name: en2sv features: - name: translation dtype: translation: languages: - en - sv splits: - name: train num_bytes: 546349 num_examples: 2527 download_size: 4286636 dataset_size: 546349 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://ec.europa.eu/jrc/en/language-technologies/ecdc-translation-memory](https://ec.europa.eu/jrc/en/language-technologies/ecdc-translation-memory) - Paper: [https://link.springer.com/article/10.1007/s10579-014-9277-0](https://link.springer.com/article/10.1007/s10579-014-9277-0) - Point of Contact: [Ralf Steinberger](mailto:Ralf.Steinberger@jrc.ec.europa.eu) ### Dataset Summary In October 2012, the European Union (EU) agency 'European Centre for Disease Prevention and Control' (ECDC) released a translation memory (TM), i.e. a collection of sentences and their professionally produced translations, in twenty-five languages. ECDC-TM covers 25 languages: the 23 official languages of the EU plus Norwegian (Norsk) and Icelandic. ECDC-TM was created by translating from English into the following 24 languages: Bulgarian, Czech, Danish, Dutch, English, Estonian, Gaelige (Irish), German, Greek, Finnish, French, Hungarian, Icelandic, Italian, Latvian, Lithuanian, Maltese, Norwegian (NOrsk), Polish, Portuguese, Romanian, Slovak, Slovenian, Spanish and Swedish. All documents and sentences were originally written in English. They were then translated into the other languages by professional translators from the Translation Centre CdT in Luxembourg. To load a language pair that is not part of the config, just specify the language code as language pair. For example, if you want to translate Czech to Greek: `dataset = load_dataset("europa_ecdc_tm", language_pair=("cs", "el"))` ### Supported Tasks and Leaderboards - `text2text-generation`: the dataset can be used to train a model for `machine-translation`. Machine translation models are usually evaluated using metrics such as [BLEU](https://huggingface.co/metrics/bleu), [ROUGE](https://huggingface.co/metrics/rouge) or [SacreBLEU](https://huggingface.co/metrics/sacrebleu). You can use the [mBART](https://huggingface.co/facebook/mbart-large-cc25) model for this task. This task has active leaderboards which can be found at [https://paperswithcode.com/task/machine-translation](https://paperswithcode.com/task/machine-translation), which usually rank models based on [BLEU score](https://huggingface.co/metrics/bleu). ### Languages All documents and sentences were originally written in English (`en`). They were then translated into the other languages by professional translators from the Translation Centre CdT in Luxembourg. Translations are available in these languages: `en`, `bg`, `cs`, `da`, `de`, `el`, `en`, `es`, `et`, `fi`, `fr`, `ga`, `hu`, `is`, `it`, `lt`, `lv`, `mt`, `nl`, `no`, `pl`, `pt`, `ro`, `sk`, `sl`, `sv`. ## Dataset Structure ### Data Instances ``` { "translation": { "<source_language>":"Sentence to translate", "<target_language>": "Translated sentence", }, } ``` ### Data Fields - `translation`: a multilingual `string` variable, with possible languages including `en`, `bg`, `cs`, `da`, `de`, `el`, `en`, `es`, `et`, `fi`, `fr`, `ga`, `hu`, `is`, `it`, `lt`, `lv`, `mt`, `nl`, `no`, `pl`, `pt`, `ro`, `sk`, `sl`, `sv`. ### Data Splits The data is not splitted (only the `train` split is available). ## Dataset Creation ### Curation Rationale The ECDC-TM is relatively small compared to the JRC-Acquis and to DGT-TM, but it has the advantage that it focuses on a very different domain, namely that of public health. Also, it includes translation units for the languages Irish (Gaelige, GA), Norwegian (Norsk, NO) and Icelandic (IS). ### Source Data #### Initial Data Collection and Normalization ECDC-TM was built on the basis of the website of the European Centre for Disease Prevention and Control (ECDC). The major part of the documents talks about health-related topics (anthrax, botulism, cholera, dengue fever, hepatitis, etc.), but some of the web pages also describe the organisation ECDC (e.g. its organisation, job opportunities) and its activities (e.g. epidemic intelligence, surveillance). #### Who are the source language producers? All documents and sentences were originally written in English, by the ECDC website content producers. ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? All documents and sentences were thus originally written in English. They were then translated into the other languages by professional translators from the Translation Centre CdT in Luxembourg. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset Contains translations of sentences in the public healthcare domain, including technical terms (disease and treatment names for example). ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Copyright © EU / ECDC, 2020 #### Copyright The Work (as defined below) is provided under the terms of this Licence (or later versions of this Licence published by the European Commission). The work is protected by copyright and/or other applicable law. Any use of the work other than as authorised under this Licence or copyright law is prohibited. The terms provided herein conform to the reuse policy established by the Commission's Reuse Decision (2011/833/EU). By exercising any rights to the work provided here, you accept and agree to be bound by the terms of this Licence. The Owner (as defined below) grants You the rights conferred by this Licence in consideration of your acceptance of such terms and conditions. #### Definitions The ‘Owner’ shall mean jointly the European Union represented by the European Commission and the European Centre for Disease Prevention and Control, which are the original licensors and/or control the copyright and any other intellectual and industrial property rights related to the Work. 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It includes ‘re‐Use’, meaning the use, communication to the public and/or distribution of the Works for purposes other than the initial purpose for which the Work was produced. #### Rights You are herewith granted a worldwide, royalty‐free, perpetual, non‐exclusive Licence to Use and re‐Use the Works and any modifications thereof for any commercial and non‐ commercial purpose allowed by the law, provided that the following conditions are met: a) Unmodified distributions must retain the above Copyright Notice; b) Unmodified distributions must retain the following ‘No Warranty’ disclaimer; c) You will not use the name of the Owner to endorse or promote products and services derived from Use of the Work without specific prior written permission. #### No warranty Each Work is provided ‘as is’ without, to the full extent permitted by law, representations, warranties, obligations and liabilities of any kind, either express or implied, including, but not limited to, any implied warranty of merchantability, integration, satisfactory quality and fitness for a particular purpose. Except in the cases of wilful misconduct or damages directly caused to natural persons, the Owner will not be liable for any incidental, consequential, direct or indirect damages, including, but not limited to, the loss of data, lost profits or any other financial loss arising from the use of, or inability to use, the Work even if the Owner has been notified of the possibility of such loss, damages, claims or costs, or for any claim by any third party. The Owner may be liable under national statutory product liability laws as far as such laws apply to the Work. ### Citation Information ``` @Article{Steinberger2014, author={Steinberger, Ralf and Ebrahim, Mohamed and Poulis, Alexandros and Carrasco-Benitez, Manuel and Schl{\"u}ter, Patrick and Przybyszewski, Marek and Gilbro, Signe}, title={An overview of the European Union's highly multilingual parallel corpora}, journal={Language Resources and Evaluation}, year={2014}, month={Dec}, day={01}, volume={48}, number={4}, pages={679-707}, issn={1574-0218}, doi={10.1007/s10579-014-9277-0}, url={https://doi.org/10.1007/s10579-014-9277-0} } ``` ### Contributions Thanks to [@SBrandeis](https://github.com/SBrandeis) for adding this dataset.
europarl_bilingual	--- annotations_creators: - found language_creators: - found language: - bg - cs - da - de - el - en - es - et - fi - fr - hu - it - lt - lv - nl - pl - pt - ro - sk - sl - sv license: - unknown multilinguality: - translation size_categories: - 100K<n<1M source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: europarl-bilingual dataset_info: - config_name: bg-cs features: - name: translation dtype: translation: languages: - bg - cs splits: - name: train num_bytes: 175372131 num_examples: 402657 download_size: 77543700 dataset_size: 175372131 - config_name: bg-da features: - name: translation dtype: translation: languages: - bg - da splits: - name: train num_bytes: 169901335 num_examples: 393449 download_size: 161209111 dataset_size: 169901335 - config_name: bg-de features: - name: translation dtype: translation: languages: - bg - de splits: - name: train num_bytes: 179830695 num_examples: 393298 download_size: 173031810 dataset_size: 179830695 - config_name: bg-el features: - name: translation dtype: translation: languages: - bg - el splits: - name: train num_bytes: 232659899 num_examples: 377341 download_size: 164911397 dataset_size: 232659899 - config_name: bg-en features: - name: translation dtype: translation: languages: - bg - en splits: - name: train num_bytes: 175002243 num_examples: 408290 download_size: 175210123 dataset_size: 175002243 - config_name: bg-es features: - name: translation dtype: translation: languages: - bg - es splits: - name: train num_bytes: 175608108 num_examples: 388226 download_size: 167299422 dataset_size: 175608108 - config_name: bg-et features: - name: translation dtype: translation: languages: - bg - et splits: - name: train num_bytes: 169828337 num_examples: 400712 download_size: 74382173 dataset_size: 169828337 - config_name: bg-fi features: - name: translation dtype: translation: languages: - bg - fi splits: - name: train num_bytes: 173345926 num_examples: 396624 download_size: 159647184 dataset_size: 173345926 - config_name: bg-fr features: - name: translation dtype: translation: languages: - bg - fr splits: - name: train num_bytes: 179518097 num_examples: 393644 download_size: 173290519 dataset_size: 179518097 - config_name: bg-hu features: - name: translation dtype: translation: languages: - bg - hu splits: - name: train num_bytes: 173346636 num_examples: 382773 download_size: 77741287 dataset_size: 173346636 - config_name: bg-it features: - name: translation dtype: translation: languages: - bg - it splits: - name: train num_bytes: 178372027 num_examples: 377822 download_size: 167706004 dataset_size: 178372027 - config_name: bg-lt features: - name: translation dtype: translation: languages: - bg - lt splits: - name: train num_bytes: 168242178 num_examples: 392554 download_size: 74614251 dataset_size: 168242178 - config_name: bg-lv features: - name: translation dtype: translation: languages: - bg - lv splits: - name: train num_bytes: 173267674 num_examples: 398355 download_size: 74564662 dataset_size: 173267674 - config_name: bg-nl features: - name: translation dtype: translation: languages: - bg - nl splits: - name: train num_bytes: 174737553 num_examples: 388273 download_size: 170765314 dataset_size: 174737553 - config_name: bg-pl features: - name: translation dtype: translation: languages: - bg - pl splits: - name: train num_bytes: 175528692 num_examples: 395269 download_size: 78179477 dataset_size: 175528692 - config_name: bg-pt features: - name: translation dtype: translation: languages: - bg - pt splits: - name: train num_bytes: 174578955 num_examples: 388972 download_size: 170237403 dataset_size: 174578955 - config_name: bg-ro features: - name: translation dtype: translation: languages: - bg - ro splits: - name: train num_bytes: 175218264 num_examples: 389381 download_size: 60489220 dataset_size: 175218264 - config_name: bg-sk features: - name: translation dtype: translation: languages: - bg - sk splits: - name: train num_bytes: 170977227 num_examples: 393815 download_size: 77065166 dataset_size: 170977227 - config_name: bg-sl features: - name: translation dtype: translation: languages: - bg - sl splits: - name: train num_bytes: 159371534 num_examples: 380231 download_size: 72025259 dataset_size: 159371534 - config_name: bg-sv features: - name: translation dtype: translation: languages: - bg - sv splits: - name: train num_bytes: 172562375 num_examples: 398236 download_size: 160015782 dataset_size: 172562375 - config_name: cs-da features: - name: translation dtype: translation: languages: - cs - da splits: - name: train num_bytes: 189814103 num_examples: 618055 download_size: 174829844 dataset_size: 189814103 - config_name: cs-de features: - name: translation dtype: translation: languages: - cs - de splits: - name: train num_bytes: 187747987 num_examples: 568589 download_size: 186471876 dataset_size: 187747987 - config_name: cs-el features: - name: translation dtype: translation: languages: - cs - el splits: - name: train num_bytes: 289333860 num_examples: 599489 download_size: 178443921 dataset_size: 289333860 - config_name: cs-en features: - name: translation dtype: translation: languages: - cs - en splits: - name: train num_bytes: 196378085 num_examples: 647095 download_size: 188756690 dataset_size: 196378085 - config_name: cs-es features: - name: translation dtype: translation: languages: - cs - es splits: - name: train num_bytes: 201972536 num_examples: 619774 download_size: 180848885 dataset_size: 201972536 - config_name: cs-et features: - name: translation dtype: translation: languages: - cs - et splits: - name: train num_bytes: 189852839 num_examples: 636512 download_size: 87913231 dataset_size: 189852839 - config_name: cs-fi features: - name: translation dtype: translation: languages: - cs - fi splits: - name: train num_bytes: 193370836 num_examples: 619320 download_size: 173216683 dataset_size: 193370836 - config_name: cs-fr features: - name: translation dtype: translation: languages: - cs - fr splits: - name: train num_bytes: 207043213 num_examples: 628200 download_size: 186873132 dataset_size: 207043213 - config_name: cs-hu features: - name: translation dtype: translation: languages: - cs - hu splits: - name: train num_bytes: 201392624 num_examples: 616160 download_size: 91341961 dataset_size: 201392624 - config_name: cs-it features: - name: translation dtype: translation: languages: - cs - it splits: - name: train num_bytes: 203150534 num_examples: 607017 download_size: 181266237 dataset_size: 203150534 - config_name: cs-lt features: - name: translation dtype: translation: languages: - cs - lt splits: - name: train num_bytes: 189504979 num_examples: 624292 download_size: 88260876 dataset_size: 189504979 - config_name: cs-lv features: - name: translation dtype: translation: languages: - cs - lv splits: - name: train num_bytes: 193888740 num_examples: 627873 download_size: 88126869 dataset_size: 193888740 - config_name: cs-nl features: - name: translation dtype: translation: languages: - cs - nl splits: - name: train num_bytes: 199512564 num_examples: 618414 download_size: 184381636 dataset_size: 199512564 - config_name: cs-pl features: - name: translation dtype: translation: languages: - cs - pl splits: - name: train num_bytes: 197967454 num_examples: 621387 download_size: 91806300 dataset_size: 197967454 - config_name: cs-pt features: - name: translation dtype: translation: languages: - cs - pt splits: - name: train num_bytes: 197178140 num_examples: 609729 download_size: 183745721 dataset_size: 197178140 - config_name: cs-ro features: - name: translation dtype: translation: languages: - cs - ro splits: - name: train num_bytes: 127321661 num_examples: 392085 download_size: 73245197 dataset_size: 127321661 - config_name: cs-sk features: - name: translation dtype: translation: languages: - cs - sk splits: - name: train num_bytes: 196186957 num_examples: 636128 download_size: 90623958 dataset_size: 196186957 - config_name: cs-sl features: - name: translation dtype: translation: languages: - cs - sl splits: - name: train num_bytes: 179909545 num_examples: 611624 download_size: 85558670 dataset_size: 179909545 - config_name: cs-sv features: - name: translation dtype: translation: languages: - cs - sv splits: - name: train num_bytes: 194656792 num_examples: 631544 download_size: 173672259 dataset_size: 194656792 - config_name: da-de features: - name: translation dtype: translation: languages: - da - de splits: - name: train num_bytes: 624355083 num_examples: 1928414 download_size: 276778385 dataset_size: 624355083 - config_name: da-el features: - name: translation dtype: translation: languages: - da - el splits: - name: train num_bytes: 604008313 num_examples: 1280579 download_size: 265542591 dataset_size: 604008313 - config_name: da-en features: - name: translation dtype: translation: languages: - da - en splits: - name: train num_bytes: 612701093 num_examples: 1991647 download_size: 279497322 dataset_size: 612701093 - config_name: da-es features: - name: translation dtype: translation: languages: - da - es splits: - name: train num_bytes: 631311642 num_examples: 1943931 download_size: 271357896 dataset_size: 631311642 - config_name: da-et features: - name: translation dtype: translation: languages: - da - et splits: - name: train num_bytes: 182908097 num_examples: 635018 download_size: 171538628 dataset_size: 182908097 - config_name: da-fi features: - name: translation dtype: translation: languages: - da - fi splits: - name: train num_bytes: 599820497 num_examples: 1917260 download_size: 263430295 dataset_size: 599820497 - config_name: da-fr features: - name: translation dtype: translation: languages: - da - fr splits: - name: train num_bytes: 658108095 num_examples: 1992590 download_size: 277504353 dataset_size: 658108095 - config_name: da-hu features: - name: translation dtype: translation: languages: - da - hu splits: - name: train num_bytes: 196114245 num_examples: 617519 download_size: 174981657 dataset_size: 196114245 - config_name: da-it features: - name: translation dtype: translation: languages: - da - it splits: - name: train num_bytes: 630400040 num_examples: 1876703 download_size: 271654671 dataset_size: 630400040 - config_name: da-lt features: - name: translation dtype: translation: languages: - da - lt splits: - name: train num_bytes: 184071192 num_examples: 614923 download_size: 171931855 dataset_size: 184071192 - config_name: da-lv features: - name: translation dtype: translation: languages: - da - lv splits: - name: train num_bytes: 188638250 num_examples: 627809 download_size: 171781368 dataset_size: 188638250 - config_name: da-nl features: - name: translation dtype: translation: languages: - da - nl splits: - name: train num_bytes: 634339405 num_examples: 1987498 download_size: 275140635 dataset_size: 634339405 - config_name: da-pl features: - name: translation dtype: translation: languages: - da - pl splits: - name: train num_bytes: 193218656 num_examples: 642544 download_size: 175344681 dataset_size: 193218656 - config_name: da-pt features: - name: translation dtype: translation: languages: - da - pt splits: - name: train num_bytes: 631413013 num_examples: 1930454 download_size: 274286241 dataset_size: 631413013 - config_name: da-ro features: - name: translation dtype: translation: languages: - da - ro splits: - name: train num_bytes: 124974166 num_examples: 388156 download_size: 156965207 dataset_size: 124974166 - config_name: da-sk features: - name: translation dtype: translation: languages: - da - sk splits: - name: train num_bytes: 190277240 num_examples: 621907 download_size: 174378230 dataset_size: 190277240 - config_name: da-sl features: - name: translation dtype: translation: languages: - da - sl splits: - name: train num_bytes: 173968152 num_examples: 595944 download_size: 169356730 dataset_size: 173968152 - config_name: da-sv features: - name: translation dtype: translation: languages: - da - sv splits: - name: train num_bytes: 567189130 num_examples: 1871171 download_size: 263342660 dataset_size: 567189130 - config_name: de-el features: - name: translation dtype: translation: languages: - de - el splits: - name: train num_bytes: 603303137 num_examples: 1223026 download_size: 277232265 dataset_size: 603303137 - config_name: de-en features: - name: translation dtype: translation: languages: - de - en splits: - name: train num_bytes: 641864487 num_examples: 1961119 download_size: 291376506 dataset_size: 641864487 - config_name: de-es features: - name: translation dtype: translation: languages: - de - es splits: - name: train num_bytes: 651057814 num_examples: 1887879 download_size: 283096221 dataset_size: 651057814 - config_name: de-et features: - name: translation dtype: translation: languages: - de - et splits: - name: train num_bytes: 181554876 num_examples: 578248 download_size: 183218377 dataset_size: 181554876 - config_name: de-fi features: - name: translation dtype: translation: languages: - de - fi splits: - name: train num_bytes: 621960938 num_examples: 1871185 download_size: 275244245 dataset_size: 621960938 - config_name: de-fr features: - name: translation dtype: translation: languages: - de - fr splits: - name: train num_bytes: 680963340 num_examples: 1942666 download_size: 289325334 dataset_size: 680963340 - config_name: de-hu features: - name: translation dtype: translation: languages: - de - hu splits: - name: train num_bytes: 193068884 num_examples: 563571 download_size: 186625855 dataset_size: 193068884 - config_name: de-it features: - name: translation dtype: translation: languages: - de - it splits: - name: train num_bytes: 653857504 num_examples: 1832989 download_size: 283411719 dataset_size: 653857504 - config_name: de-lt features: - name: translation dtype: translation: languages: - de - lt splits: - name: train num_bytes: 182429076 num_examples: 565892 download_size: 183552115 dataset_size: 182429076 - config_name: de-lv features: - name: translation dtype: translation: languages: - de - lv splits: - name: train num_bytes: 186374102 num_examples: 573226 download_size: 183437158 dataset_size: 186374102 - config_name: de-nl features: - name: translation dtype: translation: languages: - de - nl splits: - name: train num_bytes: 655711533 num_examples: 1934111 download_size: 286849380 dataset_size: 655711533 - config_name: de-pl features: - name: translation dtype: translation: languages: - de - pl splits: - name: train num_bytes: 189642761 num_examples: 579166 download_size: 187004630 dataset_size: 189642761 - config_name: de-pt features: - name: translation dtype: translation: languages: - de - pt splits: - name: train num_bytes: 654723289 num_examples: 1884176 download_size: 286068045 dataset_size: 654723289 - config_name: de-ro features: - name: translation dtype: translation: languages: - de - ro splits: - name: train num_bytes: 133686126 num_examples: 385663 download_size: 168794955 dataset_size: 133686126 - config_name: de-sk features: - name: translation dtype: translation: languages: - de - sk splits: - name: train num_bytes: 187484752 num_examples: 569381 download_size: 186001546 dataset_size: 187484752 - config_name: de-sl features: - name: translation dtype: translation: languages: - de - sl splits: - name: train num_bytes: 171891826 num_examples: 546212 download_size: 180994167 dataset_size: 171891826 - config_name: de-sv features: - name: translation dtype: translation: languages: - de - sv splits: - name: train num_bytes: 590635137 num_examples: 1842026 download_size: 275145356 dataset_size: 590635137 - config_name: el-en features: - name: translation dtype: translation: languages: - el - en splits: - name: train num_bytes: 606689426 num_examples: 1292180 download_size: 279571396 dataset_size: 606689426 - config_name: el-es features: - name: translation dtype: translation: languages: - el - es splits: - name: train num_bytes: 621773509 num_examples: 1272383 download_size: 271592910 dataset_size: 621773509 - config_name: el-et features: - name: translation dtype: translation: languages: - el - et splits: - name: train num_bytes: 282330974 num_examples: 599915 download_size: 175257825 dataset_size: 282330974 - config_name: el-fi features: - name: translation dtype: translation: languages: - el - fi splits: - name: train num_bytes: 583209381 num_examples: 1227612 download_size: 263682672 dataset_size: 583209381 - config_name: el-fr features: - name: translation dtype: translation: languages: - el - fr splits: - name: train num_bytes: 637660521 num_examples: 1290796 download_size: 277664049 dataset_size: 637660521 - config_name: el-hu features: - name: translation dtype: translation: languages: - el - hu splits: - name: train num_bytes: 293591416 num_examples: 586250 download_size: 178679940 dataset_size: 293591416 - config_name: el-it features: - name: translation dtype: translation: languages: - el - it splits: - name: train num_bytes: 619754868 num_examples: 1231222 download_size: 271890467 dataset_size: 619754868 - config_name: el-lt features: - name: translation dtype: translation: languages: - el - lt splits: - name: train num_bytes: 281773875 num_examples: 590850 download_size: 175584581 dataset_size: 281773875 - config_name: el-lv features: - name: translation dtype: translation: languages: - el - lv splits: - name: train num_bytes: 287747485 num_examples: 596929 download_size: 175479598 dataset_size: 287747485 - config_name: el-nl features: - name: translation dtype: translation: languages: - el - nl splits: - name: train num_bytes: 619747333 num_examples: 1277297 download_size: 275234928 dataset_size: 619747333 - config_name: el-pl features: - name: translation dtype: translation: languages: - el - pl splits: - name: train num_bytes: 291216179 num_examples: 591069 download_size: 179121800 dataset_size: 291216179 - config_name: el-pt features: - name: translation dtype: translation: languages: - el - pt splits: - name: train num_bytes: 619089974 num_examples: 1261188 download_size: 274510323 dataset_size: 619089974 - config_name: el-ro features: - name: translation dtype: translation: languages: - el - ro splits: - name: train num_bytes: 186445257 num_examples: 372839 download_size: 160638758 dataset_size: 186445257 - config_name: el-sk features: - name: translation dtype: translation: languages: - el - sk splits: - name: train num_bytes: 290180513 num_examples: 600684 download_size: 178030033 dataset_size: 290180513 - 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config_name: sk-sv features: - name: translation dtype: translation: languages: - sk - sv splits: - name: train num_bytes: 195200876 num_examples: 636353 download_size: 173202439 dataset_size: 195200876 - config_name: sl-sv features: - name: translation dtype: translation: languages: - sl - sv splits: - name: train num_bytes: 178446367 num_examples: 608740 download_size: 168196323 dataset_size: 178446367 --- # Dataset Card for europarl-bilingual ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Statmt](http://www.statmt.org/europarl/) - Repository: [OPUS Europarl](https://opus.nlpl.eu/Europarl.php) - Paper: [Aclweb](https://www.aclweb.org/anthology/L12-1246/) - Leaderboard: [Needs More Information] - Point of Contact: [Needs More Information] ### Dataset Summary A parallel corpus extracted from the European Parliament web site by Philipp Koehn (University of Edinburgh). The main intended use is to aid statistical machine translation research. To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs. You can find the valid pairs in Homepage section of Dataset Description: https://opus.nlpl.eu/Europarl.php E.g. `dataset = load_dataset("europarl_bilingual", lang1="fi", lang2="fr")` ### Supported Tasks and Leaderboards Tasks: Machine Translation, Cross Lingual Word Embeddings (CWLE) Alignment ### Languages - 21 languages, 211 bitexts - total number of files: 207,775 - total number of tokens: 759.05M - total number of sentence fragments: 30.32M Every pair of the following languages is available: - bg - cs - da - de - el - en - es - et - fi - fr - hu - it - lt - lv - nl - pl - pt - ro - sk - sl - sv ## Dataset Structure ### Data Instances Here is an example from the en-fr pair: ``` { 'translation': { 'en': 'Resumption of the session', 'fr': 'Reprise de la session' } } ``` ### Data Fields - `translation`: a dictionary containing two strings paired with a key indicating the corresponding language. ### Data Splits - `train`: only train split is provided. Authors did not provide a separation of examples in `train`, `dev` and `test`. ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information The data set comes with the same license as the original sources. Please, check the information about the source that is given on http://opus.nlpl.eu/Europarl-v8.php ### Citation Information ``` @InProceedings{TIEDEMANN12.463, author = {J�rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } ``` ### Contributions Thanks to [@lucadiliello](https://github.com/lucadiliello) for adding this dataset.
event2Mind	--- annotations_creators: - crowdsourced language: - en language_creators: - found license: - unknown multilinguality: - monolingual pretty_name: Event2Mind size_categories: - 10K<n<100K source_datasets: - original task_categories: - text2text-generation task_ids: [] paperswithcode_id: event2mind tags: - common-sense-inference dataset_info: features: - name: Source dtype: string - name: Event dtype: string - name: Xintent dtype: string - name: Xemotion dtype: string - name: Otheremotion dtype: string - name: Xsent dtype: string - name: Osent dtype: string splits: - name: test num_bytes: 649273 num_examples: 5221 - name: train num_bytes: 5916384 num_examples: 46472 - name: validation num_bytes: 672365 num_examples: 5401 download_size: 1300770 dataset_size: 7238022 --- # Dataset Card for "event2Mind" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://uwnlp.github.io/event2mind/](https://uwnlp.github.io/event2mind/) - Repository: https://github.com/uwnlp/event2mind - Paper: [Event2Mind: Commonsense Inference on Events, Intents, and Reactions](https://arxiv.org/abs/1805.06939) - Point of Contact: [Hannah Rashkin](mailto:hrashkin@cs.washington.edu), [Maarten Sap](mailto:msap@cs.washington.edu) - Size of downloaded dataset files: 1.30 MB - Size of the generated dataset: 7.24 MB - Total amount of disk used: 8.54 MB ### Dataset Summary In Event2Mind, we explore the task of understanding stereotypical intents and reactions to events. Through crowdsourcing, we create a large corpus with 25,000 events and free-form descriptions of their intents and reactions, both of the event's subject and (potentially implied) other participants. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 1.30 MB - Size of the generated dataset: 7.24 MB - Total amount of disk used: 8.54 MB An example of 'validation' looks as follows. ``` { "Event": "It shrinks in the wash", "Osent": "1", "Otheremotion": "[\"upset\", \"angry\"]", "Source": "it_events", "Xemotion": "[\"none\"]", "Xintent": "[\"none\"]", "Xsent": "" } ``` ### Data Fields The data fields are the same among all splits. #### default - `Source`: a `string` feature. - `Event`: a `string` feature. - `Xintent`: a `string` feature. - `Xemotion`: a `string` feature. - `Otheremotion`: a `string` feature. - `Xsent`: a `string` feature. - `Osent`: a `string` feature. ### Data Splits \| name \|train\|validation\|test\| \|-------\|----:\|---------:\|---:\| \|default\|46472\| 5401\|5221\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{rashkin-etal-2018-event2mind, title = "{E}vent2{M}ind: Commonsense Inference on Events, Intents, and Reactions", author = "Rashkin, Hannah and Sap, Maarten and Allaway, Emily and Smith, Noah A. and Choi, Yejin", booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2018", address = "Melbourne, Australia", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P18-1043", doi = "10.18653/v1/P18-1043", pages = "463--473", } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.
evidence_infer_treatment	--- pretty_name: Evidence Infer Treatment annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-retrieval task_ids: - fact-checking-retrieval paperswithcode_id: null dataset_info: - config_name: '2.0' features: - name: Text dtype: string - name: PMCID dtype: int32 - name: Prompts sequence: - name: PromptID dtype: int32 - name: PMCID dtype: int32 - name: Outcome dtype: string - name: Intervention dtype: string - name: Comparator dtype: string - name: Annotations sequence: - name: UserID dtype: int32 - name: PromptID dtype: int32 - name: PMCID dtype: int32 - name: Valid Label dtype: bool - name: Valid Reasoning dtype: bool - name: Label dtype: string - name: Annotations dtype: string - name: Label Code dtype: int32 - name: In Abstract dtype: bool - name: Evidence Start dtype: int32 - name: Evidence End dtype: int32 splits: - name: train num_bytes: 77045294 num_examples: 2690 - name: test num_bytes: 9436674 num_examples: 334 - name: validation num_bytes: 10113982 num_examples: 340 download_size: 163515689 dataset_size: 96595950 - config_name: '1.1' features: - name: Text dtype: string - name: PMCID dtype: int32 - name: Prompts sequence: - name: PromptID dtype: int32 - name: PMCID dtype: int32 - name: Outcome dtype: string - name: Intervention dtype: string - name: Comparator dtype: string - name: Annotations sequence: - name: UserID dtype: int32 - name: PromptID dtype: int32 - name: PMCID dtype: int32 - name: Valid Label dtype: bool - name: Valid Reasoning dtype: bool - name: Label dtype: string - name: Annotations dtype: string - name: Label Code dtype: int32 - name: In Abstract dtype: bool - name: Evidence Start dtype: int32 - name: Evidence End dtype: int32 splits: - name: train num_bytes: 55375971 num_examples: 1931 - name: test num_bytes: 6877338 num_examples: 240 - name: validation num_bytes: 7359847 num_examples: 248 download_size: 114452688 dataset_size: 69613156 --- # Dataset Card for Evidence Infer ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: http://evidence-inference.ebm-nlp.com/ - Repository: https://github.com/jayded/evidence-inference - Paper: [Evidence Inference 2.0: More Data, Better Models](https://arxiv.org/abs/2005.04177) - Leaderboard: http://evidence-inference.ebm-nlp.com/leaderboard/ - Point of Contact: []() ### Dataset Summary Data and code from our "Inferring Which Medical Treatments Work from Reports of Clinical Trials", NAACL 2019. This work concerns inferring the results reported in clinical trials from text. The dataset consists of biomedical articles describing randomized control trials (RCTs) that compare multiple treatments. Each of these articles will have multiple questions, or 'prompts' associated with them. These prompts will ask about the relationship between an intervention and comparator with respect to an outcome, as reported in the trial. For example, a prompt may ask about the reported effects of aspirin as compared to placebo on the duration of headaches. For the sake of this task, we assume that a particular article will report that the intervention of interest either significantly increased, significantly decreased or had significant effect on the outcome, relative to the comparator. The dataset could be used for automatic data extraction of the results of a given RCT. This would enable readers to discover the effectiveness of different treatments without needing to read the paper. We have recently collected additional data for this task (https://arxiv.org/abs/2005.04177), which we will present at BioNLP 2020. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages - English (`en`). ## Dataset Structure ### Data Instances ``` {'Text': "TITLE: Liraglutide, a once-daily human GLP-1 analogue, added to a sulphonylurea over 26 weeks produces greater improvements in glycaemic and weight control compared with adding rosiglitazone or placebo in subjects with Type 2 diabetes (LEAD-1 SU)\n\n ABSTRACT.AIM:\nTo compare the effects of combining liraglutide (0.6, 1.2 or 1.8 mg/day) or rosiglitazone 4 mg/day (all n ≥ 228) or placebo (n = 114) with glimepiride (2–4 mg/day) on glycaemic control, body weight and safety in Type 2 diabetes.\n\nABSTRACT.METHODS:\nIn total, 1041 adults (mean ± sd), age 56 ± 10 years, weight 82 ± 17 kg and glycated haemoglobin (HbA1c) 8.4 ± 1.0% at 116 sites in 21 countries were stratified based on previous oral glucose-lowering mono : combination therapies (30 : 70%) to participate in a five-arm, 26-week, double-dummy, randomized study.\n\nABSTRACT.RESULTS:\nLiraglutide (1.2 or 1.8 mg) produced greater reductions in HbA1c from baseline, (−1.1%, baseline 8.5%) compared with placebo (+0.2%, P < 0.0001, baseline 8.4%) or rosiglitazone (−0.4%, P < 0.0001, baseline 8.4%) when added to glimepiride. Liraglutide 0.6 mg was less effective (−0.6%, baseline 8.4%). Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l). Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). Changes in body weight with liraglutide 1.8 mg (−0.2 kg, baseline 83.0 kg), 1.2 mg (+0.3 kg, baseline 80.0 kg) or placebo (−0.1 kg, baseline 81.9 kg) were less than with rosiglitazone (+2.1 kg, P < 0.0001, baseline 80.6 kg). Main adverse events for all treatments were minor hypoglycaemia (< 10%), nausea (< 11%), vomiting (< 5%) and diarrhoea (< 8%).\n\nABSTRACT.CONCLUSIONS:\nLiraglutide added to glimepiride was well tolerated and provided improved glycaemic control and favourable weight profile.\n\nBODY.INTRODUCTION:\nMost drugs that target Type 2 diabetes (T2D) also cause weight gain or hypoglycaemia, or both, with the risk increasing with combination therapy. Glucagon-like peptide-1 (GLP-1)-based therapies stimulate insulin secretion and reduce glucagon secretion only during hyperglycaemia. GLP-1 also slows gastric emptying and reduces appetite [1]. Although American Diabetes Association (ADA)/European Association for the Study of Diabetes (EASD) guidelines recommend lifestyle and metformin as initial therapy for T2D [2], sulphonylureas are used widely, particularly when metformin or thiazolidinediones are not tolerated. Glycaemic control eventually deteriorates with sulphonylureas while hypoglycaemia and weight gain are common [3]. Incretin therapy improves glycaemic control with low hypoglycaemic risk, while delayed gastric emptying and reduced appetite can reduce weight [1,4]. Liraglutide is a once-daily human GLP-1 analogue with 97% linear amino-acid sequence homology to human GLP-1 [5] and half-life of 13 h after subcutaneous administration that produces 24-h blood glucose control [6]. Liraglutide monotherapy for 14 weeks reduced glycated haemoglobin (HbA1c) by 1.7% and fasting plasma glucose (FPG) by 3.4 mmol/l without causing hypoglycaemia, along with weight loss (∼3 kg) compared with placebo [7]. Improvements in pancreatic B-cell function [7–9] and blood pressure [7], along with decreased glucagon secretion [7,10], also occurred. As part of the phase 3 programme [the Liraglutide Effect and Action in Diabetes (LEAD) programme] with liraglutide in > 4000 subjects with T2D as monotherapy or in combination therapy, this 26-week trial examined liraglutide plus glimepiride compared with either placebo or rosiglitazone added to glimepiride on glycaemic control and body weight.\n\nBODY.SUBJECTS AND METHODS.STUDY PARTICIPANTS:\nInclusion criteria: T2D treated with oral glucose-lowering agents (OGLAs) for ≥ 3 months; 18–80 years of age; HbA1c 7.0–11.0% (previous OGLA monotherapy) or 7.0–10.0% (previous OGLA combination therapy); body mass index (BMI) ≤ 45.0 kg/m2. Exclusion criteria: used insulin within 3 months, impaired liver or renal function, uncontrolled hypertension (≥ 180/100 mmHg), cancer or used any drugs apart from OGLAs likely to affect glucose concentrations. Subjects provided written informed consent. The study was conducted in accordance with good clinical practice guidelines and approved by independent ethics committees.\n\nBODY.SUBJECTS AND METHODS.STUDY DESIGN:\nThe study was a 26-week, double-blind, double-dummy, randomized, active-control, five-armed parallel (116 sites in 21 countries, primarily Europe and Asia) trial enrolling 1041 subjects (1–37 subjects per centre), all receiving glimepiride (2–4 mg/day) in combination with (Fig. 1): FIGURE 1Overview of trial design and treatment arms. one of three liraglutide doses [0.6, 1.2 or 1.8 mg, injected subcutaneously (Novo Nordisk, Bagsvaerd, Denmark) and rosiglitazone placebo];liraglutide placebo and rosiglitazone placebo;liraglutide placebo and rosiglitazone 4 mg/day (rosiglitazone; AvandiaTM; GlaxoSmithKline, London, UK). The doses of rosiglitazone and glimepiride used were determined by the highest doses approved in all participating counties. After discontinuing previous OGLAs except glimepiride, separate 2-week titration and maintenance periods with glimepiride (open-label) preceded randomization (Fig. 1). Subjects were stratified according to previous treatment (monotherapy or combination therapy). After randomization, 2-week treatment titration and 24-week treatment (maintenance) phases (Fig. 1) were completed. Liraglutide was up-titrated weekly in 0.6-mg increments until allocated doses were reached. Glimepiride could be adjusted between 2 and 4 mg/day in case of hypoglycaemia or other adverse events (AEs), while other drug doses were fixed. Liraglutide (active and placebo) was supplied in 3-ml pre-filled pens with 31G needles (Novo Nordisk). Subjects were encouraged to inject liraglutide into the upper arm, thigh or abdomen at the same time each day. Rosiglitazone and glimepiride were taken in the morning or with the first meal.\n\nBODY.SUBJECTS AND METHODS.STUDY MEASUREMENTS.EFFICACY:\nThe primary endpoint was change from baseline HbA1c after 26 weeks of treatment. Secondary endpoints included: percentages of subjects reaching HbA1c (< 7.0%, ≤ 6.5%), FPG (5.0 to ≤ 7.2 mmol/l) and postprandial plasma glucose (PPG; 10.0 mmol/l) targets [11–13]; changes in body weight, FPG, mean PPG, indices of pancreatic B-cell function [pro-insulin : insulin ratio and homeostasis model assessment (HOMA)-B], HOMA-insulin resistance (HOMA-IR) and blood pressure (BP). HbA1c was measured centrally (MDS Pharma Services, King of Prussia, PA, USA) by high performance liquid chromatography while plasma glucose (PG) was self-measured using MediSense® glucose meters (Abbott Diagnostics Inc., Abbott Park, IL, USA). Insulin and C-peptide were measured by chemiluminescence, proinsulin by ELISA, while glucagon was measured in aprotinin-treated plasma by radioimmunoassay. The proinsulin : insulin ratio was calculated from fasting insulin and fasting proinsulin. HOMA-B and HOMA-IR were both calculated from FPG and fasting insulin. Samples measured centrally were collected and transported according to detailed procedures in the MDS Pharma Services manual. Samples stored at ambient temperature were shipped by courier to the central laboratory on the same day as collection, while frozen samples were shipped every 3 weeks.\n\nBODY.SUBJECTS AND METHODS.STUDY MEASUREMENTS.SAFETY:\nSafety variables included hypoglycaemic episodes based on PG levels (< 3.1 mmol/l), liraglutide antibodies including cross-reacting and neutralizing antibodies, tolerability (gastrointestinal complaints) and pulse. AEs, vital signs, electrocardiogram (ECG), biochemical and haematology measures including calcitonin were also monitored. Self-treated hypoglycaemic episodes were classified as minor, while those requiring third-party assistance were considered major. Serum antibodies against liraglutide were measured by radioimmunoprecipitation assay.\n\nBODY.SUBJECTS AND METHODS.STATISTICAL ANALYSES:\nAll efficacy and safety analyses were based on intent-to-treat criteria, defined as subjects who were exposed to ≥ 1 dose of trial product(s). Efficacy endpoints were analysed by ancova with treatment, country and previous glucose-lowering treatment as fixed effects and baseline values as covariates. Missing data were imputed by last observation carried forward (LOCF). Sample size calculations were based on predicted HbA1c and body weight after trial completion. As the three liraglutide + glimepiride groups were to be compared with both rosiglitazone + glimepiride and glimepiride monotherapy, two calculations were performed. These sample size calculations assumed a standard deviation of 1.2% of HbA1c, the non-inferiority/superiority margin vs. active control was set to 0.4% and the difference to detect (superiority vs. placebo) was set to 0.5%. For body weight, a coefficient of variation of 3% (based on phase 2a trials for liraglutide) and a difference to detect of 3% were assumed. A combined power (calculated as the product of the marginal powers for HbA1c and body weight) of at least 85% was required. These calculations indicated that at least 168 and 81 patients completing the study would be needed for the combination and glimepiride monotherapy groups, respectively. Assuming a drop-out rate of 25%, targets for randomization were 228 in each of the combination therapy groups and 114 in the placebo group (total n = 1026). To protect against Type 1 errors, HbA1c was analysed using hierarchical testing for descending doses of liraglutide. First, superiority of liraglutide 1.8 mg to placebo was tested and, only if superior to placebo, non-inferiority to rosiglitazone was tested. If non-inferiority was obtained, superiority to rosiglitazone for liraglutide 1.8 mg was tested and superiority to placebo for liraglutide 1.2 mg was tested. If superiority was confirmed, non-inferiority to rosiglitazone would be tested and so on (i.e. testing sequence was stopped when hypotheses could not be rejected). Superiority was concluded when upper limits of two-sided 95% confidence intervals (CIs) for treatment differences were below 0%; non-inferiority was concluded if these values were < 0.4%; for secondary endpoints, Type 1 errors were controlled by estimating simultaneous CIs using Dunnett's method. Proportions of subjects achieving HbA1c (HbA1c < 7.0%, and ≤ 6.5%) and FPG (5.0 ≤ FPG ≤ 7.2 mmol/l) targets [13] were compared between treatments using logistic regression with allocated treatment and baseline values as covariates. Chi-square analyses assessed differences in treatments for percentages of subjects achieving no, one, two or three PPG values < 10 mmol/l [13]. Hypoglycaemic episodes were analysed under the assumption that number per subject were negatively binomially distributed using a generalized linear model, including treatment and country as fixed effects. Other safety data were compared by descriptive statistics. Values for descriptive statistics are expressed as means ± sd, while ancova results are expressed as least square means ± SEM or with 95% CI unless otherwise noted. Significance levels were set to 5% for two-sided tests and 2.5% for one-sided tests.\n\nBODY.RESULTS.DISPOSITION AND DEMOGRAPHICS:\nThe treatment groups were well balanced (Table 1). Of 1712 subjects screened, 1041 were randomized and 1040 were exposed to trial drugs; 147 subjects (14.1%) withdrew (Fig. 2). Withdrawals were higher with placebo (27%) and rosiglitazone treatment (16%) compared with liraglutide 0.6 mg (11%), liraglutide 1.2 mg (14%) and liraglutide 1.8 mg (9%) treatment. Thirty-eight subjects (3.7%) withdrew as a result of AEs (Fig. 2). Table 1 Demographic characteristics of study participants Liraglutide 0.6 mg ( n = 233) Liraglutide 1.2 mg ( n = 228) Liraglutide 1.8 mg ( n = 234) Placebo ( n = 114) Rosiglitazone ( n = 232) Male : female (%) 54 : 46 45 : 55 53 : 47 47 : 53 47 : 53 Age (years) 55.7 ± 9.9 57.7 ± 9.0 55.6 ± 10.0 54.7 ± 10.0 56.0 ± 9.8 Duration of diabetes (years) 6.5 (4.0,10.2) 6.7 (4.0,10.7) 6.5 (3.7,10.5) 6.5 (4.5,10.6) 6.6 (4.3,10.7) Previous on mono : combi (%) 30 : 70 31 : 69 27 : 73 32 : 68 32 : 68 FPG (mmol/l) 10.0 ± 2.4 9.8 ± 2.7 9.7 ± 2.4 9.5 ± 2.0 9.9 ± 2.5 HbA 1c (%) 8.4 ± 1.0 8.5 ± 1.1 8.5 ± 0.9 8.4 ± 1.0 8.4 ± 1.0 Diabetic retinopathy (%) 17.2 14.9 12.0 13.2 16.4 Hypertension (%) 69.1 68.0 69.7 64.9 66.8 BMI (kg/m 2 ) 30.0 ± 5.0 29.8 ± 5.1 30.0 ± 5.1 30.3 ± 5.4 29.4 ± 4.8 Weight (kg) 82.6 ± 17.7 80.0 ± 17.1 83.0 ± 18.1 81.9 ± 17.1 80.6 ± 17.0 Systolic blood pressure (mmHg) 131 ± 16 133 ± 15 132 ± 16 131 ± 15.3 133 ± 15 Data are mean ± sd and percentages, except for duration of diabetes, where data are median, 25th and 75th percentile. BMI, body mass index; FPG, fasting plasma glucose; HbA 1c , glycated haemoglobin; mono : combi, previous treatment with either monotherapy or combination therapy; sd , standard deviation. FIGURE 2Flow of patients through the study.\n\nBODY.RESULTS.EFFICACY.HBA:\nHbA1c decreased rapidly with all doses of liraglutide when added to glimepiride compared with either rosiglitazone or placebo (i.e. glimepiride monotherapy), irrespective of previous therapy. The greatest decreases occurred with liraglutide 1.2 and 1.8 mg (Fig. 3a–c). After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). Estimated treatment differences and 95% CIs to placebo were: liraglutide 1.8 mg: −1.4% (1.6, −1.1); liraglutide 1.2 mg: −1.3% (1.5, −1.1); liraglutide 0.6 mg: −0.8% (−1.1, −0.6); rosiglitazone: −0.7% (−0.9, −0.4). All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001). Liraglutide 0.6 mg was non-inferior to rosiglitazone. Rosiglitazone also was superior to placebo (P < 0.0001). FIGURE 3Mean glycated haemoglobin (HbA1c) by treatment and week (intent-to-treat population with last observation carried forward): (a) overall population; (b) previously on monotherapy; or (c) previously on combination therapy; (d) mean changes in HbA1c from baseline after 26 weeks of treatment. Keys: (a–c) liraglutide 0.6 mg: grey dotted line with squares; liraglutide 1.2 mg: black solid line with triangles; liraglutide 1.8 mg: black dotted line with squares; rosiglitazone: grey solid line with circles; placebo: black solid line with circles. (d) liraglutide 0.6 mg: black stripes on white; liraglutide 1.2 mg: white stripes on black, liraglutide 1.8 mg: grey tint; rosiglitazone: white; placebo: black. **P < 0.0001 compared with placebo; ††††P < 0.0001 compared with rosiglitazone. HbA1c decreases were greater for subjects who entered from monotherapy compared with combination therapy (Fig. 3d). However, because the increase with placebo was higher for individuals entering on combination therapy (0.7 vs. 0.23%), the differences between treatment groups in favour of liraglutide were similar irrespective of whether subjects were treated previously with monotherapy or combination therapy. Neither age, gender nor BMI affected these trends.\n\nBODY.RESULTS.EFFICACY.PERCENTAGE REACHING AN HBA:\nThe percentage of subjects reaching ADA [2] and International Diabetes Federation (IDF)/American Association of Clinical Endocrinologists (AACE) [11,12] treatment HbA1c goals with liraglutide was dose dependent (Fig. 4). At week 26, 42% and 21% of subjects treated with liraglutide 1.8 mg reached an HbA1c < 7.0% and ≤ 6.5%, respectively, compared with 8% and 4% for placebo (Fig. 4). The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). FIGURE 4Subjects achieving specified glycated haemoglobin (HbA1c) levels: (a) percentage reaching HbA1c < 7.0% (American Diabetes Association/European Association for the Study of Diabetes target); (b) percentage reaching HbA1c < 6.5% (International Diabetes Federation/American Association of Clinical Endocrinologists targets); (c) cumulative distribution of HbA1c at 26 weeks for the intent-to-treat (ITT) population; and (d) for the ITT last observation carried forward (LOCF) population. Keys: (a, b) liraglutide 0.6 mg: black stripes on white; liraglutide 1.2 mg: white stripes on black, liraglutide 1.8 mg: grey tint; rosiglitazone: white; placebo: black. (c, d) liraglutide 0.6 mg: pale grey solid line; liraglutide 1.2 mg: grey solid line, liraglutide 1.8 mg: black solid line; rosiglitazone: dotted black line; placebo: dotted grey line; baseline visit: long dashed black line. P < 0.0001 or P < 0.01 compared with placebo; ††††P < 0.0001 or †††P = 0.0005 compared with rosiglitazone.\n\nBODY.RESULTS.EFFICACY.FASTING PLASMA GLUCOSE:\nBy week 2, subjects treated with liraglutide had rapid and larger decreases in FPG vs. comparator treatment. At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001), while only liraglutide 1.2 or 1.8 mg produced greater reductions than rosiglitazone. FPG treatment differences to placebo were 1.7 mmol/l for liraglutide 0.6 mg and 2.6 mmol/l for both liraglutide 1.2 and 1.8 mg. An 0.7-mmol/l greater reduction in FPG was achieved with either liraglutide 1.2 or 1.8 mg compared with rosiglitazone (P ≤ 0.006) after 26 weeks. FIGURE 5Mean changes from baseline in fasting plasma glucose after 26 weeks of treatment. ***P < 0.0001 compared with placebo; ††P < 0.01 compared with rosiglitazone. The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). The liraglutide 1.2 and 1.8 mg treatment groups also had more subjects achieving the same FPG target at end of treatment compared with rosiglitazone (26%) (P = 0.007 and P = 0.01, respectively).\n\nBODY.RESULTS.EFFICACY.POSTPRANDIAL PLASMA GLUCOSE:\nPPG was reduced similarly after each meal. The greatest reductions in mean PPG values from baseline (average of values obtained 90 min after breakfast, lunch and evening meal) occurred with liraglutide 1.2 mg (2.5 mmol/l) and liraglutide 1.8 mg (2.7 mmol/l). By comparison, the reduction from baseline in mean PPG values was 1.8 mmol/l for rosiglitazone and liraglutide 0.6 mg and 0.4 mmol/l for placebo. Treatment differences for PPG were greater with all doses of liraglutide compared with placebo (1.5–2.4 mmol/l; P < 0.0001) and greater with liraglutide 1.2 mg (0.64 mmol/l; P = 0.043) and 1.8 mg (0.87 mmol/l;P = 0.0022) compared with rosiglitazone.\n\nBODY.RESULTS.EFFICACY.PPG MEASUREMENTS < 10.0 MMOL/L:\nThe percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.\n\nBODY.RESULTS.BODY WEIGHT:\nMean weight at baseline was 81.6 kg. Mean reductions in weight from baseline to end of treatment were 0.2 kg with liraglutide 1.8 mg and 0.1 kg with placebo treatment, while increases occurred with either liraglutide 0.6 mg (0.7 kg), liraglutide 1.2 mg (0.3 kg) or rosiglitazone (2.1 kg) (Fig. 6). Unlike rosiglitazone, weight did not increase substantially with liraglutide and the differences between rosiglitazone and liraglutide were statistically significant (−2.3 to −1.4 kg; P < 0.0001), although there were no significant differences compared with placebo. Gender appeared to have no influence on the results, as indicated when added as a fixed effect in the ancova model. FIGURE 6Mean changes in body weight from baseline after 26 weeks of treatment. P < 0.05 compared with placebo; ††††P < 0.0001 compared with rosiglitazone.\n\nBODY.RESULTS.INDICES OF PANCREATIC B-CELL FUNCTION AND INSULIN RESISTANCE:\nReductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051). There were no significant differences between treatments for HOMA-IR. Table 2 Selected indices of pancreatic B-cell function Variable Treatment Baseline Week 26 (LOCF) Least square difference from placebo (95% CI) Least square difference from rosiglitazone (95% CI) Proinsulin : insulin ratio Liraglutide 0.6 mg 0.42 ± 0.22 0.38 ± 0.24 −0.05 (−0.11; 0.00) −0.02 (−0.06; 0.03) Liraglutide 1.2 mg 0.45 ± 0.31 0.33 ± 0.20 −0.10 (−0.16; −0.05) † −0.07 (−0.11; −0.02) * Liraglutide 1.8 mg 0.48 ± 0.33 0.36 ± 0.20 −0.09 (−0.15; −0.03) * −0.05 (−0.10; −0.01) * Placebo 0.44 ± 0.27 0.46 ± 0.29 Rosiglitazone 0.45 ± 0.29 0.40 ± 0.20 HOMA-B (%) Liraglutide 0.6 mg 51 ± 43.3 70 ± 88.6 15 (−19.10; 49.0) 11 (−16.7; 39.0) Liraglutide 1.2 mg 71 ± 254.3 99 ± 184.3 43 (8.10; 76.9) * 39 (10.3; 67.0) * Liraglutide 1.8 mg 56 ± 84.6 91 ± 108.2 34 (−0.23; 68.5) 30 (2.00; 58.6) * Placebo 56 ± 103.3 52 ± 107.3 Rosiglitazone 46 ± 36.2 59 ± 63.3 * P ≤ 0.05; † P < 0.0001. CI, confidence interval; HOMA, homeostatis model assessment; LOCF, last observation carried forward. \n\nBODY.RESULTS.BLOOD PRESSURE AND PULSE:\nAlthough decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments. Pulse increases above baseline ranged from 2 to 4 beats/min with the three doses of liraglutide and 1 beat/min with rosiglitazone, while pulse decreased by 1 beat/min with placebo. Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).\n\nBODY.RESULTS.SAFETY:\nThe most common treatment-emergent AEs that were considered by investigators to be either possibly or probably related to liraglutide were gastrointestinal (diarrhoea, nausea, dyspepsia and constipation) and nervous system disorders (headache and dizziness), particularly during the first 4 weeks. Nausea was highest with liraglutide 1.2 mg (10.5%) and lowest with placebo (1.8%). Vomiting (4.4%) and diarrhoea (7.9%) were also higher with liraglutide 1.2 mg. Withdrawals because of nausea ranged from 0.9–2.2%, vomiting 0.4–0.9% and diarrhoea 0–1.3%. Nausea was more common with liraglutide compared with placebo and rosiglitazone, particularly during the first 4 weeks (Fig. 7). Frequency of nausea was less in the liraglutide 0.6 mg treatment group compared with the higher doses of liraglutide. Generally, the occurrence of nausea dissipated from 4 to 26 weeks of treatment in all groups using liraglutide (Fig. 7). FIGURE 7Percentage of subjects experiencing nausea over the course of the study. Key: liraglutide 0.6 mg with glimepiride: black line with filled circles; liraglutide 1.2 mg with glimepiride: black line with filled triangles; liraglutide 1.8 mg with glimepiride: grey line with hollow circles; glimepiride grey lines with filled squares; rosiglitazone and glimepiride: grey line with hollow triangles. The incidence of serious AEs ranged between 3 and 5%: placebo (3%), rosiglitazone (3%), liraglutide 0.6 mg (3%), liraglutide 1.2 mg (4%) and liraglutide 1.8 mg (5%). Most treatment-emergent serious AEs were judged by investigators to be unlikely to be related to trial products. No deaths were reported during the trial. One subject developed chronic pancreatitis whilst taking liraglutide 0.6 mg; the person had no reported previous history of pancreatitis. The subject continued on liraglutide therapy and completed the trial. At screening, five patients had been previously diagnosed with pancreatitis. As pancreatitis was not an exclusion criterion, these patients were randomized as follows: one to liraglutide 0.6 mg, one to liraglutide 1.2 mg, two to liraglutide 1.8 mg and one to rosiglitazone + glimepiride. All five patients completed the trial without reporting pancreatitis as an adverse event. Hypoglycaemia was infrequent with all treatments. One major hypoglycaemic episode (self-measured blood glucose = 3.0 mmol/l) occurred 9 days after treatment started in a subject receiving liraglutide 1.8 mg in combination with glimepiride. Although medical assistance was not needed, the subject required third-party assistance. The investigator judged the episode as likely to be related to glimepiride and reduced the dose from 4 to 3 mg after the incident. Minor hypoglycaemia occurred in < 10% of subjects for any treatment. The proportion of subjects experiencing minor hypoglycaemia during the trial was lowest with placebo (i.e. glimepiride monotherapy 2.6%; 0.17 events/subject-year), comparable with liraglutide 0.6 mg (5.2%, 0.17 events/subject-year) and rosiglitazone (4.3%, 0.12 events/subject-year) groups and similar between the liraglutide 1.2 mg (9.2%, 0.51 events/subject-year) and liraglutide 1.8 mg (8.1%, 0.47 events/subject-year) treatment groups. Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values. Antibodies to liraglutide were found in 9–13% of subjects treated with liraglutide. No significant effects of these antibodies on HbA1c were found in pooled analyses of four trials including the current study. There were no clinically relevant changes in ophthalmoscopy, biochemistry, urinalysis, haematology or ECG assessments. No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.\n\nBODY.DISCUSSION:\nTreatment with liraglutide plus glimepiride was superior to glimepiride monotherapy at all doses of liraglutide and superior to rosiglitazone plus glimepiride for the two higher liraglutide doses for improving HbA1c. Similar findings for reductions in FPG and PPG highlight improved 24-h glucose control with once-daily liraglutide, with substantially more subjects reaching glycaemic targets, particularly with liraglutide 1.8 mg. Improvements in pancreatic B-cell function were larger with liraglutide 1.2 and 1.8 mg compared with rosiglitazone. Liraglutide was well tolerated and occurrence of gastrointestinal AEs was low overall, particularly after week 4. Although rates of hypoglycaemia were low in all treatment groups (< 10%), minor hypoglycaemic events occurred more often in patients treated with glimepiride plus liraglutide 1.2 or 1.8 mg than with glimepiride alone. It should be noted, however, that patients treated with liraglutide 1.2 or 1.8 mg achieved a lower HbA1c than those receiving glimepiride monotherapy. At lower HbA1c levels, sulphonylureas are known to elicit hypoglycaemia more readily than at higher levels. In clinical practice it may be possible to reduce the dose of sulphonylurea (when used with liraglutide) to minimize risk of hypoglycaemia and maintain HbA1cimprovements. Although weight effects were modest, liraglutide produced more favourable weight effects compared with rosiglitazone, which produced substantial weight gain. In other studies with liraglutide, subjects adding a 1.8-mg dose to metformin lost 2.8 kg [14], while those adding both metformin and glimepiride lost 1.8 kg compared with placebo [15] (both over 26 weeks) and those on liraglutide monotherapy (1.8 mg) lost 2.45 kg over 52 weeks [16]. In our study, because sulphonylureas usually cause weight gain, inclusion or optimization of glimepiride but not metformin may have mitigated the weight benefits typically associated with liraglutide. Lack of weight effects could be secondary to lower baseline body weight, withdrawal of previous metformin treatment or defensive snacking to minimize risk of hypoglycaemia. It might have been expected that the greater weight gain with rosiglitazone compared with liraglutide 1.8 mg would be associated with a concurrent increase in insulin resistance with rosiglitazone. The absence of this effect could reflect the insulin-sensitizing nature of rosiglitazone. Improvements in pancreatic B-cell function associated with liraglutide are consistent with other studies [7–9]. Study strengths include inclusion of both placebo and active (rosiglitazone) comparators and that OGLAs were optimized (not maximized) before randomization to minimize risk of hypoglycaemia. Limitations of the study include short duration of the trial and restriction on glimepiride and rosiglitazone in some countries that precluded maximal dosing. The impact of using other GLP-1-based treatments [such as exenatide, or the dipeptidyl peptidase-4 (DPP-4) inhibitor, sitagliptin] with sulphonylureas in subjects with T2D has been studied. In a 30-week American trial where exenatide twice a day was added to sulphonylureas, HbA1c was reduced by 0.46% from baseline with 5 μg and 0.86% with 10 μg [17] compared with 1.1% with liraglutide 1.8 or 1.2 mg. This reduction in HbA1c with liraglutide is consistent with other LEAD trials investigating liraglutide as monotherapy or in combination with various OGLA drugs. In these trials, HbA1c was reduced by 1–1.5%[14,16,18–20]. Reductions in FPG with exenatide were 0.3 and 0.6 mmol/l from baseline with 5 μg and 10 μg, respectively, compared with 1.4 mmol/l with liraglutide 1.8 mg; weight loss of 1.6 kg occurred with exenatide 10 μg compared with 0.2 kg for liraglutide 1.8 mg [17]. Differences in weight effects may be as a result of lower baseline weight in this trial (82 kg) compared with exenatide (96 kg) and discontinuation of previous metformin therapy, unlike the exenatide trial where exenatide was added to previous sulphonylurea monotherapy [17]. Other large-scale trials with liraglutide in combination with sulphonylureas have demonstrated weight loss of 2–3 kg [18,20]. Withdrawals from exenatide trials ranged from 24–30% compared with 9–14% with liraglutide in this study. Nausea with exenatide ranged from 39% with 5 μg to 51% with 10 μg [17] compared with 10.5% for liraglutide. Furthermore, 41% were positive for anti-exenatide antibodies compared with 9–13% with anti-liraglutide antibodies. With sitagliptin 100 mg once daily for 24 weeks, HbA1c decreased by 0.3% from baseline in subjects receiving glimepiride, with 11% achieving an HbA1c < 7.0%[21]. Reductions in FPG and PPG from baseline were 0.05 and 1.4 mmol/l, respectively, while weight increased by 0.8 kg and the prevalence of nausea was < 1%. Although head-to-head trials are required to test true differences between these agents, the marked effects of liraglutide on FPG may be as a result of consistent blood levels of liraglutide maintained over 24 h compared with exenatide which has to be administered 60 min before breakfast and dinner and has a half-life of 1.5–3.6 h [22]. In a recent 26-week head-to-head trial comparing liraglutide with exenatide, liraglutide produced a 0.3% greater decrease on HbA1c (P < 0.0001) [20]. Because DPP-4 inhibitors inhibit the degradation of GLP-1, the efficacy of sitagliptin is dependent on levels of endogenous GLP-1 which is physiologically low compared with the much higher pharmacological levels of liraglutide. Pharmacological levels may be needed to induce satiety, weight loss and possibly larger HbA1c reductions. Liraglutide is an effective and well-tolerated once-daily human GLP-1 analogue that improves overall glycaemic control and indices of pancreatic B-cell function with minimal weight gain and risk of hypoglycaemia when used in combination with a sulphonylurea for T2D.\n\nBODY.COMPETING INTERESTS:\nThe study was funded by Novo Nordisk, the manufacturer of liraglutide. In collaboration with the investigators, Novo Nordisk was responsible for the study design, protocol, statistical analysis plans, oversight, analysis and reporting of the results. Data were recorded at the clinical centres and maintained by the sponsor. The LEAD-1 SU study group had full access to the data. Final responsibility for the decision to submit the manuscript for publication was the authors. MM has received lecture fees from Novo Nordisk, Servier, MSD; JS has received honoraria, grants and lecture fees from Novo Nordisk; MB, WMWB and NAK have no conflicts to declare; JS has received lecture fees from Novo Nordisk; MZ is employed by, and holds stock in, Novo Nordisk; TLT is employed by Novo Nordisk; SC is a member of the international advisory board on liraglutide for Novo Nordisk and has received lecture fees from Novo Nordisk.", 'PMCID': 2871176, 'Prompts': {'PromptID': [150, 113, 140, 106, 142, 149, 148, 152, 154, 125, 121, 124, 107, 105, 133, 103, 126, 118, 132, 122, 141, 151, 112, 153, 102, 129, 104, 116, 136, 123, 135, 139, 101, 99, 144, 145, 147, 117, 143, 111, 137, 114, 108, 128, 134, 115, 127, 131, 109, 146, 110, 100, 138, 119, 130], 'PMCID': [2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176], 'Outcome': ['Incidence of minor hypoglycaemia', 'Patients reaching HbA1c goals less than 7.0% and equal or less than 6.5%', 'HOMA-IR', 'HbA1c level at 26 weeks', 'Reductions in systolic blood pressure', 'Pulse variations', 'Pulse variations', 'Incidence of minor hypoglycaemia', 'Changes in calcitonin at week 26', 'Postprandial plasma glucose', 'ADA fasting plasma glucose goals between 5.0 mmol/l and less than 7.2 mmol/l', 'Postprandial plasma glucose', 'HbA1c level at 26 weeks', 'HbA1c level at 26 weeks', 'Proinsulin : insulin ratio', 'Postprandial plasma glucose', 'ADA postprandial plasma glucose goals less than 10.0 mmol/l', 'ADA fasting plasma glucose goals between 5.0 mmol/l and less than 7.2 mmol/l', 'Proinsulin : insulin ratio', 'ADA fasting plasma glucose goals between 5.0 mmol/l and less than 7.2 mmol/l', 'Reductions in systolic blood pressure', 'Incidence of minor hypoglycaemia', 'Patients reaching HbA1c goals less than 7.0% and equal or less than 6.5%', 'Changes in calcitonin at week 26', 'Fasting plasma glucose at week 26', 'ADA postprandial plasma glucose goals less than 10.0 mmol/l', 'Postprandial plasma glucose', 'Fasting plasma glucose at week 26', 'HOMA-B', 'Postprandial plasma glucose', 'HOMA-B', 'HOMA-IR', 'Fasting plasma glucose at week 26', 'HbA1c level at 26 weeks', 'Reductions in systolic blood pressure', 'Decreases in diastolic blood pressure', 'Pulse variations', 'Fasting plasma glucose at week 26', 'Reductions in systolic blood pressure', 'Patients reaching HbA1c goals less than 7.0% and equal or less than 6.5%', 'HOMA-B', 'Patients reaching HbA1c goals less than 7.0% ', 'HbA1c level at 26 weeks', 'ADA postprandial plasma glucose goals less than 10.0 mmol/l', 'Proinsulin : insulin ratio', 'Fasting plasma glucose at week 26', 'ADA postprandial plasma glucose goals less than 10.0 mmol/l', 'Proinsulin : insulin ratio', 'HbA1c level at 26 weeks', 'Decreases in diastolic blood pressure', 'Patients reaching HbA1c goals less than 7.0% and equal or less than 6.5%', 'HbA1c level at 26 weeks', 'HOMA-B', 'ADA fasting plasma glucose goals between 5.0 mmol/l and less than 7.2 mmol/l', 'Weight gain'], 'Intervention': ['Liraglutide (1.2 mg) plus glimepiride', 'Liraglutide (1.8 mg) plus glimepiride', 'Liraglutide (all doses) plus glimepiride', 'Liraglutide (0.6 mg) plus glimepiride', 'Liraglutide (1.8 mg) plus glimepiride ', 'Liraglutide (1.8 mg) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride', 'Liraglutide (all doses) plus glimepiride', 'Liraglutide (1.8 mg) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride', 'Liraglutide (0.6 mg) plus glimepiride', 'Liraglutide (1.8 mg) plus glimepiride', 'Liraglutide (1.8 mg) plus glimepiride', 'Liraglutide (1.8 mg) plus glimepiride ', 'Liraglutide (1.2 mg) plus glimepiride', 'Liraglutide (0.6 mg) plus glimepiride', 'Liraglutide (0.6 mg) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride ', 'Liraglutide (1.8 mg) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride ', 'Liraglutide (1.8 mg) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride', 'Liraglutide (all doses) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride', 'Liraglutide (all doses) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride', 'Liraglutide (0.6 mg) plus glimepiride', 'Liraglutide (1.8 mg) plus glimepiride ', 'Liraglutide (1.8 mg) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride ', 'Liraglutide (all doses) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride', 'Liraglutide (1.8 mg) plus glimepiride ', 'Liraglutide (all doses) plus glimepiride', 'Liraglutide (all doses) plus glimepiride', 'Liraglutide (1.8 mg) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride ', 'Liraglutide (1.8 mg) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride ', 'Liraglutide (1.8 mg) plus glimepiride', 'Liraglutide (0.6 mg) plus glimepiride', 'Liraglutide (1.8 mg) plus glimepiride', 'Liraglutide (1.8 mg) plus glimepiride ', 'Liraglutide (1.8 mg) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride ', 'Rosiglitazone plus glimepiride', 'Liraglutide (all doses) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride', 'Liraglutide (1.8 mg) plus glimepiride ', 'Liraglutide (1.2 mg) plus glimepiride', 'Rosiglitazone plus glimepiride'], 'Comparator': ['Rosiglitazone plus glimepiride', 'Rosiglitazone plus glimepiride', 'Rosiglitazone plus glimepiride', 'Placebo plus glimepiride', 'Placebo plus glimepiride ', 'Rosiglitazone plus glimepiride', 'Rosiglitazone plus glimepiride', 'Placebo plus glimepiride', 'Placebo plus glimepiride', 'Rosiglitazone plus glimepiride', 'Rosiglitazone plus glimepiride', 'Placebo plus glimepiride', 'Rosiglitazone plus glimepiride', 'Placebo plus glimepiride', 'Rosiglitazone plus glimepiride ', 'Placebo plus glimepiride', 'Placebo plus glimepiride', 'Placebo plus glimepiride', 'Placebo plus glimepiride ', 'Rosiglitazone plus glimepiride', 'Placebo plus glimepiride ', 'Rosiglitazone plus glimepiride', 'Rosiglitazone plus glimepiride', 'Rosiglitazone plus glimepiride', 'Rosiglitazone plus glimepiride', 'Rosiglitazone plus glimepiride', 'Rosiglitazone plus glimepiride', 'Placebo plus glimepiride', 'Rosiglitazone plus glimepiride ', 'Placebo plus glimepiride', 'Rosiglitazone plus glimepiride ', 'Placebo plus glimepiride', 'Placebo plus glimepiride', 'Placebo plus glimepiride', 'Rosiglitazone plus glimepiride ', 'Placebo plus glimepiride', 'Placebo plus glimepiride', 'Rosiglitazone plus glimepiride', 'Rosiglitazone plus glimepiride ', 'Placebo plus glimepiride', 'Placebo plus glimepiride ', 'Liraglutide (1.2 mg) plus glimepiride', 'Rosiglitazone plus glimepiride', 'Placebo plus glimepiride', 'Placebo plus glimepiride ', 'Placebo plus glimepiride', 'Placebo plus glimepiride', 'Rosiglitazone plus glimepiride ', 'Placebo plus glimepiride', 'Rosiglitazone plus glimepiride', 'Placebo plus glimepiride', 'Rosiglitazone plus glimepiride', 'Placebo plus glimepiride ', 'Placebo plus glimepiride', 'Liraglutide plus glimepiride'], 'Annotations': [{'UserID': [0, 3, 2], 'PromptID': [150, 150, 150], 'PMCID': [2871176, 2871176, 2871176], 'Valid Label': [True, True, True], 'Valid Reasoning': [True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The proportion of subjects experiencing minor hypoglycaemia during the trial was lowest with placebo (i.e. glimepiride monotherapy 2.6%; 0.17 events/subject-year), comparable with liraglutide 0.6 mg (5.2%, 0.17 events/subject-year) and rosiglitazone (4.3%, 0.12 events/subject-year) groups and similar between the liraglutide 1.2 mg (9.2%, 0.51 events/subject-year) and liraglutide 1.8 mg (8.1%, 0.47 events/subject-year) treatment groups. Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.', 'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone', 'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.'], 'Label Code': [1, 1, 1], 'In Abstract': [True, True, True], 'Evidence Start': [25524, 25964, 25964], 'Evidence End': [26184, 26073, 26184]}, {'UserID': [0, 1, 3, 2], 'PromptID': [113, 113, 113, 113], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003)', 'he estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). ', 'The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), ', 'The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). '], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [16120, 16121, 16120, 16120], 'Evidence End': [16353, 16449, 16355, 16449]}, {'UserID': [0, 1, 3, 2], 'PromptID': [140, 140, 140, 140], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['There were no significant differences between treatments for HOMA-IR.', 'There were no significant differences between treatments for HOMA-IR.', 'There were no significant differences between treatments for HOMA-IR.', 'There were no significant differences between treatments for HOMA-IR.'], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [20943, 20943, 20943, 20943], 'Evidence End': [21012, 21012, 21012, 21012]}, {'UserID': [0, 1, 3, 2], 'PromptID': [106, 106, 106, 106], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['All liraglutide doses were superior to placebo (P < 0.0001)', 'Estimated treatment differences and 95% CIs to placebo were: liraglutide 1.8 mg: −1.4% (1.6, −1.1); liraglutide 1.2 mg: −1.3% (1.5, −1.1); liraglutide 0.6 mg: −0.8% (−1.1, −0.6); rosiglitazone: −0.7% (−0.9, −0.4). All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001). ', 'All liraglutide doses were superior to placebo (P < 0.0001),', 'All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001).'], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [14169, 13955, 14169, 14169], 'Evidence End': [14228, 14314, 14229, 14313]}, {'UserID': [0, 1, 3, 2], 'PromptID': [142, 142, 142, 142], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg)', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). ', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg)', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). '], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [22039, 22039, 22039, 22039], 'Evidence End': [22230, 22232, 22230, 22232]}, {'UserID': [0, 1, 3, 2], 'PromptID': [149, 149, 149, 149], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).', 'Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).', 'Pulse increases above baseline ranged from 2 to 4 beats/min with the three doses of liraglutide and 1 beat/min with rosiglitazone, while pulse decreased by 1 beat/min with placebo. Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002)', 'Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).'], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [22554, 22554, 22373, 22554], 'Evidence End': [22738, 22738, 22640, 22738]}, {'UserID': [0, 1, 3, 2], 'PromptID': [148, 148, 148, 148], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).', 'Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002)', 'Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).', 'Pulse increases above baseline ranged from 2 to 4 beats/min with the three doses of liraglutide and 1 beat/min with rosiglitazone, while pulse decreased by 1 beat/min with placebo. Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).'], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [22554, 22554, 22554, 22373], 'Evidence End': [22738, 22640, 22738, 22738]}, {'UserID': [0, 1, 3, 2], 'PromptID': [152, 152, 152, 152], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The proportion of subjects experiencing minor hypoglycaemia during the trial was lowest with placebo (i.e. glimepiride monotherapy 2.6%; 0.17 events/subject-year), comparable with liraglutide 0.6 mg (5.2%, 0.17 events/subject-year) and rosiglitazone (4.3%, 0.12 events/subject-year) groups and similar between the liraglutide 1.2 mg (9.2%, 0.51 events/subject-year) and liraglutide 1.8 mg (8.1%, 0.47 events/subject-year) treatment groups. Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.', 'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.', 'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048),', 'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.'], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [25524, 25964, 25964, 25964], 'Evidence End': [26184, 26184, 26131, 26184]}, {'UserID': [0, 1, 3, 2], 'PromptID': [154, 154, 154, 154], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.', 'No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.', 'No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.', 'No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.'], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [26515, 26515, 26515, 26515], 'Evidence End': [26703, 26703, 26703, 26703]}, {'UserID': [0, 1, 3, 2], 'PromptID': [125, 125, 125, 125], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Treatment differences for PPG were greater with all doses of liraglutide compared with placebo (1.5–2.4 mmol/l; P < 0.0001) and greater with liraglutide 1.2 mg (0.64 mmol/l; P = 0.043) and 1.8 mg (0.87 mmol/l;P = 0.0022) compared with rosiglitazone.', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). ', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). ', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). '], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [19128, 1469, 1469, 1469], 'Evidence End': [19377, 1756, 1756, 1756]}, {'UserID': [0, 3], 'PromptID': [121, 121], 'PMCID': [2871176, 2871176], 'Valid Label': [True, True], 'Valid Reasoning': [True, True], 'Label': ['significantly increased', 'significantly increased'], 'Annotations': ['The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). The liraglutide 1.2 and 1.8 mg treatment groups also had more subjects achieving the same FPG target at end of treatment compared with rosiglitazone (26%) (P = 0.007 and P = 0.01, respectively).', 'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). '], 'Label Code': [1, 1], 'In Abstract': [True, True], 'Evidence Start': [18230, 18230], 'Evidence End': [18670, 18476]}, {'UserID': [0, 1, 3, 2], 'PromptID': [124, 124, 124, 124], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Treatment differences for PPG were greater with all doses of liraglutide compared with placebo (1.5–2.4 mmol/l; P < 0.0001)', 'reatment differences for PPG were greater with all doses of liraglutide compared with placebo (1.5–2.4 mmol/l; P < 0.0001) and greater with liraglutide 1.2 mg (0.64 mmol/l; P = 0.043) and 1.8 mg (0.87 mmol/l;P = 0.0022) compared with rosiglitazone.', 'Treatment differences for PPG were greater with all doses of liraglutide compared with placebo (1.5–2.4 mmol/l; P < 0.0001) ', 'Treatment differences for PPG were greater with all doses of liraglutide compared with placebo (1.5–2.4 mmol/l; P < 0.0001) and greater with liraglutide 1.2 mg (0.64 mmol/l; P = 0.043) and 1.8 mg (0.87 mmol/l;P = 0.0022) compared with rosiglitazone.'], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [19128, 19129, 19128, 19128], 'Evidence End': [19251, 19377, 19252, 19377]}, {'UserID': [0, 1, 3, 2], 'PromptID': [107, 107, 107, 107], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Liraglutide (1.2 or 1.8 mg) produced greater reductions in HbA1c from baseline, (−1.1%, baseline 8.5%) compared with placebo (+0.2%, P < 0.0001, baseline 8.4%) or rosiglitazone (−0.4%, P < 0.0001, baseline 8.4%) when added to glimepiride.', 'After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). ', 'Liraglutide (1.2 or 1.8 mg) produced greater reductions in HbA1c from baseline, (−1.1%, baseline 8.5%) compared with placebo (+0.2%, P < 0.0001, baseline 8.4%) or rosiglitazone (−0.4%, P < 0.0001, baseline 8.4%) when added to glimepiride. ', 'After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). Estimated treatment differences and 95% CIs to placebo were: liraglutide 1.8 mg: −1.4% (1.6, −1.1); liraglutide 1.2 mg: −1.3% (1.5, −1.1); liraglutide 0.6 mg: −0.8% (−1.1, −0.6); rosiglitazone: −0.7% (−0.9, −0.4). All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001). Liraglutide 0.6 mg was non-inferior to rosiglitazone. Rosiglitazone also was superior to placebo (P < 0.0001). '], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [843, 13756, 843, 13756], 'Evidence End': [1081, 13955, 1082, 14426]}, {'UserID': [0, 1, 3, 2], 'PromptID': [105, 105, 105, 105], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Liraglutide (1.2 or 1.8 mg) produced greater reductions in HbA1c from baseline, (−1.1%, baseline 8.5%) compared with placebo (+0.2%, P < 0.0001, baseline 8.4%) or rosiglitazone (−0.4%, P < 0.0001, baseline 8.4%) when added to glimepiride.', 'After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). ', 'All liraglutide doses were superior to placebo (P < 0.0001),', 'All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001).'], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [843, 13756, 14169, 14169], 'Evidence End': [1081, 13955, 14229, 14313]}, {'UserID': [0, 1, 3, 2], 'PromptID': [133, 133, 133, 133], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02)', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). ', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02)', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). '], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [20566, 20566, 20566, 20566], 'Evidence End': [20726, 20728, 20726, 20728]}, {'UserID': [0, 1, 3, 2], 'PromptID': [103, 103, 103, 103], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l)', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). ', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) ', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). '], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [1469, 1469, 1469, 1469], 'Evidence End': [1691, 1756, 1692, 1756]}, {'UserID': [0, 1, 3, 2], 'PromptID': [126, 126, 126, 126], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone', 'The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.', 'The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05)', 'The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.'], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [19433, 19433, 19433, 19433], 'Evidence End': [19623, 19624, 19601, 19624]}, {'UserID': [0, 1, 3, 2], 'PromptID': [118, 118, 118, 118], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%).', 'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). ', 'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%)', 'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). '], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [18230, 18230, 18230, 18230], 'Evidence End': [18475, 18476, 18474, 18476]}, {'UserID': [0, 1, 2], 'PromptID': [132, 132, 132], 'PMCID': [2871176, 2871176, 2871176], 'Valid Label': [True, True, True], 'Valid Reasoning': [True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02)', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). ', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). '], 'Label Code': [-1, -1, -1], 'In Abstract': [True, True, True], 'Evidence Start': [20566, 20566, 20566], 'Evidence End': [20726, 20728, 20728]}, {'UserID': [0, 1, 1, 2], 'PromptID': [122, 122, 122, 122], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). The liraglutide 1.2 and 1.8 mg treatment groups also had more subjects achieving the same FPG target at end of treatment compared with rosiglitazone (26%) (P = 0.007 and P = 0.01, respectively).', 'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). ', 'The liraglutide 1.2 and 1.8 mg treatment groups also had more subjects achieving the same FPG target at end of treatment compared with rosiglitazone (26%) (P = 0.007 and P = 0.01, respectively).', 'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). The liraglutide 1.2 and 1.8 mg treatment groups also had more subjects achieving the same FPG target at end of treatment compared with rosiglitazone (26%) (P = 0.007 and P = 0.01, respectively).'], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [18230, 18230, 18476, 18230], 'Evidence End': [18670, 18476, 18670, 18670]}, {'UserID': [0, 1, 3, 2], 'PromptID': [141, 141, 141, 141], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg)', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). ', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo ', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). '], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [22039, 22039, 22039, 22039], 'Evidence End': [22230, 22232, 22199, 22232]}, {'UserID': [0, 1, 3, 2], 'PromptID': [151, 151, 151, 151], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The proportion of subjects experiencing minor hypoglycaemia during the trial was lowest with placebo (i.e. glimepiride monotherapy 2.6%; 0.17 events/subject-year), comparable with liraglutide 0.6 mg (5.2%, 0.17 events/subject-year) and rosiglitazone (4.3%, 0.12 events/subject-year) groups and similar between the liraglutide 1.2 mg (9.2%, 0.51 events/subject-year) and liraglutide 1.8 mg (8.1%, 0.47 events/subject-year) treatment groups. Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.', 'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.', 'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone', 'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.'], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [25524, 25964, 25964, 25964], 'Evidence End': [26184, 26184, 26073, 26184]}, {'UserID': [0, 1, 3, 2], 'PromptID': [112, 112, 112, 112], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003)', 'At week 26, 42% and 21% of subjects treated with liraglutide 1.8 mg reached an HbA1c < 7.0% and ≤ 6.5%, respectively, compared with 8% and 4% for placebo (Fig. 4). The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). ', 'The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). ', 'The percentage of subjects reaching ADA [2] and International Diabetes Federation (IDF)/American Association of Clinical Endocrinologists (AACE) [11,12] treatment HbA1c goals with liraglutide was dose dependent (Fig. 4). At week 26, 42% and 21% of subjects treated with liraglutide 1.8 mg reached an HbA1c < 7.0% and ≤ 6.5%, respectively, compared with 8% and 4% for placebo (Fig. 4). The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). '], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [16120, 15956, 16120, 15735], 'Evidence End': [16353, 16449, 16449, 16449]}, {'UserID': [0, 1, 3, 2], 'PromptID': [153, 153, 153, 153], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.', 'No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.', 'No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.', 'No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.'], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [26515, 26515, 26515, 26515], 'Evidence End': [26703, 26703, 26703, 26703]}, {'UserID': [0, 1, 3, 2], 'PromptID': [102, 102, 102, 102], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).', 'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).', 'An 0.7-mmol/l greater reduction in FPG was achieved with either liraglutide 1.2 or 1.8 mg compared with rosiglitazone (P ≤ 0.006) after 26 weeks. ', 'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).'], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [1144, 1144, 17914, 1144], 'Evidence End': [1468, 1468, 18061, 1468]}, {'UserID': [0, 1, 3, 2], 'PromptID': [129, 129, 129, 129], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.', 'The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.', 'The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.', 'The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.'], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [19433, 19433, 19433, 19433], 'Evidence End': [19624, 19624, 19624, 19624]}, {'UserID': [1, 2], 'PromptID': [104, 104], 'PMCID': [2871176, 2871176], 'Valid Label': [True, True], 'Valid Reasoning': [True, True], 'Label': ['significantly decreased', 'significantly decreased'], 'Annotations': ['Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). ', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). '], 'Label Code': [-1, -1], 'In Abstract': [True, True], 'Evidence Start': [1469, 1469], 'Evidence End': [1756, 1756]}, {'UserID': [0, 1, 3, 2], 'PromptID': [116, 116, 116, 116], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001)', 'By week 2, subjects treated with liraglutide had rapid and larger decreases in FPG vs. comparator treatment. At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001), while only liraglutide 1.2 or 1.8 mg produced greater reductions than rosiglitazone. FPG treatment differences to placebo were 1.7 mmol/l for liraglutide 0.6 mg and 2.6 mmol/l for both liraglutide 1.2 and 1.8 mg.', 'At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001),', 'At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001), while only liraglutide 1.2 or 1.8 mg produced greater reductions than rosiglitazone.'], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [17606, 17497, 17606, 17606], 'Evidence End': [17699, 17913, 17700, 17785]}, {'UserID': [0, 1, 3, 2], 'PromptID': [136, 136, 136, 136], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05)', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051).', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05),', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051).'], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [20728, 20728, 20728, 20728], 'Evidence End': [20816, 20942, 20817, 20942]}, {'UserID': [0, 1, 3, 2], 'PromptID': [123, 123, 123, 123], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l)', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). ', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) ', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). '], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [1469, 1469, 1469, 1469], 'Evidence End': [1691, 1756, 1692, 1756]}, {'UserID': [0, 1, 3, 2], 'PromptID': [135, 135, 135, 135], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05)', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051).', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05),', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051)'], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [20728, 20728, 20728, 20728], 'Evidence End': [20816, 20942, 20817, 20941]}, {'UserID': [0, 1, 3, 2], 'PromptID': [139, 139, 139, 139], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['There were no significant differences between treatments for HOMA-IR.', 'There were no significant differences between treatments for HOMA-IR.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTable 2', 'There were no significant differences between treatments for HOMA-IR.', 'There were no significant differences between treatments for HOMA-IR.'], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [20943, -1, 20943, 20943], 'Evidence End': [21012, -1, 21012, 21012]}, {'UserID': [0, 1, 3, 2], 'PromptID': [101, 101, 101, 101], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l)', 'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).', 'At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001)', 'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).'], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [1144, 1144, 17606, 1144], 'Evidence End': [1396, 1468, 17699, 1468]}, {'UserID': [0, 1, 3, 2], 'PromptID': [99, 99, 99, 99], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Liraglutide (1.2 or 1.8 mg) produced greater reductions in HbA1c from baseline, (−1.1%, baseline 8.5%) compared with placebo (+0.2%, P < 0.0001, baseline 8.4%)', 'After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). ', 'Liraglutide (1.2 or 1.8 mg) produced greater reductions in HbA1c from baseline, (−1.1%, baseline 8.5%) compared with placebo (+0.2%, P < 0.0001, baseline 8.4%) ', 'After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). Estimated treatment differences and 95% CIs to placebo were: liraglutide 1.8 mg: −1.4% (1.6, −1.1); liraglutide 1.2 mg: −1.3% (1.5, −1.1); liraglutide 0.6 mg: −0.8% (−1.1, −0.6); rosiglitazone: −0.7% (−0.9, −0.4). All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001)'], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [843, 13756, 843, 13756], 'Evidence End': [1002, 13955, 1003, 14312]}, {'UserID': [0, 1, 3, 2], 'PromptID': [144, 144, 144, 144], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg).', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). ', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). ', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). '], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [22039, 22039, 22039, 22039], 'Evidence End': [22231, 22232, 22232, 22232]}, {'UserID': [0, 1, 3, 2], 'PromptID': [145, 145, 145, 145], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments.', 'Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments. ', 'Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments. ', 'Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments. '], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [22232, 22232, 22232, 22232], 'Evidence End': [22372, 22373, 22373, 22373]}, {'UserID': [0, 1, 2], 'PromptID': [147, 147, 147], 'PMCID': [2871176, 2871176, 2871176], 'Valid Label': [True, True, True], 'Valid Reasoning': [True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).', 'Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). ', 'Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). '], 'Label Code': [1, 1, 1], 'In Abstract': [True, True, True], 'Evidence Start': [22554, 22554, 22554], 'Evidence End': [22738, 22642, 22642]}, {'UserID': [0, 1, 3, 2], 'PromptID': [117, 117, 117, 117], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).', 'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).', 'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).', 'By week 2, subjects treated with liraglutide had rapid and larger decreases in FPG vs. comparator treatment. At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001), while only liraglutide 1.2 or 1.8 mg produced greater reductions than rosiglitazone. FPG treatment differences to placebo were 1.7 mmol/l for liraglutide 0.6 mg and 2.6 mmol/l for both liraglutide 1.2 and 1.8 mg. An 0.7-mmol/l greater reduction in FPG was achieved with either liraglutide 1.2 or 1.8 mg compared with rosiglitazone (P ≤ 0.006) after 26 weeks. '], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [1144, 1144, 1144, 17497], 'Evidence End': [1468, 1468, 1468, 18061]}, {'UserID': [0, 1, 3, 2], 'PromptID': [143, 143, 143, 143], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg).', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). ', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). ', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). '], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [22039, 22039, 22039, 22039], 'Evidence End': [22231, 22232, 22232, 22232]}, {'UserID': [0, 1, 3, 2], 'PromptID': [111, 111, 111, 111], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001)', ' The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). FIGURE 4', 'At week 26, 42% and 21% of subjects treated with liraglutide 1.8 mg reached an HbA1c < 7.0% and ≤ 6.5%, respectively, compared with 8% and 4% for placebo ', 'The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). '], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [16120, 16119, 15956, 16120], 'Evidence End': [16315, 16457, 16110, 16449]}, {'UserID': [0, 1, 3, 2], 'PromptID': [137, 137, 137, 137], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051)', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051).', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01)', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051).'], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [20728, 20728, 20728, 20728], 'Evidence End': [20941, 20942, 20902, 20942]}, {'UserID': [0, 1], 'PromptID': [114, 114], 'PMCID': [2871176, 2871176], 'Valid Label': [True, True], 'Valid Reasoning': [True, True], 'Label': ['significantly increased', 'significantly increased'], 'Annotations': ['The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018).', 'At week 26, 42% and 21% of subjects treated with liraglutide 1.8 mg reached an HbA1c < 7.0% and ≤ 6.5%, respectively, compared with 8% and 4% for placebo (Fig. 4). The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). '], 'Label Code': [1, 1], 'In Abstract': [True, True], 'Evidence Start': [16120, 15956], 'Evidence End': [16447, 16449]}, {'UserID': [0, 1, 3, 2], 'PromptID': [108, 108, 108, 108], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['Liraglutide 0.6 mg was non-inferior to rosiglitazone', 'All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001). Liraglutide 0.6 mg was non-inferior to rosiglitazone.', 'Liraglutide 0.6 mg was non-inferior to rosiglitazone', '. All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001). Liraglutide 0.6 mg was non-inferior to rosiglitazone.'], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [14314, 14169, 14314, 14167], 'Evidence End': [14366, 14367, 14366, 14367]}, {'UserID': [0], 'PromptID': [128], 'PMCID': [2871176], 'Valid Label': [True], 'Valid Reasoning': [True], 'Label': ['significantly increased'], 'Annotations': ['The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone'], 'Label Code': [1], 'In Abstract': [True], 'Evidence Start': [19433], 'Evidence End': [19623]}, {'UserID': [0, 1, 2], 'PromptID': [134, 134, 134], 'PMCID': [2871176, 2871176, 2871176], 'Valid Label': [True, True, True], 'Valid Reasoning': [True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02)', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). ', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), '], 'Label Code': [-1, -1, -1], 'In Abstract': [True, True, True], 'Evidence Start': [20566, 20566, 20566], 'Evidence End': [20726, 20728, 20818]}, {'UserID': [0, 1, 3, 2], 'PromptID': [115, 115, 115, 115], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l)', 'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).', 'At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001)', 'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).'], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [1144, 1144, 17606, 1144], 'Evidence End': [1396, 1468, 17699, 1468]}, {'UserID': [0, 1, 2], 'PromptID': [127, 127, 127], 'PMCID': [2871176, 2871176, 2871176], 'Valid Label': [True, True, True], 'Valid Reasoning': [True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone', 'he percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.', 'The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.'], 'Label Code': [1, 1, 1], 'In Abstract': [True, True, True], 'Evidence Start': [19433, 19434, 19433], 'Evidence End': [19623, 19624, 19624]}, {'UserID': [0, 1, 3, 2], 'PromptID': [131, 131, 131, 131], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02)', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). ', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). ', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02)'], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [20566, 20566, 20566, 20566], 'Evidence End': [20726, 20728, 20728, 20726]}, {'UserID': [0, 1, 1, 3, 2], 'PromptID': [109, 109, 109, 109, 109], 'PMCID': [2871176, 2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True, True], 'Valid Reasoning': [True, True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Rosiglitazone also was superior to placebo (P < 0.0001)', 'Rosiglitazone also was superior to placebo (P < 0.0001).', ' The greatest decreases occurred with liraglutide 1.2 and 1.8 mg (Fig. 3a–c). After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). Estimated treatment differences and 95% CIs to placebo were: liraglutide 1.8 mg: −1.4% (1.6, −1.1); liraglutide 1.2 mg: −1.3% (1.5, −1.1); liraglutide 0.6 mg: −0.8% (−1.1, −0.6); rosiglitazone: −0.7% (−0.9, −0.4). All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001). Liraglutide 0.6 mg was non-inferior to rosiglitazone. ', 'Rosiglitazone also was superior to placebo (P < 0.0001).', 'Rosiglitazone also was superior to placebo (P < 0.0001).'], 'Label Code': [-1, -1, -1, -1, -1], 'In Abstract': [True, True, True, True, True], 'Evidence Start': [14368, 14368, 13678, 14368, 14368], 'Evidence End': [14423, 14424, 14368, 14424, 14424]}, {'UserID': [0, 1, 3, 2], 'PromptID': [146, 146, 146, 146], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments.', 'Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments. ', 'Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments. ', 'Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments. '], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [22232, 22232, 22232, 22232], 'Evidence End': [22372, 22373, 22373, 22373]}, {'UserID': [0, 1, 3, 2], 'PromptID': [110, 110, 110, 110], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001)', 'The percentage of subjects reaching ADA [2] and International Diabetes Federation (IDF)/American Association of Clinical Endocrinologists (AACE) [11,12] treatment HbA1c goals with liraglutide was dose dependent (Fig. 4). At week 26, 42% and 21% of subjects treated with liraglutide 1.8 mg reached an HbA1c < 7.0% and ≤ 6.5%, respectively, compared with 8% and 4% for placebo (Fig. 4). The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). ', 'The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). ', 'The percentage of subjects reaching ADA [2] and International Diabetes Federation (IDF)/American Association of Clinical Endocrinologists (AACE) [11,12] treatment HbA1c goals with liraglutide was dose dependent (Fig. 4). At week 26, 42% and 21% of subjects treated with liraglutide 1.8 mg reached an HbA1c < 7.0% and ≤ 6.5%, respectively, compared with 8% and 4% for placebo (Fig. 4). The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). '], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [16120, 15735, 16120, 15735], 'Evidence End': [16315, 16449, 16449, 16449]}, {'UserID': [1, 3, 2], 'PromptID': [100, 100, 100], 'PMCID': [2871176, 2871176, 2871176], 'Valid Label': [True, True, True], 'Valid Reasoning': [True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). ', 'After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) ', 'HbA1c decreased rapidly with all doses of liraglutide when added to glimepiride compared with either rosiglitazone or placebo (i.e. glimepiride monotherapy), irrespective of previous therapy. The greatest decreases occurred with liraglutide 1.2 and 1.8 mg (Fig. 3a–c). After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). Estimated treatment differences and 95% CIs to placebo were: liraglutide 1.8 mg: −1.4% (1.6, −1.1); liraglutide 1.2 mg: −1.3% (1.5, −1.1); liraglutide 0.6 mg: −0.8% (−1.1, −0.6); rosiglitazone: −0.7% (−0.9, −0.4). All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001). '], 'Label Code': [-1, -1, -1], 'In Abstract': [True, True, True], 'Evidence Start': [13756, 13756, 13487], 'Evidence End': [13955, 13944, 14314]}, {'UserID': [0, 1, 3, 2], 'PromptID': [138, 138, 138, 138], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051)', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051).', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051)', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051).'], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [20728, 20728, 20728, 20728], 'Evidence End': [20941, 20942, 20941, 20942]}, {'UserID': [0, 1, 3, 2], 'PromptID': [119, 119, 119, 119], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%).', 'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). ', 'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001)', 'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). '], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [18230, 18230, 18230, 18230], 'Evidence End': [18475, 18476, 18419, 18476]}, {'UserID': [0, 3, 2], 'PromptID': [130, 130, 130], 'PMCID': [2871176, 2871176, 2871176], 'Valid Label': [True, True, True], 'Valid Reasoning': [True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['Unlike rosiglitazone, weight did not increase substantially with liraglutide and the differences between rosiglitazone and liraglutide were statistically significant (−2.3 to −1.4 kg; P < 0.0001)', 'Changes in body weight with liraglutide 1.8 mg (−0.2 kg, baseline 83.0 kg), 1.2 mg (+0.3 kg, baseline 80.0 kg) or placebo (−0.1 kg, baseline 81.9 kg) were less than with rosiglitazone (+2.1 kg, P < 0.0001, baseline 80.6 kg)', 'Unlike rosiglitazone, weight did not increase substantially with liraglutide and the differences between rosiglitazone and liraglutide were statistically significant (−2.3 to −1.4 kg; P < 0.0001), although there were no significant differences compared with placebo. '], 'Label Code': [1, 1, 1], 'In Abstract': [True, True, True], 'Evidence Start': [19950, 1756, 19950], 'Evidence End': [20145, 1979, 20217]}]}} ``` ### Data Fields - `PMCID` (`int`): ID to identify the articles. - `Text` (`str`): Article text. - `Prompts` (`dict`): Prompts and annotations with keys: - 'PromptID': Which prompt the doctor is answering. - 'PMCID' - 'Outcome': Represent the fill-in-the-blank input for the following prompt formed "With respect to outcome, characterize the reported difference between intervention and those receiving comparator". - 'Intervention': Represent the fill-in-the-blank input for the following prompt formed "With respect to outcome, characterize the reported difference between intervention and those receiving comparator". - 'Comparator': Represent the fill-in-the-blank input for the following prompt formed "With respect to outcome, characterize the reported difference between intervention and those receiving comparator". - 'Annotations': The annotation files consist of the following headings: UserID, PromptID, PMCID, Valid Label, Valid Reasoning, Label, Annotations, Label Code, In Abstract, Start Evidence, End Evidence. ### Data Splits \| name \| train \| validation \| test \| \|------\|------:\|-----------:\|-----:\| \| 1.1 \| 1931 \| 248 \| 240 \| \| 2.0 \| 2690 \| 340 \| 334 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{lehman2019inferring, title={Inferring Which Medical Treatments Work from Reports of Clinical Trials}, author={Lehman, Eric and DeYoung, Jay and Barzilay, Regina and Wallace, Byron C}, booktitle={Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL)}, pages={3705--3717}, year={2019} } @misc{deyoung2020evidence, title={Evidence Inference 2.0: More Data, Better Models}, author={Jay DeYoung and Eric Lehman and Ben Nye and Iain J. Marshall and Byron C. Wallace}, year={2020}, eprint={2005.04177}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@Narsil](https://github.com/Narsil) for adding this dataset.
exams	--- pretty_name: EXAMS annotations_creators: - found language_creators: - found language: - ar - bg - de - es - fr - hr - hu - it - lt - mk - pl - pt - sq - sr - tr - vi license: - cc-by-sa-4.0 multilinguality: - monolingual - multilingual size_categories: - 10K<n<100K - 1K<n<10K - n<1K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: exams configs: - alignments - crosslingual_bg - crosslingual_hr - crosslingual_hu - crosslingual_it - crosslingual_mk - crosslingual_pl - crosslingual_pt - crosslingual_sq - crosslingual_sr - crosslingual_test - crosslingual_tr - crosslingual_vi - crosslingual_with_para_bg - crosslingual_with_para_hr - crosslingual_with_para_hu - crosslingual_with_para_it - crosslingual_with_para_mk - crosslingual_with_para_pl - crosslingual_with_para_pt - crosslingual_with_para_sq - crosslingual_with_para_sr - crosslingual_with_para_test - crosslingual_with_para_tr - crosslingual_with_para_vi - multilingual - multilingual_with_para dataset_info: - config_name: alignments features: - name: source_id dtype: string - name: target_id_list sequence: string splits: - name: full num_bytes: 1265280 num_examples: 10834 download_size: 169745177 dataset_size: 1265280 - config_name: multilingual features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 3385865 num_examples: 7961 - name: validation num_bytes: 1143067 num_examples: 2672 - name: test num_bytes: 5753625 num_examples: 13510 download_size: 169745177 dataset_size: 10282557 - config_name: multilingual_with_para features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 127298595 num_examples: 7961 - name: validation num_bytes: 42713069 num_examples: 2672 - name: test num_bytes: 207981218 num_examples: 13510 download_size: 169745177 dataset_size: 377992882 - config_name: crosslingual_test features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: test num_bytes: 8412531 num_examples: 19736 download_size: 169745177 dataset_size: 8412531 - config_name: crosslingual_with_para_test features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: test num_bytes: 207981218 num_examples: 13510 download_size: 169745177 dataset_size: 207981218 - config_name: crosslingual_bg features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 1078545 num_examples: 2344 - name: validation num_bytes: 282115 num_examples: 593 download_size: 169745177 dataset_size: 1360660 - config_name: crosslingual_with_para_bg features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 47068024 num_examples: 2344 - name: validation num_bytes: 11916370 num_examples: 593 download_size: 169745177 dataset_size: 58984394 - config_name: crosslingual_hr features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 808320 num_examples: 2341 - name: validation num_bytes: 176910 num_examples: 538 download_size: 169745177 dataset_size: 985230 - config_name: crosslingual_with_para_hr features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 24890820 num_examples: 2341 - name: validation num_bytes: 5695382 num_examples: 538 download_size: 169745177 dataset_size: 30586202 - config_name: crosslingual_hu features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 678447 num_examples: 1731 - name: validation num_bytes: 202324 num_examples: 536 download_size: 169745177 dataset_size: 880771 - config_name: crosslingual_with_para_hu features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 19036575 num_examples: 1731 - name: validation num_bytes: 6043577 num_examples: 536 download_size: 169745177 dataset_size: 25080152 - config_name: crosslingual_it features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 399864 num_examples: 1010 - name: validation num_bytes: 93343 num_examples: 246 download_size: 169745177 dataset_size: 493207 - config_name: crosslingual_with_para_it features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 16409787 num_examples: 1010 - name: validation num_bytes: 4018497 num_examples: 246 download_size: 169745177 dataset_size: 20428284 - config_name: crosslingual_mk features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 826582 num_examples: 1665 - name: validation num_bytes: 204570 num_examples: 410 download_size: 169745177 dataset_size: 1031152 - config_name: crosslingual_with_para_mk features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 38446774 num_examples: 1665 - name: validation num_bytes: 9673826 num_examples: 410 download_size: 169745177 dataset_size: 48120600 - config_name: crosslingual_pl features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 574246 num_examples: 1577 - name: validation num_bytes: 141877 num_examples: 394 download_size: 169745177 dataset_size: 716123 - config_name: crosslingual_with_para_pl features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 16374617 num_examples: 1577 - name: validation num_bytes: 4159076 num_examples: 394 download_size: 169745177 dataset_size: 20533693 - config_name: crosslingual_pt features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 375214 num_examples: 740 - name: validation num_bytes: 87850 num_examples: 184 download_size: 169745177 dataset_size: 463064 - config_name: crosslingual_with_para_pt features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 12185799 num_examples: 740 - name: validation num_bytes: 3093848 num_examples: 184 download_size: 169745177 dataset_size: 15279647 - config_name: crosslingual_sq features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 424388 num_examples: 1194 - name: validation num_bytes: 110293 num_examples: 311 download_size: 169745177 dataset_size: 534681 - config_name: crosslingual_with_para_sq features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 17341921 num_examples: 1194 - name: validation num_bytes: 4450152 num_examples: 311 download_size: 169745177 dataset_size: 21792073 - config_name: crosslingual_sr features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 650268 num_examples: 1323 - name: validation num_bytes: 145928 num_examples: 314 download_size: 169745177 dataset_size: 796196 - config_name: crosslingual_with_para_sr features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 24576553 num_examples: 1323 - name: validation num_bytes: 5772713 num_examples: 314 download_size: 169745177 dataset_size: 30349266 - config_name: crosslingual_tr features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 718431 num_examples: 1571 - name: validation num_bytes: 182974 num_examples: 393 download_size: 169745177 dataset_size: 901405 - config_name: crosslingual_with_para_tr features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 18597963 num_examples: 1571 - name: validation num_bytes: 4763341 num_examples: 393 download_size: 169745177 dataset_size: 23361304 - config_name: crosslingual_vi features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 954191 num_examples: 1955 - name: validation num_bytes: 232264 num_examples: 488 download_size: 169745177 dataset_size: 1186455 - config_name: crosslingual_with_para_vi features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 40884023 num_examples: 1955 - name: validation num_bytes: 10260662 num_examples: 488 download_size: 169745177 dataset_size: 51144685 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Repository: https://github.com/mhardalov/exams-qa - Paper: [EXAMS: A Multi-Subject High School Examinations Dataset for Cross-Lingual and Multilingual Question Answering](https://arxiv.org/abs/2011.03080) - Point of Contact: [hardalov@@fmi.uni-sofia.bg](hardalov@@fmi.uni-sofia.bg) ### Dataset Summary EXAMS is a benchmark dataset for multilingual and cross-lingual question answering from high school examinations. It consists of more than 24,000 high-quality high school exam questions in 16 languages, covering 8 language families and 24 school subjects from Natural Sciences and Social Sciences, among others. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The languages in the dataset are: - ar - bg - de - es - fr - hr - hu - it - lt - mk - pl - pt - sq - sr - tr - vi ## Dataset Structure ### Data Instances An example of a data instance (with support paragraphs, in Bulgarian) is: ``` {'answerKey': 'C', 'id': '35dd6b52-7e71-11ea-9eb1-54bef70b159e', 'info': {'grade': 12, 'language': 'Bulgarian', 'subject': 'Biology'}, 'question': {'choices': {'label': ['A', 'B', 'C', 'D'], 'para': ['Това води до наследствени изменения между организмите. Мирновременните вождове са наследствени. Черният, сивият и кафявият цвят на оцветяване на тялото се определя от пигмента меланин и възниква в резултат на наследствени изменения. Тези различия, според Монтескьо, не са наследствени. Те са и важни наследствени вещи в клана. Те са били наследствени архонти и управляват демократично. Реликвите са исторически, религиозни, семейни (наследствени) и технически. Общо са направени 800 изменения. Не всички наследствени аномалии на хемоглобина са вредни, т.е. Моногенните наследствени болести, които водят до мигрена, са редки. Няма наследствени владетели. Повечето от тях са наследствени и се предават на потомството. Всичките синове са ерцхерцози на всичките наследствени земи и претенденти. През 1509 г. Фраунбергите са издигнати на наследствени имперски графове. Фамилията Валдбург заради постиженията са номинирани на „наследствени имперски трушсеси“. Фамилията Валдбург заради постиженията са номинирани на „наследствени имперски трушсеси“. Описани са единични наследствени случаи, но по-често липсва фамилна обремененост. Позициите им са наследствени и се предават в рамките на клана. Внесени са изменения в конструкцията на веригите. и са направени изменения в ходовата част. На храма са правени лоши архитектурни изменения. Изменения са предприети и вътре в двореца. Имало двама наследствени вождове. Имало двама наследствени вождове. Годишният календар, „компасът“ и биологичния часовник са наследствени и при много бозайници.', 'Постепенно задълбочаващите се функционални изменения довеждат и до структурни изменения. Те се дължат както на растягането на кожата, така и на въздействието на хормоналните изменения върху кожната тъкан. тези изменения се долавят по-ясно. Впоследствие, той претърпява изменения. Ширината остава без изменения. След тяхното издаване се налагат изменения в първоначалния Кодекс, защото не е съобразен с направените в Дигестите изменения. Еволюционният преход се характеризира със следните изменения: Наблюдават се и сезонни изменения в теглото. Приемат се изменения и допълнения към Устава. Тук се размножават и предизвикват възпалителни изменения. Общо са направени 800 изменения. Бронирането не претърпява съществени изменения. При животните се откриват изменения при злокачествената форма. Срещат се и дегенеративни изменения в семенните каналчета. ТАВКР „Баку“ се строи по изменения проект 1143.4. Трансът се съпровожда с определени изменения на мозъчната дейност. На изменения е подложен и Светия Синод. Внесени са изменения в конструкцията на веригите. На храма са правени лоши архитектурни изменения. Оттогава стиховете претърпяват изменения няколко пъти. Настъпват съществени изменения в музикалната култура. По-късно той претърпява леки изменения. Настъпват съществени изменения в музикалната култура. Претърпява сериозни изменения само носовата надстройка. Хоризонталното брониране е оставено без изменения.', 'Модификациите са обратими. Тези реакции са обратими. В началните стадии тези натрупвания са обратими. Всички такива ефекти са временни и обратими. Много от реакциите са обратими и идентични с тези при гликолизата. Ако в обращение има книжни пари, те са обратими в злато при поискване . Общо са направени 800 изменения. Непоследователността е представена от принципа на "симетрия", при който взаимоотношенията са разглеждани като симетрични или обратими. Откакто формулите в клетките на електронната таблица не са обратими, тази техника е с ограничена стойност. Ефектът на Пелтие-Зеебек и ефектът Томсън са обратими (ефектът на Пелтие е обратен на ефекта на Зеебек). Плазмолизата протича в три етапа, в зависимост от силата и продължителността на въздействието:\n\nПървите два етапа са обратими. Внесени са изменения в конструкцията на веригите. и са направени изменения в ходовата част. На храма са правени лоши архитектурни изменения. Изменения са предприети и вътре в двореца. Оттогава насетне екипите не са претърпявали съществени изменения. Изменения са направени и в колесника на машината. Тези изменения са обявени през октомври 1878 година. Последните изменения са внесени през януари 2009 година. В процеса на последващото проектиране са внесени някои изменения. Сериозните изменения са в края на Втората световна война. Внесени са изменения в конструкцията на погребите и подемниците. Внесени са изменения в конструкцията на погребите и подемниците. Внесени са изменения в конструкцията на погребите и подемниците. Постепенно задълбочаващите се функционални изменения довеждат и до структурни изменения.', 'Ерозионни процеси от масов характер липсват. Обновлението в редиците на партията приема масов характер. Тя обаче няма масов характер поради спецификата на формата. Движението против десятъка придобива масов характер и в Балчишка околия. Понякога екзекутирането на „обсебените от Сатана“ взимало невероятно масов характер. Укриването на дължими като наряд продукти в селата придобива масов характер. Периодичните миграции са в повечето случаи с масов характер и са свързани със сезонните изменения в природата, а непериодичните са премествания на животни, които настъпват след пожари, замърсяване на средата, висока численост и др. Имат необратим характер. Именно по време на двувековните походи на западните рицари използването на гербовете придобива масов характер. След присъединяването на Южен Кавказ към Русия, изселването на азербайджанци от Грузия придобива масов характер. Те имат нормативен характер. Те имат установителен характер. Освобождаването на работна сила обикновено има масов характер, защото обхваща големи контингенти от носителите на труд. Валежите имат подчертано континентален характер. Имат най-често издънков характер. Приливите имат предимно полуденонощен характер. Някои от тях имат мистериален характер. Тези сведения имат случаен, епизодичен характер. Те имат сезонен или годишен характер. Временните обезпечителни мерки имат временен характер. Други имат пожелателен характер (Здравко, Слава). Ловът и събирачеството имат спомагателен характер. Фактически успяват само малко да усилят бронирането на артилерийските погреби, другите изменения носят само частен характер. Някои карикатури имат само развлекателен характер, докато други имат политически нюанси. Поемите на Хезиод имат по-приложен характер.'], 'text': ['дължат се на фенотипни изменения', 'имат масов характер', 'са наследствени', 'са обратими']}, 'stem': 'Мутационите изменения:'}} ``` ### Data Fields A data instance contains the following fields: - `id`: A question ID, unique across the dataset - `question`: the question contains the following: - `stem`: a stemmed representation of the question textual - `choices`: a set of 3 to 5 candidate answers, which each have: - `text`: the text of the answers - `label`: a label in `['A', 'B', 'C', 'D', 'E']` used to match to the `answerKey` - `para`: (optional) a supported paragraph from Wikipedia in the same language as the question and answer - `answerKey`: the key corresponding to the right answer's `label` - `info`: some additional information on the question including: - `grade`: the school grade for the exam this question was taken from - `subject`: a free text description of the academic subject - `language`: the English name of the language for this question ### Data Splits Depending on the configuration, the dataset have different splits: - "alignments": a single "full" split - "multilingual" and "multilingual_with_para": "train", "validation" and "test" splits - "crosslingual_test" and "crosslingual_with_para_test": a single "test" split - the rest of crosslingual configurations: "train" and "validation" splits ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Eχαµs was collected from official state exams prepared by the ministries of education of various countries. These exams are taken by students graduating from high school, and often require knowledge learned through the entire course. The questions cover a large variety of subjects and material based on the country’s education system. They cover major school subjects such as Biology, Chemistry, Geography, History, and Physics, but we also highly specialized ones such as Agriculture, Geology, Informatics, as well as some applied and profiled studies. Some countries allow students to take official examinations in several languages. This dataset provides 9,857 parallel question pairs spread across seven languages coming from Croatia (Croatian, Serbian, Italian, Hungarian), Hungary (Hungarian, German, French, Spanish, Croatian, Serbian, Italian), and North Macedonia (Macedonian, Albanian, Turkish). For all languages in the dataset, the first step in the process of data collection was to download the PDF files per year, per subject, and per language (when parallel languages were available in the same source), convert the PDF files to text, and select those that were well formatted and followed the document structure. Then, Regular Expressions (RegEx) were used to parse the questions, their corresponding choices and the correct answer choice. In order to ensure that all our questions are answerable using textual input only, questions that contained visual information were removed, as selected by using curated list of words such as map, table, picture, graph, etc., in the corresponding language. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The dataset, which contains paragraphs from Wikipedia, is licensed under CC-BY-SA 4.0. The code in this repository is licensed according the [LICENSE file](https://raw.githubusercontent.com/mhardalov/exams-qa/main/LICENSE). ### Citation Information ``` @article{hardalov2020exams, title={EXAMS: A Multi-subject High School Examinations Dataset for Cross-lingual and Multilingual Question Answering}, author={Hardalov, Momchil and Mihaylov, Todor and Dimitrina Zlatkova and Yoan Dinkov and Ivan Koychev and Preslav Nvakov}, journal={arXiv preprint arXiv:2011.03080}, year={2020} } ``` ### Contributions Thanks to [@yjernite](https://github.com/yjernite) for adding this dataset.
factckbr	--- annotations_creators: - expert-generated language_creators: - found language: - pt license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - fact-checking pretty_name: FACTCK BR dataset_info: features: - name: url dtype: string - name: author dtype: string - name: date dtype: string - name: claim dtype: string - name: review dtype: string - name: title dtype: string - name: rating dtype: float32 - name: best_rating dtype: float32 - name: label dtype: class_label: names: '0': falso '1': distorcido '2': impreciso '3': exagerado '4': insustentável '5': verdadeiro '6': outros '7': subestimado '8': impossível provar '9': discutível '10': sem contexto '11': de olho '12': verdadeiro, mas '13': ainda é cedo para dizer splits: - name: train num_bytes: 750646 num_examples: 1313 download_size: 721314 dataset_size: 750646 --- # Dataset Card for FACTCK BR ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/jghm-f/FACTCK.BR - Repository: https://github.com/jghm-f/FACTCK.BR - Paper: https://dl.acm.org/doi/10.1145/3323503.3361698 - Leaderboard: - Point of Contact: ### Dataset Summary A dataset to study Fake News in Portuguese, presenting a supposedly false News along with their respective fact check and classification. The data is collected from the ClaimReview, a structured data schema used by fact check agencies to share their results in search engines, enabling data collect in real time. The FACTCK.BR dataset contains 1309 claims with its corresponding label. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@hugoabonizio](https://github.com/hugoabonizio) for adding this dataset.
fake_news_english	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - unknown multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - text-classification task_ids: - multi-label-classification pretty_name: Fake News English dataset_info: features: - name: article_number dtype: int32 - name: url_of_article dtype: string - name: fake_or_satire dtype: class_label: names: '0': Satire '1': Fake - name: url_of_rebutting_article dtype: string splits: - name: train num_bytes: 78078 num_examples: 492 download_size: 3002233 dataset_size: 78078 --- # Dataset Card for Fake News English ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://dl.acm.org/doi/10.1145/3201064.3201100 - Repository: https://github.com/jgolbeck/fakenews/ - Paper: https://doi.org/10.1145/3201064.3201100 - Leaderboard: - Point of Contact:** Jennifer Golbeck (http://www.jengolbeck.com) ### Dataset Summary This dataset contains URLs of news articles classified as either fake or satire. The articles classified as fake also have the URL of a rebutting article. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure ### Data Instances ``` { "article_number": 102 , "url_of_article": https://newslo.com/roger-stone-blames-obama-possibility-trump-alzheimers-attacks-president-caused-severe-stress/ , "fake_or_satire": 1, # Fake "url_of_rebutting_article": https://www.snopes.com/fact-check/donald-trumps-intelligence-quotient/ } ``` ### Data Fields - article_number: An integer used as an index for each row - url_of_article: A string which contains URL of an article to be assessed and classified as either Fake or Satire - fake_or_satire: A classlabel for the above variable which can take two values- Fake (1) and Satire (0) - url_of_rebutting_article: A string which contains a URL of the article used to refute the article in question (present - in url_of_article) ### Data Splits This dataset is not split, only the train split is available. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Golbeck, Jennifer Everett, Jennine Falak, Waleed Gieringer, Carl Graney, Jack Hoffman, Kelly Huth, Lindsay Ma, Zhenya Jha, Mayanka Khan, Misbah Kori, Varsha Mauriello, Matthew Lewis, Elo Mirano, George IV, William Mussenden, Sean Nelson, Tammie Mcwillie, Sean Pant, Akshat Cheakalos, Paul ### Licensing Information [More Information Needed] ### Citation Information @inproceedings{inproceedings, author = {Golbeck, Jennifer and Everett, Jennine and Falak, Waleed and Gieringer, Carl and Graney, Jack and Hoffman, Kelly and Huth, Lindsay and Ma, Zhenya and Jha, Mayanka and Khan, Misbah and Kori, Varsha and Mauriello, Matthew and Lewis, Elo and Mirano, George and IV, William and Mussenden, Sean and Nelson, Tammie and Mcwillie, Sean and Pant, Akshat and Cheakalos, Paul}, year = {2018}, month = {05}, pages = {17-21}, title = {Fake News vs Satire: A Dataset and Analysis}, doi = {10.1145/3201064.3201100} } ### Contributions Thanks to [@MisbahKhan789](https://github.com/MisbahKhan789), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
fake_news_filipino	--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - tl license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - fact-checking paperswithcode_id: fake-news-filipino-dataset pretty_name: Fake News Filipino dataset_info: features: - name: label dtype: class_label: names: '0': '0' '1': '1' - name: article dtype: string splits: - name: train num_bytes: 3623685 num_examples: 3206 download_size: 1313458 dataset_size: 3623685 --- # Dataset Card for Fake News Filipino ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Fake News Filipino homepage](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks) - Repository: [Fake News Filipino repository](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks) - Paper: [LREC 2020 paper](http://www.lrec-conf.org/proceedings/lrec2020/index.html) - Leaderboard: - Point of Contact: [Jan Christian Cruz](mailto:jan_christian_cruz@dlsu.edu.ph) ### Dataset Summary Low-Resource Fake News Detection Corpora in Filipino. The first of its kind. Contains 3,206 expertly-labeled news samples, half of which are real and half of which are fake. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset is primarily in Filipino, with the addition of some English words commonly used in Filipino vernacular. ## Dataset Structure ### Data Instances Sample data: ``` { "label": "0", "article": "Sa 8-pahinang desisyon, pinaboran ng Sandiganbayan First Division ang petition for Writ of Preliminary Attachment/Garnishment na inihain ng prosekusyon laban sa mambabatas." } ``` ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation Fake news articles were sourced from online sites that were tagged as fake news sites by the non-profit independent media fact-checking organization Verafiles and the National Union of Journalists in the Philippines (NUJP). Real news articles were sourced from mainstream news websites in the Philippines, including Pilipino Star Ngayon, Abante, and Bandera. ### Curation Rationale We remedy the lack of a proper, curated benchmark dataset for fake news detection in Filipino by constructing and producing what we call “Fake News Filipino.” ### Source Data #### Initial Data Collection and Normalization We construct the dataset by scraping our source websites, encoding all characters into UTF-8. Preprocessing was light to keep information intact: we retain capitalization and punctuation, and do not correct any misspelled words. #### Who are the source language producers? Jan Christian Blaise Cruz, Julianne Agatha Tan, and Charibeth Cheng ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [Jan Christian Cruz](mailto:jan_christian_cruz@dlsu.edu.ph), Julianne Agatha Tan, and Charibeth Cheng ### Licensing Information [More Information Needed] ### Citation Information @inproceedings{cruz2020localization, title={Localization of Fake News Detection via Multitask Transfer Learning}, author={Cruz, Jan Christian Blaise and Tan, Julianne Agatha and Cheng, Charibeth}, booktitle={Proceedings of The 12th Language Resources and Evaluation Conference}, pages={2596--2604}, year={2020} } ### Contributions Thanks to [@anaerobeth](https://github.com/anaerobeth) for adding this dataset.
farsi_news	--- annotations_creators: - found language_creators: - found language: - fa license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: null pretty_name: FarsiNews dataset_info: features: - name: title dtype: string - name: summary dtype: string - name: link dtype: string - name: tags sequence: string splits: - name: hamshahri num_bytes: 1267659 num_examples: 2203 - name: radiofarda num_bytes: 265272 num_examples: 284 download_size: 1648337 dataset_size: 1532931 --- # Dataset Card for FarsiNews ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: []() - Repository: [link](https://github.com/sci2lab/Farsi-datasets) - Paper: []() - Leaderboard: []() - Point of Contact: []() ### Dataset Summary https://github.com/sci2lab/Farsi-datasets Contains Farsi (Persian) datasets for Machine Learning tasks, particularly NLP. These datasets have been extracted from the RSS feed of two Farsi news agency websites: - Hamshahri - RadioFarda ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure [More Information Needed] ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information https://github.com/sci2lab/Farsi-datasets ### Contributions Thanks to [@Narsil](https://github.com/Narsil) for adding this dataset.
fashion_mnist	--- annotations_creators: - expert-generated language_creators: - found language: - en license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - image-classification task_ids: - multi-class-image-classification paperswithcode_id: fashion-mnist pretty_name: FashionMNIST dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': T - shirt / top '1': Trouser '2': Pullover '3': Dress '4': Coat '5': Sandal '6': Shirt '7': Sneaker '8': Bag '9': Ankle boot config_name: fashion_mnist splits: - name: train num_bytes: 31296655 num_examples: 60000 - name: test num_bytes: 5233818 num_examples: 10000 download_size: 30878645 dataset_size: 36530473 --- # Dataset Card for FashionMNIST ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [GitHub](https://github.com/zalandoresearch/fashion-mnist) - Repository: [GitHub](https://github.com/zalandoresearch/fashion-mnist) - Paper: [arXiv](https://arxiv.org/pdf/1708.07747.pdf) - Leaderboard: - Point of Contact: ### Dataset Summary Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits. ### Supported Tasks and Leaderboards - `image-classification`: The goal of this task is to classify a given image of Zalando's article into one of 10 classes. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-fashion-mnist). ### Languages [More Information Needed] ## Dataset Structure ### Data Instances A data point comprises an image and its label. ``` { 'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=28x28 at 0x27601169DD8>, 'label': 9 } ``` ### Data Fields - `image`: A `PIL.Image.Image` object containing the 28x28 image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]`. - `label`: an integer between 0 and 9 representing the classes with the following mapping: \| Label \| Description \| \| --- \| --- \| \| 0 \| T-shirt/top \| \| 1 \| Trouser \| \| 2 \| Pullover \| \| 3 \| Dress \| \| 4 \| Coat \| \| 5 \| Sandal \| \| 6 \| Shirt \| \| 7 \| Sneaker \| \| 8 \| Bag \| \| 9 \| Ankle boot \| ### Data Splits The data is split into training and test set. The training set contains 60,000 images and the test set 10,000 images. ## Dataset Creation ### Curation Rationale From the arXiv paper: The original MNIST dataset contains a lot of handwritten digits. Members of the AI/ML/Data Science community love this dataset and use it as a benchmark to validate their algorithms. In fact, MNIST is often the first dataset researchers try. "If it doesn't work on MNIST, it won't work at all", they said. "Well, if it does work on MNIST, it may still fail on others." Here are some good reasons: - MNIST is too easy. Convolutional nets can achieve 99.7% on MNIST. Classic machine learning algorithms can also achieve 97% easily. Check out our side-by-side benchmark for Fashion-MNIST vs. MNIST, and read "Most pairs of MNIST digits can be distinguished pretty well by just one pixel." - MNIST is overused. In this April 2017 Twitter thread, Google Brain research scientist and deep learning expert Ian Goodfellow calls for people to move away from MNIST. - MNIST can not represent modern CV tasks, as noted in this April 2017 Twitter thread, deep learning expert/Keras author François Chollet. ### Source Data #### Initial Data Collection and Normalization From the arXiv paper: Fashion-MNIST is based on the assortment on Zalando’s website. Every fashion product on Zalando has a set of pictures shot by professional photographers, demonstrating different aspects of the product, i.e. front and back looks, details, looks with model and in an outfit. The original picture has a light-gray background (hexadecimal color: #fdfdfd) and stored in 762 × 1000 JPEG format. For efficiently serving different frontend components, the original picture is resampled with multiple resolutions, e.g. large, medium, small, thumbnail and tiny. We use the front look thumbnail images of 70,000 unique products to build Fashion-MNIST. Those products come from different gender groups: men, women, kids and neutral. In particular, whitecolor products are not included in the dataset as they have low contrast to the background. The thumbnails (51 × 73) are then fed into the following conversion pipeline: 1. Converting the input to a PNG image. 2. Trimming any edges that are close to the color of the corner pixels. The “closeness” is defined by the distance within 5% of the maximum possible intensity in RGB space. 3. Resizing the longest edge of the image to 28 by subsampling the pixels, i.e. some rows and columns are skipped over. 4. Sharpening pixels using a Gaussian operator of the radius and standard deviation of 1.0, with increasing effect near outlines. 5. Extending the shortest edge to 28 and put the image to the center of the canvas. 6. Negating the intensities of the image. 7. Converting the image to 8-bit grayscale pixels. #### Who are the source language producers? From the arXiv paper: Every fashion product on Zalando has a set of pictures shot by professional photographers, demonstrating different aspects of the product, i.e. front and back looks, details, looks with model and in an outfit. ### Annotations #### Annotation process From the arXiv paper: For the class labels, they use the silhouette code of the product. The silhouette code is manually labeled by the in-house fashion experts and reviewed by a separate team at Zalando. Each product Zalando is the Europe’s largest online fashion platform. Each product contains only one silhouette code. #### Who are the annotators? From the arXiv paper: The silhouette code is manually labeled by the in-house fashion experts and reviewed by a separate team at Zalando. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Han Xiao and Kashif Rasul and Roland Vollgraf ### Licensing Information MIT Licence ### Citation Information ``` @article{DBLP:journals/corr/abs-1708-07747, author = {Han Xiao and Kashif Rasul and Roland Vollgraf}, title = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms}, journal = {CoRR}, volume = {abs/1708.07747}, year = {2017}, url = {http://arxiv.org/abs/1708.07747}, archivePrefix = {arXiv}, eprint = {1708.07747}, timestamp = {Mon, 13 Aug 2018 16:47:27 +0200}, biburl = {https://dblp.org/rec/bib/journals/corr/abs-1708-07747}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@gchhablani](https://github.com/gchablani) for adding this dataset.
fever	--- language: - en paperswithcode_id: fever annotations_creators: - crowdsourced language_creators: - found license: - cc-by-sa-3.0 - gpl-3.0 multilinguality: - monolingual pretty_name: FEVER size_categories: - 100K<n<1M source_datasets: - extended\|wikipedia task_categories: - text-classification task_ids: [] tags: - knowledge-verification dataset_info: - config_name: v1.0 features: - name: id dtype: int32 - name: label dtype: string - name: claim dtype: string - name: evidence_annotation_id dtype: int32 - name: evidence_id dtype: int32 - name: evidence_wiki_url dtype: string - name: evidence_sentence_id dtype: int32 splits: - name: train num_bytes: 29591412 num_examples: 311431 - name: labelled_dev num_bytes: 3643157 num_examples: 37566 - name: unlabelled_dev num_bytes: 1548965 num_examples: 19998 - name: unlabelled_test num_bytes: 1617002 num_examples: 19998 - name: paper_dev num_bytes: 1821489 num_examples: 18999 - name: paper_test num_bytes: 1821668 num_examples: 18567 download_size: 44853972 dataset_size: 40043693 - config_name: v2.0 features: - name: id dtype: int32 - name: label dtype: string - name: claim dtype: string - name: evidence_annotation_id dtype: int32 - name: evidence_id dtype: int32 - name: evidence_wiki_url dtype: string - name: evidence_sentence_id dtype: int32 splits: - name: validation num_bytes: 306243 num_examples: 2384 download_size: 392466 dataset_size: 306243 - config_name: wiki_pages features: - name: id dtype: string - name: text dtype: string - name: lines dtype: string splits: - name: wikipedia_pages num_bytes: 7254115038 num_examples: 5416537 download_size: 1713485474 dataset_size: 7254115038 --- # Dataset Card for "fever" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://fever.ai/](https://fever.ai/) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Dataset Summary With billions of individual pages on the web providing information on almost every conceivable topic, we should have the ability to collect facts that answer almost every conceivable question. However, only a small fraction of this information is contained in structured sources (Wikidata, Freebase, etc.) – we are therefore limited by our ability to transform free-form text to structured knowledge. There is, however, another problem that has become the focus of a lot of recent research and media coverage: false information coming from unreliable sources. The FEVER workshops are a venue for work in verifiable knowledge extraction and to stimulate progress in this direction. - FEVER Dataset: FEVER (Fact Extraction and VERification) consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims are classified as Supported, Refuted or NotEnoughInfo. For the first two classes, the annotators also recorded the sentence(s) forming the necessary evidence for their judgment. - FEVER 2.0 Adversarial Attacks Dataset: The FEVER 2.0 Dataset consists of 1174 claims created by the submissions of participants in the Breaker phase of the 2019 shared task. Participants (Breakers) were tasked with generating adversarial examples that induce classification errors for the existing systems. Breakers submitted a dataset of up to 1000 instances with equal number of instances for each of the three classes (Supported, Refuted NotEnoughInfo). Only novel claims (i.e. not contained in the original FEVER dataset) were considered as valid entries to the shared task. The submissions were then manually evaluated for Correctness (grammatical, appropriately labeled and meet the FEVER annotation guidelines requirements). ### Supported Tasks and Leaderboards The task is verification of textual claims against textual sources. When compared to textual entailment (TE)/natural language inference, the key difference is that in these tasks the passage to verify each claim is given, and in recent years it typically consists a single sentence, while in verification systems it is retrieved from a large set of documents in order to form the evidence. ### Languages The dataset is in English. ## Dataset Structure ### Data Instances #### v1.0 - Size of downloaded dataset files: 44.86 MB - Size of the generated dataset: 40.05 MB - Total amount of disk used: 84.89 MB An example of 'train' looks as follows. ``` 'claim': 'Nikolaj Coster-Waldau worked with the Fox Broadcasting Company.', 'evidence_wiki_url': 'Nikolaj_Coster-Waldau', 'label': 'SUPPORTS', 'id': 75397, 'evidence_id': 104971, 'evidence_sentence_id': 7, 'evidence_annotation_id': 92206} ``` #### v2.0 - Size of downloaded dataset files: 0.39 MB - Size of the generated dataset: 0.30 MB - Total amount of disk used: 0.70 MB An example of 'validation' looks as follows. ``` {'claim': "There is a convicted statutory rapist called Chinatown's writer.", 'evidence_wiki_url': '', 'label': 'NOT ENOUGH INFO', 'id': 500000, 'evidence_id': -1, 'evidence_sentence_id': -1, 'evidence_annotation_id': 269158} ``` #### wiki_pages - Size of downloaded dataset files: 1.71 GB - Size of the generated dataset: 7.25 GB - Total amount of disk used: 8.97 GB An example of 'wikipedia_pages' looks as follows. ``` {'text': 'The following are the football -LRB- soccer -RRB- events of the year 1928 throughout the world . ', 'lines': '0\tThe following are the football -LRB- soccer -RRB- events of the year 1928 throughout the world .\n1\t', 'id': '1928_in_association_football'} ``` ### Data Fields The data fields are the same among all splits. #### v1.0 - `id`: a `int32` feature. - `label`: a `string` feature. - `claim`: a `string` feature. - `evidence_annotation_id`: a `int32` feature. - `evidence_id`: a `int32` feature. - `evidence_wiki_url`: a `string` feature. - `evidence_sentence_id`: a `int32` feature. #### v2.0 - `id`: a `int32` feature. - `label`: a `string` feature. - `claim`: a `string` feature. - `evidence_annotation_id`: a `int32` feature. - `evidence_id`: a `int32` feature. - `evidence_wiki_url`: a `string` feature. - `evidence_sentence_id`: a `int32` feature. #### wiki_pages - `id`: a `string` feature. - `text`: a `string` feature. - `lines`: a `string` feature. ### Data Splits #### v1.0 \| \| train \| unlabelled_dev \| labelled_dev \| paper_dev \| unlabelled_test \| paper_test \| \|------\|-------:\|---------------:\|-------------:\|----------:\|----------------:\|-----------:\| \| v1.0 \| 311431 \| 19998 \| 37566 \| 18999 \| 19998 \| 18567 \| #### v2.0 \| \| validation \| \|------\|-----------:\| \| v2.0 \| 2384 \| #### wiki_pages \| \| wikipedia_pages \| \|------------\|----------------:\| \| wiki_pages \| 5416537 \| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information FEVER license: ``` These data annotations incorporate material from Wikipedia, which is licensed pursuant to the Wikipedia Copyright Policy. These annotations are made available under the license terms described on the applicable Wikipedia article pages, or, where Wikipedia license terms are unavailable, under the Creative Commons Attribution-ShareAlike License (version 3.0), available at http://creativecommons.org/licenses/by-sa/3.0/ (collectively, the â€œLicense Termsâ€). You may not use these files except in compliance with the applicable License Terms. ``` ### Citation Information If you use "FEVER Dataset", please cite: ```bibtex @inproceedings{Thorne18Fever, author = {Thorne, James and Vlachos, Andreas and Christodoulopoulos, Christos and Mittal, Arpit}, title = {{FEVER}: a Large-scale Dataset for Fact Extraction and {VERification}}, booktitle = {NAACL-HLT}, year = {2018} } ``` If you use "FEVER 2.0 Adversarial Attacks Dataset", please cite: ```bibtex @inproceedings{Thorne19FEVER2, author = {Thorne, James and Vlachos, Andreas and Cocarascu, Oana and Christodoulopoulos, Christos and Mittal, Arpit}, title = {The {FEVER2.0} Shared Task}, booktitle = {Proceedings of the Second Workshop on {Fact Extraction and VERification (FEVER)}}, year = {2018} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
few_rel	--- annotations_creators: - crowdsourced - machine-generated language_creators: - found language: - en license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K - n<1K source_datasets: - original task_categories: - other task_ids: [] paperswithcode_id: fewrel pretty_name: Few-Shot Relation Classification Dataset configs: - default - pid2name tags: - relation-extraction dataset_info: - config_name: default features: - name: relation dtype: string - name: tokens sequence: string - name: head struct: - name: text dtype: string - name: type dtype: string - name: indices sequence: sequence: int64 - name: tail struct: - name: text dtype: string - name: type dtype: string - name: indices sequence: sequence: int64 - name: names sequence: string splits: - name: train_wiki num_bytes: 19923155 num_examples: 44800 - name: val_nyt num_bytes: 1385642 num_examples: 2500 - name: val_pubmed num_bytes: 488502 num_examples: 1000 - name: val_semeval num_bytes: 2646249 num_examples: 8851 - name: val_wiki num_bytes: 5147348 num_examples: 11200 - name: pubmed_unsupervised num_bytes: 1117703 num_examples: 2500 download_size: 22674323 dataset_size: 30708599 - config_name: pid2name features: - name: relation dtype: string - name: names sequence: string splits: - name: pid2name num_bytes: 81607 num_examples: 744 download_size: 22674323 dataset_size: 81607 --- # Dataset Card for few_rel ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [GitHub Page](https://thunlp.github.io/) - Repository: [GitHub](https://github.com/thunlp/FewRel) - Paper: [FewRel](https://arxiv.org/abs/1810.10147), [FewRel 2.0](https://arxiv.org/abs/1910.07124) - Leaderboard: [GitHub Leaderboard](https://thunlp.github.io/fewrel.html) - Point of Contact: [Needs More Information] ### Dataset Summary FewRel is a large-scale few-shot relation extraction dataset, which contains more than one hundred relations and tens of thousands of annotated instances cross different domains. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages The dataset contaings English text, as used by writers on Wikipedia, and crowdsourced English annotations. ## Dataset Structure ### Data Instances An instance from `train_wiki` split: ``` {'head': {'indices': [[16]], 'text': 'tjq', 'type': 'Q1331049'}, 'names': ['place served by transport hub', 'territorial entity or entities served by this transport hub (airport, train station, etc.)'], 'relation': 'P931', 'tail': {'indices': [[13, 14]], 'text': 'tanjung pandan', 'type': 'Q3056359'}, 'tokens': ['Merpati', 'flight', '106', 'departed', 'Jakarta', '(', 'CGK', ')', 'on', 'a', 'domestic', 'flight', 'to', 'Tanjung', 'Pandan', '(', 'TJQ', ')', '.']} ``` ### Data Fields For `default`: - `relation`: a `string` feature containing PID of the relation. - `tokens`: a `list` of `string` features containing tokens for the text. - `head`: a dictionary containing: - `text`: a `string` feature representing the head entity. - `type`: a `string` feature representing the type of the head entity. - `indices`: a `list` containing `list` of token indices. - `tail`: a dictionary containing: - `text`: a `string` feature representing the tail entity. - `type`: a `string` feature representing the type of the tail entity. - `indices`: a `list` containing `list` of token indices. - `names`: a `list` of `string` features containing relation names. For `pubmed_unsupervised` split, this is set to a `list` with an empty `string`. For `val_semeval` and `val_pubmed` split, this is set to a `list` with the `string` from the `relation` field. ### Data Splits `train_wiki`: 44800 `val_nyt`: 2500 `val_pubmed`: 1000 `val_semeval`: 8851 `val_wiki`: 11200 `pubmed_unsupervised`: 2500 ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators For FewRel: Han, Xu and Zhu, Hao and Yu, Pengfei and Wang, Ziyun and Yao, Yuan and Liu, Zhiyuan and Sun, Maosong For FewRel 2.0: Gao, Tianyu and Han, Xu and Zhu, Hao and Liu, Zhiyuan and Li, Peng and Sun, Maosong and Zhou, Jie ### Licensing Information ``` MIT License Copyright (c) 2018 THUNLP Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ``` ### Citation Information ``` @inproceedings{han-etal-2018-fewrel, title = "{F}ew{R}el: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation", author = "Han, Xu and Zhu, Hao and Yu, Pengfei and Wang, Ziyun and Yao, Yuan and Liu, Zhiyuan and Sun, Maosong", booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing", month = oct # "-" # nov, year = "2018", address = "Brussels, Belgium", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D18-1514", doi = "10.18653/v1/D18-1514", pages = "4803--4809" } ``` ``` @inproceedings{gao-etal-2019-fewrel, title = "{F}ew{R}el 2.0: Towards More Challenging Few-Shot Relation Classification", author = "Gao, Tianyu and Han, Xu and Zhu, Hao and Liu, Zhiyuan and Li, Peng and Sun, Maosong and Zhou, Jie", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D19-1649", doi = "10.18653/v1/D19-1649", pages = "6251--6256" } ``` ### Contributions Thanks to [@gchhablani](https://github.com/gchhablani) for adding this dataset.
financial_phrasebank	--- annotations_creators: - expert-generated language_creators: - found language: - en license: - cc-by-nc-sa-3.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification - sentiment-classification pretty_name: FinancialPhrasebank dataset_info: - config_name: sentences_allagree features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': negative '1': neutral '2': positive splits: - name: train num_bytes: 303371 num_examples: 2264 download_size: 681890 dataset_size: 303371 - config_name: sentences_75agree features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': negative '1': neutral '2': positive splits: - name: train num_bytes: 472703 num_examples: 3453 download_size: 681890 dataset_size: 472703 - config_name: sentences_66agree features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': negative '1': neutral '2': positive splits: - name: train num_bytes: 587152 num_examples: 4217 download_size: 681890 dataset_size: 587152 - config_name: sentences_50agree features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': negative '1': neutral '2': positive splits: - name: train num_bytes: 679240 num_examples: 4846 download_size: 681890 dataset_size: 679240 --- # Dataset Card for financial_phrasebank ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Kaggle](https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news) [ResearchGate](https://www.researchgate.net/publication/251231364_FinancialPhraseBank-v10) - Repository: - Paper: [Arxiv](https://arxiv.org/abs/1307.5336) - Leaderboard: [Kaggle](https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news/code) [PapersWithCode](https://paperswithcode.com/sota/sentiment-analysis-on-financial-phrasebank) = - Point of Contact: [Pekka Malo](mailto:pekka.malo@aalto.fi) [Ankur Sinha](mailto:ankur.sinha@aalto.fi) ### Dataset Summary Polar sentiment dataset of sentences from financial news. The dataset consists of 4840 sentences from English language financial news categorised by sentiment. The dataset is divided by agreement rate of 5-8 annotators. ### Supported Tasks and Leaderboards Sentiment Classification ### Languages English ## Dataset Structure ### Data Instances ``` { "sentence": "Pharmaceuticals group Orion Corp reported a fall in its third-quarter earnings that were hit by larger expenditures on R&D and marketing .", "label": "negative" } ``` ### Data Fields - sentence: a tokenized line from the dataset - label: a label corresponding to the class as a string: 'positive', 'negative' or 'neutral' ### Data Splits There's no train/validation/test split. However the dataset is available in four possible configurations depending on the percentage of agreement of annotators: `sentences_50agree`; Number of instances with >=50% annotator agreement: 4846 `sentences_66agree`: Number of instances with >=66% annotator agreement: 4217 `sentences_75agree`: Number of instances with >=75% annotator agreement: 3453 `sentences_allagree`: Number of instances with 100% annotator agreement: 2264 ## Dataset Creation ### Curation Rationale The key arguments for the low utilization of statistical techniques in financial sentiment analysis have been the difficulty of implementation for practical applications and the lack of high quality training data for building such models. Especially in the case of finance and economic texts, annotated collections are a scarce resource and many are reserved for proprietary use only. To resolve the missing training data problem, we present a collection of ∼ 5000 sentences to establish human-annotated standards for benchmarking alternative modeling techniques. The objective of the phrase level annotation task was to classify each example sentence into a positive, negative or neutral category by considering only the information explicitly available in the given sentence. Since the study is focused only on financial and economic domains, the annotators were asked to consider the sentences from the view point of an investor only; i.e. whether the news may have positive, negative or neutral influence on the stock price. As a result, sentences which have a sentiment that is not relevant from an economic or financial perspective are considered neutral. ### Source Data #### Initial Data Collection and Normalization The corpus used in this paper is made out of English news on all listed companies in OMX Helsinki. The news has been downloaded from the LexisNexis database using an automated web scraper. Out of this news database, a random subset of 10,000 articles was selected to obtain good coverage across small and large companies, companies in different industries, as well as different news sources. Following the approach taken by Maks and Vossen (2010), we excluded all sentences which did not contain any of the lexicon entities. This reduced the overall sample to 53,400 sentences, where each has at least one or more recognized lexicon entity. The sentences were then classified according to the types of entity sequences detected. Finally, a random sample of ∼5000 sentences was chosen to represent the overall news database. #### Who are the source language producers? The source data was written by various financial journalists. ### Annotations #### Annotation process This release of the financial phrase bank covers a collection of 4840 sentences. The selected collection of phrases was annotated by 16 people with adequate background knowledge on financial markets. Given the large number of overlapping annotations (5 to 8 annotations per sentence), there are several ways to define a majority vote based gold standard. To provide an objective comparison, we have formed 4 alternative reference datasets based on the strength of majority agreement: #### Who are the annotators? Three of the annotators were researchers and the remaining 13 annotators were master's students at Aalto University School of Business with majors primarily in finance, accounting, and economics. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases All annotators were from the same institution and so interannotator agreement should be understood with this taken into account. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/. If you are interested in commercial use of the data, please contact the following authors for an appropriate license: - [Pekka Malo](mailto:pekka.malo@aalto.fi) - [Ankur Sinha](mailto:ankur.sinha@aalto.fi) ### Citation Information ``` @article{Malo2014GoodDO, title={Good debt or bad debt: Detecting semantic orientations in economic texts}, author={P. Malo and A. Sinha and P. Korhonen and J. Wallenius and P. Takala}, journal={Journal of the Association for Information Science and Technology}, year={2014}, volume={65} } ``` ### Contributions Thanks to [@frankier](https://github.com/frankier) for adding this dataset.
finer	--- annotations_creators: - expert-generated language_creators: - other language: - fi license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition paperswithcode_id: finer pretty_name: Finnish News Corpus for Named Entity Recognition dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-DATE '2': B-EVENT '3': B-LOC '4': B-ORG '5': B-PER '6': B-PRO '7': I-DATE '8': I-EVENT '9': I-LOC '10': I-ORG '11': I-PER '12': I-PRO - name: nested_ner_tags sequence: class_label: names: '0': O '1': B-DATE '2': B-EVENT '3': B-LOC '4': B-ORG '5': B-PER '6': B-PRO '7': I-DATE '8': I-EVENT '9': I-LOC '10': I-ORG '11': I-PER '12': I-PRO config_name: finer splits: - name: train num_bytes: 5159550 num_examples: 13497 - name: validation num_bytes: 387494 num_examples: 986 - name: test num_bytes: 1327354 num_examples: 3512 - name: test_wikipedia num_bytes: 1404397 num_examples: 3360 download_size: 3733127 dataset_size: 8278795 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Github](https://github.com/mpsilfve/finer-data) - Repository: [Github](https://github.com/mpsilfve/finer-data) - Paper: [Arxiv](https://arxiv.org/abs/1908.04212) - Leaderboard: - Point of Contact: ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields Each row consists of the following fields: * `id`: The sentence id * `tokens`: An ordered list of tokens from the full text * `ner_tags`: Named entity recognition tags for each token * `nested_ner_tags`: Nested named entity recognition tags for each token Note that by design, the length of `tokens`, `ner_tags`, and `nested_ner_tags` will always be identical. `ner_tags` and `nested_ner_tags` correspond to the list below: ``` [ "O", "B-DATE", "B-EVENT", "B-LOC", "B-ORG", "B-PER", "B-PRO", "I-DATE", "I-EVENT", "I-LOC", "I-ORG", "I-PER", "I-PRO" ] ``` IOB2 labeling scheme is used. ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@stefan-it](https://github.com/stefan-it) for adding this dataset.
flores	--- pretty_name: Flores annotations_creators: - found language_creators: - found language: - en - ne - si license: - cc-by-4.0 multilinguality: - translation size_categories: - 1K<n<10K source_datasets: - extended\|wikipedia - extended\|opus_gnome - extended\|opus_ubuntu - extended\|open_subtitles - extended\|paracrawl - extended\|bible_para - extended\|kde4 - extended\|other-global-voices - extended\|other-common-crawl task_categories: - translation task_ids: [] paperswithcode_id: flores configs: - neen - sien dataset_info: - config_name: neen features: - name: translation dtype: translation: languages: - ne - en splits: - name: validation num_bytes: 849380 num_examples: 2560 - name: test num_bytes: 999063 num_examples: 2836 download_size: 1542781 dataset_size: 1848443 - config_name: sien features: - name: translation dtype: translation: languages: - si - en splits: - name: validation num_bytes: 1031158 num_examples: 2899 - name: test num_bytes: 983563 num_examples: 2767 download_size: 1542781 dataset_size: 2014721 --- # Dataset Card for "flores" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://github.com/facebookresearch/flores/](https://github.com/facebookresearch/flores/) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 3.08 MB - Size of the generated dataset: 3.87 MB - Total amount of disk used: 6.95 MB ### Dataset Summary Evaluation datasets for low-resource machine translation: Nepali-English and Sinhala-English. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### neen - Size of downloaded dataset files: 1.54 MB - Size of the generated dataset: 1.86 MB - Total amount of disk used: 3.40 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "translation": "{\"en\": \"This is the wrong translation!\", \"ne\": \"यस वाहेक आगम पूजा, तारा पूजा, व्रत आदि पनि घरभित्र र वाहिर दुवै स्थानमा गरेको पा..." } ``` #### sien - Size of downloaded dataset files: 1.54 MB - Size of the generated dataset: 2.01 MB - Total amount of disk used: 3.57 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "translation": "{\"en\": \"This is the wrong translation!\", \"si\": \"එවැනි ආවරණයක් ලබාදීමට රක්ෂණ සපයන්නෙකු කැමති වුවත් ඒ සාමාන් යයෙන් බොහෝ රටවල පොදු ..." } ``` ### Data Fields The data fields are the same among all splits. #### neen - `translation`: a multilingual `string` variable, with possible languages including `ne`, `en`. #### sien - `translation`: a multilingual `string` variable, with possible languages including `si`, `en`. ### Data Splits \|name\|validation\|test\| \|----\|---------:\|---:\| \|neen\| 2560\|2836\| \|sien\| 2899\|2767\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @misc{guzmn2019new, title={Two New Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English}, author={Francisco Guzman and Peng-Jen Chen and Myle Ott and Juan Pino and Guillaume Lample and Philipp Koehn and Vishrav Chaudhary and Marc'Aurelio Ranzato}, year={2019}, eprint={1902.01382}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.
flue	--- annotations_creators: - crowdsourced - machine-generated language_creators: - crowdsourced language: - fr license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - intent-classification - semantic-similarity-classification - sentiment-classification pretty_name: FLUE configs: - CLS - PAWS-X - WSD-V - XNLI tags: - Word Sense Disambiguation for Verbs dataset_info: - config_name: CLS features: - name: text dtype: string - name: label dtype: class_label: names: '0': negative '1': positive - name: idx dtype: int32 splits: - name: train num_bytes: 3853279 num_examples: 5997 - name: test num_bytes: 3852344 num_examples: 5999 download_size: 314687066 dataset_size: 7705623 - config_name: PAWS-X features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int32 - name: idx dtype: int32 splits: - name: validation num_bytes: 522013 num_examples: 1988 - name: test num_bytes: 526953 num_examples: 2000 - name: train num_bytes: 13096677 num_examples: 49399 download_size: 30282057 dataset_size: 14145643 - config_name: XNLI features: - name: premise dtype: string - name: hypo dtype: string - name: label dtype: class_label: names: '0': contradiction '1': entailment '2': neutral - name: idx dtype: int32 splits: - name: validation num_bytes: 520022 num_examples: 2490 - name: test num_bytes: 1048999 num_examples: 5010 - name: train num_bytes: 87373154 num_examples: 392702 download_size: 483963712 dataset_size: 88942175 - config_name: WSD-V features: - name: sentence sequence: string - name: pos_tags sequence: string - name: lemmas sequence: string - name: fine_pos_tags sequence: string - name: disambiguate_tokens_ids sequence: int32 - name: disambiguate_labels sequence: string - name: idx dtype: string splits: - name: train num_bytes: 206869215 num_examples: 269821 - name: test num_bytes: 2722232 num_examples: 3121 download_size: 38303600 dataset_size: 209591447 --- # Dataset Card for FLUE ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [homepage](https://github.com/getalp/Flaubert/tree/master/flue) - Repository:[github](https://github.com/getalp/Flaubert/tree/master/flue) - Paper:[paper](https://arxiv.org/abs/1912.05372) - Leaderboard:[leaderboard](https://github.com/getalp/Flaubert/tree/master/flue/leaderboard) - Point of Contact:[Hang Le](thi-phuong-hang.le@univ-grenoble-alpes.fr) ### Dataset Summary FLUE is an evaluation setup for French NLP systems similar to the popular GLUE benchmark. The goal is to enable further reproducible experiments in the future and to share models and progress on the French language. The tasks and data are obtained from existing works, please refer to our Flaubert paper for a complete list of references. ### Supported Tasks and Leaderboards The supported tasks are: Text Classification, Paraphrasing, Natural Language Inference, Constituency Parsing, Dependency Parsing, Verb Sense Disambiguation and Noun Sense Disambiguation ### Languages The datasets are all in French. ## Dataset Structure ### Text Classification (CLS) This is a binary classification task. It consists in classifying Amazon reviews for three product categories: books, DVD, and music. Each sample contains a review text and the associated rating from 1 to 5 stars. Reviews rated above 3 is labeled as positive, and those rated less than 3 is labeled as negative. #### Data Instances An instance looks like: ``` { 'idx': 1, 'label': 0, 'text': 'Bilan plus que mitigé pour cet album fourre-tout qui mêle quelques bonnes idées (les parodies d\'oeuvres d\'art) et des scènetes qui ne font que faire écho paresseusement aux précédents albums. Uderzo n\'a pas pris de risque pour cet album, mais, au vu des précédents, on se dit que c\'est peut-être un moindre mal ... L\'album semble n\'avoir été fait que pour permettre à Uderzo de rappeler avec une insistance suspecte qu\'il est bien l\'un des créateurs d\'Astérix (comme lorsqu\'il se met en scène lui même dans la BD) et de traiter ses critiques d\' "imbéciles" dans une préface un rien aigrie signée "Astérix". Préface dans laquelle Uderzo feint de croire que ce qu\'on lui reproche est d\'avoir fait survivre Asterix à la disparition de Goscinny (reproche naturellement démenti par la fidélité des lecteurs - démonstration imparable !). On aurait tant aimé qu\'Uderzo accepte de s\'entourer d\'un scénariste compétent et respectueux de l\'esprit Goscinnien (cela doit se trouver !) et nous propose des albums plus ambitieux ...' } ``` #### Data Fields The dataset is composed of two fields: - text: the field that represents the text to classify. - label: the sentiment represented by the text, here positive or negative. #### Data Splits The train and test sets are balanced, including around 1k positive and 1k negative reviews for a total of 2k reviews in each dataset. We take the French portion to create the binary text classification task in FLUE and report the accuracy on the test set. ### Paraphrasing (PAWS-X) The task consists in identifying whether the two sentences in a pair are semantically equivalent or not. #### Data Instances An instance looks like: ``` { 'idx': 1, 'label': 0, 'sentence1': "À Paris, en octobre 1560, il rencontra secrètement l'ambassadeur d'Angleterre, Nicolas Throckmorton, lui demandant un passeport pour retourner en Angleterre en passant par l'Écosse.", 'sentence2': "En octobre 1560, il rencontra secrètement l'ambassadeur d'Angleterre, Nicolas Throckmorton, à Paris, et lui demanda un passeport pour retourner en Écosse par l'Angleterre." } ``` #### Data Fields The dataset is compososed of three fields: - sentence1: The first sentence of an example - sentence2: The second sentence of an example - lalel: 0 if the two sentences are not paraphrasing each other, 1 otherwise. #### Data Splits The train set includes 49.4k examples, the dev and test sets each comprises nearly 2k examples. We take the related datasets for French to perform the paraphrasing task and report the accuracy on the test set. ### Natural Language Inference (XNLI) The Natural Language Inference (NLI) task, also known as recognizing textual entailment (RTE), is to determine whether a premise entails, contradicts or neither entails nor contradicts a hypothesis. We take the French part of the XNLI corpus to form the development and test sets for the NLI task in FLUE. #### Data Instances An instance looks like: ``` { 'idx': 1, 'label': 2, 'hypo': 'Le produit et la géographie sont ce qui fait travailler la crème de la crème .', 'premise': "L' écrémage conceptuel de la crème a deux dimensions fondamentales : le produit et la géographie ." } ``` #### Data Fields The dataset is composed of three fields: - premise: Premise sentence. - hypo: Hypothesis sentence. - label: contradiction if the two sentences are contradictory, entailment if the two sentences entails, neutral if they neither entails or contradict each other. #### Data Splits The train set includes 392.7k examples, the dev and test sets comprises 2.5k and 5k examples respectively. We take the related datasets for French to perform the NLI task and report the accuracy on the test set. ### Word Sense Disambiguation for Verbs (WSD-V) The FrenchSemEval (FSE) dataset aims to evaluate the Word Sense Disambiguation for Verbs task for the French language. Extracted from Wiktionary. #### Data Instances An instance looks like: ``` { 'idx': 'd000.s001', 'sentence': ['"', 'Ce', 'ne', 'fut', 'pas', 'une', 'révolution', '2.0', ',', 'ce', 'fut', 'une', 'révolution', 'de', 'rue', '.'], 'fine_pos_tags': [27, 26, 18, 13, 18, 0, 6, 22, 27, 26, 13, 0, 6, 4, 6, 27], 'lemmas': ['"', 'ce', 'ne', 'être', 'pas', 'un', 'révolution', '2.0', ',', 'ce', 'être', 'un', 'révolution', 'de', 'rue', '.'], 'pos_tags': [13, 11, 14, 0, 14, 9, 15, 4, 13, 11, 0, 9, 15, 7, 15, 13], 'disambiguate_labels': ['__ws_1_2.0__adj__1'], 'disambiguate_tokens_ids': [7], } ``` #### Data Fields The dataset is composed of six fields: - sentence: The sentence to process split in tokens. - pos_tags: The corresponding POS tags for each tokens. - lemmas: The corresponding lemma for each tokens. - fine_pos_tags: Fined (more specific) POS tags for each tokens. - disambiguate_tokens_ids: The ID of the token in the sentence to disambiguate. - disambiguate_labels: The label in the form of sentenceID __ws_sentence-number_token__pos__number-of-time-the-token-appeared-across-all-the-sentences (i.e. d000.s404.t000 __ws_2_agir__verb__1). #### Data Splits The train set includes 269821 examples, the test set includes 3121 examples. ## Considerations for Using the Data ### Social Impact of Dataset The goal is to enable further reproducible experiments in the future and to share models and progress on the French language. ## Additional Information ### Licensing Information The licenses are: - The licensing status of the data, especially the news source text, is unknown for CLS - The dataset may be freely used for any purpose, although acknowledgement of Google LLC ("Google") as the data source would be appreciated. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset. for PAWS-X - CC BY-NC 4.0 for XNLI - The licensing status of the data, especially the news source text, is unknown for Verb Sense Disambiguation ### Citation Information ``` @misc{le2019flaubert, title={FlauBERT: Unsupervised Language Model Pre-training for French}, author={Hang Le and Loïc Vial and Jibril Frej and Vincent Segonne and Maximin Coavoux and Benjamin Lecouteux and Alexandre Allauzen and Benoît Crabbé and Laurent Besacier and Didier Schwab}, year={2019}, eprint={1912.05372}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@jplu](https://github.com/jplu) for adding this dataset.
food101	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended\|other-foodspotting task_categories: - image-classification task_ids: - multi-class-image-classification paperswithcode_id: food-101 pretty_name: Food-101 dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': apple_pie '1': baby_back_ribs '2': baklava '3': beef_carpaccio '4': beef_tartare '5': beet_salad '6': beignets '7': bibimbap '8': bread_pudding '9': breakfast_burrito '10': bruschetta '11': caesar_salad '12': cannoli '13': caprese_salad '14': carrot_cake '15': ceviche '16': cheesecake '17': cheese_plate '18': chicken_curry '19': chicken_quesadilla '20': chicken_wings '21': chocolate_cake '22': chocolate_mousse '23': churros '24': clam_chowder '25': club_sandwich '26': crab_cakes '27': creme_brulee '28': croque_madame '29': cup_cakes '30': deviled_eggs '31': donuts '32': dumplings '33': edamame '34': eggs_benedict '35': escargots '36': falafel '37': filet_mignon '38': fish_and_chips '39': foie_gras '40': french_fries '41': french_onion_soup '42': french_toast '43': fried_calamari '44': fried_rice '45': frozen_yogurt '46': garlic_bread '47': gnocchi '48': greek_salad '49': grilled_cheese_sandwich '50': grilled_salmon '51': guacamole '52': gyoza '53': hamburger '54': hot_and_sour_soup '55': hot_dog '56': huevos_rancheros '57': hummus '58': ice_cream '59': lasagna '60': lobster_bisque '61': lobster_roll_sandwich '62': macaroni_and_cheese '63': macarons '64': miso_soup '65': mussels '66': nachos '67': omelette '68': onion_rings '69': oysters '70': pad_thai '71': paella '72': pancakes '73': panna_cotta '74': peking_duck '75': pho '76': pizza '77': pork_chop '78': poutine '79': prime_rib '80': pulled_pork_sandwich '81': ramen '82': ravioli '83': red_velvet_cake '84': risotto '85': samosa '86': sashimi '87': scallops '88': seaweed_salad '89': shrimp_and_grits '90': spaghetti_bolognese '91': spaghetti_carbonara '92': spring_rolls '93': steak '94': strawberry_shortcake '95': sushi '96': tacos '97': takoyaki '98': tiramisu '99': tuna_tartare '100': waffles splits: - name: train num_bytes: 3845865322 num_examples: 75750 - name: validation num_bytes: 1276249954 num_examples: 25250 download_size: 4998236572 dataset_size: 5122115276 --- # Dataset Card for Food-101 ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Food-101 Dataset](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/) - Repository: - Paper: [Paper](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/static/bossard_eccv14_food-101.pdf) - Leaderboard: - Point of Contact: ### Dataset Summary This dataset consists of 101 food categories, with 101'000 images. For each class, 250 manually reviewed test images are provided as well as 750 training images. On purpose, the training images were not cleaned, and thus still contain some amount of noise. This comes mostly in the form of intense colors and sometimes wrong labels. All images were rescaled to have a maximum side length of 512 pixels. ### Supported Tasks and Leaderboards - `image-classification`: The goal of this task is to classify a given image of a dish into one of 101 classes. The leaderboard is available [here](https://paperswithcode.com/sota/fine-grained-image-classification-on-food-101). ### Languages English ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x276021C5EB8>, 'label': 23 } ``` ### Data Fields The data instances have the following fields: - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]`. - `label`: an `int` classification label. <details> <summary>Class Label Mappings</summary> ```json { "apple_pie": 0, "baby_back_ribs": 1, "baklava": 2, "beef_carpaccio": 3, "beef_tartare": 4, "beet_salad": 5, "beignets": 6, "bibimbap": 7, "bread_pudding": 8, "breakfast_burrito": 9, "bruschetta": 10, "caesar_salad": 11, "cannoli": 12, "caprese_salad": 13, "carrot_cake": 14, "ceviche": 15, "cheesecake": 16, "cheese_plate": 17, "chicken_curry": 18, "chicken_quesadilla": 19, "chicken_wings": 20, "chocolate_cake": 21, "chocolate_mousse": 22, "churros": 23, "clam_chowder": 24, "club_sandwich": 25, "crab_cakes": 26, "creme_brulee": 27, "croque_madame": 28, "cup_cakes": 29, "deviled_eggs": 30, "donuts": 31, "dumplings": 32, "edamame": 33, "eggs_benedict": 34, "escargots": 35, "falafel": 36, "filet_mignon": 37, "fish_and_chips": 38, "foie_gras": 39, "french_fries": 40, "french_onion_soup": 41, "french_toast": 42, "fried_calamari": 43, "fried_rice": 44, "frozen_yogurt": 45, "garlic_bread": 46, "gnocchi": 47, "greek_salad": 48, "grilled_cheese_sandwich": 49, "grilled_salmon": 50, "guacamole": 51, "gyoza": 52, "hamburger": 53, "hot_and_sour_soup": 54, "hot_dog": 55, "huevos_rancheros": 56, "hummus": 57, "ice_cream": 58, "lasagna": 59, "lobster_bisque": 60, "lobster_roll_sandwich": 61, "macaroni_and_cheese": 62, "macarons": 63, "miso_soup": 64, "mussels": 65, "nachos": 66, "omelette": 67, "onion_rings": 68, "oysters": 69, "pad_thai": 70, "paella": 71, "pancakes": 72, "panna_cotta": 73, "peking_duck": 74, "pho": 75, "pizza": 76, "pork_chop": 77, "poutine": 78, "prime_rib": 79, "pulled_pork_sandwich": 80, "ramen": 81, "ravioli": 82, "red_velvet_cake": 83, "risotto": 84, "samosa": 85, "sashimi": 86, "scallops": 87, "seaweed_salad": 88, "shrimp_and_grits": 89, "spaghetti_bolognese": 90, "spaghetti_carbonara": 91, "spring_rolls": 92, "steak": 93, "strawberry_shortcake": 94, "sushi": 95, "tacos": 96, "takoyaki": 97, "tiramisu": 98, "tuna_tartare": 99, "waffles": 100 } ``` </details> ### Data Splits \| \|train\|validation\| \|----------\|----:\|---------:\| \|# of examples\|75750\|25250\| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information LICENSE AGREEMENT ================= - The Food-101 data set consists of images from Foodspotting [1] which are not property of the Federal Institute of Technology Zurich (ETHZ). Any use beyond scientific fair use must be negociated with the respective picture owners according to the Foodspotting terms of use [2]. [1] http://www.foodspotting.com/ [2] http://www.foodspotting.com/terms/ ### Citation Information ``` @inproceedings{bossard14, title = {Food-101 -- Mining Discriminative Components with Random Forests}, author = {Bossard, Lukas and Guillaumin, Matthieu and Van Gool, Luc}, booktitle = {European Conference on Computer Vision}, year = {2014} } ``` ### Contributions Thanks to [@nateraw](https://github.com/nateraw) for adding this dataset.
fquad	--- annotations_creators: - crowdsourced language_creators: - crowdsourced - found language: - fr license: - cc-by-nc-sa-3.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering - text-retrieval task_ids: - extractive-qa - closed-domain-qa paperswithcode_id: fquad pretty_name: 'FQuAD: French Question Answering Dataset' dataset_info: features: - name: context dtype: string - name: questions sequence: string - name: answers sequence: - name: texts dtype: string - name: answers_starts dtype: int32 splits: - name: train num_bytes: 5898752 num_examples: 4921 - name: validation num_bytes: 1031456 num_examples: 768 download_size: 0 dataset_size: 6930208 --- # Dataset Card for FQuAD ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://fquad.illuin.tech/](https://fquad.illuin.tech/) - Paper: [FQuAD: French Question Answering Dataset](https://arxiv.org/abs/2002.06071) - Point of Contact: [https://www.illuin.tech/contact/](https://www.illuin.tech/contact/) - Size of downloaded dataset files: 3.29 MB - Size of the generated dataset: 6.94 MB - Total amount of disk used: 10.23 MB ### Dataset Summary FQuAD: French Question Answering Dataset We introduce FQuAD, a native French Question Answering Dataset. FQuAD contains 25,000+ question and answer pairs. Finetuning CamemBERT on FQuAD yields a F1 score of 88% and an exact match of 77.9%. Developped to provide a SQuAD equivalent in the French language. Questions are original and based on high quality Wikipedia articles. Please, note this dataset is licensed for non-commercial purposes and users must agree to the following terms and conditions: 1. Use FQuAD only for internal research purposes. 2. Not make any copy except a safety one. 3. Not redistribute it (or part of it) in any way, even for free. 4. Not sell it or use it for any commercial purpose. Contact us for a possible commercial licence. 5. Mention the corpus origin and Illuin Technology in all publications about experiments using FQuAD. 6. Redistribute to Illuin Technology any improved or enriched version you could make of that corpus. Request manually download of the data from: https://fquad.illuin.tech/ ### Supported Tasks and Leaderboards - `closed-domain-qa`, `text-retrieval`: This dataset is intended to be used for `closed-domain-qa`, but can also be used for information retrieval tasks. ### Languages This dataset is exclusively in French, with context data from Wikipedia and questions from French university students (`fr`). ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 3.29 MB - Size of the generated dataset: 6.94 MB - Total amount of disk used: 10.23 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "answers": { "answers_starts": [161, 46, 204], "texts": ["La Vierge aux rochers", "documents contemporains", "objets de spéculations"] }, "context": "\"Les deux tableaux sont certes décrits par des documents contemporains à leur création mais ceux-ci ne le font qu'indirectement ...", "questions": ["Que concerne principalement les documents ?", "Par quoi sont décrit les deux tableaux ?", "Quels types d'objets sont les deux tableaux aux yeux des chercheurs ?"] } ``` ### Data Fields The data fields are the same among all splits. #### default - `context`: a `string` feature. - `questions`: a `list` of `string` features. - `answers`: a dictionary feature containing: - `texts`: a `string` feature. - `answers_starts`: a `int32` feature. ### Data Splits The FQuAD dataset has 3 splits: _train_, _validation_, and _test_. The _test_ split is however not released publicly at the moment. The splits contain disjoint sets of articles. The following table contains stats about each split. Dataset Split \| Number of Articles in Split \| Number of paragraphs in split \| Number of questions in split --------------\|------------------------------\|--------------------------\|------------------------- Train \| 117 \| 4921 \| 20731 Validation \| 768 \| 51.0% \| 3188 Test \| 10 \| 532 \| 2189 ## Dataset Creation ### Curation Rationale The FQuAD dataset was created by Illuin technology. It was developped to provide a SQuAD equivalent in the French language. Questions are original and based on high quality Wikipedia articles. ### Source Data The text used for the contexts are from the curated list of French High-Quality Wikipedia [articles](https://fr.wikipedia.org/wiki/Cat%C3%A9gorie:Article_de_qualit%C3%A9). ### Annotations Annotations (spans and questions) are written by students of the CentraleSupélec school of engineering. Wikipedia articles were scraped and Illuin used an internally-developped tool to help annotators ask questions and indicate the answer spans. Annotators were given paragraph sized contexts and asked to generate 4/5 non-trivial questions about information in the context. ### Personal and Sensitive Information No personal or sensitive information is included in this dataset. This has been manually verified by the dataset curators. ## Considerations for Using the Data Users should consider this dataset is sampled from Wikipedia data which might not be representative of all QA use cases. ### Social Impact of Dataset The social biases of this dataset have not yet been investigated. ### Discussion of Biases The social biases of this dataset have not yet been investigated, though articles have been selected by their quality and objectivity. ### Other Known Limitations The limitations of the FQuAD dataset have not yet been investigated. ## Additional Information ### Dataset Curators Illuin Technology: [https://fquad.illuin.tech/](https://fquad.illuin.tech/) ### Licensing Information The FQuAD dataset is licensed under the [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/fr/) license. It allows personal and academic research uses of the dataset, but not commercial uses. So concretely, the dataset cannot be used to train a model that is then put into production within a business or a company. For this type of commercial use, we invite FQuAD users to contact [the authors](https://www.illuin.tech/contact/) to discuss possible partnerships. ### Citation Information ``` @ARTICLE{2020arXiv200206071 author = {Martin, d'Hoffschmidt and Maxime, Vidal and Wacim, Belblidia and Tom, Brendlé}, title = "{FQuAD: French Question Answering Dataset}", journal = {arXiv e-prints}, keywords = {Computer Science - Computation and Language}, year = "2020", month = "Feb", eid = {arXiv:2002.06071}, pages = {arXiv:2002.06071}, archivePrefix = {arXiv}, eprint = {2002.06071}, primaryClass = {cs.CL} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova) for adding this dataset. Thanks to [@ManuelFay](https://github.com/manuelfay) for providing information on the dataset creation process.
freebase_qa	--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended\|trivia_qa task_categories: - question-answering task_ids: - open-domain-qa paperswithcode_id: freebaseqa pretty_name: FreebaseQA dataset_info: features: - name: Question-ID dtype: string - name: RawQuestion dtype: string - name: ProcessedQuestion dtype: string - name: Parses sequence: - name: Parse-Id dtype: string - name: PotentialTopicEntityMention dtype: string - name: TopicEntityName dtype: string - name: TopicEntityMid dtype: string - name: InferentialChain dtype: string - name: Answers sequence: - name: AnswersMid dtype: string - name: AnswersName sequence: string splits: - name: train num_bytes: 10235375 num_examples: 20358 - name: test num_bytes: 1987874 num_examples: 3996 - name: validation num_bytes: 1974114 num_examples: 3994 download_size: 33204999 dataset_size: 14197363 --- # Dataset Card for FreebaseQA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: - Repository: [FreebaseQA repository](https://github.com/kelvin-jiang/FreebaseQA) - Paper: [FreebaseQA ACL paper](https://www.aclweb.org/anthology/N19-1028.pdf) - Leaderboard: - Point of Contact: [Kelvin Jiang](https://github.com/kelvin-jiang) ### Dataset Summary FreebaseQA is a dataset for open-domain factoid question answering (QA) tasks over structured knowledge bases, like Freebase. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure ### Data Instances Here is an example from the dataset: ``` {'Parses': {'Answers': [{'AnswersMid': ['m.01npcx'], 'AnswersName': [['goldeneye']]}, {'AnswersMid': ['m.01npcx'], 'AnswersName': [['goldeneye']]}], 'InferentialChain': ['film.film_character.portrayed_in_films..film.performance.film', 'film.actor.film..film.performance.film'], 'Parse-Id': ['FreebaseQA-train-0.P0', 'FreebaseQA-train-0.P1'], 'PotentialTopicEntityMention': ['007', 'pierce brosnan'], 'TopicEntityMid': ['m.0clpml', 'm.018p4y'], 'TopicEntityName': ['james bond', 'pierce brosnan']}, 'ProcessedQuestion': "what was pierce brosnan's first outing as 007", 'Question-ID': 'FreebaseQA-train-0', 'RawQuestion': "What was Pierce Brosnan's first outing as 007?"} ``` ### Data Fields - `Question-ID`: a `string` feature representing ID of each question. - `RawQuestion`: a `string` feature representing the original question collected from data sources. - `ProcessedQuestion`: a `string` feature representing the question processed with some operations such as removal of trailing question mark and decapitalization. - `Parses`: a dictionary feature representing the semantic parse(s) for the question containing: - `Parse-Id`: a `string` feature representing the ID of each semantic parse. - `PotentialTopicEntityMention`: a `string` feature representing the potential topic entity mention in the question. - `TopicEntityName`: a `string` feature representing name or alias of the topic entity in the question from Freebase. - `TopicEntityMid`: a `string` feature representing the Freebase MID of the topic entity in the question. - `InferentialChain`: a `string` feature representing path from the topic entity node to the answer node in Freebase, labeled as a predicate. - `Answers`: a dictionary feature representing the answer found from this parse containing: - `AnswersMid`: a `string` feature representing the Freebase MID of the answer. - `AnswersName`: a `list` of `string` features representing the answer string from the original question-answer pair. ### Data Splits This data set contains 28,348 unique questions that are divided into three subsets: train (20,358), dev (3,994) and eval (3,996), formatted as JSON files: FreebaseQA-[train\|dev\|eval].json ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The data set is generated by matching trivia-type question-answer pairs with subject-predicateobject triples in Freebase. For each collected question-answer pair, we first tag all entities in each question and search for relevant predicates that bridge a tagged entity with the answer in Freebase. Finally, human annotation is used to remove false positives in these matched triples. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Kelvin Jiang - Currently at University of Waterloo. Work was done at York University. ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{jiang-etal-2019-freebaseqa, title = "{F}reebase{QA}: A New Factoid {QA} Data Set Matching Trivia-Style Question-Answer Pairs with {F}reebase", author = "Jiang, Kelvin and Wu, Dekun and Jiang, Hui", booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)", month = jun, year = "2019", address = "Minneapolis, Minnesota", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/N19-1028", doi = "10.18653/v1/N19-1028", pages = "318--323", abstract = "In this paper, we present a new data set, named FreebaseQA, for open-domain factoid question answering (QA) tasks over structured knowledge bases, like Freebase. The data set is generated by matching trivia-type question-answer pairs with subject-predicate-object triples in Freebase. For each collected question-answer pair, we first tag all entities in each question and search for relevant predicates that bridge a tagged entity with the answer in Freebase. Finally, human annotation is used to remove any false positive in these matched triples. Using this method, we are able to efficiently generate over 54K matches from about 28K unique questions with minimal cost. Our analysis shows that this data set is suitable for model training in factoid QA tasks beyond simpler questions since FreebaseQA provides more linguistically sophisticated questions than other existing data sets.", } ``` ### Contributions Thanks to [@gchhablani](https://github.com/gchhablani) and [@anaerobeth](https://github.com/anaerobeth) for adding this dataset.
gap	--- annotations_creators: - crowdsourced language: - en language_creators: - found license: - unknown multilinguality: - monolingual pretty_name: GAP Benchmark Suite size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - coreference-resolution paperswithcode_id: gap dataset_info: features: - name: ID dtype: string - name: Text dtype: string - name: Pronoun dtype: string - name: Pronoun-offset dtype: int32 - name: A dtype: string - name: A-offset dtype: int32 - name: A-coref dtype: bool - name: B dtype: string - name: B-offset dtype: int32 - name: B-coref dtype: bool - name: URL dtype: string splits: - name: train num_bytes: 1095623 num_examples: 2000 - name: validation num_bytes: 248329 num_examples: 454 - name: test num_bytes: 1090462 num_examples: 2000 download_size: 2401971 dataset_size: 2434414 --- # Dataset Card for "gap" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://github.com/google-research-datasets/gap-coreference](https://github.com/google-research-datasets/gap-coreference) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns](https://arxiv.org/abs/1810.05201) - Point of Contact: [gap-coreference@google.com](mailto:gap-coreference@google.com) - Size of downloaded dataset files: 2.40 MB - Size of the generated dataset: 2.43 MB - Total amount of disk used: 4.83 MB ### Dataset Summary GAP is a gender-balanced dataset containing 8,908 coreference-labeled pairs of (ambiguous pronoun, antecedent name), sampled from Wikipedia and released by Google AI Language for the evaluation of coreference resolution in practical applications. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 2.40 MB - Size of the generated dataset: 2.43 MB - Total amount of disk used: 4.83 MB An example of 'validation' looks as follows. ``` { "A": "aliquam ultrices sagittis", "A-coref": false, "A-offset": 208, "B": "elementum curabitur vitae", "B-coref": false, "B-offset": 435, "ID": "validation-1", "Pronoun": "condimentum mattis pellentesque", "Pronoun-offset": 948, "Text": "Lorem ipsum dolor", "URL": "sem fringilla ut" } ``` ### Data Fields The data fields are the same among all splits. #### default - `ID`: a `string` feature. - `Text`: a `string` feature. - `Pronoun`: a `string` feature. - `Pronoun-offset`: a `int32` feature. - `A`: a `string` feature. - `A-offset`: a `int32` feature. - `A-coref`: a `bool` feature. - `B`: a `string` feature. - `B-offset`: a `int32` feature. - `B-coref`: a `bool` feature. - `URL`: a `string` feature. ### Data Splits \| name \|train\|validation\|test\| \|-------\|----:\|---------:\|---:\| \|default\| 2000\| 454\|2000\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{webster-etal-2018-mind, title = "Mind the {GAP}: A Balanced Corpus of Gendered Ambiguous Pronouns", author = "Webster, Kellie and Recasens, Marta and Axelrod, Vera and Baldridge, Jason", journal = "Transactions of the Association for Computational Linguistics", volume = "6", year = "2018", address = "Cambridge, MA", publisher = "MIT Press", url = "https://aclanthology.org/Q18-1042", doi = "10.1162/tacl_a_00240", pages = "605--617", } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@otakumesi](https://github.com/otakumesi), [@lewtun](https://github.com/lewtun) for adding this dataset.
gem	--- annotations_creators: - crowdsourced - found language_creators: - crowdsourced - found - machine-generated language: - cs - de - en - es - ru - tr - vi license: - other multilinguality: - monolingual - multilingual size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K source_datasets: - extended\|other-vision-datasets - original task_categories: - fill-mask - summarization - table-to-text - tabular-to-text - text-generation - text2text-generation task_ids: - dialogue-modeling - rdf-to-text - news-articles-summarization - text-simplification paperswithcode_id: gem pretty_name: GEM configs: - common_gen - cs_restaurants - dart - e2e_nlg - mlsum_de - mlsum_es - schema_guided_dialog - totto - web_nlg_en - web_nlg_ru - wiki_auto_asset_turk - wiki_lingua_es_en - wiki_lingua_ru_en - wiki_lingua_tr_en - wiki_lingua_vi_en - xsum tags: - intent-to-text - meaning-representation-to-text - concepts-to-text dataset_info: - config_name: mlsum_de features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: text dtype: string - name: topic dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 858060337 num_examples: 220748 - name: validation num_bytes: 49712791 num_examples: 11392 - name: test num_bytes: 49146354 num_examples: 10695 - name: challenge_train_sample num_bytes: 1894220 num_examples: 500 - name: challenge_validation_sample num_bytes: 2202723 num_examples: 500 - name: challenge_test_covid num_bytes: 19771285 num_examples: 5058 download_size: 362783528 dataset_size: 980787710 - config_name: mlsum_es features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: text dtype: string - name: topic dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 1211240956 num_examples: 259888 - name: validation num_bytes: 51611723 num_examples: 9977 - name: test num_bytes: 72117564 num_examples: 13366 - name: challenge_train_sample num_bytes: 2366443 num_examples: 500 - name: challenge_validation_sample num_bytes: 2658596 num_examples: 500 - name: challenge_test_covid num_bytes: 13576624 num_examples: 1938 download_size: 525621426 dataset_size: 1353571906 - config_name: wiki_lingua_es_en_v0 features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: source dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 215665468 num_examples: 79515 - name: validation num_bytes: 25891008 num_examples: 8835 - name: test num_bytes: 50195305 num_examples: 19797 download_size: 169406387 dataset_size: 291751781 - config_name: wiki_lingua_ru_en_v0 features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: source dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 159631205 num_examples: 36898 - name: validation num_bytes: 18626973 num_examples: 4100 - name: test num_bytes: 34865311 num_examples: 9094 download_size: 169406387 dataset_size: 213123489 - config_name: wiki_lingua_tr_en_v0 features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: source dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 7689845 num_examples: 3193 - name: validation num_bytes: 942122 num_examples: 355 - name: test num_bytes: 1875110 num_examples: 808 download_size: 169406387 dataset_size: 10507077 - config_name: wiki_lingua_vi_en_v0 features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: source dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 31599580 num_examples: 9206 - name: validation num_bytes: 3618660 num_examples: 1023 - name: test num_bytes: 6267359 num_examples: 2167 download_size: 169406387 dataset_size: 41485599 - config_name: wiki_lingua_arabic_ar features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: source_aligned dtype: translation: languages: - ar - en - name: target_aligned dtype: translation: languages: - ar - en - name: source dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 208106335 num_examples: 20441 - name: validation num_bytes: 31126187 num_examples: 2919 - name: test num_bytes: 60915220 num_examples: 5841 download_size: 58984103 dataset_size: 300147742 - config_name: wiki_lingua_chinese_zh features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: source_aligned dtype: translation: languages: - zh - en - name: target_aligned dtype: translation: languages: - zh - en - name: source dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 86130302 num_examples: 13211 - name: validation num_bytes: 13060918 num_examples: 1886 - name: test num_bytes: 25310021 num_examples: 3775 download_size: 32899156 dataset_size: 124501241 - config_name: wiki_lingua_czech_cs features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: source_aligned dtype: translation: languages: - cs - en - name: target_aligned dtype: translation: languages: - cs - en - name: source dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 41107318 num_examples: 5033 - name: validation num_bytes: 6305328 num_examples: 718 - name: test num_bytes: 12124770 num_examples: 1438 download_size: 14515534 dataset_size: 59537416 - config_name: wiki_lingua_dutch_nl features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: source_aligned dtype: translation: languages: - nl - en - name: target_aligned dtype: translation: languages: - nl - en - name: source dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 169067454 num_examples: 21866 - name: validation num_bytes: 25521003 num_examples: 3123 - name: test num_bytes: 49165151 num_examples: 6248 download_size: 56492150 dataset_size: 243753608 - config_name: wiki_lingua_english_en features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: source_aligned dtype: translation: languages: - en - en - name: target_aligned dtype: translation: languages: - en - en - name: source dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 464171624 num_examples: 99020 - name: validation num_bytes: 67652281 num_examples: 13823 - name: test num_bytes: 138944243 num_examples: 28614 download_size: 118031903 dataset_size: 670768148 - config_name: wiki_lingua_french_fr features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: source_aligned dtype: translation: languages: - fr - en - name: target_aligned dtype: translation: languages: - fr - en - name: source dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 372039357 num_examples: 44556 - name: validation num_bytes: 54992250 num_examples: 6364 - name: test num_bytes: 108831855 num_examples: 12731 download_size: 118758047 dataset_size: 535863462 - config_name: wiki_lingua_german_de features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: source_aligned dtype: translation: languages: - de - en - name: target_aligned dtype: translation: languages: - de - en - name: source dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 322276536 num_examples: 40839 - name: validation num_bytes: 47631883 num_examples: 5833 - name: test num_bytes: 93715331 num_examples: 11669 download_size: 107638803 dataset_size: 463623750 - config_name: wiki_lingua_hindi_hi features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: source_aligned dtype: translation: languages: - hi - en - name: target_aligned dtype: translation: languages: - hi - en - name: source dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 99672133 num_examples: 6942 - name: validation num_bytes: 14706378 num_examples: 991 - name: test num_bytes: 28543048 num_examples: 1984 download_size: 21042040 dataset_size: 142921559 - config_name: wiki_lingua_indonesian_id features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: source_aligned dtype: translation: languages: - id - en - name: target_aligned dtype: translation: languages: - id - en - name: source dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 263974954 num_examples: 33237 - name: validation num_bytes: 39297987 num_examples: 4747 - name: test num_bytes: 76567819 num_examples: 9497 download_size: 83968162 dataset_size: 379840760 - config_name: wiki_lingua_italian_it features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: source_aligned dtype: translation: languages: - it - en - name: target_aligned dtype: translation: languages: - it - en - name: source dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 267090482 num_examples: 35661 - name: validation num_bytes: 39227425 num_examples: 5093 - name: test num_bytes: 76840429 num_examples: 10189 download_size: 88921209 dataset_size: 383158336 - config_name: wiki_lingua_japanese_ja features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: source_aligned dtype: translation: languages: - ja - en - name: target_aligned dtype: translation: languages: - ja - en - name: source dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 73871019 num_examples: 8853 - name: validation num_bytes: 10807006 num_examples: 1264 - name: test num_bytes: 21175951 num_examples: 2530 download_size: 22803299 dataset_size: 105853976 - config_name: wiki_lingua_korean_ko features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: source_aligned dtype: translation: languages: - ko - en - name: target_aligned dtype: translation: languages: - ko - en - name: source dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 73106687 num_examples: 8524 - name: validation num_bytes: 10788276 num_examples: 1216 - name: test num_bytes: 21172641 num_examples: 2436 download_size: 23336917 dataset_size: 105067604 - config_name: wiki_lingua_portuguese_pt features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: source_aligned dtype: translation: languages: - pt - en - name: target_aligned dtype: translation: languages: - pt - en - name: source dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 405546332 num_examples: 57159 - name: validation num_bytes: 59729210 num_examples: 8165 - name: test num_bytes: 117775356 num_examples: 16331 download_size: 137542940 dataset_size: 583050898 - config_name: wiki_lingua_russian_ru features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: source_aligned dtype: translation: languages: - ru - en - name: target_aligned dtype: translation: languages: - ru - en - name: source dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 406299624 num_examples: 37028 - name: validation num_bytes: 59651340 num_examples: 5288 - name: test num_bytes: 116330937 num_examples: 10580 download_size: 106281321 dataset_size: 582281901 - config_name: wiki_lingua_spanish_es features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: source_aligned dtype: translation: languages: - es - en - name: target_aligned dtype: translation: languages: - es - en - name: source dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 604276564 num_examples: 79212 - name: validation num_bytes: 88677656 num_examples: 11316 - name: test num_bytes: 177096288 num_examples: 22632 download_size: 198247534 dataset_size: 870050508 - config_name: wiki_lingua_thai_th features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: source_aligned dtype: translation: languages: - th - en - name: target_aligned dtype: translation: languages: - th - en - name: source dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 139287649 num_examples: 10325 - name: validation num_bytes: 21097845 num_examples: 1475 - name: test num_bytes: 40049968 num_examples: 2950 download_size: 29988180 dataset_size: 200435462 - config_name: wiki_lingua_turkish_tr features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: source_aligned dtype: translation: languages: - tr - en - name: target_aligned dtype: translation: languages: - tr - en - name: source dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 21987247 num_examples: 3148 - name: validation num_bytes: 3229714 num_examples: 449 - name: test num_bytes: 6197850 num_examples: 900 download_size: 7055820 dataset_size: 31414811 - config_name: wiki_lingua_vietnamese_vi features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: source_aligned dtype: translation: languages: - vi - en - name: target_aligned dtype: translation: languages: - vi - en - name: source dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 128025008 num_examples: 13707 - name: validation num_bytes: 19414734 num_examples: 1957 - name: test num_bytes: 37430208 num_examples: 3917 download_size: 38035490 dataset_size: 184869950 - config_name: xsum features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: xsum_id dtype: string - name: document dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 66299136 num_examples: 23206 - name: validation num_bytes: 2270306 num_examples: 1117 - name: test num_bytes: 2598509 num_examples: 1166 - name: challenge_train_sample num_bytes: 1429145 num_examples: 500 - name: challenge_validation_sample num_bytes: 1012689 num_examples: 500 - name: challenge_test_backtranslation num_bytes: 1262047 num_examples: 500 - name: challenge_test_bfp_02 num_bytes: 1090364 num_examples: 500 - name: challenge_test_bfp_05 num_bytes: 1078076 num_examples: 500 - name: challenge_test_nopunc num_bytes: 1127796 num_examples: 500 - name: challenge_test_covid num_bytes: 1867180 num_examples: 401 download_size: 258277147 dataset_size: 80035248 - config_name: common_gen features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: concept_set_id dtype: int32 - name: concepts list: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 10475926 num_examples: 67389 - name: validation num_bytes: 405872 num_examples: 993 - name: test num_bytes: 153170 num_examples: 1497 - name: challenge_train_sample num_bytes: 85413 num_examples: 500 - name: challenge_validation_sample num_bytes: 215192 num_examples: 500 - name: challenge_test_scramble num_bytes: 60411 num_examples: 500 download_size: 1933517 dataset_size: 11395984 - config_name: cs_restaurants features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: dialog_act dtype: string - name: dialog_act_delexicalized dtype: string - name: target_delexicalized dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 873145 num_examples: 3569 - name: validation num_bytes: 288222 num_examples: 781 - name: test num_bytes: 295696 num_examples: 842 - name: challenge_train_sample num_bytes: 127869 num_examples: 500 - name: challenge_validation_sample num_bytes: 193239 num_examples: 500 - name: challenge_test_scramble num_bytes: 185574 num_examples: 500 download_size: 1531111 dataset_size: 1963745 - config_name: dart features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: dart_id dtype: int32 - name: tripleset list: list: string - name: subtree_was_extended dtype: bool - name: target_sources list: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 23047610 num_examples: 62659 - name: validation num_bytes: 1934054 num_examples: 2768 - name: test num_bytes: 3476953 num_examples: 5097 download_size: 29939366 dataset_size: 28458617 - config_name: e2e_nlg features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: meaning_representation dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 9129030 num_examples: 33525 - name: validation num_bytes: 1856097 num_examples: 4299 - name: test num_bytes: 2133695 num_examples: 4693 - name: challenge_train_sample num_bytes: 145319 num_examples: 500 - name: challenge_validation_sample num_bytes: 226525 num_examples: 500 - name: challenge_test_scramble num_bytes: 236199 num_examples: 500 download_size: 14668048 dataset_size: 13726865 - config_name: totto features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: totto_id dtype: int32 - name: table_page_title dtype: string - name: table_webpage_url dtype: string - name: table_section_title dtype: string - name: table_section_text dtype: string - name: table list: list: - name: column_span dtype: int32 - name: is_header dtype: bool - name: row_span dtype: int32 - name: value dtype: string - name: highlighted_cells list: list: int32 - name: example_id dtype: string - name: sentence_annotations list: - name: original_sentence dtype: string - name: sentence_after_deletion dtype: string - name: sentence_after_ambiguity dtype: string - name: final_sentence dtype: string - name: overlap_subset dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 676032144 num_examples: 121153 - name: validation num_bytes: 50736204 num_examples: 7700 - name: test num_bytes: 41330062 num_examples: 7700 - name: challenge_train_sample num_bytes: 2283076 num_examples: 500 - name: challenge_validation_sample num_bytes: 3398639 num_examples: 500 - name: challenge_test_scramble num_bytes: 2638966 num_examples: 500 download_size: 189534609 dataset_size: 776419091 - config_name: web_nlg_en features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: input list: string - name: target dtype: string - name: references list: string - name: category dtype: string - name: webnlg_id dtype: string splits: - name: train num_bytes: 13067615 num_examples: 35426 - name: validation num_bytes: 1153995 num_examples: 1667 - name: test num_bytes: 1403601 num_examples: 1779 - name: challenge_train_sample num_bytes: 193198 num_examples: 502 - name: challenge_validation_sample num_bytes: 359868 num_examples: 499 - name: challenge_test_scramble num_bytes: 402407 num_examples: 500 - name: challenge_test_numbers num_bytes: 409213 num_examples: 500 download_size: 13181969 dataset_size: 16989897 - config_name: web_nlg_ru features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: input list: string - name: target dtype: string - name: references list: string - name: category dtype: string - name: webnlg_id dtype: string splits: - name: train num_bytes: 6888009 num_examples: 14630 - name: validation num_bytes: 795998 num_examples: 790 - name: test num_bytes: 1145282 num_examples: 1102 - name: challenge_train_sample num_bytes: 247089 num_examples: 501 - name: challenge_validation_sample num_bytes: 514117 num_examples: 500 - name: challenge_test_scramble num_bytes: 521625 num_examples: 500 download_size: 7854845 dataset_size: 10112120 - config_name: wiki_auto_asset_turk features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: source dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 161095379 num_examples: 483801 - name: validation num_bytes: 8211308 num_examples: 20000 - name: test_asset num_bytes: 475336 num_examples: 359 - name: test_turk num_bytes: 406842 num_examples: 359 - name: challenge_train_sample num_bytes: 219542 num_examples: 500 - name: challenge_validation_sample num_bytes: 213048 num_examples: 500 - name: challenge_test_asset_backtranslation num_bytes: 436820 num_examples: 359 - name: challenge_test_asset_bfp02 num_bytes: 432742 num_examples: 359 - name: challenge_test_asset_bfp05 num_bytes: 432742 num_examples: 359 - name: challenge_test_asset_nopunc num_bytes: 432735 num_examples: 359 - name: challenge_test_turk_backtranslation num_bytes: 417204 num_examples: 359 - name: challenge_test_turk_bfp02 num_bytes: 414381 num_examples: 359 - name: challenge_test_turk_bfp05 num_bytes: 414383 num_examples: 359 - name: challenge_test_turk_nopunc num_bytes: 414388 num_examples: 359 download_size: 126927527 dataset_size: 174016850 - config_name: schema_guided_dialog features: - name: gem_id dtype: string - name: gem_parent_id dtype: string - name: dialog_acts list: - name: act dtype: class_label: names: '0': AFFIRM '1': AFFIRM_INTENT '2': CONFIRM '3': GOODBYE '4': INFORM '5': INFORM_COUNT '6': INFORM_INTENT '7': NEGATE '8': NEGATE_INTENT '9': NOTIFY_FAILURE '10': NOTIFY_SUCCESS '11': OFFER '12': OFFER_INTENT '13': REQUEST '14': REQUEST_ALTS '15': REQ_MORE '16': SELECT '17': THANK_YOU - name: slot dtype: string - name: values list: string - name: context list: string - name: dialog_id dtype: string - name: service dtype: string - name: turn_id dtype: int32 - name: prompt dtype: string - name: target dtype: string - name: references list: string splits: - name: train num_bytes: 146648117 num_examples: 164982 - name: validation num_bytes: 9376504 num_examples: 10000 - name: test num_bytes: 10160596 num_examples: 10000 - name: challenge_train_sample num_bytes: 441326 num_examples: 500 - name: challenge_validation_sample num_bytes: 491492 num_examples: 500 - name: challenge_test_backtranslation num_bytes: 512834 num_examples: 500 - name: challenge_test_bfp02 num_bytes: 529404 num_examples: 500 - name: challenge_test_bfp05 num_bytes: 515151 num_examples: 500 - name: challenge_test_nopunc num_bytes: 509332 num_examples: 500 - name: challenge_test_scramble num_bytes: 514644 num_examples: 500 download_size: 17826468 dataset_size: 169699400 --- # Dataset Card for GEM ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://gem-benchmark.github.io/](https://gem-benchmark.github.io/) - Repository: - Paper: [The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics](https://arxiv.org/abs/2102.01672) - Point of Contact: [Sebastian Gehrman](gehrmann@google.com) - Size of downloaded dataset files: 2.19 GB - Size of the generated dataset: 3.92 GB - Total amount of disk used: 6.10 GB ### Dataset Summary GEM is a benchmark environment for Natural Language Generation with a focus on its Evaluation, both through human annotations and automated Metrics. GEM aims to: - measure NLG progress across 13 datasets spanning many NLG tasks and languages. - provide an in-depth analysis of data and models presented via data statements and challenge sets. - develop standards for evaluation of generated text using both automated and human metrics. It is our goal to regularly update GEM and to encourage toward more inclusive practices in dataset development by extending existing data or developing datasets for additional languages. You can find more complete information in the dataset cards for each of the subsets: - [CommonGen](https://gem-benchmark.com/data_cards/common_gen) - [Czech Restaurant](https://gem-benchmark.com/data_cards/cs_restaurants) - [DART](https://gem-benchmark.com/data_cards/dart) - [E2E](https://gem-benchmark.com/data_cards/e2e_nlg) - [MLSum](https://gem-benchmark.com/data_cards/mlsum) - [Schema-Guided Dialog](https://gem-benchmark.com/data_cards/schema_guided_dialog) - [WebNLG](https://gem-benchmark.com/data_cards/web_nlg) - [Wiki-Auto/ASSET/TURK](https://gem-benchmark.com/data_cards/wiki_auto_asset_turk) - [WikiLingua](https://gem-benchmark.com/data_cards/wiki_lingua) - [XSum](https://gem-benchmark.com/data_cards/xsum) The subsets are organized by task: ``` { "summarization": { "mlsum": ["mlsum_de", "mlsum_es"], "wiki_lingua": ["wiki_lingua_es_en", "wiki_lingua_ru_en", "wiki_lingua_tr_en", "wiki_lingua_vi_en"], "xsum": ["xsum"], }, "struct2text": { "common_gen": ["common_gen"], "cs_restaurants": ["cs_restaurants"], "dart": ["dart"], "e2e": ["e2e_nlg"], "totto": ["totto"], "web_nlg": ["web_nlg_en", "web_nlg_ru"], }, "simplification": { "wiki_auto_asset_turk": ["wiki_auto_asset_turk"], }, "dialog": { "schema_guided_dialog": ["schema_guided_dialog"], }, } ``` Each example has one `target` per example in its training set, and a set of `references` (with one or more items) in its validation and test set. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### common_gen - Size of downloaded dataset files: 1.85 MB - Size of the generated dataset: 9.23 MB - Total amount of disk used: 11.07 MB An example of `validation` looks as follows. ``` {'concept_set_id': 0, 'concepts': ['field', 'look', 'stand'], 'gem_id': 'common_gen-validation-0', 'references': ['The player stood in the field looking at the batter.', 'The coach stands along the field, looking at the goalkeeper.', 'I stood and looked across the field, peacefully.', 'Someone stands, looking around the empty field.'], 'target': 'The player stood in the field looking at the batter.'} ``` #### cs_restaurants - Size of downloaded dataset files: 1.47 MB - Size of the generated dataset: 1.31 MB - Total amount of disk used: 2.77 MB An example of `validation` looks as follows. ``` {'dialog_act': '?request(area)', 'dialog_act_delexicalized': '?request(area)', 'gem_id': 'cs_restaurants-validation-0', 'references': ['Jakou lokalitu hledáte ?'], 'target': 'Jakou lokalitu hledáte ?', 'target_delexicalized': 'Jakou lokalitu hledáte ?'} ``` #### dart - Size of downloaded dataset files: 29.37 MB - Size of the generated dataset: 27.44 MB - Total amount of disk used: 56.81 MB An example of `validation` looks as follows. ``` {'dart_id': 0, 'gem_id': 'dart-validation-0', 'references': ['A school from Mars Hill, North Carolina, joined in 1973.'], 'subtree_was_extended': True, 'target': 'A school from Mars Hill, North Carolina, joined in 1973.', 'target_sources': ['WikiSQL_decl_sents'], 'tripleset': [['Mars Hill College', 'JOINED', '1973'], ['Mars Hill College', 'LOCATION', 'Mars Hill, North Carolina']]} ``` #### e2e_nlg - Size of downloaded dataset files: 14.60 MB - Size of the generated dataset: 12.14 MB - Total amount of disk used: 26.74 MB An example of `validation` looks as follows. ``` {'gem_id': 'e2e_nlg-validation-0', 'meaning_representation': 'name[Alimentum], area[city centre], familyFriendly[no]', 'references': ['There is a place in the city centre, Alimentum, that is not family-friendly.'], 'target': 'There is a place in the city centre, Alimentum, that is not family-friendly.'} ``` #### mlsum_de - Size of downloaded dataset files: 347.36 MB - Size of the generated dataset: 951.06 MB - Total amount of disk used: 1.30 GB An example of `validation` looks as follows. ``` {'date': '00/04/2019', 'gem_id': 'mlsum_de-validation-0', 'references': ['In einer Kleinstadt auf der Insel Usedom war eine junge Frau tot in ihrer Wohnung gefunden worden. Nun stehen zwei Bekannte unter Verdacht.'], 'target': 'In einer Kleinstadt auf der Insel Usedom war eine junge Frau tot in ihrer Wohnung gefunden worden. Nun stehen zwei Bekannte unter Verdacht.', 'text': 'Kerzen und Blumen stehen vor dem Eingang eines Hauses, in dem eine 18-jährige Frau tot aufgefunden wurde. In einer Kleinstadt auf der Insel Usedom war eine junge Frau tot in ...', 'title': 'Tod von 18-Jähriger auf Usedom: Zwei Festnahmen', 'topic': 'panorama', 'url': 'https://www.sueddeutsche.de/panorama/usedom-frau-tot-festnahme-verdaechtige-1.4412256'} ``` #### mlsum_es - Size of downloaded dataset files: 514.11 MB - Size of the generated dataset: 1.31 GB - Total amount of disk used: 1.83 GB An example of `validation` looks as follows. ``` {'date': '05/01/2019', 'gem_id': 'mlsum_es-validation-0', 'references': ['El diseñador que dio carta de naturaleza al estilo genuinamente americano celebra el medio siglo de su marca entre grandes fastos y problemas financieros. Conectar con las nuevas generaciones es el regalo que precisa más que nunca'], 'target': 'El diseñador que dio carta de naturaleza al estilo genuinamente americano celebra el medio siglo de su marca entre grandes fastos y problemas financieros. Conectar con las nuevas generaciones es el regalo que precisa más que nunca', 'text': 'Un oso de peluche marcándose un heelflip de monopatín es todo lo que Ralph Lauren necesitaba esta Navidad. Estampado en un jersey de lana azul marino, supone la guinda que corona ...', 'title': 'Ralph Lauren busca el secreto de la eterna juventud', 'topic': 'elpais estilo', 'url': 'http://elpais.com/elpais/2019/01/04/estilo/1546617396_933318.html'} ``` #### schema_guided_dialog - Size of downloaded dataset files: 8.64 MB - Size of the generated dataset: 45.78 MB - Total amount of disk used: 54.43 MB An example of `validation` looks as follows. ``` {'dialog_acts': [{'act': 2, 'slot': 'song_name', 'values': ['Carnivore']}, {'act': 2, 'slot': 'playback_device', 'values': ['TV']}], 'dialog_id': '10_00054', 'gem_id': 'schema_guided_dialog-validation-0', 'prompt': 'Yes, I would.', 'references': ['Please confirm the song Carnivore on tv.'], 'target': 'Please confirm the song Carnivore on tv.', 'turn_id': 15} ``` #### totto - Size of downloaded dataset files: 187.73 MB - Size of the generated dataset: 757.99 MB - Total amount of disk used: 945.72 MB An example of `validation` looks as follows. ``` {'example_id': '7391450717765563190', 'gem_id': 'totto-validation-0', 'highlighted_cells': [[3, 0], [3, 2], [3, 3]], 'overlap_subset': 'True', 'references': ['Daniel Henry Chamberlain was the 76th Governor of South Carolina from 1874.', 'Daniel Henry Chamberlain was the 76th Governor of South Carolina, beginning in 1874.', 'Daniel Henry Chamberlain was the 76th Governor of South Carolina who took office in 1874.'], 'sentence_annotations': [{'final_sentence': 'Daniel Henry Chamberlain was the 76th Governor of South Carolina from 1874.', 'original_sentence': 'Daniel Henry Chamberlain (June 23, 1835 – April 13, 1907) was an American planter, lawyer, author and the 76th Governor of South Carolina ' 'from 1874 until 1877.', 'sentence_after_ambiguity': 'Daniel Henry Chamberlain was the 76th Governor of South Carolina from 1874.', 'sentence_after_deletion': 'Daniel Henry Chamberlain was the 76th Governor of South Carolina from 1874.'}, ... ], 'table': [[{'column_span': 1, 'is_header': True, 'row_span': 1, 'value': '#'}, {'column_span': 2, 'is_header': True, 'row_span': 1, 'value': 'Governor'}, {'column_span': 1, 'is_header': True, 'row_span': 1, 'value': 'Took Office'}, {'column_span': 1, 'is_header': True, 'row_span': 1, 'value': 'Left Office'}], [{'column_span': 1, 'is_header': True, 'row_span': 1, 'value': '74'}, {'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '-'}, {'column_span': 1, 'is_header': False, 'row_span': 1, 'value': 'Robert Kingston Scott'}, {'column_span': 1, 'is_header': False, 'row_span': 1, 'value': 'July 6, 1868'}], ... ], 'table_page_title': 'List of Governors of South Carolina', 'table_section_text': 'Parties Democratic Republican', 'table_section_title': 'Governors under the Constitution of 1868', 'table_webpage_url': 'http://en.wikipedia.org/wiki/List_of_Governors_of_South_Carolina', 'target': 'Daniel Henry Chamberlain was the 76th Governor of South Carolina from 1874.', 'totto_id': 0} ``` #### web_nlg_en - Size of downloaded dataset files: 12.95 MB - Size of the generated dataset: 14.63 MB - Total amount of disk used: 27.57 MB An example of `validation` looks as follows. ``` {'category': 'Airport', 'gem_id': 'web_nlg_en-validation-0', 'input': ['Aarhus \| leader \| Jacob_Bundsgaard'], 'references': ['The leader of Aarhus is Jacob Bundsgaard.'], 'target': 'The leader of Aarhus is Jacob Bundsgaard.', 'webnlg_id': 'dev/Airport/1/Id1'} ``` #### web_nlg_ru - Size of downloaded dataset files: 7.63 MB - Size of the generated dataset: 8.41 MB - Total amount of disk used: 16.04 MB An example of `validation` looks as follows. ``` {'category': 'Airport', 'gem_id': 'web_nlg_ru-validation-0', 'input': ['Punjab,_Pakistan \| leaderTitle \| Provincial_Assembly_of_the_Punjab'], 'references': ['Пенджаб, Пакистан, возглавляется Провинциальной ассамблеей Пенджаба.', 'Пенджаб, Пакистан возглавляется Провинциальной ассамблеей Пенджаба.'], 'target': 'Пенджаб, Пакистан, возглавляется Провинциальной ассамблеей Пенджаба.', 'webnlg_id': 'dev/Airport/1/Id1'} ``` #### wiki_auto_asset_turk - Size of downloaded dataset files: 127.27 MB - Size of the generated dataset: 152.77 MB - Total amount of disk used: 280.04 MB An example of `validation` looks as follows. ``` {'gem_id': 'wiki_auto_asset_turk-validation-0', 'references': ['The Gandalf Awards honor excellent writing in in fantasy literature.'], 'source': 'The Gandalf Awards, honoring achievement in fantasy literature, were conferred by the World Science Fiction Society annually from 1974 to 1981.', 'source_id': '350_691837-1-0-0', 'target': 'The Gandalf Awards honor excellent writing in in fantasy literature.', 'target_id': '350_691837-0-0-0'} ``` #### wiki_lingua_es_en - Size of downloaded dataset files: 169.41 MB - Size of the generated dataset: 287.60 MB - Total amount of disk used: 457.01 MB An example of `validation` looks as follows. ``` 'references': ["Practice matted hair prevention from early in your cat's life. Make sure that your cat is grooming itself effectively. Keep a close eye on cats with long hair."], 'source': 'Muchas personas presentan problemas porque no cepillaron el pelaje de sus gatos en una etapa temprana de su vida, ya que no lo consideraban necesario. Sin embargo, a medida que...', 'target': "Practice matted hair prevention from early in your cat's life. Make sure that your cat is grooming itself effectively. Keep a close eye on cats with long hair."} ``` #### wiki_lingua_ru_en - Size of downloaded dataset files: 169.41 MB - Size of the generated dataset: 211.21 MB - Total amount of disk used: 380.62 MB An example of `validation` looks as follows. ``` {'gem_id': 'wiki_lingua_ru_en-val-0', 'references': ['Get immediate medical care if you notice signs of a complication. Undergo diagnostic tests to check for gallstones and complications. Ask your doctor about your treatment ' 'options.'], 'source': 'И хотя, скорее всего, вам не о чем волноваться, следует незамедлительно обратиться к врачу, если вы подозреваете, что у вас возникло осложнение желчекаменной болезни. Это ...', 'target': 'Get immediate medical care if you notice signs of a complication. Undergo diagnostic tests to check for gallstones and complications. Ask your doctor about your treatment ' 'options.'} ``` #### wiki_lingua_tr_en - Size of downloaded dataset files: 169.41 MB - Size of the generated dataset: 10.35 MB - Total amount of disk used: 179.75 MB An example of `validation` looks as follows. ``` {'gem_id': 'wiki_lingua_tr_en-val-0', 'references': ['Open Instagram. Go to the video you want to download. Tap ⋮. Tap Copy Link. Open Google Chrome. Tap the address bar. Go to the SaveFromWeb site. Tap the "Paste Instagram Video" text box. Tap and hold the text box. Tap PASTE. Tap Download. Download the video. Find the video on your Android.'], 'source': 'Instagram uygulamasının çok renkli kamera şeklindeki simgesine dokun. Daha önce giriş yaptıysan Instagram haber kaynağı açılır. Giriş yapmadıysan istendiğinde e-posta adresini ...', 'target': 'Open Instagram. Go to the video you want to download. Tap ⋮. Tap Copy Link. Open Google Chrome. Tap the address bar. Go to the SaveFromWeb site. Tap the "Paste Instagram Video" text box. Tap and hold the text box. Tap PASTE. Tap Download. Download the video. Find the video on your Android.'} ``` #### wiki_lingua_vi_en - Size of downloaded dataset files: 169.41 MB - Size of the generated dataset: 41.02 MB - Total amount of disk used: 210.43 MB An example of `validation` looks as follows. ``` {'gem_id': 'wiki_lingua_vi_en-val-0', 'references': ['Select the right time of year for planting the tree. You will usually want to plant your tree when it is dormant, or not flowering, during cooler or colder times of year.'], 'source': 'Bạn muốn cung cấp cho cây cơ hội tốt nhất để phát triển và sinh tồn. Trồng cây đúng thời điểm trong năm chính là yếu tố then chốt. Thời điểm sẽ thay đổi phụ thuộc vào loài cây ...', 'target': 'Select the right time of year for planting the tree. You will usually want to plant your tree when it is dormant, or not flowering, during cooler or colder times of year.'} ``` #### xsum - Size of downloaded dataset files: 254.89 MB - Size of the generated dataset: 70.67 MB - Total amount of disk used: 325.56 MB An example of `validation` looks as follows. ``` {'document': 'Burberry reported pre-tax profits of £166m for the year to March. A year ago it made a loss of £16.1m, hit by charges at its Spanish operations.\n' 'In the past year it has opened 21 new stores and closed nine. It plans to open 20-30 stores this year worldwide.\n' 'The group has also focused on promoting the Burberry brand online...', 'gem_id': 'xsum-validation-0', 'references': ['Luxury fashion designer Burberry has returned to profit after opening new stores and spending more on online marketing'], 'target': 'Luxury fashion designer Burberry has returned to profit after opening new stores and spending more on online marketing', 'xsum_id': '10162122'} ``` ### Data Fields The data fields are the same among all splits. #### common_gen - `gem_id`: a `string` feature. - `concept_set_id`: a `int32` feature. - `concepts`: a `list` of `string` features. - `target`: a `string` feature. - `references`: a `list` of `string` features. #### cs_restaurants - `gem_id`: a `string` feature. - `dialog_act`: a `string` feature. - `dialog_act_delexicalized`: a `string` feature. - `target_delexicalized`: a `string` feature. - `target`: a `string` feature. - `references`: a `list` of `string` features. #### dart - `gem_id`: a `string` feature. - `dart_id`: a `int32` feature. - `tripleset`: a `list` of `string` features. - `subtree_was_extended`: a `bool` feature. - `target_sources`: a `list` of `string` features. - `target`: a `string` feature. - `references`: a `list` of `string` features. #### e2e_nlg - `gem_id`: a `string` feature. - `meaning_representation`: a `string` feature. - `target`: a `string` feature. - `references`: a `list` of `string` features. #### mlsum_de - `gem_id`: a `string` feature. - `text`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. - `target`: a `string` feature. - `references`: a `list` of `string` features. #### mlsum_es - `gem_id`: a `string` feature. - `text`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. - `target`: a `string` feature. - `references`: a `list` of `string` features. #### schema_guided_dialog - `gem_id`: a `string` feature. - `act`: a classification label, with possible values including `AFFIRM` (0), `AFFIRM_INTENT` (1), `CONFIRM` (2), `GOODBYE` (3), `INFORM` (4). - `slot`: a `string` feature. - `values`: a `list` of `string` features. - `dialog_id`: a `string` feature. - `turn_id`: a `int32` feature. - `prompt`: a `string` feature. - `target`: a `string` feature. - `references`: a `list` of `string` features. #### totto - `gem_id`: a `string` feature. - `totto_id`: a `int32` feature. - `table_page_title`: a `string` feature. - `table_webpage_url`: a `string` feature. - `table_section_title`: a `string` feature. - `table_section_text`: a `string` feature. - `column_span`: a `int32` feature. - `is_header`: a `bool` feature. - `row_span`: a `int32` feature. - `value`: a `string` feature. - `highlighted_cells`: a `list` of `int32` features. - `example_id`: a `string` feature. - `original_sentence`: a `string` feature. - `sentence_after_deletion`: a `string` feature. - `sentence_after_ambiguity`: a `string` feature. - `final_sentence`: a `string` feature. - `overlap_subset`: a `string` feature. - `target`: a `string` feature. - `references`: a `list` of `string` features. #### web_nlg_en - `gem_id`: a `string` feature. - `input`: a `list` of `string` features. - `target`: a `string` feature. - `references`: a `list` of `string` features. - `category`: a `string` feature. - `webnlg_id`: a `string` feature. #### web_nlg_ru - `gem_id`: a `string` feature. - `input`: a `list` of `string` features. - `target`: a `string` feature. - `references`: a `list` of `string` features. - `category`: a `string` feature. - `webnlg_id`: a `string` feature. #### wiki_auto_asset_turk - `gem_id`: a `string` feature. - `source_id`: a `string` feature. - `target_id`: a `string` feature. - `source`: a `string` feature. - `target`: a `string` feature. - `references`: a `list` of `string` features. #### wiki_lingua_es_en - `gem_id`: a `string` feature. - `source`: a `string` feature. - `target`: a `string` feature. - `references`: a `list` of `string` features. #### wiki_lingua_ru_en - `gem_id`: a `string` feature. - `source`: a `string` feature. - `target`: a `string` feature. - `references`: a `list` of `string` features. #### wiki_lingua_tr_en - `gem_id`: a `string` feature. - `source`: a `string` feature. - `target`: a `string` feature. - `references`: a `list` of `string` features. #### wiki_lingua_vi_en - `gem_id`: a `string` feature. - `source`: a `string` feature. - `target`: a `string` feature. - `references`: a `list` of `string` features. #### xsum - `gem_id`: a `string` feature. - `xsum_id`: a `string` feature. - `document`: a `string` feature. - `target`: a `string` feature. - `references`: a `list` of `string` features. ### Data Splits #### common_gen \| \|train\|validation\|test\| \|----------\|----:\|---------:\|---:\| \|common_gen\|67389\| 993\|1497\| #### cs_restaurants \| \|train\|validation\|test\| \|--------------\|----:\|---------:\|---:\| \|cs_restaurants\| 3569\| 781\| 842\| #### dart \| \|train\|validation\|test\| \|----\|----:\|---------:\|---:\| \|dart\|62659\| 2768\|6959\| #### e2e_nlg \| \|train\|validation\|test\| \|-------\|----:\|---------:\|---:\| \|e2e_nlg\|33525\| 4299\|4693\| #### mlsum_de \| \|train \|validation\|test \| \|--------\|-----:\|---------:\|----:\| \|mlsum_de\|220748\| 11392\|10695\| #### mlsum_es \| \|train \|validation\|test \| \|--------\|-----:\|---------:\|----:\| \|mlsum_es\|259886\| 9977\|13365\| #### schema_guided_dialog \| \|train \|validation\|test \| \|--------------------\|-----:\|---------:\|----:\| \|schema_guided_dialog\|164982\| 10000\|10000\| #### totto \| \|train \|validation\|test\| \|-----\|-----:\|---------:\|---:\| \|totto\|121153\| 7700\|7700\| #### web_nlg_en \| \|train\|validation\|test\| \|----------\|----:\|---------:\|---:\| \|web_nlg_en\|35426\| 1667\|1779\| #### web_nlg_ru \| \|train\|validation\|test\| \|----------\|----:\|---------:\|---:\| \|web_nlg_ru\|14630\| 790\|1102\| #### wiki_auto_asset_turk \| \|train \|validation\|test_asset\|test_turk\| \|--------------------\|-----:\|---------:\|---------:\|--------:\| \|wiki_auto_asset_turk\|373801\| 73249\| 359\| 359\| #### wiki_lingua_es_en \| \|train\|validation\|test \| \|-----------------\|----:\|---------:\|----:\| \|wiki_lingua_es_en\|79515\| 8835\|19797\| #### wiki_lingua_ru_en \| \|train\|validation\|test\| \|-----------------\|----:\|---------:\|---:\| \|wiki_lingua_ru_en\|36898\| 4100\|9094\| #### wiki_lingua_tr_en \| \|train\|validation\|test\| \|-----------------\|----:\|---------:\|---:\| \|wiki_lingua_tr_en\| 3193\| 355\| 808\| #### wiki_lingua_vi_en \| \|train\|validation\|test\| \|-----------------\|----:\|---------:\|---:\| \|wiki_lingua_vi_en\| 9206\| 1023\|2167\| #### xsum \| \|train\|validation\|test\| \|----\|----:\|---------:\|---:\| \|xsum\|23206\| 1117\|1166\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information CC-BY-SA-4.0 ### Citation Information ``` @article{gem_benchmark, author = {Sebastian Gehrmann and Tosin P. Adewumi and Karmanya Aggarwal and Pawan Sasanka Ammanamanchi and Aremu Anuoluwapo and Antoine Bosselut and Khyathi Raghavi Chandu and Miruna{-}Adriana Clinciu and Dipanjan Das and Kaustubh D. Dhole and Wanyu Du and Esin Durmus and Ondrej Dusek and Chris Emezue and Varun Gangal and Cristina Garbacea and Tatsunori Hashimoto and Yufang Hou and Yacine Jernite and Harsh Jhamtani and Yangfeng Ji and Shailza Jolly and Dhruv Kumar and Faisal Ladhak and Aman Madaan and Mounica Maddela and Khyati Mahajan and Saad Mahamood and Bodhisattwa Prasad Majumder and Pedro Henrique Martins and Angelina McMillan{-}Major and Simon Mille and Emiel van Miltenburg and Moin Nadeem and Shashi Narayan and Vitaly Nikolaev and Rubungo Andre Niyongabo and Salomey Osei and Ankur P. Parikh and Laura Perez{-}Beltrachini and Niranjan Ramesh Rao and Vikas Raunak and Juan Diego Rodriguez and Sashank Santhanam and Jo{\~{a}}o Sedoc and Thibault Sellam and Samira Shaikh and Anastasia Shimorina and Marco Antonio Sobrevilla Cabezudo and Hendrik Strobelt and Nishant Subramani and Wei Xu and Diyi Yang and Akhila Yerukola and Jiawei Zhou}, title = {The {GEM} Benchmark: Natural Language Generation, its Evaluation and Metrics}, journal = {CoRR}, volume = {abs/2102.01672}, year = {2021}, url = {https://arxiv.org/abs/2102.01672}, archivePrefix = {arXiv}, eprint = {2102.01672} } ``` ### Contributions Thanks to [@yjernite](https://github.com/yjernite) for adding this dataset.
generated_reviews_enth	--- annotations_creators: - expert-generated - machine-generated language_creators: - machine-generated language: - en - th license: - cc-by-sa-4.0 multilinguality: - translation size_categories: - 100K<n<1M source_datasets: - original task_categories: - translation - text-classification task_ids: - multi-class-classification - semantic-similarity-classification pretty_name: generated_reviews_enth dataset_info: features: - name: translation dtype: translation: languages: - en - th - name: review_star dtype: int32 - name: correct dtype: class_label: names: '0': neg '1': pos config_name: generated_reviews_enth splits: - name: train num_bytes: 147673215 num_examples: 141369 - name: validation num_bytes: 16409966 num_examples: 15708 - name: test num_bytes: 18133523 num_examples: 17453 download_size: 59490601 dataset_size: 182216704 --- # Dataset Card for generated_reviews_enth ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: ttp://airesearch.in.th/ - Repository: https://github.com/vistec-ai/generated_reviews_enth - Paper: https://arxiv.org/pdf/2007.03541.pdf - Leaderboard: - Point of Contact: [AIResearch](http://airesearch.in.th/) ### Dataset Summary `generated_reviews_enth` is created as part of [scb-mt-en-th-2020](https://arxiv.org/pdf/2007.03541.pdf) for machine translation task. This dataset (referred to as `generated_reviews_yn` in [scb-mt-en-th-2020](https://arxiv.org/pdf/2007.03541.pdf)) are English product reviews generated by [CTRL](https://arxiv.org/abs/1909.05858), translated by Google Translate API and annotated as accepted or rejected (`correct`) based on fluency and adequacy of the translation by human annotators. This allows it to be used for English-to-Thai translation quality esitmation (binary label), machine translation, and sentiment analysis. ### Supported Tasks and Leaderboards English-to-Thai translation quality estimation (binary label) is the intended use. Other uses include machine translation and sentiment analysis. ### Languages English, Thai ## Dataset Structure ### Data Instances ``` {'correct': 0, 'review_star': 4, 'translation': {'en': "I had a hard time finding a case for my new LG Lucid 2 but finally found this one on amazon. The colors are really pretty and it works just as well as, if not better than the otterbox. Hopefully there will be more available by next Xmas season. Overall, very cute case. I love cheetah's. :)", 'th': 'ฉันมีปัญหาในการหาเคสสำหรับ LG Lucid 2 ใหม่ของฉัน แต่ในที่สุดก็พบเคสนี้ใน Amazon สีสวยมากและใช้งานได้ดีเช่นเดียวกับถ้าไม่ดีกว่านาก หวังว่าจะมีให้มากขึ้นในช่วงเทศกาลคริสต์มาสหน้า โดยรวมแล้วน่ารักมาก ๆ ฉันรักเสือชีตาห์ :)'}} {'correct': 0, 'review_star': 1, 'translation': {'en': "This is the second battery charger I bought as a Christmas present, that came from Amazon, after one purchased before for my son. His was still working. The first charger, received in July, broke apart and wouldn't charge anymore. Just found out two days ago they discontinued it without warning. It took quite some time to find the exact replacement charger. Too bad, really liked it. One of these days, will purchase an actual Nikon product, or go back to buying batteries.", 'th': 'นี่เป็นเครื่องชาร์จแบตเตอรี่ก้อนที่สองที่ฉันซื้อเป็นของขวัญคริสต์มาสซึ่งมาจากอเมซอนหลังจากที่ซื้อมาเพื่อลูกชายของฉัน เขายังทำงานอยู่ เครื่องชาร์จแรกที่ได้รับในเดือนกรกฎาคมแตกเป็นชิ้น ๆ และจะไม่ชาร์จอีกต่อไป เพิ่งค้นพบเมื่อสองวันก่อนพวกเขาหยุดมันโดยไม่มีการเตือนล่วงหน้า ใช้เวลาพอสมควรในการหาที่ชาร์จที่ถูกต้อง แย่มากชอบมาก สักวันหนึ่งจะซื้อผลิตภัณฑ์ Nikon จริงหรือกลับไปซื้อแบตเตอรี่'}} {'correct': 1, 'review_star': 1, 'translation': {'en': 'I loved the idea of having a portable computer to share pictures with family and friends on my big screen. It worked really well for about 3 days, then when i opened it one evening there was water inside where all the wires came out. I cleaned that up and put some tape over that, so far, no leaks. My husband just told me yesterday, however, that this thing is trash.', 'th': 'ฉันชอบไอเดียที่มีคอมพิวเตอร์พกพาเพื่อแชร์รูปภาพกับครอบครัวและเพื่อน ๆ บนหน้าจอขนาดใหญ่ของฉัน มันใช้งานได้ดีจริง ๆ ประมาณ 3 วันจากนั้นเมื่อฉันเปิดมันในเย็นวันหนึ่งมีน้ำอยู่ภายในที่ซึ่งสายไฟทั้งหมดออกมา ฉันทำความสะอาดมันแล้ววางเทปไว้ที่นั่นจนถึงตอนนี้ไม่มีรอยรั่ว สามีของฉันเพิ่งบอกฉันเมื่อวานนี้ว่าสิ่งนี้เป็นขยะ'}} ``` ### Data Fields - `translation`: - `en`: English product reviews generated by [CTRL](https://arxiv.org/abs/1909.05858) - `th`: Thai product reviews translated from `en` by Google Translate API - `review_star`: Stars of the generated reviews, put as condition for [CTRL](https://arxiv.org/abs/1909.05858) - `correct`: 1 if the English-to-Thai translation is accepted (`correct`) based on fluency and adequacy of the translation by human annotators else 0 ### Data Splits \| \| train \| valid \| test \| \|-----------------\|--------\|-------\|-------\| \| # samples \| 141369 \| 15708 \| 17453 \| \| # correct:0 \| 99296 \| 10936 \| 12208 \| \| # correct:1 \| 42073 \| 4772 \| 5245 \| \| # review_star:1 \| 50418 \| 5628 \| 6225 \| \| # review_star:2 \| 22876 \| 2596 \| 2852 \| \| # review_star:3 \| 22825 \| 2521 \| 2831 \| \| # review_star:1 \| 22671 \| 2517 \| 2778 \| \| # review_star:5 \| 22579 \| 2446 \| 2767 \| ## Dataset Creation ### Curation Rationale `generated_reviews_enth` is created as part of [scb-mt-en-th-2020](https://arxiv.org/pdf/2007.03541.pdf) for machine translation task. This dataset (referred to as `generated_reviews_yn` in [scb-mt-en-th-2020](https://arxiv.org/pdf/2007.03541.pdf)) are English product reviews generated by [CTRL](https://arxiv.org/abs/1909.05858), translated by Google Translate API and annotated as accepted or rejected (`correct`) based on fluency and adequacy of the translation by human annotators. This allows it to be used for English-to-Thai translation quality esitmation (binary label), machine translation, and sentiment analysis. ### Source Data #### Initial Data Collection and Normalization The data generation process is as follows: - `en` is generated using conditional generation of [CTRL](https://arxiv.org/abs/1909.05858), stating a star review for each generated product review. - `th` is translated from `en` using Google Translate API - `correct` is annotated as accepted or rejected (1 or 0) based on fluency and adequacy of the translation by human annotators For this specific dataset for translation quality estimation task, we apply the following preprocessing: - Drop duplciates on `en`,`th`,`review_star`,`correct`; duplicates might exist because the translation checking is done by annotators. - Remove reviews that are not between 1-5 stars. - Remove reviews whose `correct` are not 0 or 1. - Deduplicate on `en` which contains the source sentences. #### Who are the source language producers? [CTRL](https://arxiv.org/abs/1909.05858) ### Annotations #### Annotation process Annotators are given English and Thai product review pairs. They are asked to label the pair as acceptable translation or not based on fluency and adequacy of the translation. #### Who are the annotators? Human annotators of [Hope Data Annotations](https://www.hopedata.org/) hired by [AIResearch.in.th](http://airesearch.in.th/) ### Personal and Sensitive Information The authors do not expect any personal or sensitive information to be in the generated product reviews, but they could slip through from pretraining of [CTRL](https://arxiv.org/abs/1909.05858). ## Considerations for Using the Data ### Social Impact of Dataset - English-Thai translation quality estimation for machine translation - Product review classification for Thai ### Discussion of Biases [More Information Needed] ### Other Known Limitations Due to annotation process constraints, the number of one-star reviews are notably higher than other-star reviews. This makes the dataset slighly imbalanced. ## Additional Information ### Dataset Curators The dataset was created by [AIResearch.in.th](http://airesearch.in.th/) ### Licensing Information CC BY-SA 4.0 ### Citation Information ``` @article{lowphansirikul2020scb, title={scb-mt-en-th-2020: A Large English-Thai Parallel Corpus}, author={Lowphansirikul, Lalita and Polpanumas, Charin and Rutherford, Attapol T and Nutanong, Sarana}, journal={arXiv preprint arXiv:2007.03541}, year={2020} } ``` ### Contributions Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset.
generics_kb	--- annotations_creators: - machine-generated language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K - 1M<n<10M source_datasets: - original task_categories: - other task_ids: [] paperswithcode_id: genericskb pretty_name: GenericsKB configs: - generics_kb - generics_kb_best - generics_kb_simplewiki - generics_kb_waterloo tags: - knowledge-base dataset_info: - config_name: generics_kb_best features: - name: source dtype: string - name: term dtype: string - name: quantifier_frequency dtype: string - name: quantifier_number dtype: string - name: generic_sentence dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 99897719 num_examples: 1020868 download_size: 94850525 dataset_size: 99897719 - config_name: generics_kb features: - name: source dtype: string - name: term dtype: string - name: quantifier_frequency dtype: string - name: quantifier_number dtype: string - name: generic_sentence dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 348158966 num_examples: 3433000 download_size: 343284785 dataset_size: 348158966 - config_name: generics_kb_simplewiki features: - name: source_name dtype: string - name: sentence dtype: string - name: sentences_before sequence: string - name: sentences_after sequence: string - name: concept_name dtype: string - name: quantifiers sequence: string - name: id dtype: string - name: bert_score dtype: float64 - name: headings sequence: string - name: categories sequence: string splits: - name: train num_bytes: 10039355 num_examples: 12765 download_size: 16437369 dataset_size: 10039355 - config_name: generics_kb_waterloo features: - name: source_name dtype: string - name: sentence dtype: string - name: sentences_before sequence: string - name: sentences_after sequence: string - name: concept_name dtype: string - name: quantifiers sequence: string - name: id dtype: string - name: bert_score dtype: float64 splits: - name: train num_bytes: 4277214701 num_examples: 3666725 download_size: 0 dataset_size: 4277214701 --- # Dataset Card for Generics KB ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Homepage](https://allenai.org/data/genericskb) - Repository: [Repository](https://drive.google.com/drive/folders/1vqfVXhJXJWuiiXbUa4rZjOgQoJvwZUoT) - Paper: [Paper](https://arxiv.org/pdf/2005.00660.pdf) - Point of Contact:[Sumithra Bhakthavatsalam](sumithrab@allenai.org) [Chloe Anastasiades](chloea@allenai.org) [Peter Clark](peterc@allenai.org) Alternatively email_at info@allenai.org ### Dataset Summary Dataset contains a large (3.5M+ sentence) knowledge base of generic sentences. This is the first large resource to contain naturally occurring generic sentences, rich in high-quality, general, semantically complete statements. All GenericsKB sentences are annotated with their topical term, surrounding context (sentences), and a (learned) confidence. We also release GenericsKB-Best (1M+ sentences), containing the best-quality generics in GenericsKB augmented with selected, synthesized generics from WordNet and ConceptNet. This demonstrates that GenericsKB can be a useful resource for NLP applications, as well as providing data for linguistic studies of generics and their semantics. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset is in English. ## Dataset Structure ### Data Instances The GENERICSKB contains 3,433,000 sentences. GENERICS-KB-BEST comprises of GENERICSKB generics with a score > 0.234, augmented with short generics synthesized from three other resources for all the terms (generic categories) in GENERICSKB- BEST. GENERICSKB-BEST contains 1,020,868 generics (774,621 from GENERICSKB plus 246,247 synthesized). SimpleWikipedia is a filtered scrape of SimpleWikipedia pages (simple.wikipedia.org). The Waterloo corpus is 280GB of English plain text, gathered by Charles Clarke (Univ. Waterloo) using a webcrawler in 2001 from .edu domains. ###### Sample SimpleWikipedia/ Waterloo config look like this ``` {'source_name': 'SimpleWikipedia', 'sentence': 'Sepsis happens when the bacterium enters the blood and make it form tiny clots.', 'sentences_before': [], 'sentences_after': [], 'concept_name': 'sepsis', 'quantifiers': {}, 'id': 'SimpleWikipedia--tmp-sw-rs1-with-bug-fixes-initialprocessing-inputs-articles-with-clean-sentences-jsonl-c27816b298e1e0b5326916ee4e2fd0f1603caa77-100-Bubonic-plague--Different-kinds-of-the-same-disease--Septicemic-plague-0-0-039fbe9c11adde4ff9a829376ca7e0ed-1560874903-47882-/Users/chloea/Documents/aristo/commonsense/kbs/simplewikipedia/commonsense-filtered-good-rs1.jsonl-1f33b1e84018a2b1bfdf446f9a6491568b5585da-1561086091.8220549', 'bert_score': 0.8396177887916565} ``` ###### Sample instance for Generics KB datasets look like this: ``` {'source': 'Waterloo', 'term': 'aardvark', 'quantifier_frequency': '', 'quantifier_number': '', 'generic_sentence': 'Aardvarks are very gentle animals.', 'score': '0.36080607771873474'} {'source': 'TupleKB', 'term': 'aardvark', 'quantifier_frequency': '', 'quantifier_number': '', 'generic_sentence': 'Aardvarks dig burrows.', 'score': '1.0'} ``` ### Data Fields The fields in GenericsKB-Best.tsv and GenericsKB.tsv are as follows: - `SOURCE`: denotes the source of the generic - `TERM`: denotes the category that is the topic of the generic. - `GENERIC SENTENCE`: is the sentence itself. - `SCORE`: Is the BERT-trained score, measuring the degree to which the generic represents a "useful, general truth" about the world (as judged by crowdworkers). Score ranges from 0 (worst) to 1 (best). Sentences with scores below 0.23 (corresponding to an "unsure" vote by crowdworkers) are in GenericsKB, but are not part of GenericsKB-Best due to their unreliability. - `QUANTIFIER_FREQUENCY`:For generics with explicit quantifiers (all, most, etc.) the quantifier is listed - Frequency contains values such as 'usually', 'often', 'frequently' - `QUANTIFIER_NUMBER`: For generics with explicit quantifiers (all, most, etc.) with values such as 'all'\|'any'\|'most'\|'much'\|'some' etc... The SimpleWiki/Waterloo generics from GenericsKB.tsv, but expanded to also include their surrounding context (before/after sentences). The Waterloo generics are the majority of GenericsKB. This zip file is 1.4GB expanding to 5.5GB. There is a json representation for every generic statement in the Generics KB. The generic statement is stored under the `sentence` field within the `knowledge` object. There is also a `bert_score` associated with each sentence which is the BERT-based classifier's score for the 'genericness' of the statement. This score is meant to reflect how much generalized world knowledge/commonsense the statement captures vs only being contextually meaningful. Detailed description of each of the fields: - `source_name`: The name of the corpus the generic statement was picked from. - `sentence`: The generic sentence. - `sentences_before`: Provides context information surrounding the generic statement from the original corpus.Up to five sentences preceding the generic sentence in the original corpus. - `sentences_after`: Up to five sentences following the generic sentence in the original corpus. - `concept_name`: A concept that is the subject of the generic statement. - `quantifiers`: The quantifiers for the key concept of the generic statement. There can be multiple quantifiers to allow for statements such as "All bats sometimes fly", where 'all' and 'sometimes' are both quantifiers reflecting number and frequency respectively. - `id`: Unique identifier for a generic statement in the kb. - `bert_score`: Score for the generic statement from the BERT-based generics classifier. <br>Additional fields that apply only to SimpleWiki dataset - `headings`: A breadcrumb of section/subsection headings from the top down to the location of the generic statement in the corpus. It applies to SimpleWikipedia which has a hierarchical structure. - `categories`:The listed categories under which the source article falls. Applies to SimpleWikipedia. ### Data Splits There are no splits. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Data was crawled. SimpleWikipedia is a filtered scrape of SimpleWikipedia pages (simple.wikipedia.org). The Waterloo corpus is 280GB of English plain text, gathered by Charles Clarke (Univ. Waterloo) using a webcrawler in 2001 from .edu domains. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process Bert was used to decide whether the sentence is useful or not. Every sentence has a bert score. #### Who are the annotators? No annotations were made. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The GenericsKB is available under the Creative Commons - Attribution 4.0 International - licence. As an informal summary, from https://creativecommons.org/licenses/by/4.0/, you are free to: Share ― copy and redistribute the material in any medium or format Adapt ― remix, transform, and build upon the material for any purpose, even commercially. under the following terms: Attribution ― You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. No additional restrictions ― You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. For details, see https://creativecommons.org/licenses/by/4.0/ or the or the included file "Creative Commons ― Attribution 4.0 International ― CC BY 4.0.pdf" in this folder. ### Citation Information ``` @InProceedings{huggingface:dataset, title = {GenericsKB: A Knowledge Base of Generic Statements}, authors={Sumithra Bhakthavatsalam, Chloe Anastasiades, Peter Clark}, year={2020}, publisher = {Allen Institute for AI}, } ``` ### Contributions Thanks to [@bpatidar](https://github.com/bpatidar) for adding this dataset.
german_legal_entity_recognition	--- annotations_creators: - expert-generated language_creators: - found language: - de license: - cc-by-4.0 multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition paperswithcode_id: legal-documents-entity-recognition pretty_name: Legal Documents Entity Recognition dataset_info: - config_name: bag features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-AN '1': B-EUN '2': B-GRT '3': B-GS '4': B-INN '5': B-LD '6': B-LDS '7': B-LIT '8': B-MRK '9': B-ORG '10': B-PER '11': B-RR '12': B-RS '13': B-ST '14': B-STR '15': B-UN '16': B-VO '17': B-VS '18': B-VT '19': I-AN '20': I-EUN '21': I-GRT '22': I-GS '23': I-INN '24': I-LD '25': I-LDS '26': I-LIT '27': I-MRK '28': I-ORG '29': I-PER '30': I-RR '31': I-RS '32': I-ST '33': I-STR '34': I-UN '35': I-VO '36': I-VS '37': I-VT '38': O splits: - name: train download_size: 4392913 dataset_size: 0 - config_name: bfh features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-AN '1': B-EUN '2': B-GRT '3': B-GS '4': B-INN '5': B-LD '6': B-LDS '7': B-LIT '8': B-MRK '9': B-ORG '10': B-PER '11': B-RR '12': B-RS '13': B-ST '14': B-STR '15': B-UN '16': B-VO '17': B-VS '18': B-VT '19': I-AN '20': I-EUN '21': I-GRT '22': I-GS '23': I-INN '24': I-LD '25': I-LDS '26': I-LIT '27': I-MRK '28': I-ORG '29': I-PER '30': I-RR '31': I-RS '32': I-ST '33': I-STR '34': I-UN '35': I-VO '36': I-VS '37': I-VT '38': O splits: - name: train download_size: 4392913 dataset_size: 0 - config_name: bgh features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-AN '1': B-EUN '2': B-GRT '3': B-GS '4': B-INN '5': B-LD '6': B-LDS '7': B-LIT '8': B-MRK '9': B-ORG '10': B-PER '11': B-RR '12': B-RS '13': B-ST '14': B-STR '15': B-UN '16': B-VO '17': B-VS '18': B-VT '19': I-AN '20': I-EUN '21': I-GRT '22': I-GS '23': I-INN '24': I-LD '25': I-LDS '26': I-LIT '27': I-MRK '28': I-ORG '29': I-PER '30': I-RR '31': I-RS '32': I-ST '33': I-STR '34': I-UN '35': I-VO '36': I-VS '37': I-VT '38': O splits: - name: train download_size: 4392913 dataset_size: 0 - config_name: bpatg features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-AN '1': B-EUN '2': B-GRT '3': B-GS '4': B-INN '5': B-LD '6': B-LDS '7': B-LIT '8': B-MRK '9': B-ORG '10': B-PER '11': B-RR '12': B-RS '13': B-ST '14': B-STR '15': B-UN '16': B-VO '17': B-VS '18': B-VT '19': I-AN '20': I-EUN '21': I-GRT '22': I-GS '23': I-INN '24': I-LD '25': I-LDS '26': I-LIT '27': I-MRK '28': I-ORG '29': I-PER '30': I-RR '31': I-RS '32': I-ST '33': I-STR '34': I-UN '35': I-VO '36': I-VS '37': I-VT '38': O splits: - name: train download_size: 4392913 dataset_size: 0 - config_name: bsg features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-AN '1': B-EUN '2': B-GRT '3': B-GS '4': B-INN '5': B-LD '6': B-LDS '7': B-LIT '8': B-MRK '9': B-ORG '10': B-PER '11': B-RR '12': B-RS '13': B-ST '14': B-STR '15': B-UN '16': B-VO '17': B-VS '18': B-VT '19': I-AN '20': I-EUN '21': I-GRT '22': I-GS '23': I-INN '24': I-LD '25': I-LDS '26': I-LIT '27': I-MRK '28': I-ORG '29': I-PER '30': I-RR '31': I-RS '32': I-ST '33': I-STR '34': I-UN '35': I-VO '36': I-VS '37': I-VT '38': O splits: - name: train download_size: 4392913 dataset_size: 0 - config_name: bverfg features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-AN '1': B-EUN '2': B-GRT '3': B-GS '4': B-INN '5': B-LD '6': B-LDS '7': B-LIT '8': B-MRK '9': B-ORG '10': B-PER '11': B-RR '12': B-RS '13': B-ST '14': B-STR '15': B-UN '16': B-VO '17': B-VS '18': B-VT '19': I-AN '20': I-EUN '21': I-GRT '22': I-GS '23': I-INN '24': I-LD '25': I-LDS '26': I-LIT '27': I-MRK '28': I-ORG '29': I-PER '30': I-RR '31': I-RS '32': I-ST '33': I-STR '34': I-UN '35': I-VO '36': I-VS '37': I-VT '38': O splits: - name: train download_size: 4392913 dataset_size: 0 - config_name: bverwg features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-AN '1': B-EUN '2': B-GRT '3': B-GS '4': B-INN '5': B-LD '6': B-LDS '7': B-LIT '8': B-MRK '9': B-ORG '10': B-PER '11': B-RR '12': B-RS '13': B-ST '14': B-STR '15': B-UN '16': B-VO '17': B-VS '18': B-VT '19': I-AN '20': I-EUN '21': I-GRT '22': I-GS '23': I-INN '24': I-LD '25': I-LDS '26': I-LIT '27': I-MRK '28': I-ORG '29': I-PER '30': I-RR '31': I-RS '32': I-ST '33': I-STR '34': I-UN '35': I-VO '36': I-VS '37': I-VT '38': O splits: - name: train download_size: 4392913 dataset_size: 0 - config_name: all features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-AN '1': B-EUN '2': B-GRT '3': B-GS '4': B-INN '5': B-LD '6': B-LDS '7': B-LIT '8': B-MRK '9': B-ORG '10': B-PER '11': B-RR '12': B-RS '13': B-ST '14': B-STR '15': B-UN '16': B-VO '17': B-VS '18': B-VT '19': I-AN '20': I-EUN '21': I-GRT '22': I-GS '23': I-INN '24': I-LD '25': I-LDS '26': I-LIT '27': I-MRK '28': I-ORG '29': I-PER '30': I-RR '31': I-RS '32': I-ST '33': I-STR '34': I-UN '35': I-VO '36': I-VS '37': I-VT '38': O splits: - name: train download_size: 4392913 dataset_size: 0 --- # Dataset Card for Legal Documents Entity Recognition ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/elenanereiss/Legal-Entity-Recognition - Repository: None - Paper: https://link.springer.com/chapter/10.1007/978-3-030-33220-4_20 - Leaderboard: [If the dataset supports an active leaderboard, add link here]() - Point of Contact: Georg Rehm (georg.rehm@dfki.de) ### Dataset Summary <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Deprecated:</b> Dataset "german_legal_entity_recognition" is deprecated and will be deleted. Use <a href="https://huggingface.co/datasets/elenanereiss/german-ler">"elenanereiss/german-ler"</a> instead.</p> </div> ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
germaner	--- annotations_creators: - crowdsourced language_creators: - found language: - de license: - apache-2.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: GermaNER dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-LOC '1': B-ORG '2': B-OTH '3': B-PER '4': I-LOC '5': I-ORG '6': I-OTH '7': I-PER '8': O splits: - name: train num_bytes: 9059606 num_examples: 26200 download_size: 4363657 dataset_size: 9059606 --- # Dataset Card for GermaNER ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Repository: https://github.com/tudarmstadt-lt/GermaNER - Paper: https://pdfs.semanticscholar.org/b250/3144ed2152830f6c64a9f797ab3c5a34fee5.pdf - Point of Contact: [Darina Benikova](mailto:benikova@aiphes.tu-darmstadt.de) ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages German ## Dataset Structure ### Data Instances An example instance looks as follows: ``` { 'id': '3', 'ner_tags': [1, 5, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], 'tokens': ['Bayern', 'München', 'ist', 'wieder', 'alleiniger', 'Top-', 'Favorit', 'auf', 'den', 'Gewinn', 'der', 'deutschen', 'Fußball-Meisterschaft', '.'] } ``` ### Data Fields Each instance in the dataset has: - `id`: an id as a string - `tokens`: sequence of tokens - `ner_tags`: NER tags for each token (encoded as IOB) NER tags can be: 'B-LOC' (0), 'B-ORG' (1), 'B-OTH' (2), 'B-PER' (3), 'I-LOC' (4), 'I-ORG' (5), 'I-OTH' (6), 'I-PER' (7), 'O' (8) ### Data Splits Dataset provides only train part (26200 data instances). ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information License of GermaNER: ``` GermaNER is licensed under ASL 2.0 and other lenient licenses, allowing its use for academic and commercial purposes without restrictions. The licenses of its compenents are mixed licensed and are individually listed in Data/Licenses. Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. 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While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS ``` ### Citation Information ```bibtex @inproceedings{Benikova2015GermaNERFO, title={GermaNER: Free Open German Named Entity Recognition Tool}, author={Darina Benikova and Seid Muhie Yimam and P. Santhanam and Chris Biemann}, booktitle={GSCL}, year={2015} } ``` ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
germeval_14	--- annotations_creators: - crowdsourced language_creators: - found language: - de license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition paperswithcode_id: nosta-d-named-entity-annotation-for-german pretty_name: GermEval14 dataset_info: features: - name: id dtype: string - name: source dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-LOC '2': I-LOC '3': B-LOCderiv '4': I-LOCderiv '5': B-LOCpart '6': I-LOCpart '7': B-ORG '8': I-ORG '9': B-ORGderiv '10': I-ORGderiv '11': B-ORGpart '12': I-ORGpart '13': B-OTH '14': I-OTH '15': B-OTHderiv '16': I-OTHderiv '17': B-OTHpart '18': I-OTHpart '19': B-PER '20': I-PER '21': B-PERderiv '22': I-PERderiv '23': B-PERpart '24': I-PERpart - name: nested_ner_tags sequence: class_label: names: '0': O '1': B-LOC '2': I-LOC '3': B-LOCderiv '4': I-LOCderiv '5': B-LOCpart '6': I-LOCpart '7': B-ORG '8': I-ORG '9': B-ORGderiv '10': I-ORGderiv '11': B-ORGpart '12': I-ORGpart '13': B-OTH '14': I-OTH '15': B-OTHderiv '16': I-OTHderiv '17': B-OTHpart '18': I-OTHpart '19': B-PER '20': I-PER '21': B-PERderiv '22': I-PERderiv '23': B-PERpart '24': I-PERpart config_name: germeval_14 splits: - name: train num_bytes: 13816714 num_examples: 24000 - name: validation num_bytes: 1266974 num_examples: 2200 - name: test num_bytes: 2943201 num_examples: 5100 download_size: 10288972 dataset_size: 18026889 --- # Dataset Card for "germeval_14" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://sites.google.com/site/germeval2014ner/](https://sites.google.com/site/germeval2014ner/) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [https://pdfs.semanticscholar.org/b250/3144ed2152830f6c64a9f797ab3c5a34fee5.pdf](https://pdfs.semanticscholar.org/b250/3144ed2152830f6c64a9f797ab3c5a34fee5.pdf) - Point of Contact: [Darina Benikova](mailto:benikova@aiphes.tu-darmstadt.de) - Size of downloaded dataset files: 10.29 MB - Size of the generated dataset: 18.03 MB - Total amount of disk used: 28.31 MB ### Dataset Summary The GermEval 2014 NER Shared Task builds on a new dataset with German Named Entity annotation with the following properties: - The data was sampled from German Wikipedia and News Corpora as a collection of citations. - The dataset covers over 31,000 sentences corresponding to over 590,000 tokens. - The NER annotation uses the NoSta-D guidelines, which extend the Tübingen Treebank guidelines, using four main NER categories with sub-structure, and annotating embeddings among NEs such as [ORG FC Kickers [LOC Darmstadt]]. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages German ## Dataset Structure ### Data Instances #### germeval_14 - Size of downloaded dataset files: 10.29 MB - Size of the generated dataset: 18.03 MB - Total amount of disk used: 28.31 MB An example of 'train' looks as follows. This example was too long and was cropped: ```json { "id": "11", "ner_tags": [13, 14, 14, 14, 14, 0, 0, 0, 0, 0, 0, 0, 19, 20, 13, 0, 1, 0, 0, 0, 0, 0, 19, 20, 20, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], "nested_ner_tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], "source": "http://de.wikipedia.org/wiki/Liste_von_Filmen_mit_homosexuellem_Inhalt [2010-01-11] ", "tokens": "[\"Scenes\", \"of\", \"a\", \"Sexual\", \"Nature\", \"(\", \"GB\", \"2006\", \")\", \"-\", \"Regie\", \":\", \"Ed\", \"Blum\", \"Shortbus\", \"(\", \"USA\", \"2006..." } ``` ### Data Fields The data fields are the same among all splits. #### germeval_14 - `id`: a `string` feature. - `source`: a `string` feature. - `tokens`: a `list` of `string` features. - `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-LOC` (1), `I-LOC` (2), `B-LOCderiv` (3), `I-LOCderiv` (4). - `nested_ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-LOC` (1), `I-LOC` (2), `B-LOCderiv` (3), `I-LOCderiv` (4). ### Data Splits \| name \|train\|validation\|test\| \|-----------\|----:\|---------:\|---:\| \|germeval_14\|24000\| 2200\|5100\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/) ### Citation Information ``` @inproceedings{benikova-etal-2014-nosta, title = {NoSta-D Named Entity Annotation for German: Guidelines and Dataset}, author = {Benikova, Darina and Biemann, Chris and Reznicek, Marc}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)}, month = {may}, year = {2014}, address = {Reykjavik, Iceland}, publisher = {European Language Resources Association (ELRA)}, url = {http://www.lrec-conf.org/proceedings/lrec2014/pdf/276_Paper.pdf}, pages = {2524--2531}, } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@jplu](https://github.com/jplu), [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq), [@stefan-it](https://github.com/stefan-it), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.
giga_fren	--- annotations_creators: - found language_creators: - found language: - en - fr license: - unknown multilinguality: - multilingual size_categories: - 10M<n<100M source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: GigaFren dataset_info: features: - name: id dtype: string - name: translation dtype: translation: languages: - en - fr config_name: en-fr splits: - name: train num_bytes: 8690296821 num_examples: 22519904 download_size: 2701536198 dataset_size: 8690296821 --- # Dataset Card for GigaFren ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: http://opus.nlpl.eu/giga-fren.php - Repository: None - Paper: http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf - Leaderboard: [More Information Needed] - Point of Contact: [More Information Needed] ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances Here are some examples of questions and facts: ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
gigaword	--- annotations_creators: - found language_creators: - found language: - en license: - mit multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - extended\|gigaword_2003 task_categories: - summarization task_ids: [] paperswithcode_id: null pretty_name: Gigaword train-eval-index: - config: default task: summarization task_id: summarization splits: train_split: train eval_split: test col_mapping: document: text summary: target metrics: - type: rouge name: Rouge tags: - headline-generation dataset_info: features: - name: document dtype: string - name: summary dtype: string splits: - name: train num_bytes: 915249388 num_examples: 3803957 - name: validation num_bytes: 45767096 num_examples: 189651 - name: test num_bytes: 450782 num_examples: 1951 download_size: 578402958 dataset_size: 961467266 --- # Dataset Card for Gigaword ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Repository: [Gigaword repository](https://github.com/harvardnlp/sent-summary) - Leaderboard: [Gigaword leaderboard](https://paperswithcode.com/sota/text-summarization-on-gigaword) - Paper: [A Neural Attention Model for Abstractive Sentence Summarization](https://arxiv.org/abs/1509.00685) - Point of Contact: [Alexander Rush](mailto:arush@cornell.edu) - Size of downloaded dataset files: 578.41 MB - Size of the generated dataset: 962.96 MB - Total amount of disk used: 1.54 GB ### Dataset Summary Headline-generation on a corpus of article pairs from Gigaword consisting of around 4 million articles. Use the 'org_data' provided by https://github.com/microsoft/unilm/ which is identical to https://github.com/harvardnlp/sent-summary but with better format. ### Supported Tasks and Leaderboards - `summarization`: This dataset can be used for Summarization, where given a dicument, the goal is to predict its summery. The model performance is evaluated using the [ROUGE](https://huggingface.co/metrics/rouge) metric. The leaderboard for this task is available [here](https://paperswithcode.com/sota/text-summarization-on-gigaword). ### Languages English. ## Dataset Structure ### Data Instances An example of 'train' looks as follows. ``` { 'document': "australia 's current account deficit shrunk by a record #.## billion dollars -lrb- #.## billion us -rrb- in the june quarter due to soaring commodity prices , figures released monday showed .", 'summary': 'australian current account deficit narrows sharply' } ``` ### Data Fields The data fields are the same among all splits. - `document`: a `string` feature. - `summary`: a `string` feature. ### Data Splits \| name \| train \|validation\|test\| \|-------\|------:\|---------:\|---:\| \|default\|3803957\| 189651\|1951\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization From the paper: > For our training set, we pair the headline of each article with its first sentence to create an inputsummary pair. While the model could in theory be trained on any pair, Gigaword contains many spurious headline-article pairs. We therefore prune training based on the following heuristic filters: (1) Are there no non-stop-words in common? (2) Does the title contain a byline or other extraneous editing marks? (3) Does the title have a question mark or colon? After applying these filters, the training set consists of roughly J = 4 million title-article pairs. We apply a minimal preprocessing step using PTB tokenization, lower-casing, replacing all digit characters with #, and replacing of word types seen less than 5 times with UNK. We also remove all articles from the time-period of the DUC evaluation. release. The complete input training vocabulary consists of 119 million word tokens and 110K unique word types with an average sentence size of 31.3 words. The headline vocabulary consists of 31 million tokens and 69K word types with the average title of length 8.3 words (note that this is significantly shorter than the DUC summaries). On average there are 4.6 overlapping word types between the headline and the input; although only 2.6 in the first 75-characters of the input. #### Who are the source language producers? From the paper: > For training data for both tasks, we utilize the annotated Gigaword data set (Graff et al., 2003; Napoles et al., 2012), which consists of standard Gigaword, preprocessed with Stanford CoreNLP tools (Manning et al., 2014). ### Annotations #### Annotation process Annotations are inherited from the annotatated Gigaword data set. Additional information from the paper: > Our model only uses annotations for tokenization and sentence separation, although several of the baselines use parsing and tagging as well. #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ```bibtex @article{graff2003english, title={English gigaword}, author={Graff, David and Kong, Junbo and Chen, Ke and Maeda, Kazuaki}, journal={Linguistic Data Consortium, Philadelphia}, volume={4}, number={1}, pages={34}, year={2003} } @article{Rush_2015, title={A Neural Attention Model for Abstractive Sentence Summarization}, url={http://dx.doi.org/10.18653/v1/D15-1044}, DOI={10.18653/v1/d15-1044}, journal={Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing}, publisher={Association for Computational Linguistics}, author={Rush, Alexander M. and Chopra, Sumit and Weston, Jason}, year={2015} } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
glucose	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended\|other-ROC-stories task_categories: - fill-mask - text-generation paperswithcode_id: glucose pretty_name: GLUCOSE tags: - commonsense-inference dataset_info: features: - name: experiment_id dtype: string - name: story_id dtype: string - name: worker_id dtype: int64 - name: worker_ids dtype: string - name: submission_time_normalized dtype: string - name: worker_quality_assessment dtype: int64 - name: selected_sentence_index dtype: int64 - name: story dtype: string - name: selected_sentence dtype: string - name: number_filled_in dtype: int64 - name: 1_specificNL dtype: string - name: 1_specificStructured dtype: string - name: 1_generalNL dtype: string - name: 1_generalStructured dtype: string - name: 2_specificNL dtype: string - name: 2_specificStructured dtype: string - name: 2_generalNL dtype: string - name: 2_generalStructured dtype: string - name: 3_specificNL dtype: string - name: 3_specificStructured dtype: string - name: 3_generalNL dtype: string - name: 3_generalStructured dtype: string - name: 4_specificNL dtype: string - name: 4_specificStructured dtype: string - name: 4_generalNL dtype: string - name: 4_generalStructured dtype: string - name: 5_specificNL dtype: string - name: 5_specificStructured dtype: string - name: 5_generalNL dtype: string - name: 5_generalStructured dtype: string - name: 6_specificNL dtype: string - name: 6_specificStructured dtype: string - name: 6_generalNL dtype: string - name: 6_generalStructured dtype: string - name: 7_specificNL dtype: string - name: 7_specificStructured dtype: string - name: 7_generalNL dtype: string - name: 7_generalStructured dtype: string - name: 8_specificNL dtype: string - name: 8_specificStructured dtype: string - name: 8_generalNL dtype: string - name: 8_generalStructured dtype: string - name: 9_specificNL dtype: string - name: 9_specificStructured dtype: string - name: 9_generalNL dtype: string - name: 9_generalStructured dtype: string - name: 10_specificNL dtype: string - name: 10_specificStructured dtype: string - name: 10_generalNL dtype: string - name: 10_generalStructured dtype: string config_name: glucose splits: - name: train num_bytes: 204605370 num_examples: 65522 - name: test num_bytes: 355757 num_examples: 500 download_size: 30362105 dataset_size: 204961127 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - [Repository](https://github.com/TevenLeScao/glucose) - [Paper](https://arxiv.org/abs/2009.07758) - Point of Contact: [glucose@elementalcognition.com](mailto:glucose@elementalcognition.com) ### Dataset Summary GLUCOSE: GeneraLized and COntextualized Story Explanations, is a novel conceptual framework and dataset for commonsense reasoning. Given a short story and a sentence X in the story, GLUCOSE captures ten dimensions of causal explanation related to X. These dimensions, inspired by human cognitive psychology, cover often-implicit causes and effects of X, including events, location, possession, and other attributes. ### Supported Tasks and Leaderboards Common sense inference of: 1. Causes 2. Emotions motivating an event 3. Locations enabling an event 4. Possession states enabling an event 5. Other attributes enabling an event 6. Consequences 7. Emotions caused by an event 8. Changes in location caused by an event 9. Changes in possession caused by an event 10. Other attributes that may be changed by an event ### Languages English, monolingual ## Dataset Structure ### Data Instances ``` { "experiment_id": "e56c7c3e-4660-40fb-80d0-052d566d676a__4", "story_id": "e56c7c3e-4660-40fb-80d0-052d566d676a", "worker_id": 19, "submission_time_normalized": "20190930", "worker_quality_rating": 3, "selected_sentence_index": 4, "story": "It was bedtime at our house. Two of the three kids hit the pillow and fall asleep. The third is a trouble maker. For two hours he continues to get out of bed and want to play. Finally he becomes tired and falls asleep." selected_sentence: "Finally he becomes tired and falls asleep.", "1_specificNL": "The third kid continues to get out of bed and wants to play >Causes/Enables> The kid finally becomes tired and falls asleep", "1_specificStructured": "{The third kid}_[subject] {continues}_[verb] {to }_[preposition1] {get out of bed}_[object1] {and wants to play}_[object2] >Causes/Enables> {The kid}_[subject] {finally becomes}_[verb] {tired}_[object1] {and falls asleep}_[object2]", "1_generalNL": "Someone_A doesn't want to go to sleep >Causes/Enables> Someone_A finally falls asleep", "1_generalStructured": "{Someone_A}_[subject] {doesn't want}_[verb] {to }_[preposition1] {go to sleep}_[object1] >Causes/Enables> {Someone_A}_[subject] {finally falls}_[verb] {asleep}_[object1]", "2_specificNL": "escaped", "2_specificStructured": "escaped", "2_generalNL": "escaped", "2_generalStructured": "escaped", "3_specificNL": "The third kid is in bed >Enables> The kid finally becomes tired and falls asleep", "3_specificStructured": "{The third kid}_[subject] {is}_[verb] {in}_[preposition] {bed}_[object] >Enables> {The kid}_[subject] {finally becomes}_[verb] {tired}_[object1] {and falls asleep}_[object2]", "3_generalNL": "Someone_A is in bed >Enables> Someone_A falls asleep", "3_generalStructured": "{Someone_A}_[subject] {is}_[verb] {in}_[preposition] {bed}_[object] >Enables> {Someone_A}_[subject] {falls}_[verb] {asleep}_[object1]", "4_specificNL": "escaped", "4_specificStructured": "escaped", "4_generalNL": "escaped", "4_generalStructured": "escaped", "5_specificNL": "escaped", "5_specificStructured": "escaped", "5_generalNL": "escaped", "5_generalStructured": "escaped", "6_specificNL": "escaped", "6_specificStructured": "escaped", "6_generalNL": "escaped", "6_generalStructured": "escaped", "7_specificNL": "escaped", "7_specificStructured": "escaped", "7_generalNL": "escaped", "7_generalStructured": "escaped", "8_specificNL": "escaped", "8_specificStructured": "escaped", "8_generalNL": "escaped", "8_generalStructured": "escaped", "9_specificNL": "escaped", "9_specificStructured": "escaped", "9_generalNL": "escaped", "9_generalStructured": "escaped", "10_specificNL": "escaped", "10_specificStructured": "escaped", "10_generalNL": "escaped", "10_generalStructured": "escaped", "number_filled_in": 7 } ``` ### Data Fields - __experiment_id__: a randomly generated alphanumeric sequence for a given story with the sentence index appended at the end after two underscores. Example: cbee2b5a-f2f9-4bca-9630-6825b1e36c13__0 - __story_id__: a random alphanumeric identifier for the story. Example: e56c7c3e-4660-40fb-80d0-052d566d676a - __worker_id__: each worker has a unique identificaiton number. Example: 21 - __submission_time_normalized__: the time of submission in the format YYYYMMDD. Example: 20200115 - __worker_quality_assessment__: rating for the worker on the assignment in the row. Example: 2 - __selected_sentence_index__: the index of a given sentence in a story. Example: 0 - __story__: contains the full text of the ROC story that was used for the HIT. Example: It was bedtime at our house. Two of the three kids hit the pillow and fall asleep. The third is a trouble maker. For two hours he continues to get out of bed and want to play. Finally he becomes tired and falls asleep. - __selected_sentence__: the sentence from the story that is being annotated. Example: It was bedtime at our house. - __[1-10]\_[specific/general][NL/Structured]__: This is the primary data collected. It provides the common sense knowledge about the related stories and those general rules about the world derived from the specific statements. For each of the ten relationships, there are four columns. The specific columns give the specific statements from the story. The general statements give the corresponding generalization. The NL columns are formatted in natural language, whereas the structured columns contain indications of the slots used to fill in the data. Example: - __1_specificNL__: "The school has a football team >Causes/Enables> The football game was last weekend" - __1_specificStructured__: "{The school }\_[subject] {has }\_[verb] {a football team }\_[object1] >Causes/Enables> {The football game }\_[subject] {was last weekend }\_[verb]" - __1_generalNL__: "Somewhere_A (that is a school ) has Something_A (that is a sports team ) >Causes/Enables> The game was last weekend" - __1_generalStructured__: "{Somewhere_A \|\|that is a school \|\|}\_[subject] {has }\_[verb] {Something_A \|\|that is a sports team \|\|}\_[object1] >Causes/Enables> {The game }\_[subject] {was last weekend }\_[verb]" - __number\_filled\_in__: number of dimensions filled in for the assignment. Example: 4 ### Data Splits Train split: 65,521 examples Test splits: 500 examples, without worker id and rating, number filled in, and structured text. ## Dataset Creation ### Curation Rationale When humans read or listen, they make implicit commonsense inferences that frame their understanding of what happened and why. As a step toward AI systems that can build similar mental models, we introduce GLUCOSE, a large-scale dataset of implicit commonsense causal knowledge, encoded as causal mini-theories about the world, each grounded in a narrative context. ### Source Data #### Initial Data Collection and Normalization Initial text from ROCStories #### Who are the source language producers? Amazon Mechanical Turk. ### Annotations #### Annotation process To enable developing models that can build mental models of narratives, we aimed to crowdsource a large, quality-monitored dataset. Beyond the scalability benefits, using crowd workers (as opposed to a small set of expert annotators) ensures diversity of thought, thus broadening coverage of a common-sense knowledge resource. The annotation task is complex: it requires annotators to understand different causal dimensions in a variety of contexts and to come up with generalized theories beyond the story context. For strict quality control, we designed a three-stage knowledge acquisition pipeline for crowdsourcing the GLUCOSE dataset on the Amazon Mechanical Turk Platform. The workers first go through a qualification test where they must score at least 90% on 10 multiple-choice questions on select GLUCOSE dimensions. Next, qualified workers can work on the main GLUCOSE data collection task: given a story S and a story sentence X, they are asked to fill in (allowing for non-applicable) all ten GLUCOSE dimensions, getting step-by-step guidance from the GLUCOSE data acquisition. To ensure data consistency, the same workers answer all dimensions for an S, X pair. Finally, the submissions are reviewed by an expert who rates each worker on a scale from 0 to 3, and provides feedback on how to improve. Our final UIs are the result of more than six rounds of pilot studies, iteratively improving the interaction elements, functionality, dimension definitions, instructions, and examples. #### Who are the annotators? Amazon Mechanical Turk workers, with feedback from an expert. ### Personal and Sensitive Information No personal or sensitive information. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Nasrin Mostafazadeh, Aditya Kalyanpur, Lori Moon, David Buchanan, Lauren Berkowitz, Or Biran, Jennifer Chu-Carroll, from Elemental Cognition ### Licensing Information Creative Commons Attribution-NonCommercial 4.0 International Public License ### Citation Information ``` @inproceedings{mostafazadeh2020glucose, title={GLUCOSE: GeneraLized and COntextualized Story Explanations}, author={Nasrin Mostafazadeh and Aditya Kalyanpur and Lori Moon and David Buchanan and Lauren Berkowitz and Or Biran and Jennifer Chu-Carroll}, year={2020}, booktitle={The Conference on Empirical Methods in Natural Language Processing}, publisher={Association for Computational Linguistics} } ``` ### Contributions Thanks to [@TevenLeScao](https://github.com/TevenLeScao) for adding this dataset.
glue	--- annotations_creators: - other language_creators: - other language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - acceptability-classification - natural-language-inference - semantic-similarity-scoring - sentiment-classification - text-scoring paperswithcode_id: glue pretty_name: GLUE (General Language Understanding Evaluation benchmark) configs: - ax - cola - mnli - mnli_matched - mnli_mismatched - mrpc - qnli - qqp - rte - sst2 - stsb - wnli tags: - qa-nli - coreference-nli - paraphrase-identification dataset_info: - config_name: cola features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': unacceptable '1': acceptable - name: idx dtype: int32 splits: - name: test num_bytes: 61049 num_examples: 1063 - name: train num_bytes: 489149 num_examples: 8551 - name: validation num_bytes: 60850 num_examples: 1043 download_size: 376971 dataset_size: 611048 - config_name: sst2 features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': negative '1': positive - name: idx dtype: int32 splits: - name: test num_bytes: 217556 num_examples: 1821 - name: train num_bytes: 4715283 num_examples: 67349 - name: validation num_bytes: 106692 num_examples: 872 download_size: 7439277 dataset_size: 5039531 - config_name: mrpc features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': not_equivalent '1': equivalent - name: idx dtype: int32 splits: - name: test num_bytes: 443498 num_examples: 1725 - name: train num_bytes: 946146 num_examples: 3668 - name: validation num_bytes: 106142 num_examples: 408 download_size: 1494541 dataset_size: 1495786 - config_name: qqp features: - name: question1 dtype: string - name: question2 dtype: string - name: label dtype: class_label: names: '0': not_duplicate '1': duplicate - name: idx dtype: int32 splits: - name: train num_bytes: 50901116 num_examples: 363846 - name: validation num_bytes: 5653794 num_examples: 40430 - name: test num_bytes: 55171431 num_examples: 390965 download_size: 41696084 dataset_size: 111726341 - config_name: stsb features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float32 - name: idx dtype: int32 splits: - name: test num_bytes: 170847 num_examples: 1379 - name: train num_bytes: 758394 num_examples: 5749 - name: validation num_bytes: 217012 num_examples: 1500 download_size: 802872 dataset_size: 1146253 - config_name: mnli features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: idx dtype: int32 splits: - name: test_matched num_bytes: 1854787 num_examples: 9796 - name: test_mismatched num_bytes: 1956866 num_examples: 9847 - name: train num_bytes: 74865118 num_examples: 392702 - name: validation_matched num_bytes: 1839926 num_examples: 9815 - name: validation_mismatched num_bytes: 1955384 num_examples: 9832 download_size: 312783507 dataset_size: 82472081 - config_name: mnli_mismatched features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: idx dtype: int32 splits: - name: test num_bytes: 1956866 num_examples: 9847 - name: validation num_bytes: 1955384 num_examples: 9832 download_size: 312783507 dataset_size: 3912250 - config_name: mnli_matched features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: idx dtype: int32 splits: - name: test num_bytes: 1854787 num_examples: 9796 - name: validation num_bytes: 1839926 num_examples: 9815 download_size: 312783507 dataset_size: 3694713 - config_name: qnli features: - name: question dtype: string - name: sentence dtype: string - name: label dtype: class_label: names: '0': entailment '1': not_entailment - name: idx dtype: int32 splits: - name: test num_bytes: 1376516 num_examples: 5463 - name: train num_bytes: 25677924 num_examples: 104743 - name: validation num_bytes: 1371727 num_examples: 5463 download_size: 10627589 dataset_size: 28426167 - config_name: rte features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': entailment '1': not_entailment - name: idx dtype: int32 splits: - name: test num_bytes: 975936 num_examples: 3000 - name: train num_bytes: 848888 num_examples: 2490 - name: validation num_bytes: 90911 num_examples: 277 download_size: 697150 dataset_size: 1915735 - config_name: wnli features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': not_entailment '1': entailment - name: idx dtype: int32 splits: - name: test num_bytes: 37992 num_examples: 146 - name: train num_bytes: 107517 num_examples: 635 - name: validation num_bytes: 12215 num_examples: 71 download_size: 28999 dataset_size: 157724 - config_name: ax features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: idx dtype: int32 splits: - name: test num_bytes: 238392 num_examples: 1104 download_size: 222257 dataset_size: 238392 train-eval-index: - config: cola task: text-classification task_id: binary_classification splits: train_split: train eval_split: validation col_mapping: sentence: text label: target - config: sst2 task: text-classification task_id: binary_classification splits: train_split: train eval_split: validation col_mapping: sentence: text label: target - config: mrpc task: text-classification task_id: natural_language_inference splits: train_split: train eval_split: validation col_mapping: sentence1: text1 sentence2: text2 label: target - config: qqp task: text-classification task_id: natural_language_inference splits: train_split: train eval_split: validation col_mapping: question1: text1 question2: text2 label: target - config: stsb task: text-classification task_id: natural_language_inference splits: train_split: train eval_split: validation col_mapping: sentence1: text1 sentence2: text2 label: target - config: mnli task: text-classification task_id: natural_language_inference splits: train_split: train eval_split: validation_matched col_mapping: premise: text1 hypothesis: text2 label: target - config: mnli_mismatched task: text-classification task_id: natural_language_inference splits: train_split: train eval_split: validation col_mapping: premise: text1 hypothesis: text2 label: target - config: mnli_matched task: text-classification task_id: natural_language_inference splits: train_split: train eval_split: validation col_mapping: premise: text1 hypothesis: text2 label: target - config: qnli task: text-classification task_id: natural_language_inference splits: train_split: train eval_split: validation col_mapping: question: text1 sentence: text2 label: target - config: rte task: text-classification task_id: natural_language_inference splits: train_split: train eval_split: validation col_mapping: sentence1: text1 sentence2: text2 label: target - config: wnli task: text-classification task_id: natural_language_inference splits: train_split: train eval_split: validation col_mapping: sentence1: text1 sentence2: text2 label: target --- # Dataset Card for GLUE ## Table of Contents - [Dataset Card for GLUE](#dataset-card-for-glue) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [ax](#ax) - [cola](#cola) - [mnli](#mnli) - [mnli_matched](#mnli_matched) - [mnli_mismatched](#mnli_mismatched) - [mrpc](#mrpc) - [qnli](#qnli) - [qqp](#qqp) - [rte](#rte) - [sst2](#sst2) - [stsb](#stsb) - [wnli](#wnli) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [ax](#ax-1) - [cola](#cola-1) - [mnli](#mnli-1) - [mnli_matched](#mnli_matched-1) - [mnli_mismatched](#mnli_mismatched-1) - [mrpc](#mrpc-1) - [qnli](#qnli-1) - [qqp](#qqp-1) - [rte](#rte-1) - [sst2](#sst2-1) - [stsb](#stsb-1) - [wnli](#wnli-1) - [Data Fields](#data-fields) - [ax](#ax-2) - [cola](#cola-2) - [mnli](#mnli-2) - [mnli_matched](#mnli_matched-2) - [mnli_mismatched](#mnli_mismatched-2) - [mrpc](#mrpc-2) - [qnli](#qnli-2) - [qqp](#qqp-2) - [rte](#rte-2) - [sst2](#sst2-2) - [stsb](#stsb-2) - [wnli](#wnli-2) - [Data Splits](#data-splits) - [ax](#ax-3) - [cola](#cola-3) - [mnli](#mnli-3) - [mnli_matched](#mnli_matched-3) - [mnli_mismatched](#mnli_mismatched-3) - [mrpc](#mrpc-3) - [qnli](#qnli-3) - [qqp](#qqp-3) - [rte](#rte-3) - [sst2](#sst2-3) - [stsb](#stsb-3) - [wnli](#wnli-3) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://nyu-mll.github.io/CoLA/](https://nyu-mll.github.io/CoLA/) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 1.00 GB - Size of the generated dataset: 240.84 MB - Total amount of disk used: 1.24 GB ### Dataset Summary GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems. ### Supported Tasks and Leaderboards The leaderboard for the GLUE benchmark can be found [at this address](https://gluebenchmark.com/). It comprises the following tasks: #### ax A manually-curated evaluation dataset for fine-grained analysis of system performance on a broad range of linguistic phenomena. This dataset evaluates sentence understanding through Natural Language Inference (NLI) problems. Use a model trained on MulitNLI to produce predictions for this dataset. #### cola The Corpus of Linguistic Acceptability consists of English acceptability judgments drawn from books and journal articles on linguistic theory. Each example is a sequence of words annotated with whether it is a grammatical English sentence. #### mnli The Multi-Genre Natural Language Inference Corpus is a crowdsourced collection of sentence pairs with textual entailment annotations. Given a premise sentence and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis (entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are gathered from ten different sources, including transcribed speech, fiction, and government reports. The authors of the benchmark use the standard test set, for which they obtained private labels from the RTE authors, and evaluate on both the matched (in-domain) and mismatched (cross-domain) section. They also uses and recommend the SNLI corpus as 550k examples of auxiliary training data. #### mnli_matched The matched validation and test splits from MNLI. See the "mnli" BuilderConfig for additional information. #### mnli_mismatched The mismatched validation and test splits from MNLI. See the "mnli" BuilderConfig for additional information. #### mrpc The Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent. #### qnli The Stanford Question Answering Dataset is a question-answering dataset consisting of question-paragraph pairs, where one of the sentences in the paragraph (drawn from Wikipedia) contains the answer to the corresponding question (written by an annotator). The authors of the benchmark convert the task into sentence pair classification by forming a pair between each question and each sentence in the corresponding context, and filtering out pairs with low lexical overlap between the question and the context sentence. The task is to determine whether the context sentence contains the answer to the question. This modified version of the original task removes the requirement that the model select the exact answer, but also removes the simplifying assumptions that the answer is always present in the input and that lexical overlap is a reliable cue. #### qqp The Quora Question Pairs2 dataset is a collection of question pairs from the community question-answering website Quora. The task is to determine whether a pair of questions are semantically equivalent. #### rte The Recognizing Textual Entailment (RTE) datasets come from a series of annual textual entailment challenges. The authors of the benchmark combined the data from RTE1 (Dagan et al., 2006), RTE2 (Bar Haim et al., 2006), RTE3 (Giampiccolo et al., 2007), and RTE5 (Bentivogli et al., 2009). Examples are constructed based on news and Wikipedia text. The authors of the benchmark convert all datasets to a two-class split, where for three-class datasets they collapse neutral and contradiction into not entailment, for consistency. #### sst2 The Stanford Sentiment Treebank consists of sentences from movie reviews and human annotations of their sentiment. The task is to predict the sentiment of a given sentence. It uses the two-way (positive/negative) class split, with only sentence-level labels. #### stsb The Semantic Textual Similarity Benchmark (Cer et al., 2017) is a collection of sentence pairs drawn from news headlines, video and image captions, and natural language inference data. Each pair is human-annotated with a similarity score from 1 to 5. #### wnli The Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task in which a system must read a sentence with a pronoun and select the referent of that pronoun from a list of choices. The examples are manually constructed to foil simple statistical methods: Each one is contingent on contextual information provided by a single word or phrase in the sentence. To convert the problem into sentence pair classification, the authors of the benchmark construct sentence pairs by replacing the ambiguous pronoun with each possible referent. The task is to predict if the sentence with the pronoun substituted is entailed by the original sentence. They use a small evaluation set consisting of new examples derived from fiction books that was shared privately by the authors of the original corpus. While the included training set is balanced between two classes, the test set is imbalanced between them (65% not entailment). Also, due to a data quirk, the development set is adversarial: hypotheses are sometimes shared between training and development examples, so if a model memorizes the training examples, they will predict the wrong label on corresponding development set example. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence between a model's score on this task and its score on the unconverted original task. The authors of the benchmark call converted dataset WNLI (Winograd NLI). ### Languages The language data in GLUE is in English (BCP-47 `en`) ## Dataset Structure ### Data Instances #### ax - Size of downloaded dataset files: 0.22 MB - Size of the generated dataset: 0.24 MB - Total amount of disk used: 0.46 MB An example of 'test' looks as follows. ``` { "premise": "The cat sat on the mat.", "hypothesis": "The cat did not sit on the mat.", "label": -1, "idx: 0 } ``` #### cola - Size of downloaded dataset files: 0.38 MB - Size of the generated dataset: 0.61 MB - Total amount of disk used: 0.99 MB An example of 'train' looks as follows. ``` { "sentence": "Our friends won't buy this analysis, let alone the next one we propose.", "label": 1, "id": 0 } ``` #### mnli - Size of downloaded dataset files: 312.78 MB - Size of the generated dataset: 82.47 MB - Total amount of disk used: 395.26 MB An example of 'train' looks as follows. ``` { "premise": "Conceptually cream skimming has two basic dimensions - product and geography.", "hypothesis": "Product and geography are what make cream skimming work.", "label": 1, "idx": 0 } ``` #### mnli_matched - Size of downloaded dataset files: 312.78 MB - Size of the generated dataset: 3.69 MB - Total amount of disk used: 316.48 MB An example of 'test' looks as follows. ``` { "premise": "Hierbas, ans seco, ans dulce, and frigola are just a few names worth keeping a look-out for.", "hypothesis": "Hierbas is a name worth looking out for.", "label": -1, "idx": 0 } ``` #### mnli_mismatched - Size of downloaded dataset files: 312.78 MB - Size of the generated dataset: 3.91 MB - Total amount of disk used: 316.69 MB An example of 'test' looks as follows. ``` { "premise": "What have you decided, what are you going to do?", "hypothesis": "So what's your decision?, "label": -1, "idx": 0 } ``` #### mrpc [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### qnli [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### qqp [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### rte [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### sst2 [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### stsb [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### wnli [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Data Fields The data fields are the same among all splits. #### ax - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). - `idx`: a `int32` feature. #### cola - `sentence`: a `string` feature. - `label`: a classification label, with possible values including `unacceptable` (0), `acceptable` (1). - `idx`: a `int32` feature. #### mnli - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). - `idx`: a `int32` feature. #### mnli_matched - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). - `idx`: a `int32` feature. #### mnli_mismatched - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). - `idx`: a `int32` feature. #### mrpc [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### qnli [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### qqp [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### rte [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### sst2 [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### stsb [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### wnli [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Data Splits #### ax \| \|test\| \|---\|---:\| \|ax \|1104\| #### cola \| \|train\|validation\|test\| \|----\|----:\|---------:\|---:\| \|cola\| 8551\| 1043\|1063\| #### mnli \| \|train \|validation_matched\|validation_mismatched\|test_matched\|test_mismatched\| \|----\|-----:\|-----------------:\|--------------------:\|-----------:\|--------------:\| \|mnli\|392702\| 9815\| 9832\| 9796\| 9847\| #### mnli_matched \| \|validation\|test\| \|------------\|---------:\|---:\| \|mnli_matched\| 9815\|9796\| #### mnli_mismatched \| \|validation\|test\| \|---------------\|---------:\|---:\| \|mnli_mismatched\| 9832\|9847\| #### mrpc [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### qnli [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### qqp [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### rte [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### sst2 [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### stsb [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### wnli [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{warstadt2018neural, title={Neural Network Acceptability Judgments}, author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R}, journal={arXiv preprint arXiv:1805.12471}, year={2018} } @inproceedings{wang2019glue, title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding}, author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.}, note={In the Proceedings of ICLR.}, year={2019} } Note that each GLUE dataset has its own citation. Please see the source to see the correct citation for each contained dataset. ``` ### Contributions Thanks to [@patpizio](https://github.com/patpizio), [@jeswan](https://github.com/jeswan), [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.
gnad10	--- annotations_creators: - crowdsourced language_creators: - found language: - de license: - cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended\|other-from-One-Million-Posts-Corpus task_categories: - text-classification task_ids: - topic-classification pretty_name: 10k German News Articles Datasets dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': Web '1': Panorama '2': International '3': Wirtschaft '4': Sport '5': Inland '6': Etat '7': Wissenschaft '8': Kultur splits: - name: train num_bytes: 24418224 num_examples: 9245 - name: test num_bytes: 2756405 num_examples: 1028 download_size: 27160809 dataset_size: 27174629 --- # Dataset Card for 10k German News Articles Datasets ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [10k German News Article Dataset](https://tblock.github.io/10kGNAD/) - Repository: [10k German News Article Dataset](https://github.com/tblock/10kGNAD) - Point of Contact: [Steven Liu](stevhliu@gmail.com) ### Dataset Summary The 10k German News Article Dataset consists of 10273 German language news articles from the online Austrian newspaper website DER Standard. Each news article has been classified into one of 9 categories by professional forum moderators employed by the newspaper. This dataset is extended from the original [One Million Posts Corpus](https://ofai.github.io/million-post-corpus/). The dataset was created to support topic classification in German because a classifier effective on a English dataset may not be as effective on a German dataset due to higher inflections and longer compound words. Additionally, this dataset can be used as a benchmark dataset for German topic classification. ### Supported Tasks and Leaderboards This dataset can be used to train a model, like [BERT](https://huggingface.co/bert-base-uncased) for `topic classification` on German news articles. There are 9 possible categories. ### Languages The text is in German and it comes from an online Austrian newspaper website. The BCP-47 code for German is `de-DE`. ## Dataset Structure ### Data Instances An example data instance contains a German news article (title and article are concatenated) and it's corresponding topic category. ``` {'text': ''Die Gewerkschaft GPA-djp lanciert den "All-in-Rechner" und findet, dass die Vertragsform auf die Führungsebene beschränkt gehört. Wien – Die Gewerkschaft GPA-djp sieht Handlungsbedarf bei sogenannten All-in-Verträgen.' 'label': 'Wirtschaft' } ``` ### Data Fields * `text`: contains the title and content of the article * `label`: can be one of 9 possible topic categories (`Web`, `Panorama`, `International`, `Wirtschaft`, `Sport`, `Inland`, `Etat`, `Wissenschaft`, `Kultur`) ### Data Splits The data is split into a training set consisting of 9245 articles and a test set consisting of 1028 articles. ## Dataset Creation ### Curation Rationale The dataset was created to support topic classification in the German language. English text classification datasets are common ([AG News](https://huggingface.co/datasets/ag_news) and [20 Newsgroup](https://huggingface.co/datasets/newsgroup)), but German datasets are less common. A classifier trained on an English dataset may not work as well on a set of German text due to grammatical differences. Thus there is a need for a German dataset for effectively assessing model performance. ### Source Data #### Initial Data Collection and Normalization The 10k German News Article Dataset is extended from the One Million Posts Corpus. 10273 German news articles were collected from this larger corpus. In the One Million Posts Corpus, each article has a topic path like `Newsroom/Wirtschaft/Wirtschaftpolitik/Finanzmaerkte/Griechenlandkrise`. The 10kGNAD uses the second part of the topic path as the topic label. Article title and texts are concatenated into one text and author names are removed to avoid keyword classification on authors who write frequently on a particular topic. #### Who are the source language producers? The language producers are the authors of the Austrian newspaper website DER Standard. ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators This dataset was curated by Timo Block. ### Licensing Information This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 license. ### Citation Information Please consider citing the authors of the "One Million Post Corpus" if you use the dataset.: ``` @InProceedings{Schabus2017, Author = {Dietmar Schabus and Marcin Skowron and Martin Trapp}, Title = {One Million Posts: A Data Set of German Online Discussions}, Booktitle = {Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)}, Pages = {1241--1244}, Year = {2017}, Address = {Tokyo, Japan}, Doi = {10.1145/3077136.3080711}, Month = aug } ``` ### Contributions Thanks to [@stevhliu](https://github.com/stevhliu) for adding this dataset.
go_emotions	--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - apache-2.0 multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification - multi-label-classification paperswithcode_id: goemotions pretty_name: GoEmotions configs: - raw - simplified tags: - emotion dataset_info: - config_name: raw features: - name: text dtype: string - name: id dtype: string - name: author dtype: string - name: subreddit dtype: string - name: link_id dtype: string - name: parent_id dtype: string - name: created_utc dtype: float32 - name: rater_id dtype: int32 - name: example_very_unclear dtype: bool - name: admiration dtype: int32 - name: amusement dtype: int32 - name: anger dtype: int32 - name: annoyance dtype: int32 - name: approval dtype: int32 - name: caring dtype: int32 - name: confusion dtype: int32 - name: curiosity dtype: int32 - name: desire dtype: int32 - name: disappointment dtype: int32 - name: disapproval dtype: int32 - name: disgust dtype: int32 - name: embarrassment dtype: int32 - name: excitement dtype: int32 - name: fear dtype: int32 - name: gratitude dtype: int32 - name: grief dtype: int32 - name: joy dtype: int32 - name: love dtype: int32 - name: nervousness dtype: int32 - name: optimism dtype: int32 - name: pride dtype: int32 - name: realization dtype: int32 - name: relief dtype: int32 - name: remorse dtype: int32 - name: sadness dtype: int32 - name: surprise dtype: int32 - name: neutral dtype: int32 splits: - name: train num_bytes: 55343630 num_examples: 211225 download_size: 42742918 dataset_size: 55343630 - config_name: simplified features: - name: text dtype: string - name: labels sequence: class_label: names: '0': admiration '1': amusement '2': anger '3': annoyance '4': approval '5': caring '6': confusion '7': curiosity '8': desire '9': disappointment '10': disapproval '11': disgust '12': embarrassment '13': excitement '14': fear '15': gratitude '16': grief '17': joy '18': love '19': nervousness '20': optimism '21': pride '22': realization '23': relief '24': remorse '25': sadness '26': surprise '27': neutral - name: id dtype: string splits: - name: train num_bytes: 4224198 num_examples: 43410 - name: validation num_bytes: 527131 num_examples: 5426 - name: test num_bytes: 524455 num_examples: 5427 download_size: 4394818 dataset_size: 5275784 --- # Dataset Card for GoEmotions ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/google-research/google-research/tree/master/goemotions - Repository: https://github.com/google-research/google-research/tree/master/goemotions - Paper: https://arxiv.org/abs/2005.00547 - Leaderboard: - Point of Contact: [Dora Demszky](https://nlp.stanford.edu/~ddemszky/index.html) ### Dataset Summary The GoEmotions dataset contains 58k carefully curated Reddit comments labeled for 27 emotion categories or Neutral. The raw data is included as well as the smaller, simplified version of the dataset with predefined train/val/test splits. ### Supported Tasks and Leaderboards This dataset is intended for multi-class, multi-label emotion classification. ### Languages The data is in English. ## Dataset Structure ### Data Instances Each instance is a reddit comment with a corresponding ID and one or more emotion annotations (or neutral). ### Data Fields The simplified configuration includes: - `text`: the reddit comment - `labels`: the emotion annotations - `comment_id`: unique identifier of the comment (can be used to look up the entry in the raw dataset) In addition to the above, the raw data includes: * `author`: The Reddit username of the comment's author. * `subreddit`: The subreddit that the comment belongs to. * `link_id`: The link id of the comment. * `parent_id`: The parent id of the comment. * `created_utc`: The timestamp of the comment. * `rater_id`: The unique id of the annotator. * `example_very_unclear`: Whether the annotator marked the example as being very unclear or difficult to label (in this case they did not choose any emotion labels). In the raw data, labels are listed as their own columns with binary 0/1 entries rather than a list of ids as in the simplified data. ### Data Splits The simplified data includes a set of train/val/test splits with 43,410, 5426, and 5427 examples respectively. ## Dataset Creation ### Curation Rationale From the paper abstract: > Understanding emotion expressed in language has a wide range of applications, from building empathetic chatbots to detecting harmful online behavior. Advancement in this area can be improved using large-scale datasets with a fine-grained typology, adaptable to multiple downstream tasks. ### Source Data #### Initial Data Collection and Normalization Data was collected from Reddit comments via a variety of automated methods discussed in 3.1 of the paper. #### Who are the source language producers? English-speaking Reddit users. ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? Annotations were produced by 3 English-speaking crowdworkers in India. ### Personal and Sensitive Information This dataset includes the original usernames of the Reddit users who posted each comment. Although Reddit usernames are typically disasociated from personal real-world identities, this is not always the case. It may therefore be possible to discover the identities of the individuals who created this content in some cases. ## Considerations for Using the Data ### Social Impact of Dataset Emotion detection is a worthwhile problem which can potentially lead to improvements such as better human/computer interaction. However, emotion detection algorithms (particularly in computer vision) have been abused in some cases to make erroneous inferences in human monitoring and assessment applications such as hiring decisions, insurance pricing, and student attentiveness (see [this article](https://www.unite.ai/ai-now-institute-warns-about-misuse-of-emotion-detection-software-and-other-ethical-issues/)). ### Discussion of Biases From the authors' github page: > Potential biases in the data include: Inherent biases in Reddit and user base biases, the offensive/vulgar word lists used for data filtering, inherent or unconscious bias in assessment of offensive identity labels, annotators were all native English speakers from India. All these likely affect labelling, precision, and recall for a trained model. Anyone using this dataset should be aware of these limitations of the dataset. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Researchers at Amazon Alexa, Google Research, and Stanford. See the [author list](https://arxiv.org/abs/2005.00547). ### Licensing Information The GitHub repository which houses this dataset has an [Apache License 2.0](https://github.com/google-research/google-research/blob/master/LICENSE). ### Citation Information @inproceedings{demszky2020goemotions, author = {Demszky, Dorottya and Movshovitz-Attias, Dana and Ko, Jeongwoo and Cowen, Alan and Nemade, Gaurav and Ravi, Sujith}, booktitle = {58th Annual Meeting of the Association for Computational Linguistics (ACL)}, title = {{GoEmotions: A Dataset of Fine-Grained Emotions}}, year = {2020} } ### Contributions Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset.
gooaq	--- annotations_creators: - expert-generated language_creators: - machine-generated language: - en license: - apache-2.0 multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - question-answering task_ids: - open-domain-qa paperswithcode_id: gooaq pretty_name: 'GooAQ: Open Question Answering with Diverse Answer Types' dataset_info: features: - name: id dtype: int32 - name: question dtype: string - name: short_answer dtype: string - name: answer dtype: string - name: answer_type dtype: class_label: names: '0': feat_snip '1': collection '2': knowledge '3': unit_conv '4': time_conv '5': curr_conv splits: - name: train num_bytes: 974320061 num_examples: 3112679 - name: validation num_bytes: 444553 num_examples: 2500 - name: test num_bytes: 445810 num_examples: 2500 download_size: 2111358901 dataset_size: 975210424 --- # Dataset Card for GooAQ ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [GooAQ 🥑: Google Answers to Google Questions!](https://github.com/allenai/gooaq) - Repository: [GooAQ 🥑: Google Answers to Google Questions!](https://github.com/allenai/gooaq) - Paper: [GOOAQ: Open Question Answering with Diverse Answer Types](https://arxiv.org/abs/2104.08727) - Point of Contact: [Daniel Khashabi](danielk@allenai.org) ### Dataset Summary GooAQ is a large-scale dataset with a variety of answer types. This dataset contains over 5 million questions and 3 million answers collected from Google. GooAQ questions are collected semi-automatically from the Google search engine using its autocomplete feature. This results in naturalistic questions of practical interest that are nonetheless short and expressed using simple language. GooAQ answers are mined from Google's responses to our collected questions, specifically from the answer boxes in the search results. This yields a rich space of answer types, containing both textual answers (short and long) as well as more structured ones such as collections. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset contains samples in English only. ## Dataset Structure ### Data Instances Each row of the data file should look like this: ``` { "id": 3339543, "question": "what is the difference between collagen and whey protein?", "short_answer": None, "answer": "The main differences between the amino acid profiles of whey and collagen are that whey contains all 9 essential amino acids, while collagen only has 8. ... Collagen is a fibrous protein found in the skin, cartilage, and bones of animals whereas whey comes from milk.", "answer_type": "feat_snip" } ``` where the questions `question` are collected via Google auto-complete. The answers responses (`short_answer` and `answer`) were collected from Google's answer boxes. The answer types (`answer_type`) are inferred based on the html content of Google's response. Here is the dominant types in the current dataset: - `feat_snip`: explanatory responses; the majoriy the question/responses are of this type. - `collection`: list responses (e.g., steps to accomplish something). - `knowledge`: typically short responses for knowledge seeking questions. - `unit_conv`: questions about converting units. - `time_conv`: questions about converting times. - `curr_conv`: questions about converting currencies. Dataset instances which are not part of dominant types are marked with -1 label. ### Data Fields - `id`: an `int` feature. - `question`: a `string` feature. - `short_answer`: a `string` feature (could be None as well in some cases). - `answer`: a `string` feature (could be None as well in some cases). - `answer_type`: a `string` feature. ### Data Splits Number of samples in train/validation/test set are given below: \| Split \| Number of samples \| \|------------\|-------------------\| \| Train \| 3112679 \| \| Validation \| 2500 \| \| Test \| 2500 \| ## Dataset Creation ### Curation Rationale While day-to-day questions come with a variety of answer types, the current question-answering (QA) literature has failed to adequately address the answer diversity of questions. Many of the everyday questions that humans deal with and pose to search engines have a more diverse set of responses. Their answer can be a multi-sentence description (a snippet) (e.g., ‘what is’ or ‘can you’ questions), a collection of items such as ingredients (‘what are’, ‘things to’) or of steps towards a goal such as unlocking a phone (‘how to’), etc. Even when the answer is short, it can have richer types, e.g., unit conversion, time zone conversion, or various kinds of knowledge look-up (‘how much’, ‘when is’, etc.). Such answer type diversity is not represented in any existing dataset. ### Source Data #### Initial Data Collection and Normalization Construction this dataset involved two main steps, extracting questions from search auto-complete and extracting answers from answer boxes. 1) Query Extraction: To extract a rich yet natural set of questions they used Google auto-completion. They start with a seed set of question terms (e.g., “who”, “where”, etc.). They bootstrap based on this set, by repeatedly querying prefixes of previously extracted questions, in order to discover longer and richer sets of questions. Such questions extracted from the autocomplete algorithm are highly reflective of popular questions posed by users of Google. They filter out any questions shorter than 5 tokens as they are often in-complete questions. This process yields over ∼5M questions, which were collected over a span of 6 months. The average length of the questions is about 8 tokens. 2) Answer Extraction: They rely on the Google answer boxes shown on top of the search results when the questions are issued to Google. There are a variety of answer boxes. The most common kind involves highlighted sentences (extracted from various websites) that contain the answer to a given question. These form the snippet and collection answers in GOOAQ. In some cases, the answer box shows the answer directly, possibly in addition to the textual snippet. These form theshort answers in GOOAQ. They first scrape the search results for all questions. This is the main extraction bottleneck, which was done over a span of 2 months. Subsequently, they extract answer strings from the HTML content of the search results. Answer types are also inferred at this stage, based on the HTML tags around the answer. #### Who are the source language producers? Answered above. ### Annotations #### Annotation process Answered in above section. #### Who are the annotators? Since their task is focused on English, they required workers to be based in a country with a population predominantly of native English speakers (e.g., USA, Canada, UK, and Australia) and have completed at least 5000 HITs with ≥ 99% assignment approval rate. Additionally, they have a qualification test with half-a-dozen questions all of which need to be answered correctly by the annotators. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases To prevent biased judgements, they also ask the annotators to avoid using Google search (which is what they used when mined GOOAQ) when annotating the quality of shown instances. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators List the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here. ### Licensing Information Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. ### Citation Information ``` @article{gooaq2021, title={GooAQ: Open Question Answering with Diverse Answer Types}, author={Khashabi, Daniel and Ng, Amos and Khot, Tushar and Sabharwal, Ashish and Hajishirzi, Hannaneh and Callison-Burch, Chris}, journal={arXiv preprint}, year={2021} } ``` ### Contributions Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset.
google_wellformed_query	--- task_categories: - text-classification multilinguality: - monolingual task_ids: - text-scoring language: - en annotations_creators: - crowdsourced source_datasets: - extended size_categories: - 10K<n<100K license: - cc-by-sa-4.0 paperswithcode_id: null pretty_name: GoogleWellformedQuery language_creators: - found dataset_info: features: - name: rating dtype: float32 - name: content dtype: string splits: - name: train num_bytes: 857391 num_examples: 17500 - name: test num_bytes: 189503 num_examples: 3850 - name: validation num_bytes: 184110 num_examples: 3750 download_size: 1157019 dataset_size: 1231004 --- # Dataset Card for Google Query-wellformedness Dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [GitHub](https://github.com/google-research-datasets/query-wellformedness) - Repository: [GitHub](https://github.com/google-research-datasets/query-wellformedness) - Paper: [ARXIV](https://arxiv.org/abs/1808.09419) - Leaderboard: - Point of Contact: ### Dataset Summary Google's query wellformedness dataset was created by crowdsourcing well-formedness annotations for 25,100 queries from the Paralex corpus. Every query was annotated by five raters each with 1/0 rating of whether or not the query is well-formed. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure ### Data Instances ``` {'rating': 0.2, 'content': 'The European Union includes how many ?'} ``` ### Data Fields - `rating`: a `float` between 0-1 - `sentence`: query which you want to rate ### Data Splits \| \| Train \| Valid \| Test \| \| ----- \| ------ \| ----- \| ---- \| \| Input Sentences \| 17500 \| 3750 \| 3850 \| ## Dataset Creation ### Curation Rationale Understanding search queries is a hard problem as it involves dealing with “word salad” text ubiquitously issued by users. However, if a query resembles a well-formed question, a natural language processing pipeline is able to perform more accurate interpretation, thus reducing downstream compounding errors. Hence, identifying whether or not a query is well formed can enhance query understanding. This dataset introduce a new task of identifying a well-formed natural language question. ### Source Data Used the Paralex corpus (Fader et al., 2013) that contains pairs of noisy paraphrase questions. These questions were issued by users in WikiAnswers (a Question-Answer forum) and consist of both web-search query like constructs (“5 parts of chloroplast?”) and well-formed questions (“What is the punishment for grand theft?”). #### Initial Data Collection and Normalization Selected 25,100 queries from the unique list of queries extracted from the corpus such that no two queries in the selected set are paraphrases. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process The queries are annotated into well-formed or non-wellformed questions if it satisfies the following: 1. Query is grammatical. 2. Query is an explicit question. 3. Query does not contain spelling errors. #### Who are the annotators? Every query was labeled by five different crowdworkers with a binary label indicating whether a query is well-formed or not. And average of the ratings of the five annotators was reported, to get the probability of a query being well-formed. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Query-wellformedness dataset is licensed under CC BY-SA 4.0. Any third party content or data is provided “As Is” without any warranty, express or implied. ### Citation Information ``` @InProceedings{FaruquiDas2018, title = {{Identifying Well-formed Natural Language Questions}}, author = {Faruqui, Manaal and Das, Dipanjan}, booktitle = {Proc. of EMNLP}, year = {2018} } ``` ### Contributions Thanks to [@vasudevgupta7](https://github.com/vasudevgupta7) for adding this dataset.
grail_qa	--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: [] paperswithcode_id: null pretty_name: Grail QA tags: - knowledge-base-qa dataset_info: features: - name: qid dtype: string - name: question dtype: string - name: answer sequence: - name: answer_type dtype: string - name: answer_argument dtype: string - name: entity_name dtype: string - name: function dtype: string - name: num_node dtype: int32 - name: num_edge dtype: int32 - name: graph_query struct: - name: nodes sequence: - name: nid dtype: int32 - name: node_type dtype: string - name: id dtype: string - name: class dtype: string - name: friendly_name dtype: string - name: question_node dtype: int32 - name: function dtype: string - name: edges sequence: - name: start dtype: int32 - name: end dtype: int32 - name: relation dtype: string - name: friendly_name dtype: string - name: sparql_query dtype: string - name: domains sequence: string - name: level dtype: string - name: s_expression dtype: string splits: - name: train num_bytes: 69433121 num_examples: 44337 - name: validation num_bytes: 9800544 num_examples: 6763 - name: test num_bytes: 2167256 num_examples: 13231 download_size: 17636773 dataset_size: 81400921 --- # Dataset Card for Grail QA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Grail QA](https://dki-lab.github.io/GrailQA/) - Repository: - Paper: [GrailQA paper (Gu et al. '20)](https://arxiv.org/abs/2011.07743) - Leaderboard: - Point of Contact: ### Dataset Summary #### What is GrailQA? Strongly Generalizable Question Answering (GrailQA) is a new large-scale, high-quality dataset for question answering on knowledge bases (KBQA) on Freebase with 64,331 questions annotated with both answers and corresponding logical forms in different syntax (i.e., SPARQL, S-expression, etc.). It can be used to test three levels of generalization in KBQA: i.i.d., compositional, and zero-shot. #### Why GrailQA? GrailQA is by far the largest crowdsourced KBQA dataset with questions of high diversity (i.e., questions in GrailQA can have up to 4 relations and optionally have a function from counting, superlatives and comparatives). It also has the highest coverage over Freebase; it widely covers 3,720 relations and 86 domains from Freebase. Last but not least, our meticulous data split allows GrailQA to test not only i.i.d. generalization, but also compositional generalization and zero-shot generalization, which are critical for practical KBQA systems. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English and Graph query ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - `qid` (`str`) - `question` (`str`) - `answer` (`List`): Defaults to `[]` in test split. - `answer_type` (`str`) - `answer_argument` (`str`) - `entity_name` (`str`): Defauts to `""` if `answer_type` is not `Entity`. - `function` (`string`): Defaults to `""` in test split. - `num_node` (`int`): Defaults to `-1` in test split. - `num_edge` (`int`): Defaults to `-1` in test split. - `graph_query` (`Dict`) - `nodes` (`List`): Defaults to `[]` in test split. - `nid` (`int`) - `node_type` (`str`) - `id` (`str`) - `class` (`str`) - `friendly_name` (`str`) - `question_node` (`int`) - `function` (`str`) - `edges` (`List`): Defaults to `[]` in test split. - `start` (`int`) - `end` (`int`) - `relation` (`str`) - `friendly_name` (`str`) - `sqarql_query` (`str`): Defaults to `""` in test split. - `domains` (`List[str]`): Defaults to `[]` in test split. - `level` (`str`): Only available in validation split. Defaults to `""` in others. - `s_expression` (`str`): Defaults to `""` in test split. Notes: Only `qid` and `question` available in test split. ### Data Splits Dataset Split \| Number of Instances in Split --------------\|-------------------------------------------- Train \| 44,337 Validation \| 6,763 Test \| 13,231 ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@mattbui](https://github.com/mattbui) for adding this dataset.
great_code	--- annotations_creators: - expert-generated language_creators: - found language: - en license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - table-to-text task_ids: [] paperswithcode_id: null pretty_name: GREAT dataset_info: features: - name: id dtype: int32 - name: source_tokens sequence: string - name: has_bug dtype: bool - name: error_location dtype: int32 - name: repair_candidates sequence: string - name: bug_kind dtype: int32 - name: bug_kind_name dtype: string - name: repair_targets sequence: int32 - name: edges list: list: - name: before_index dtype: int32 - name: after_index dtype: int32 - name: edge_type dtype: int32 - name: edge_type_name dtype: string - name: provenances list: - name: datasetProvenance struct: - name: datasetName dtype: string - name: filepath dtype: string - name: license dtype: string - name: note dtype: string splits: - name: train num_bytes: 14705534822 num_examples: 1798742 - name: validation num_bytes: 1502956919 num_examples: 185656 - name: test num_bytes: 7880762248 num_examples: 968592 download_size: 23310374002 dataset_size: 24089253989 --- # Dataset Card for GREAT ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: None - Repository: https://github.com/google-research-datasets/great - Paper: https://openreview.net/forum?id=B1lnbRNtwr - Leaderboard: [More Information Needed] - Point of Contact: [More Information Needed] ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances Here are some examples of questions and facts: ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
greek_legal_code	--- annotations_creators: - found language_creators: - found language: - el license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification - topic-classification pretty_name: Greek Legal Code dataset_info: - config_name: volume features: - name: text dtype: string - name: label dtype: class_label: names: '0': ΚΟΙΝΩΝΙΚΗ ΠΡΟΝΟΙΑ '1': ΓΕΩΡΓΙΚΗ ΝΟΜΟΘΕΣΙΑ '2': ΡΑΔΙΟΦΩΝΙΑ ΚΑΙ ΤΥΠΟΣ '3': ΒΙΟΜΗΧΑΝΙΚΗ ΝΟΜΟΘΕΣΙΑ '4': ΥΓΕΙΟΝΟΜΙΚΗ ΝΟΜΟΘΕΣΙΑ '5': ΠΟΛΕΜΙΚΟ ΝΑΥΤΙΚΟ '6': ΤΑΧΥΔΡΟΜΕΙΑ - ΤΗΛΕΠΙΚΟΙΝΩΝΙΕΣ '7': ΔΑΣΗ ΚΑΙ ΚΤΗΝΟΤΡΟΦΙΑ '8': ΕΛΕΓΚΤΙΚΟ ΣΥΝΕΔΡΙΟ ΚΑΙ ΣΥΝΤΑΞΕΙΣ '9': ΠΟΛΕΜΙΚΗ ΑΕΡΟΠΟΡΙΑ '10': ΝΟΜΙΚΑ ΠΡΟΣΩΠΑ ΔΗΜΟΣΙΟΥ ΔΙΚΑΙΟΥ '11': ΝΟΜΟΘΕΣΙΑ ΑΝΩΝΥΜΩΝ ΕΤΑΙΡΕΙΩΝ ΤΡΑΠΕΖΩΝ ΚΑΙ ΧΡΗΜΑΤΙΣΤΗΡΙΩΝ '12': ΠΟΛΙΤΙΚΗ ΑΕΡΟΠΟΡΙΑ '13': ΕΜΜΕΣΗ ΦΟΡΟΛΟΓΙΑ '14': ΚΟΙΝΩΝΙΚΕΣ ΑΣΦΑΛΙΣΕΙΣ '15': ΝΟΜΟΘΕΣΙΑ ΔΗΜΩΝ ΚΑΙ ΚΟΙΝΟΤΗΤΩΝ '16': ΝΟΜΟΘΕΣΙΑ ΕΠΙΜΕΛΗΤΗΡΙΩΝ ΣΥΝΕΤΑΙΡΙΣΜΩΝ ΚΑΙ ΣΩΜΑΤΕΙΩΝ '17': ΔΗΜΟΣΙΑ ΕΡΓΑ '18': ΔΙΟΙΚΗΣΗ ΔΙΚΑΙΟΣΥΝΗΣ '19': ΑΣΦΑΛΙΣΤΙΚΑ ΤΑΜΕΙΑ '20': ΕΚΚΛΗΣΙΑΣΤΙΚΗ ΝΟΜΟΘΕΣΙΑ '21': ΕΚΠΑΙΔΕΥΤΙΚΗ ΝΟΜΟΘΕΣΙΑ '22': ΔΗΜΟΣΙΟ ΛΟΓΙΣΤΙΚΟ '23': ΤΕΛΩΝΕΙΑΚΗ ΝΟΜΟΘΕΣΙΑ '24': ΣΥΓΚΟΙΝΩΝΙΕΣ '25': ΕΘΝΙΚΗ ΑΜΥΝΑ '26': ΣΤΡΑΤΟΣ ΞΗΡΑΣ '27': ΑΓΟΡΑΝΟΜΙΚΗ ΝΟΜΟΘΕΣΙΑ '28': ΔΗΜΟΣΙΟΙ ΥΠΑΛΛΗΛΟΙ '29': ΠΕΡΙΟΥΣΙΑ ΔΗΜΟΣΙΟΥ ΚΑΙ ΝΟΜΙΣΜΑ '30': ΟΙΚΟΝΟΜΙΚΗ ΔΙΟΙΚΗΣΗ '31': ΛΙΜΕΝΙΚΗ ΝΟΜΟΘΕΣΙΑ '32': ΑΣΤΙΚΗ ΝΟΜΟΘΕΣΙΑ '33': ΠΟΛΙΤΙΚΗ ΔΙΚΟΝΟΜΙΑ '34': ΔΙΠΛΩΜΑΤΙΚΗ ΝΟΜΟΘΕΣΙΑ '35': ΔΙΟΙΚΗΤΙΚΗ ΝΟΜΟΘΕΣΙΑ '36': ΑΜΕΣΗ ΦΟΡΟΛΟΓΙΑ '37': ΤΥΠΟΣ ΚΑΙ ΤΟΥΡΙΣΜΟΣ '38': ΕΘΝΙΚΗ ΟΙΚΟΝΟΜΙΑ '39': ΑΣΤΥΝΟΜΙΚΗ ΝΟΜΟΘΕΣΙΑ '40': ΑΓΡΟΤΙΚΗ ΝΟΜΟΘΕΣΙΑ '41': ΕΡΓΑΤΙΚΗ ΝΟΜΟΘΕΣΙΑ '42': ΠΟΙΝΙΚΗ ΝΟΜΟΘΕΣΙΑ '43': ΕΜΠΟΡΙΚΗ ΝΟΜΟΘΕΣΙΑ '44': ΕΠΙΣΤΗΜΕΣ ΚΑΙ ΤΕΧΝΕΣ '45': ΕΜΠΟΡΙΚΗ ΝΑΥΤΙΛΙΑ '46': ΣΥΝΤΑΓΜΑΤΙΚΗ ΝΟΜΟΘΕΣΙΑ splits: - name: train num_bytes: 216757887 num_examples: 28536 - name: test num_bytes: 71533786 num_examples: 9516 - name: validation num_bytes: 68824457 num_examples: 9511 download_size: 45606292 dataset_size: 357116130 - config_name: chapter features: - name: text dtype: string - name: label dtype: class_label: names: '0': ΜΕΤΑΛΛΕΙΑ ΚΑΙ ΟΡΥΧΕΙΑ '1': ΣΤΑΤΙΩΤΙΚΕΣ ΣΧΟΛΕΣ '2': ΠΑΡΟΧΕΣ ΑΝΕΡΓΙΑΣ '3': ΣΙΔΗΡΟΔΡΟΜΙΚΑ ΔΙΚΤΥΑ '4': ΕΙΔΙΚΑ ΣΤΡΑΤΙΩΤΙΚΑ ΑΔΙΚΗΜΑΤΑ '5': ΚΡΑΤΙΚΕΣ ΠΡΟΜΗΘΕΙΕΣ '6': ΑΓΡΟΤΙΚΗ ΑΠΟΚΑΤΑΣΤΑΣΗ '7': ΑΞΙΩΜΑΤΙΚΟΙ ΠΟΛΕΜΙΚΟΥ ΝΑΥΤΙΚΟΥ '8': ΣΧΕΔΙΑ ΠΟΛΕΩΝ '9': ΣΥΚΑ '10': ΠΡΟΛΗΨΙΣ ΚΑΙ ΔΙΩΞΙΣ ΤΟΥ ΕΓΚΛΗΜΑΤΟΣ '11': ΔΙΕΘΝΕΙΣ ΜΕΤΑΦΟΡΕΣ '12': ΓΕΝΙΚΗ ΣΥΓΚΟΙΝΩΝΙΑ ΚΑΙ ΔΙΑΤΑΞΕΙΣ '13': ΚΛΗΡΟΝΟΜΙΚΟ ΔΙΚΑΙΟ '14': ΚΟΙΝΩΝΙΚΗ ΑΝΤΙΛΗΨΗ '15': ΝΑΥΤΙΛΙΑΚΕΣ ΣΗΜΑΝΣΕΙΣ '16': ΔΙΕΘΝΕΣ ΠΟΙΝΙΚΟ ΔΙΚΑΙΟ '17': ΑΣΦΑΛΙΣΤΙΚΟΙ ΟΡΓΑΝΙΣΜΟΙ Ε.Ν '18': ΣΩΜΑΤΙΚΗ ΑΓΩΓΗ '19': ΣΠΟΡΟΠΑΡΑΓΩΓΗ '20': ΥΠΗΡΕΣΙΑΙ ΔΗΜΟΣΙΩΝ ΕΡΓΩΝ '21': ΤΑΜΕΙΑ ΣΥΝΤΑΞΕΩΝ ΤΡΑΠΕΖΩΝ '22': ΠΥΡΟΣΒΕΣΤΙΚΟ ΣΩΜΑ '23': ΔΙΑΦΟΡΕΣ ΒΙΟΜΗΧΑΝΙΕΣ '24': ΕΚΤΕΛΕΣΗ ΚΑΙ ΣΥΝΕΠΕΙΕΣ ΤΗΣ ΠΟΙΝΗΣ '25': ΔΙΕΘΝΕΙΣ ΑΣΦΑΛΙΣΤΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '26': ΝΟΜΟΘΕΣΙΑ '27': ΒΑΜΒΑΚΙ '28': ΠΑΡΟΧΕΣ ΣΥΝΤΑΞΕΩΝ '29': ΝΟΜΙΣΜΑ '30': ΣΥΜΒΑΣΗ ΝΑΥΤΙΚΗΣ ΕΡΓΑΣΙΑΣ '31': ΟΡΓΑΝΙΣΜΟΊ ΚΟΙΝΩΝΙΚΉΣ ΑΣΦΑΛΊΣΕΩΣ '32': ΑΓΡΟΤΙΚΗ ΑΣΦΑΛΕΙΑ '33': ΥΓΕΙΟΝΟΜΙΚΟΣ ΕΛΕΓΧΟΣ ΕΙΣΕΡΧΟΜΕΝΩΝ '34': ΜΟΥΣΕΙΑ ΚΑΙ ΣΥΛΛΟΓΕΣ '35': ΠΡΟΣΩΠΙΚΟ Ι.Κ.Α '36': ΞΕΝΟΔΟΧΕΙΑ '37': ΚΡΑΤΙΚΗ ΑΣΦΑΛΕΙΑ '38': ΣΥΝΕΤΑΙΡΙΣΜΟΙ '39': ΠΟΛΥΕΘΝΕΙΣ ΣΥΜΦΩΝΙΕΣ '40': ΕΤΕΡΟΔΟΞΟΙ '41': ΜΕΣΗ ΕΚΠΑΙΔΕΥΣΙΣ '42': ΓΕΩΡΓΙΚΟΙ ΟΡΓΑΝΙΣΜΟΙ '43': ΓΕΝΙΚΟ ΛΟΓΙΣΤΗΡΙΟ '44': ΡΥΘΜΙΣΗ ΤΗΣ ΑΓΟΡΑΣ ΕΡΓΑΣΙΑΣ '45': ΠΑΡΟΧΟΙ ΚΙΝΗΤΩΝ ΤΗΛΕΠΙΚΟΙΝΩΝΙΩΝ '46': ΕΜΠΡΑΓΜΑΤΟΣ ΑΣΦΑΛΕΙΑ '47': ΦΟΡΟΛΟΓΙΑ ΑΚΑΘΑΡΙΣΤΟΥ ΠΡΟΣΟΔΟΥ '48': ΚΤΗΜΑΤΙΚΕΣ ΤΡΑΠΕΖΕΣ '49': ΣΤΑΤΙΣΤΙΚΗ '50': ΚΕΡΑΙΕΣ – ΣΤΑΘΜΟΙ ΚΕΡΑΙΩΝ '51': ΠΟΙΝΙΚΟΣ ΝΟΜΟΣ '52': ΜΕΣΑ ΔΙΔΑΣΚΑΛΙΑΣ '53': ΕΜΠΟΡΙΟ ΦΑΡΜΑΚΩΝ '54': ΔΙΑΦΟΡΑ '55': ΔΗΜΟΣΙΑ ΚΤΗΜΑΤΑ '56': ΕΙΣΦΟΡΕΣ Ι.Κ.Α '57': ΚΑΤΑΓΓΕΛΙΑ ΣΥΜΒΑΣΕΩΣ ΕΡΓΑΣΙΑΣ '58': ΠΡΟΣΩΠΙΚΟ ΠΟΛΙΤΙΚΗΣ ΑΕΡΟΠΟΡΙΑΣ '59': ΔΗΜΟΣΙΟ ΧΡΕΟΣ '60': ΑΠΟΤΑΜΙΕΥΣΗ '61': ΑΛΛΟΘΡΗΣΚΟΙ '62': ΠΛΟΗΓΙΚΗ ΥΠΗΡΕΣΙΑ '63': ΤΥΠΟΣ ΚΑΙ ΠΛΗΡΟΦΟΡΙΕΣ '64': ΤΡΟΠΟΠΟΙΗΣΗ ΚΑΙ ΚΑΤΑΡΓΗΣΗ ΤΗΣ ΠΟΙΝΗΣ '65': ΑΣΦΑΛΙΣΤΙΚΑ ΤΑΜΕΙΑ ΤΥΠΟΥ '66': ΟΙΚΟΓΕΝΕΙΑΚΟ ΔΙΚΑΙΟ '67': ΔΙΟΙΚΗΣΗ ΕΘΝΙΚΗΣ ΟΙΚΟΝΟΜΙΑΣ '68': ΥΠΟΥΡΓΕΙΟ ΕΘΝΙΚΗΣ ΑΜΥΝΑΣ '69': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ ΠΡΟΝΟΙΑΣ '70': ΠΡΟΣΩΠΙΚΟ ΤΩΝ ΔΙΚΑΣΤΗΡΙΩΝ '71': ΠΡΟΣΤΑΣΙΑ ΠΡΟΣΩΠΩΝ ΕΙΔΙΚΩΝ ΚΑΤΗΓΟΡΙΩΝ '72': ΠΑΡΟΧΕΣ ΑΣΘΕΝΕΙΑΣ '73': ΜΕΤΑΝΑΣΤΕΥΣΗ '74': ΥΠΟΥΡΓΕΙΟ ΠΑΙΔΕΙΑΣ '75': ΑΣΦΑΛΕΙΑ ΝΑΥΣΙΠΛΟΪΑΣ '76': ΟΔΟΠΟΙΪΑ '77': ΣΤΡΑΤΟΔΙΚΕΙΑ '78': ΜΙΣΘΩΣΗ '79': ΕΙΣΠΡΑΞΗ ΔΗΜΟΣΙΩΝ ΕΣΟΔΩΝ '80': ΟΠΛΙΤΕΣ ΚΑΙ ΑΝΘΥΠΑΣΠΙΣΤΕΣ '81': ΟΡΓΑΝΙΣΜΟΣ ΤΗΛΕΠΙΚΟΙΝΩΝΙΩΝ ΕΛΛΑΔΑΣ (Ο.Τ.Ε.) '82': ΌΡΓΑΝΑ ΆΣΚΗΣΗΣ ΔΙΑΧΕΙΡΙΣΤΙΚΟΎ ΕΛΈΓΧΟΥ ΟΡΓΑΝΙΣΜΏΝ ΚΑΙ ΕΠΙΧΕΙΡΉΣΕΩΝ '83': ΠΟΙΝΙΚΗ ΝΟΜΟΘΕΣΙΑ ΤΥΠΟΥ '84': ΕΞΑΓΩΓΙΚΟ ΕΜΠΟΡΙΟ '85': ΑΕΡΟΠΟΡΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '86': ΓΕΩΡΓΙΚΟΙ ΣΥΝΕΤΑΙΡΙΣΜΟΙ ΑΓΡΟΤΙΚΕΣ ΣΥΝΕΤΑΙΡΙΣΤΙΚΕΣ ΟΡΓΑΝΩΣΕΙΣ '87': ΟΙΚΟΝΟΜΙΚΕΣ ΥΠΗΡΕΣΙΕΣ '88': ΟΧΥΡΩΣΕΙΣ '89': ΕΚΤΑΚΤΟΙ ΠΟΙΝΙΚΟΙ ΝΟΜΟΙ '90': ΕΚΤΕΛΕΣΗ '91': ΔΙΟΙΚΗΤΙΚΟΙ ΚΑΝΟΝΙΣΜΟΙ '92': ΥΔΡΑΥΛΙΚΑ ΕΡΓΑ '93': ΑΣΦΑΛΙΣΤΙΚΑ ΤΑΜΕΙΑ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ '94': ΕΚΚΑΘΑΡΙΣΕΙΣ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ '95': ΔΙΟΙΚΗΣΗ ΕΜΠΟΡΙΚΟΥ ΝΑΥΤΙΚΟΥ '96': ΑΝΩΤΑΤΟ ΕΙΔΙΚΟ ΔΙΚΑΣΤΗΡΙΟ '97': ΑΡΤΟΣ '98': ΕΙΣΑΓΩΓΙΚΟ ΕΜΠΟΡΙΟ '99': ΑΛΙΕΙΑ '100': ΕΚΚΛΗΣΙΑΣΤΙΚΗ ΠΕΡΙΟΥΣΙΑ '101': ΔΙΑΦΟΡΑ ΔΗΜΟΣΙΑ ΕΡΓΑ '102': ΜΟΝΕΣ '103': ΠΡΟΕΔΡΟΣ ΤΗΣ ΔΗΜΟΚΡΑΤΙΑΣ ΚΑΙ ΠΡΟΕΔΡΙΑ ΤΗΣ ΔΗΜΟΚΡΑΤΙΑΣ '104': ΠΟΛΥΕΘΝΕΙΣ ΟΡΓΑΝΙΣΜΟΙ '105': ΑΡΧΑΙΟΤΗΤΕΣ '106': ΝΑΟΙ ΚΑΙ ΛΕΙΤΟΥΡΓΟΙ ΑΥΤΩΝ '107': ΕΚΚΛΗΣΙΑΣΤΙΚΗ ΕΚΠΑΙΔΕΥΣΗ '108': ΕΝΙΣΧΥΣΙΣ ΤΗΣ ΓΕΩΡΓΙΑΣ '109': ΕΚΘΕΣΕΙΣ '110': ΠΡΟΣΤΑΣΙΑ ΤΩΝ ΣΥΝΑΛΛΑΓΩΝ '111': ΑΣΦΑΛΙΣΗ '112': ΚΤΗΝΟΤΡΟΦΙΑ '113': ΕΚΠΑΙΔΕΥΤΙΚΑ ΤΕΛΗ '114': ΔΙΟΙΚΗΣΗ ΕΚΠΑΙΔΕΥΣΕΩΣ '115': ΤΑΜΕΙΟ ΠΑΡΑΚΑΤΑΘΗΚΩΝ ΚΑΙ ΔΑΝΕΙΩΝ '116': ΑΓΑΘΟΕΡΓΑ ΙΔΡΥΜΑΤΑ '117': ΦΟΡΟΛΟΓΙΚΑ ΔΙΚΑΣΤΗΡΙΑ '118': ΦΟΡΟΙ ΚΑΤΑΝΑΛΩΣΕΩΣ '119': ΒΙΒΛΙΟΘΗΚΕΣ-ΠΡΟΣΤΑΣΙΑ ΒΙΒΛΙΟΥ-ΔΙΑΔΟΣΗ ΛΟΓΟΤΕΧΝΙΑΣ '120': ΤΗΛΕΠΙΚΟΙΝΩΝΙΑΚΕΣ ΚΑΙ ΤΑΧΥΔΡΟΜΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '121': ΙΔΙΩΤΙΚΗ ΕΚΠΑΙΔΕΥΣΗ '122': ΤΗΛΕΠΙΚΟΙΝΩΝΙΕΣ '123': ΑΣΥΡΜΑΤΟΣ '124': ΑΠΟΔΟΧΕΣ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΩΝ '125': ΥΓΕΙΟΝΟΜΙΚΗ ΥΠΗΡΕΣΙΑ ΣΤΡΑΤΟΥ '126': ΦΑΡΜΑΚΕΙΑ '127': ΔΗΜΟΣΙΟ ΛΟΓΙΣΤΙΚΟ '128': ΝΑΥΤΙΚΗ ΕΚΠΑΙΔΕΥΣΗ '129': ΕΞΥΠΗΡΕΤΗΣΗ ΠΟΛΙΤΙΚΗΣ ΑΕΡΟΠΟΡΙΑΣ '130': ΠΑΡΟΧΕΣ Ι.Κ.Α '131': ΓΕΝΙΚΑ ΥΓΕΙΟΝΟΜΙΚΑ ΜΕΤΡΑ '132': ΕΚΜΕΤΑΛΛΕΥΣΗ ΘΑΛΑΣΣΙΩΝ ΣΥΓΚΟΙΝΩΝΙΩΝ '133': ΠΡΟΣΩΠΙΚΟ ΤΑΧΥΔΡΟΜΕΙΩΝ '134': ΕΚΤΕΛΕΣΤΙΚΗ ΕΞΟΥΣΙΑ '135': ΣΥΣΤΑΣΗ ΚΑΙ ΕΔΡΑ ΤΟΥ ΚΡΑΤΟΥΣ '136': ΦΟΡΟΛΟΓΙΑ ΔΙΑΣΚΕΔΑΣΕΩΝ '137': ΤΗΛΕΦΩΝΑ '138': ΣΤΡΑΤΟΛΟΓΙΑ '139': ΕΚΠΑΙΔΕΥΣΗ ΕΡΓΑΤΩΝ '140': ΥΠΟΥΡΓΕΙΟ ΠΟΛΙΤΙΣΜΟΥ '141': ΦΟΡΟΛΟΓΙΑ ΟΙΝΟΠΝΕΥΜΑΤΩΔΩΝ ΠΟΤΩΝ '142': ΥΠΟΥΡΓΕΙΟ ΓΕΩΡΓΙΑΣ '143': ΣΩΜΑΤΕΙΑ '144': ΕΙΔΙΚΕΣ ΜΟΡΦΕΣ ΑΠΑΣΧΟΛΗΣΗΣ '145': ΥΠΟΥΡΓΕΙΟ ΔΙΚΑΙΟΣΥΝΗΣ '146': ΝΑΥΤΙΛΙΑΚΟΙ ΟΡΓΑΝΙΣΜΟΙ '147': ΤΟΥΡΙΣΜΟΣ '148': ΚΑΠΝΟΣ '149': ΠΡΟΣΤΑΣΙΑ ΗΘΩΝ '150': ΕΙΔΙΚΕΣ ΥΠΗΡΕΣΙΕΣ ΝΑΥΤΙΚΟΥ '151': ΑΠΟΔΟΧΕΣ ΣΤΡΑΤΙΩΤΙΚΩΝ '152': ΠΡΟΝΟΙΑ ΠΛΗΡΩΜΑΤΩΝ Ε.Ν '153': ΕΙΔΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΠΕΡΙ ΑΝΩΝ.ΕΤΑΙΡΕΙΩΝ '154': ΔΗΜΟΣΙΑ ΔΙΟΙΚΗΣΗ '155': ΤΟΠΙΚΑ ΣΧΕΔΙΑ ΠΟΛΕΩΝ '156': ΠΡΟΣΤΑΣΙΑ ΠΑΙΔΙΚΗΣ ΗΛΙΚΙΑΣ '157': ΕΛΛΗΝΙΚΗ ΑΣΤΥΝΟΜΙΑ '158': ΛΙΜΕΝΙΚΟ ΣΩΜΑ '159': ΤΟΥΡΙΣΤΙΚΗ ΑΣΤΥΝΟΜΙΑ '160': ΒΙΟΜΗΧΑΝΙΑ '161': ΣΧΟΛΕΣ ΠΑΝΕΠΙΣΤΗΜΙΟΥ ΑΘΗΝΩΝ '162': ΑΣΦΑΛΙΣΤΙΚΟΙ ΟΡΓΑΝΙΣΜΟΙ ΣΤΡΑΤΟΥ '163': ΑΛΥΚΕΣ '164': ΕΣΩΤΕΡΙΚΟ ΕΜΠΟΡΙΟ '165': ΕΘΝΙΚΟ ΣΥΣΤΗΜΑ ΥΓΕΙΑΣ '166': ΝΟΜΟΘΕΤΙΚΗ ΕΞΟΥΣΙΑ '167': ΔΙΟΙΚΗΣH ΚΟΙΝΩΝIKΗΣ ΠΡΟΝΟΙΑΣ '168': ΠΛΗΡΩΜΑΤΑ '169': ΜΑΘΗΤΙΚΗ ΠΡΟΝΟΙΑ '170': ΔΙΟΙΚΗΣΗ ΤΥΠΟΥ ΚΑΙ ΤΟΥΡΙΣΜΟΥ '171': ΕΠΟΙΚΙΣΜΟΣ '172': ΤΡΟΧΙΟΔΡΟΜΟΙ '173': ΕΠΑΓΓΕΛΜΑΤΙΚΗ ΕΚΠΑΙΔΕΥΣΗ '174': ΑΕΡΟΠΟΡΙΚΗ ΕΚΠΑΙΔΕΥΣΗ '175': ΥΠΟΥΡΓΕΙΟ ΕΘΝΙΚΗΣ ΟΙΚΟΝΟΜΙΑΣ '176': ΘΕΑΤΡΟ '177': ΥΔΡΕΥΣΗ '178': ΔΙΕΘΝΕΙΣ ΣΤΡΑΤΙΩΤΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '179': ΕΘΝΙΚΟ ΜΕΤΣΟΒΙΟ ΠΟΛΥΤΕΧΝΕΙΟ '180': ΥΠΟΥΡΓΕΙΟ ΕΞΩΤΕΡΙΚΩΝ '181': ΕΥΡΩΠΑΪΚΟΙ ΠΟΛΥΕΘΝΕΙΣ ΟΡΓΑΝΙΣΜΟΙ '182': ΕΛΕΥΘΕΡΙΑ ΤΗΣ ΕΡΓΑΣΙΑΣ '183': ΥΠΟΥΡΓΕΙΟ ΕΣΩΤΕΡΙΚΩΝ ΔΗΜ.ΔΙΟΙΚΗΣΗΣ ΚΑΙ ΑΠΟΚΕΝΤΡΩΣΗΣ '184': ΔΙΑΦΟΡΕΣ ΕΝΟΧΙΚΕΣ ΣΧΕΣΕΙΣ '185': ΛΗΞΙΑΡΧΕΙΑ '186': ΕΙΔΙΚΟΙ ΚΑΝΟΝΙΣΜΟΙ '187': ΤΕΛΩΝΕΙΑΚΕΣ ΣΥΜΒΑΣΕΙΣ '188': ΝΑΥΤΙΚΟ ΠΟΙΝΙΚΟ ΔΙΚΑΙΟ '189': ΣΤΕΓΑΣΗ ΔΗΜΟΣΙΩΝ ΥΠΗΡΕΣΙΩΝ '190': ΠΛΗΡΩΜΑΤΑ ΠΟΛΕΜΙΚΟΥ ΝΑΥΤΙΚΟΥ '191': ΣΥΝΤΑΓΜΑΤΙΚΟΣ ΧΑΡΤΗΣ '192': ΗΛΕΚΤΡΙΣΜΟΣ '193': ΑΣΦΑΛΙΣΤΙΚΑ ΔΙΚΑΣΤΗΡΙΑ '194': ΛΕΣΧΕΣ ΕΛΛΗΝΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ '195': ΥΠΟΥΡΓΕΙΟ ΔΗΜΟΣΙΑΣ TAΞΗΣ '196': ΕΚΤΕΛΕΣ ΔΗΜΟΣΙΩΝ ΕΡΓΩΝ '197': ΠΑΝΕΠΙΣΤΗΜΙΟ ΘΕΣΣΑΛΟΝΙΚΗΣ '198': ΔΑΣΙΚΗ ΝΟΜΟΘΕΣΙΑ '199': ΕΙΔΙΚΕΣ ΑΝΩΤΑΤΕΣ ΣΧΟΛΕΣ '200': ΕΔΑΦΟΣ ΤΟΥ ΕΛΛΗΝΙΚΟΥ ΚΡΑΤΟΥΣ '201': ΔΙΚΗΓΟΡΟΙ '202': ΔΙΚΑΙΟ ΤΩΝ ΠΡΟΣΩΠΩΝ '203': ΔΙΟΙΚΗΣΗ ΤΑΧΥΔΡΟΜΙΚΗΣ, ΤΗΛΕΓΡΑΦΙΚΗΣ '204': ΣΧΟΛΙΚΑ ΚΤΙΡΙΑ ΚΑΙ ΤΑΜΕΙΑ '205': ΑΕΡΟΛΙΜΕΝΕΣ '206': ΥΠΟΘΗΚΟΦΥΛΑΚΕΙΑ '207': ΑΣΦΑΛΙΣΤΙΚΑ ΤΑΜΕΙΑ ΠΡΟΣΩΠΙΚΟΥ ΥΠΟΥΡΓΕΙΟΥ ΔΗΜΟΣΙΑΣ ΤΑΞΗΣ '208': ΔΙΑΧΕΙΡΙΣΕΙΣ ΤΟΥ ΔΗΜΟΣΙΟΥ '209': ΕΜΠΡΑΓΜΑΤΟ ΔΙΚΑΙΟ '210': ΦΟΡΤΟΕΚΦΟΡΤΩΣΕΙΣ '211': ΑΝΩΝΥΜΕΣ ΕΤΑΙΡΕΙΕΣ '212': ΕΙΔΙΚΟΙ ΕΠΙΣΙΤΙΣΤΙΚΟΙ ΝΟΜΟΙ '213': ΕΚΚΛΗΣΙΕΣ ΑΛΛΟΔΑΠΗΣ '214': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ '215': ΟΡΓΑΝΙΣΜΟΣ ΑΣΦΑΛΙΣΗΣ ΕΛΕΥΘΕΡΩΝ ΕΠΑΓΓΕΛΜΑΤΙΩΝ '216': ΑΣΦΑΛΕΙΑ ΑΕΡΟΠΛΟΪΑΣ '217': ΤΑΜΕΙΑ ΑΣΦΑΛΙΣΕΩΣ ΚΑΙ ΑΡΩΓΗΣ '218': ΑΝΩΤΑΤΗ ΕΚΠΑΙΔΕΥΣΗ '219': ΠΟΛΕΜΙΚΗ ΔΙΑΘΕΣΙΜΟΤΗΤΑ '220': ΠΟΙΝΙΚΟ ΚΑΙ ΠΕΙΘΑΡΧΙΚΟ ΔΙΚΑΙΟ '221': ΦΟΡΟΛΟΓΙΑ ΕΠΙΤΗΔΕΥΜΑΤΟΣ '222': ΕΚΤΑΚΤΕΣ ΦΟΡΟΛΟΓΙΕΣ '223': ΠΟΙΝΙΚΗ ΔΙΚΟΝΟΜΙΑ '224': ΣΤΟΙΧΕΙΩΔΗΣ ΕΚΠΑΙΔΕΥΣΗ '225': ΣΥΜΒΟΥΛΙΟ ΕΠΙΚΡΑΤΕΙΑΣ ΚΑΙ ΔΙΟΙΚΗΤΙΚΑ ΔΙΚΑΣΤΗΡΙΑ '226': ΝΟΜΙΚΑ ΠΡΟΣΩΠΑ ΚΑΙ ΕΚΜΕΤΑΛΛΕΥΣΕΙΣ '227': ΟΙΚΟΝΟΜΙΚΗ ΔΙΟΙΚΗΣΗ ΝΑΥΤΙΚΟΥ '228': ΤΥΠΟΣ '229': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΕΠΑΓΓΕΛΜΑΤΙΩΝ '230': ΠΑΝΕΠΙΣΤΗΜΙΟ ΙΩΑΝΝΙΝΩΝ '231': ΧΡΕΩΓΡΑΦΑ '232': ΠΡΟΪΟΝΤΑ ΕΛΑΙΑΣ '233': ΕΚΚΛΗΣΙΑ ΙΟΝΙΩΝ ΝΗΣΩΝ '234': ΔΙΟΙΚΗΣH ΥΓΙΕΙΝΗΣ '235': ΑΕΡΟΠΟΡΙΚΟ ΠΟΙΝΙΚΟ ΔΙΚΑΙΟ '236': ΚΑΤΑΠΟΛΕΜΗΣΗ ΝΟΣΩΝ ΚΑΤ’ ΙΔΙΑΝ '237': ΕΙΔΙΚΟΙ ΠΟΙΝΙΚΟΙ ΝΟΜΟΙ '238': ΘΗΡΑ '239': ΥΓΙΕΙΝΗ ΚΑΙ ΑΣΦΑΛΕΙΑ ΕΡΓΑΖΟΜΕΝΩΝ '240': ΔΙΟΙΚΗΣΗ ΣΥΓΚΟΙΝΩΝΙΩΝ '241': ΑΠΟΣΤΟΛΙΚΗ ΔΙΑΚΟΝΙΑ ΕΚΚΛΗΣΙΑΣ ΤΗΣ ΕΛΛΑΔΟΣ '242': ΠΡΟΣΩΡΙΝΕΣ ΑΤΕΛΕΙΕΣ '243': ΤΑΧΥΔΡΟΜΙΚΑ ΤΑΜΙΕΥΤΗΡΙΑ '244': ΑΝΩΤΑΤΗ ΣΧΟΛΗ ΚΑΛΩΝ ΤΕΧΝΩΝ '245': ΔΙΟΙΚΗΣΗ ΕΡΓΑΣΙΑΣ '246': ΑΓΙΟΝ ΟΡΟΣ '247': ΣΧΟΛΕΣ Π. ΝΑΥΤΙΚΟΥ '248': ΤΡΑΠΕΖΕΣ '249': ΕΛΕΓΧΟΣ ΚΙΝΗΣΕΩΣ ΜΕ ΤΟ ΕΞΩΤΕΡΙΚΟ '250': ΕΙΔΙΚΑΙ ΚΑΤΗΓΟΡΙΑΙ ΠΛΟΙΩΝ '251': ΓΕΩΡΓΙΚΗ ΥΓΙΕΙΝΗ '252': ΕΞΟΔΑ ΠΟΙΝΙΚΗΣ ΔΙΑΔΙΚΑΣΙΑΣ '253': ΕΡΓΑΣΙΑ ΓΥΝΑΙΚΩΝ ΚΑΙ ΑΝΗΛΙΚΩΝ '254': ΔΙΟΙΚΗΣΗ ΕΦΟΔΙΑΣΜΟΥ '255': ΕΜΠΟΡΙΚΑ ΕΠΑΓΓΕΛΜΑΤΑ '256': ΕΚΤΕΛΩΝΙΣΤΕΣ '257': ΦΟΡΟΛΟΓΙΑ ΚΛΗΡΟΝΟΜΙΩΝ, ΔΩΡΕΩΝ ΚΛΠ '258': ΟΡΓΑΝΙΣΜΟΙ ΥΠΟΥΡΓΕΙΟΥ ΕΡΓΑΣΙΑΣ '259': ΕΝΙΣΧΥΣΗ ΕΠΙΣΤΗΜΩΝ ΚΑΙ ΤΕΧΝΩΝ '260': ΔΙΑΦΟΡΟΙ ΦΟΡΟΛΟΓΙΚΟΙ ΝΟΜΟΙ '261': ΤΕΧΝΙΚΕΣ ΠΡΟΔΙΑΓΡΑΦΕΣ '262': ΜΗΤΡΩΑ ΔΗΜΟΤΩΝ '263': ΚΑΤΑΣΤΑΣΗ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ '264': ΠΡΟΣΩΠΙΚΟΝ ΔΗΜΩΝ ΚΑΙ ΚΟΙΝΟΤΗΤΩΝ '265': ΥΓΕΙΟΝΟΜΙΚΗ ΑΝΤΙΛΗΨΗ '266': ΤΕΛΗ ΧΑΡΤΟΣΗΜΟΥ '267': ΣΤΡΑΤΙΩΤΙΚΟΙ ΓΕΝΙΚΑ '268': ΛΙΜΕΝΙΚΕΣ ΑΡΧΕΣ '269': ΕΛΕΓΧΟΣ ΚΥΚΛΟΦΟΡΙΑΣ '270': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΣ ΚΑΙ ΑΥΤΑΣΦΑΛΙΣΕΩΣ ΥΓΕΙΟΝΟΜΙΚΩΝ '271': ΠΟΛΙΤΙΚΗ ΚΑΙ ΟΙΚΟΝΟΜΙΚΗ ΕΠΙΣΤΡΑΤΕΥΣΗ '272': ΤΗΛΕΓΡΑΦΟΙ '273': ΣΕΙΣΜΟΠΛΗΚΤΟΙ '274': ΙΑΜΑΤΙΚΕΣ ΠΗΓΕΣ '275': ΙΔΙΩΤΙΚΟ ΝΑΥΤΙΚΟ ΔΙΚΑΙΟ '276': ΔΙΕΘΝΕΙΣ ΥΓΕΙΟΝΟΜΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '277': ΝΟΜΙΚΑ ΠΡΟΣΩΠΑ ΔΗΜΟΣΙΟΥ ΔΙΚΑΙΟΥ '278': ΕΚΚΛΗΣΙΑ ΚΡΗΤΗΣ '279': ΠΡΟΣΤΑΣΙΑ ΝΟΜΙΣΜΑΤΟΣ '280': ΠΡΟΣΤΑΣΙΑ ΠΡΟΪΟΝΤΩΝ ΑΜΠΕΛΟΥ '281': ΑΝΑΠΗΡΟΙ ΚΑΙ ΘΥΜΑΤΑ ΠΟΛΕΜΟΥ '282': ΠΑΡΟΧΕΣ ΔΙΑΦΟΡΕΣ '283': ΤΟΠΙΚΗ ΑΥΤΟΔΙΟΙΚΗΣΗ '284': OΡΓΑΝΩΣΗ ΣΤΡΑΤΟΥ ΞΗΡΑΣ '285': ΔΙΑΚΟΠΕΣ ΤΗΣ ΕΡΓΑΣΙΑΣ '286': ΟΡΓΑΝΙΣΜΟΣ ΠΟΛΕΜΙΚΗΣ ΑΕΡΟΠΟΡΙΑΣ '287': ΕΠΙΜΕΛΗΤΗΡΙΑ '288': ΕΚΚΛΗΣΙΑ ΤΗΣ ΕΛΛΑΔΟΣ '289': ΝΑΡΚΩΤΙΚΑ '290': ΕΚΜΕΤΑΛΛΕΥΣΗ ΤΑΧΥΔΡΟΜΕΙΩΝ '291': ΜΟΥΣΙΚΗ '292': ΝΟΜΑΡΧΙΕΣ '293': ΠΡΟΣΩΠΙΚΟ ΠΟΛΕΜΙΚΟΥ ΝΑΥΤΙΚΟΥ '294': ΓΕΝΙΚΟ ΧΗΜΕΙΟ ΤΟΥ ΚΡΑΤΟΥΣ '295': ΚΡΑΤΙΚΗ '296': ΔΙΟΙΚΗΣΗ ΠΟΛΕΜΙΚΟΥ ΝΑΥΤΙΚΟΥ '297': ΠΑΡΟΧΟΙ ΣΤΑΘΕΡΩΝ ΗΛΕΚΤΡΟΝΙΚΩΝ ΕΠΙΚΟΙΝΩΝΙΩΝ '298': ΕΠΑΓΓΕΛΜΑΤΙΚΟΣ ΚΙΝΔΥΝΟΣ '299': ΕΝΟΧΕΣ ΣΕ ΧΡΥΣΟ ΚΑΙ ΣΥΝΑΛΛΑΓΜΑ '300': ΙΠΠΟΠΑΡΑΓΩΓΗ '301': ΑΥΤΟΚΙΝΗΤΑ '302': ΑΓΟΡΑΝΟΜΙΚΕΣ ΔΙΑΤΑΞΕΙΣ '303': ΠΡΟΣΦΥΓΕΣ '304': ΔΙΑΦΟΡΑ ΣΤΡΑΤΙΩΤΙΚΑ ΘΕΜΑΤΑ '305': ΓΕΝ. ΓΡΑΜΜ. ΒΙΟΜΗΧΑΝΙΑΣ - ΓΕΝ. ΓΡΑΜΜ. ΕΡΕΥΝΑΣ ΚΑΙ ΤΕΧΝΟΛΟΓΙΑΣ '306': ΔΙΑΜΕΤΑΚΟΜΙΣΗ '307': ΔΙΚΑΙΟΣΤΑΣΙΟ '308': ΥΔΑΤΑ '309': ΦΟΡΟΛΟΓΙΚΕΣ ΔΙΕΥΚΟΛΥΝΣΕΙΣ ΚΑΙ ΑΠΑΛΛΑΓΕΣ '310': ΜΟΝΟΠΩΛΙΑ '311': ΕΙΔΙΚΕΣ ΔΙΑΔΙΚΑΣΙΕΣ '312': ΠΡΟΝΟΙΑ ΓΙΑ ΤΟΥΣ ΣΤΡΑΤΙΩΤΙΚΟΥΣ '313': ΠΟΛΙΤΙΚΗ ΔΙΚΟΝΟΜΙΑ '314': ΟΡΓΑΝΩΣΗ ΧΡΟΝΟΥ ΕΡΓΑΣΙΑΣ '315': ΠΡΟΣΩΠΙΚΟ ΤΥΠΟΥ '316': ΔΙΚΑΣΤΙΚΟΙ ΕΠΙΜΕΛΗΤΕΣ '317': ΛΟΥΤΡΟΠΟΛΕΙΣ '318': ΤΕΛΩΝΕΙΑΚΟΣ ΚΩΔΙΚΑΣ '319': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΝΟΜΙΚΩΝ '320': ΔΙΑΦΟΡΟΙ ΤΕΛΩΝΕΙΑΚΟΙ ΝΟΜΟΙ '321': ΔΙΟΙΚΗΣΗ ΠΟΛΙΤΙΚΗΣ ΑΕΡΟΠΟΡΙΑΣ '322': ΑΕΡΟΠΟΡΙΚΕΣ ΕΚΜΕΤΑΛΛΕΥΣΕΙΣ '323': ΕΜΠΟΡΙΚΕΣ ΠΡΑΞΕΙΣ '324': ΔΙΚΑΣΤΗΡΙΑ '325': ΒΑΣΙΛΕΙΑ ΚΑΙ ΑΝΤΙΒΑΣΙΛΕΙΑ '326': ΠΡΟΣΩΠΙΚΟ ΠΟΛΕΜΙΚΗΣ ΑΕΡΟΠΟΡΙΑΣ '327': ΠΡΟΣΤΑΣΙΑ ΚΑΙ ΚΙΝΗΤΡΑ ΙΔΙΩΤΙΚΩΝ ΕΠΕΝΔΥΣΕΩΝ '328': ΒΑΣΙΛΙΚΑ ΙΔΡΥΜΑΤΑ '329': ΣΙΔΗΡΟΔΡΟΜΟΙ ΓΕΝΙΚΑ '330': ΠΝΕΥΜΑΤΙΚΗ ΙΔΙΟΚΤΗΣΙΑ '331': ΔΙΑΦΟΡΑ ΑΣΦΑΛΙΣΤΙΚΑ ΤΑΜΕΙΑ '332': ΥΓΕΙΟΝΟΜΙΚΑ ΕΠΑΓΓΕΛΜΑΤΑ '333': ΦΟΡΟΛΟΓΙΑ ΚΑΠΝΟΥ '334': ΟΙΚΟΝΟΜΙΚΗ ΔΙΟΙΚΗΣΗ '335': ΧΩΡΟΦΥΛΑΚΗ '336': ΤΕΛΩΝΕΙΑΚΗ ΥΠΗΡΕΣΙΑ '337': ΠΑΝΕΠΙΣΤΗΜΙΟ ΠΑΤΡΩΝ '338': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΑΣΦΑΛΙΣΤΩΝ '339': ΑΣΦΑΛΙΣΤΙΚΟΙ ΟΡΓΑΝΙΣΜΟΙ '340': ΣΤΡΑΤΙΩΤΙΚΑ ΕΡΓΑ ΚΑΙ ΠΡΟΜΗΘΕΙΕΣ '341': ΥΠΟΝΟΜΟΙ '342': ΦΟΡΟΛΟΓΙΑ ΚΕΦΑΛΑΙΟΥ '343': ΕΤΑΙΡΕΙΕΣ ΠΕΡΙΩΡΙΣΜΕΝΗΣ ΕΥΘΥΝΗΣ '344': ΥΠΟΥΡΓΕΊΟ ΚΟΙΝΩΝΙΚΏΝ ΑΣΦΑΛΊΣΕΩΝ '345': ΣΥΜΒΟΛΑΙΟΓΡΑΦΟΙ '346': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΑΡΤΕΡΓΑΤΩΝ '347': ΕΡΓΑ ΚΑΙ ΠΡΟΜΗΘΕΙΕΣ ΔΗΜΩΝ ΚΑΙ ΚΟΙΝΟΤΗΤΩΝ '348': ΕΛΕΓΚΤΙΚΟ ΣΥΝΕΔΡΙΟ '349': ΔΙΑΦΟΡΑ ΕΠΙΣΤΗΜΟΝΙΚΑ ΙΔΡΥΜΑΤΑ '350': ΑΞΙΩΜΑΤΙΚΟΙ ΕΝΟΠΛΩΝ ΔΥΝΑΜΕΩΝ '351': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΕΜΠΟΡΩΝ (Τ.Α.Ε) '352': ΣΤΡΑΤΙΩΤΙΚΗ ΠΟΙΝΙΚΗ '353': ΦΟΡΟΛΟΓΙΑ ΟΙΝΟΠΝΕΥΜΑΤΟΣ '354': ΟΡΓΑΝΙΣΜΟΣ ΓΕΩΡΓΙΚΩΝ ΑΣΦΑΛΙΣΕΩΝ '355': ΣΥΛΛΟΓΙΚΕΣ ΣΥΜΒΑΣΕΙΣ ΕΡΓΑΣΙΑΣ '356': ΧΡΗΜΑΤΙΣΤΗΡΙΑ '357': ΠΟΛΙΤΙΚΑΙ ΚΑΙ ΣΤΡΑΤΙΩΤΙΚΑΙ ΣΥΝΤΑΞΕΙΣ '358': ΚΟΙΝΩΝΙΚΗ ΣΤΕΓΑΣΤΙΚΗ ΣΥΝΔΡΟΜΗ '359': ΚΑΤΟΧΥΡΩΣΗ ΕΠΑΓΓΕΛΜΑΤΩΝ '360': ΦΟΡΟΛΟΓΙΑ ΚΑΘΑΡΑΣ ΠΡΟΣΟΔΟΥ '361': ΠΕΡΙΦΕΡΕΙΕΣ '362': ΕΚΚΛΗΣΙΑΣΤΙΚΗ ΔΙΚΑΙΟΣΥΝΗ '363': ΥΠΟΥΡΓΕΙΟ ΟΙΚΟΝΟΜΙΚΩΝ '364': ΕΘΝΙΚΑ ΚΛΗΡΟΔΟΤΗΜΑΤΑ '365': ΕΓΓΕΙΟΒΕΛΤΙΩΤΙΚΑ ΕΡΓΑ '366': ΛΙΜΕΝΕΣ '367': ΦΥΛΑΚΕΣ '368': ΓΕΩΡΓΙΚΗ ΕΚΠΑΙΔΕΥΣΗ '369': ΠΛΗΡΩΜΗ ΕΡΓΑΣΙΑΣ '370': ΕΜΠΟΡΙΚΟΣ ΝΟΜΟΣ '371': ΙΔΡΥΜΑ ΚΟΙΝΩΝΙΚΩΝ ΑΣΦΑΛΙΣΕΩΝ '372': ΑΣΦΑΛΙΣΤΙΚΑ ΤΑΜΕΙΑ ΤΡΑΠΕΖΩΝ '373': ΕΙΔΙΚΟΙ ΑΓΡΟΤΙΚΟΙ ΝΟΜΟΙ '374': ΔΙΕΘΝΕΙΣ ΔΙΚΟΝΟΜΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '375': ΥΠΟΥΡΓΕΙΑ ΜΑΚΕΔΟΝΙΑΣ–ΘΡΑΚΗΣ, ΑΙΓΑΙΟΥ Κ.Λ.Π '376': ΑΣΤΥΝΟΜΙΚΟΊ ΣΚΎΛΟΙ '377': ΔΙΑΦΟΡΑ ΘΕΜΑΤΑ '378': ΕΚΔΟΣΗ ΕΓΚΛΗΜΑΤΙΩΝ '379': ΑΓΟΡΑΝΟΜΙΑ '380': ΔΙΚΑΣΤΙΚΟ ΤΟΥ ΔΗΜΟΣΙΟΥ '381': ΑΣΤΙΚΟΣ ΚΩΔΙΚΑΣ '382': ΤΕΛΩΝΕΙΑΚΕΣ ΑΤΕΛΕΙΕΣ '383': ΑΓΡΟΤΙΚΕΣ ΜΙΣΘΩΣΕΙΣ '384': ΛΕΩΦΟΡΕΙΑ '385': ΓΕΝΙΚΟΙ ΕΠΙΣΙΤΙΣΤΙΚΟΙ ΝΟΜΟΙ '386': ΑΣΤΥΝΟΜΙΑ ΠΟΛΕΩΝ '387': ΜΗΧΑΝΙΚΟΙ ΚΑΙ ΕΡΓΟΛΑΒΟΙ '388': ΠΟΛΕΜΙΚΕΣ ΣΥΝΤΑΞΕΙΣ splits: - name: train num_bytes: 216757887 num_examples: 28536 - name: test num_bytes: 71533786 num_examples: 9516 - name: validation num_bytes: 68824457 num_examples: 9511 download_size: 45606292 dataset_size: 357116130 - config_name: subject features: - name: text dtype: string - name: label dtype: class_label: names: '0': ΜΕΤΟΧΙΚΟ ΤΑΜΕΙΟ Π.Ν '1': ΜΕΤΑΝΑΣΤΕΥΣΗ ΣΤΟ ΒΕΛΓΙΟ '2': ΝΑΥΤΙΚΕΣ ΦΥΛΑΚΕΣ '3': ΚΑΝΟΝΙΣΜΟΣ ΕΚΤΕΛΕΣΕΩΣ ΣΤΡΑΤΙΩΤΙΚΩΝ ΕΡΓΩΝ '4': ΔΙΟΙΚΗΤΙΚΗ ΚΑΙ ΟΙΚΟΝΟΜΙΚΗ ΥΠΗΡΕΣΙΑ '5': ΑΣΚΗΣΗ ΠΟΙΝΙΚΗΣ ΑΓΩΓΗΣ '6': ΚΑΝΟΝΙΣΜΟΣ ΕΣΩΤΕΡΙΚΗΣ ΥΠΗΡΕΣΙΑΣ ΕΠΙΒΑΤΗΓΩΝ ΠΛΟΙΩΝ '7': ΚΩΔΙΚΑΣ ΠΟΛΙΤΙΚΗΣ ΔΙΚΟΝΟΜΙΑΣ - ΠΑΛΑΙΟΣ '8': ΚΑΤΑΣΤΑΤΙΚΟ ΤΑΜΕΙΟΥ ΑΣΦΑΛΙΣΕΩΣ ΕΜΠΟΡΩΝ (Τ.Α.Ε) '9': ΜΗΧΑΝΟΛΟΓΟΙ, ΗΛΕΚΤΡΟΛΟΓΟΙ, ΝΑΥΠΗΓΟΙ ΚΑΙ ΜΗΧΑΝΟΔΗΓΟΙ '10': ΣΤΕΓΑΣΗ ΠΑΡΑΠΗΓΜΑΤΟΥΧΩΝ '11': ΝΟΜΙΣΜΑΤΙΚΗ ΕΠΙΤΡΟΠΗ '12': ΠΕΡΙΦΕΡΕΙΑΚΑ ΤΑΜΕΙΑ '13': ΜΗΤΡΩΑ ΑΡΡΕΝΩΝ '14': ΔΙΚΑΣΤΙΚΕΣ ΔΙΑΚΟΠΕΣ '15': ΣΥΜΦΩΝΙΑ ΠΕΡΙ ΠΡΟΞΕΝΙΚΩΝ ΣΧΕΣΕΩΝ '16': ΠΑΛΑΙΟΙ ΑΣΤΙΚΟΙ ΚΩΔΙΚΕΣ '17': ΚΛΑΔΟΣ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΔΙΚΗΓΟΡΩΝ (Κ.Ε.Α.Δ.) '18': ΟΙΚΟΝΟΜΙΚΕΣ ΑΡΜΟΔΙΟΤΗΤΕΣ ΣΤΡΑΤΙΩΤΙΚΩΝ ΑΡΧΩΝ '19': ΥΠΟΝΟΜΟΙ ΘΕΣΣΑΛΟΝΙΚΗΣ '20': ΔΙΑΦΟΡΑ ΥΔΡΑΥΛΙΚΑ ΤΑΜΕΙΑ '21': ΕΛΕΓΧΟΣ ΘΕΑΤΡΙΚΩΝ ΕΡΓΩΝ ΚΑΙ ΔΙΣΚΩΝ '22': ΥΠΗΡΕΣΙΑ ΙΠΠΟΠΑΡΑΓΩΓΗΣ '23': ΣΩΜΑΤΙΚΗ ΑΓΩΓΗ '24': ΕΚΔΙΚΑΣΗ ΤΕΛΩΝΕΙΑΚΩΝ ΠΑΡΑΒΑΣΕΩΝ '25': ΚΙΝΗΤΡΑ ΙΔΙΩΤΙΚΩΝ ΕΠΕΝΔΥΣΕΩΝ ΣΤΗΝ ΠΕΡΙΦΕΡΕΙΑ '26': ΜΕΛΗ ΟΙΚΟΓΕΝΕΙΑΣ ΑΣΦΑΛΙΣΜΕΝΩΝ '27': ΚΕΡΜΑΤΑ '28': ΕΠΙΔΟΜΑ ΑΝΑΠΡΟΣΑΡΜΟΓΗΣ '29': ΕΚΤΕΛΕΣΗ ΔΑΣΙΚΩΝ ΕΡΓΩΝ '30': ΛΙΠΑΣΜΑΤΑ '31': ΕΠΙΧΟΡΗΓΗΣΗ ΣΠΟΥΔΑΣΤΩΝ ΤΕΚΝΩΝ ΕΡΓΑΖΟΜΕΝΩΝ '32': ΠΡΟΣΤΑΣΙΑ ΟΙΝΟΥ '33': ΠΤΗΤΙΚΟ ΚΑΙ ΚΑΤΑΔΥΤΙΚΟ ΕΠΙΔΟΜΑ '34': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΥΠΑΛΛΗΛΩΝ ΕΜΠΟΡΙΚΩΝ ΚΑΤΑΣΤΗΜΑΤΩΝ (Τ.Ε.Α.Υ.Ε.Κ.) '35': ΕΚΚΟΚΚΙΣΗ ΒΑΜΒΑΚΟΣ '36': ΜΟΝΟΠΩΛΙΟ ΚΙΝΙΝΟΥ '37': ΙΝΣΤΙΤΟΥΤΑ ΔΙΕΘΝΟΥΣ ΔΙΚΑΙΟΥ '38': ΙΑΠΩΝΙΑ – ΙΝΔΙΑ –ΙΟΡΔΑΝΙΑ Κ.ΛΠ '39': ΕΠΙΔΟΜΑ ΣΤΟΛΗΣ '40': ΑΝΑΓΝΩΡΙΣΕΙΣ '41': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΕΡΓΟΛΗΠΤΩΝ '42': ΑΝΑΣΤΟΛΗ ΤΗΣ ΠΟΙΝΗΣ '43': ΠΟΤΑΜΟΠΛΟΙΑ '44': ΕΙΔΙΚΗ ΤΕΛΩΝΕΙΑΚΗ ΠΑΡΑΚΟΛΟΥΘΗΣΗ '45': ΕΠΙΘΕΩΡΗΣΗ ΦΑΡΜΑΚΕΙΩΝ '46': ΣΥΝΤΑΞΕΙΣ ΘΥΜΑΤΩΝ ΕΘΝΙΚΩΝ '47': ΑΠΛΟΠΟΙΗΣΗ ΤΕΛΩΝΕΙΑΚΩΝ ΔΙΑΤΥΠΩΣΕΩΝ '48': ΚΛΑΔΟΣ ΑΣΘΕΝΕΙΑΣ Τ.Α.Κ.Ε '49': ΥΠΗΡΕΣΙΑ ΥΠΟΔΟΧΗΣ ΠΛΟΙΩΝ ΚΑΙ ΠΟΛΕΜΙΚΗ ΧΡΗΣΗ ΛΙΜΕΝΩΝ '50': ΦΑΡΜΑΚΕΙΟ ΠΟΛΕΜΙΚΟΥ ΝΑΥΤΙΚΟΥ '51': ΤΑΜΕΙΟ ΑΠΟΚΑΤΑΣΤΑΣΕΩΣ ΠΡΟΣΦΥΓΩΝ ΣΥΜΒΟΥΛΙΟΥ ΤΗΣ ΕΥΡΩΠΗΣ '52': ΝΑΥΤΙΚΕΣ ΕΤΑΙΡΕΙΕΣ '53': ΙΣΡΑΗΛΙΤΙΚΕΣ ΚΟΙΝΟΤΗΤΕΣ '54': ΣΕΙΣΜΟΠΛΗΚΤΟΙ ΣΤΕΡΕΑΣ ΕΛΛΑΔΑΣ (ΑΤΤΙΚΗΣ, ΒΟΙΩΤΙΑΣ Κ.Λ.Π.) '55': ΔΙΑΦΟΡΕΣ ΣΧΟΛΕΣ Π.Ν '56': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΕΜΠΟΡ.ΚΑΙ ΒΙΟΜ.- ΕΠΑΓΓΕΛ. ΚΑΙ ΒΙΟΤΕΧΝ. ΕΠΙΜΕΛΗΤΗΡΙΩΝ ΤΟΥ ΚΡΑΤΟΥΣ '57': ΕΘΝΙΚΗ ΚΤΗΜΑΤΙΚΗ ΤΡΑΠΕΖΑ '58': ΝΑΥΤΙΚΟΙ ΑΚΟΛΟΥΘΟΙ '59': ΔΗΜΟΣΙΕΣ ΝΑΥΤΙΚΕΣ ΣΧΟΛΕΣ '60': ΜΙΚΡΟΦΩΤΟΓΡΑΦΙΕΣ '61': ΚΑΤΑΣΤΑΤΙΚΟΙ ΝΟΜΟΙ-Τ.Σ.Α.Υ '62': ΚΑΤΑΣΤΑΣΗ ΑΞΙΩΜΑΤΙΚΩΝ Π.Ν '63': ΕΛΛΗΝΙΚΑ ΣΧΟΛΕΙΑ ΑΛΛΟΔΑΠΗΣ '64': ΟΡΓΑΝΙΣΜΟΣ ΟΙΚΟΝΟΜΙΚΗΣ '65': ΕΘΝΙΚΗ ΤΡΑΠΕΖΑ ΤΗΣ ΕΛΛΑΔΟΣ '66': ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ Ν.Π.Δ.Δ '67': ΠΡΟΣΩΠΙΚΟ ΜΕ ΣΧΕΣΗ ΙΔΙΩΤΙΚΟΥ ΔΙΚΑΙΟΥ '68': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΕΤΑΙΡΕΙΑΣ ΥΔΡΕΥΣΗΣ ΚΑΙ ΑΠΟΧΕΤΕΥΣΗΣ ΠΡΩΤΕΥΟΥΣΗΣ (Τ.Ε.Α.Π.Ε.Υ.Α.Π.) '69': ΣΩΜΑ ΟΙΚΟΝΟΜΙΚΟΥ ΕΛΕΓΧΟΥ '70': ΣΥΜΒΑΣΗ ΠΕΡΙ ΔΙΕΚΔΙΚΗΣΕΩΣ ΔΙΑΤΡΟΦΗΣ '71': ΙΣΟΤΗΤΑ ΤΩΝ ΔΥΟ ΦΥΛΩΝ '72': ΤΑΜΕΙΟ ΑΡΩΓΗΣ ΚΑΙ ΕΠΙΚΟΥΡΙΚΟ ΤΑΜΕΙΟ '73': ΤΟΥΡΙΣΤΙΚΟ ΔΕΛΤΙΟ '74': ΔΙΑΦΟΡΟΙ ΝΟΜΟΙ '75': ΟΡΓΑΝΙΣΜΟΣ ΛΙΜΕΝΟΣ ΠΕΙΡΑΙΩΣ ΑΝΩΝΥΜΗ ΕΤΑΙΡΙΑ '76': ΕΚΚΑΘΑΡΙΣΙΣ ΔΙΟΡΙΣΜΩΝ ΚΑΙ ΠΡΟΑΓΩΓΩΝ ΚΑΤΟΧΗΣ '77': ΤΑΞΙΝΟΜΗΣΗ ΒΑΜΒΑΚΟΣ '78': ΠΡΥΤΑΝΕΙΣ ΚΑΙ ΚΟΣΜΗΤΟΡΕΣ '79': ΥΠΗΡΕΣΙΑΚΟ ΣΥΜΒΟΥΛΙΟ ΕΚΚΛΗΣΙΑΣΤΙΚΗΣ ΕΚΠΑΙΔΕΥΣΗΣ '80': ΩΡΕΣ ΕΡΓΑΣΙΑΣ ΣΤΗΝ ΒΙΟΜΗΧΑΝΙΑ ΚΑΙ ΒΙΟΤΕΧΝΙΑ '81': ΧΑΡΤΗΣ ΟΡΓΑΝΙΣΜΟΥ ΟΙΚΟΝΟΜΙΚΗΣ ΣΥΝΕΡΓΑΣΙΑΣ '82': ΓΥΜΝΑΣΙΟ ΑΠΟΔΗΜΩΝ ΕΛΛΗΝΟΠΑΙΔΩΝ '83': ΚΑΝΟΝΙΣΜΟΣ ΑΣΘΕΝΕΙΑΣ '84': ΕΚΔΟΣΕΙΣ ΥΠΟΥΡΓΕΙΟΥ ΕΜΠΟΡΙΚΗΣ ΝΑΥΤΙΛΙΑΣ '85': ΠΛΗΤΤΟΜΕΝΟΙ ΑΠΟ ΘΕΟΜΗΝΙΕΣ ΚΑΙ ΑΛΛΑ ΕΚΤΑΚΤΑ ΓΕΓΟΝΟΤΑ '86': ΩΡΕΣ ΕΡΓΑΣΙΑΣ ΠΡΟΣΩΠΙΚΟΥ '87': ΓΕΩΜΗΛΑ '88': ΦΟΡΟΛΟΓΙΑ ΑΝΑΤΙΜΗΣΗΣ ΑΚΙΝΗΤΩΝ '89': ΠΑΝΩΛΗΣ '90': ΣΧΟΛΕΣ ΝΗΠΙΑΓΩΓΩΝ '91': ΦΑΡΜΑΚΑΠΟΘΗΚΕΣ '92': ΦΡΟΝΤΙΣΤΗΡΙΑ ΝΟΜΙΚΩΝ ΣΠΟΥΔΩΝ '93': ΟΙΚΟΓΕΝΕΙΑΚΑ ΕΠΙΔΟΜΑΤΑ ΜΙΣΘΩΤΩΝ '94': ΗΛΕΚΤΡΟΚΙΝΗΤΑ ΛΕΩΦΟΡΕΙΑ ΑΘΗΝΩΝ – ΠΕΙΡΑΙΩΣ (Η.Λ.Π.Α.Π.) '95': ΑΣΤΙΚΑ ΔΙΚΑΙΩΜΑΤΑ ΑΛΛΟΔΑΠΩΝ '96': ΠΟΛΙΤΙΚΟ ΠΡΟΣΩΠΙΚΟ ΑΕΡΟΠΟΡΙΑΣ '97': ΔΙΚΑΣΤΙΚΗ ΕΚΠΡΟΣΩΠΗΣΗ Ι.Κ.Α '98': ΥΓΕΙΟΝΟΜΙΚΗ ΥΠΗΡΕΣΙΑ Π.Σ '99': ΥΓΕΙΟΝΟΜΙΚΟΙ ΣΤΑΘΜΟΙ '100': ΙΕΡΑΡΧΙΑ ΚΑΙ ΠΡΟΑΓΩΓΕΣ ΜΟΝΙΜΩΝ ΥΠΑΞΙΩΜΑΤΙΚΩΝ ΚΑΙ ΑΝΘΥΠΑΣΠΙΣΤΩΝ '101': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΕΡΓΑΤΟΤΕΧΝΙΤΩΝ ΚΑΙ ΥΠΑΛΛΗΛΩΝ ΔΕΡΜΑΤΟΣ ΕΛΛΑΔΑΣ (Τ.Ε.Α.Ε.Υ.Δ.Ε.) '102': ΠΡΑΤΗΡΙΑ ΑΡΤΟΥ '103': ΠΛΗΡΩΜΗ ΜΕ ΕΠΙΤΑΓΗ '104': ΤΕΧΝΙΚΗ ΕΚΜΕΤΑΛΛΕΥΣΗ ΕΛΙΚΟΠΤΕΡΩΝ '105': ΔΙΕΘΝΕΙΣ ΤΑΧΥΔΡΟΜΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '106': ΔΙΚΑΣΤΙΚΟΙ ΑΝΤΙΠΡΟΣΩΠΟΙ ΤΟΥ ΔΗΜΟΣΙΟΥ '107': ΩΡΕΣ ΕΡΓΑΣΙΑΣ ΣΕ ΔΙΑΦΟΡΑ ΕΠΑΓΓΕΛΜΑΤΑ '108': ΔΙΕΥΘΥΝΣΗ ΚΤΗΝΟΤΡΟΦΙΑΣ '109': ΕΠΙΘΕΩΡΗΣΗ ΣΦΑΓΙΩΝ '110': ΠΛΩΙΜΟΤΗΤΑ ΑΕΡΟΣΚΑΦΩΝ '111': ΑΓΟΡΑΝΟΜΙΚΟΣ ΚΩΔΙΚΑΣ '112': ΔΙΕΘΝΕΙΣ ΜΕΤΑΦΟΡΕΣ ΕΠΙΒΑΤΩΝ ΚΑΙ ΕΜΠΟΡΕΥΜΑΤΩΝ '113': ΠΡΟΜΗΘΕΙΕΣ '114': ΔΙΑΦΟΡΕΣ ΔΙΑΤΑΞΕΙΣ '115': ΔΙΑΙΤΗΣΙΑ ΣΥΛΛΟΓΙΚΩΝ ΔΙΑΦΟΡΩΝ - ΜΕΣΟΛΑΒΗΤΕΣ ΔΙΑΙΤΗΤΕΣ '116': ΣΟΥΛΤΑΝΙΝΑ '117': ΜΕΤΑΓΡΑΦΗ '118': ΕΙΣΑΓΩΓΗ ΕΠΙΣΤΗΜΟΝΙΚΟΥ ΥΛΙΚΟΥ '119': ΔΙΑΡΘΡΩΣΗ ΥΠΗΡΕΣΙΩΝ Ο.Γ.Α '120': ΔΙΚΑΣΤΙΚΟΙ ΛΕΙΤΟΥΡΓΟΙ - ΕΘΝΙΚΗ ΣΧΟΛΗ ΔΙΚΑΣΤΩΝ '121': ΠΙΣΤΟΠΟΙΗΤΙΚΑ ΚΑΙ ΔΙΚΑΙΟΛΟΓΗΤΙΚΑ '122': ΑΣΚΗΣΗ ΙΑΤΡΙΚΟΥ ΕΠΑΓΓΕΛΜΑΤΟΣ '123': ΣΥΝΕΤΑΙΡΙΣΜΟΙ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ '124': ΣΧΟΛΗ ΕΠΙΣΤΗΜΩΝ ΥΓΕΙΑΣ ΠΑΝΜΙΟΥ ΠΑΤΡΩΝ '125': ΑΛΛΟΔΑΠΕΣ ΝΑΥΤΙΛΙΑΚΕΣ ΕΠΙΧΕΙΡΗΣΕΙΣ '126': ΛΑΤΟΜΕΙΑ '127': ΕΚΜΕΤΑΛΛΕΥΣΗ ΙΑΜΑΤΙΚΩΝ ΠΗΓΩΝ '128': ΠΩΛΗΣΗ ΧΡΕΩΓΡΑΦΩΝ ΜΕ ΔΟΣΕΙΣ '129': ΝΟΜΟΘΕΣΙΑ ΠΕΡΙ ΤΡΑΠΕΖΩΝ (ΓΕΝΙΚΑ) '130': ΕΙΔΙΚΑ ΜΕΤΑΛΛΕΙΑ '131': YΠΟΥΡΓΕΙΟ ΥΓΙΕΙΝΗΣ '132': ΛΗΞΙΑΡΧΙΚΕΣ ΠΡΑΞΕΙΣ '133': ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΓΙΑ ΤΟΝ ΤΥΠΟ '134': ΕΘΝΙΚΟ ΣΥΣΤΗΜΑ ΕΠΑΓΓΕΛΜΑΤΙΚΗΣ ΕΚΠΑΙΔΕΥΣΗΣ-ΚΑΤΑΡΤΙΣΗΣ '135': ΑΡΟΥΡΑΙΟΙ ΚΑΙ ΑΚΡΙΔΕΣ '136': ΠΡΟΣΤΑΣΙΑ ΦΥΜΑΤΙΚΩΝ ΝΑΥΤΙΚΩΝ '137': ΑΠΟΡΡΗΤΟ ΕΠΙΣΤΟΛΩΝ ΚΑΙ ΕΠΙΚΟΙΝΩΝΙΩΝ '138': ΠΟΡΘΜΕΙΑ ΚΑΙ ΟΧΗΜΑΤΑΓΩΓΑ '139': ΜΕΤΡΑ ΕΞΟΙΚΟΝΟΜΗΣΗΣ ΕΝΕΡΓΕΙΑΣ '140': ΣΤΟΙΧΕΙΑ ΠΡΟΣΩΠΙΚΟΥ ΔΗΜΟΣΙΩΝ ΥΠΗΡΕΣΙΩΝ ΚΑΙ Ν.Π.Δ.Δ '141': ΠΑΓΙΕΣ ΑΜΟΙΒΕΣ ΔΙΚΗΓΟΡΩΝ '142': ΟΡΓΑΝΙΣΜΟΣ ΣΧΟΛΗΣ ΕΥΕΛΠΙΔΩΝ '143': ΟΙΚΟΝΟΜΙΚΟ ΕΠΙΜΕΛΗΤΗΡΙΟ ΤΗΣ ΕΛΛΑΔΑΣ '144': ΓΡΑΦΕΙΑ ΕΥΡΕΣΕΩΣ ΕΡΓΑΣΙΑΣ '145': ΔΙΑΦΗΜΙΣΕΙΣ '146': ΔΙΑΦΟΡΕΣ ΥΠΟΤΡΟΦΙΕΣ '147': ΦΟΡΤΗΓΑ ΑΚΤΟΠΛΟΙΚΑ ΠΛΟΙΑ (ΜS) ΜΕΧΡΙ 500 Κ.Ο.Χ '148': ΕΠΙΤΡΟΠΗ ΣΥΝΕΡΓΑΣΙΑΣ UNICEF '149': ΥΓΙΕΙΝΗ ΘΕΡΕΤΡΩΝ '150': ΕΠΙΣΤΗΜΟΝΙΚΗ ΕΡΕΥΝΑ ΚΑΙ ΤΕΧΝΟΛΟΓΙΑ '151': ΑΠΑΓΟΡΕΥΣΕΙΣ ΕΞΑΓΩΓΗΣ '152': ΑΜΠΕΛΟΥΡΓΙΚΟ ΚΤΗΜΑΤΟΛΟΓΙΟ '153': ΥΠΟΥΡΓΕΙΟ ΥΓΕΙΑΣ ΚΑΙ ΠΡΟΝΟΙΑΣ '154': ΔΙΕΘΝΗΣ ΝΑΥΤΙΛΙΑΚΟΣ ΟΡΓΑΝΙΣΜΟΣ '155': ΔΙΕΥΘΥΝΣΗ ΤΕΛΩΝΕΙΑΚΟΥ ΕΛΕΓΧΟΥ '156': ΔΕΛΤΙΑ ΤΑΥΤΟΤΗΤΟΣ Π. ΝΑΥΤΙΚΟΥ '157': ΑΝΩΤΑΤΗ ΥΓΕΙΟΝΟΜΙΚΗ ΕΠΙΤΡΟΠΗ '158': ΠΡΟΣΤΑΣΙΑ ΕΦΕΔΡΩΝ ΑΞΙΩΜΑΤΙΚΩΝ, ΑΝΑΠΗΡΩΝ ΠΟΛΕΜΟΥ ΚΑΙ ΑΓΩΝΙΣΤΩΝ ΕΘΝ. ΑΝΤΙΣΤΑΣΗΣ '159': ΦΟΡΟΙ ΥΠΕΡ ΤΡΙΤΩΝ '160': ΑΓΡΟΛΗΨΙΕΣ ΙΟΝΙΩΝ ΝΗΣΙΩΝ '161': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΥΠΑΛΛΗΛΩΝ ΕΜΠΟΡΙΟΥ ΤΡΟΦΙΜΩΝ (Τ.Ε.Α.Υ.Ε.Τ) '162': ΑΝΩΤΑΤΟ ΕΙΔΙΚΟ ΔΙΚΑΣΤΗΡΙΟ '163': ΕΙΣΑΓΩΓΗ ΓΥΝΑΙΚΩΝ ΣΤΙΣ ΑΝΩΤΑΤΕΣ ΣΤΡΑΤΙΩΤΙΚΕΣ ΣΧΟΛΕΣ '164': ΣΧΟΛΗ ΑΞΙΩΜΑΤΙΚΩΝ ΝΟΣΗΛΕΥΤΙΚΗΣ (Σ.Α.Ν.) '165': ΔΙΑΔΙΚΑΣΙΑ ΔΙΟΙΚΗΤΙΚΩΝ ΔΙΚΑΣΤΗΡΙΩΝ '166': ΠΡΟΣΤΑΣΙΑ ΕΡΓΑΖΟΜΕΝΟΥ ΠΑΙΔΙΟΥ '167': ΑΜΝΗΣΤΙΑ '168': ΣΧΟΛΕΣ ΚΑΛΛΙΤΕΧΝΙΚΗΣ ΕΚΠΑΙΔΕΥΣΗΣ '169': ΧΑΡΗ ΚΑΙ ΜΕΤΡΙΑΣΜΟΣ '170': ΤΥΦΛΟΙ '171': ΣΥΜΒΟΥΛΙΟ ΤΗΣ ΕΥΡΩΠΗΣ '172': ΕΡΓΟΣΤΑΣΙΑ ΕΚΡΗΚΤΙΚΩΝ ΥΛΩΝ '173': ΜΗΤΡΩΑ Π. ΝΑΥΤΙΚΟΥ '174': ΥΓΡΗ ΑΜΜΩΝΙΑ '175': ΠΕΙΡΑΜΑΤΙΚΑ ΣΧΟΛΕΙΑ '176': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΑΞΙΩΜΑΤΙΚΩΝ Ε.Ν '177': ΕΠΑΓΓΕΛΜΑΤΙΚΟΣ ΠΡΟΣΑΝΑΤΟΛΙΣΜΟΣ ΚΑΙ ΚΑΤΑΡΤΙΣΗ '178': ΤΕΛΩΝΕΙΑΚΗ ΕΠΙΒΛΕΨΗ '179': ΠΡΟΣΩΡΙΝΕΣ ΕΥΡΩΠΑΙΚΕΣ ΣΥΜΦΩΝΙΕΣ '180': ΜΟΝΟΠΩΛΙΟ ΠΑΙΓΝΙΟΧΑΡΤΩΝ '181': ΛΕΙΤΟΥΡΓΙΑ ΤΟΥΡΙΣΤΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ '182': ΕΚΠΟΙΗΣΗ ΕΚΚΛΗΣΙΑΣΤΙΚΩΝ ΚΙΝΗΤΩΝ ΚΑΙ ΑΚΙΝΗΤΩΝ '183': ΣΥΛΛΟΓΙΚΕΣ ΣΥΜΒΑΣΕΙΣ (ΓΕΝΙΚΑ) '184': ΟΔΟΙΠΟΡΙΚΑ ΚΑΙ ΑΠΟΖΗΜΙΩΣΕΙΣ ΕΚΤΟΣ ΕΔΡΑΣ '185': ΣΤΕΓΑΣΤΙΚΗ ΑΠΟΚΑΤΑΣΤΑΣΗ ΠΡΟΣΦΥΓΩΝ '186': ΑΝΩΤΑΤΑ ΣΥΜΒΟΥΛΙΑ ΕΚΠΑΙΔΕΥΣΕΩΣ '187': ΑΡΧΕΙΑ ΥΠΟΥΡΓΕΙΟΥ ΟΙΚΟΝΟΜΙΚΩΝ '188': ΓΕΝΙΚΗ ΓΡΑΜΜΑΤΕΙΑ ΥΠΟΥΡΓΙΚΟΥ ΣΥΜΒΟΥΛΙΟΥ '189': ΠΕΡΙΠΤΕΡΑ ΑΝΑΠΗΡΩΝ ΠΟΛΕΜΟΥ '190': ΕΠΑΓΓΕΛΜΑΤΙΚΕΣ ΟΡΓΑΝΩΣΕΙΣ ΕΜΠΟΡΩΝ, ΒΙΟΤΕΧΝΩΝ ΚΑΙ ΛΟΙΠΩΝ ΕΠΑΓΓΕΛΜΑΤΙΩΝ '191': ΙΔΙΩΤΙΚΟΙ ΣΤΑΘΜΟΙ ΠΑΡΑΓΩΓΗΣ ΗΛΕΚΤΡΙΚΗΣ ΕΝΕΡΓΕΙΑΣ '192': ΘΕΑΤΡΙΚΑ ΕΡΓΑ '193': ΜΕ ΤΗ ΝΕΑ ΖΗΛΑΝΔΙΑ '194': ΦΟΡΟΣ ΚΑΤΑΝΑΛΩΣΕΩΣ ΣΑΚΧΑΡΕΩΣ '195': ΝΟΜΑΡΧΙΑΚΑ ΤΑΜΕΙΑ '196': ΑΓΩΓΕΣ ΚΑΚΟΔΙΚΙΑΣ '197': ΚΩΔΙΚΑΣ ΦΟΡΟΛΟΓΙΚΗΣ ΔΙΚΟΝΟΜΙΑΣ '198': ΑΤΟΜΑ ΒΑΡΙΑ ΝΟΗΤΙΚΑ ΚΑΘΥΣΤΕΡΗΜΕΝΑ '199': ΜΕ ΤΗ ΣΟΥΗΔΙΑ '200': ΑΕΡΟΝΑΥΤΙΚΗ ΜΕΤΕΩΡΟΛΟΓΙΑ '201': ΙΔΙΩΤΙΚΕΣ ΣΧΟΛΕΣ ΓΥΜΝΑΣΤΙΚΗΣ '202': ΠΕΡΙΟΥΣΙΑ ΔΗΜΩΝ ΚΑΙ ΚΟΙΝΟΤΗΤΩΝ '203': ΑΓΟΡΑΠΩΛΗΣΙΕΣ ΚΑΤΟΧΗΣ '204': ΕΚΚΛΗΣΙΑ ΠΑΡΙΣΙΩΝ '205': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ ΠΡΟΣΤΑΣΙΑΣ ΦΥΤΩΝ '206': ΚΑΤΟΧΥΡΩΣΗ ΘΡΗΣΚΕΥΤΙΚΗΣ ΕΛΕΥΘΕΡΙΑΣ '207': ΥΓΕΙΟΝΟΜΙΚΗ ΕΞΕΤΑΣΗ ΜΗ ΙΠΤΑΜΕΝΟΥ ΠΡΟΣΩΠΙΚΟΥ '208': ΣΥΝΤΑΞΕΙΣ ΘΥΜΑΤΩΝ ΠΟΛΕΜΟΥ 1940 '209': ΥΔΡΑΥΛΙΚΕΣ ΕΓΚΑΤΑΣΤΑΣΕΙΣ '210': ΚΟΙΝΩΝΙΚΟΙ ΛΕΙΤΟΥΡΓΟΙ - ΚΟΙΝΩΝΙΚΟΙ ΣΥΜΒΟΥΛΟΙ '211': ΔΙΑΦΟΡΕΣ ΠΡΟΣΩΡΙΝΕΣ ΑΤΕΛΕΙΕΣ '212': ΟΙΚΟΝΟΜΙΚΗ ΔΙΑΧΕΙΡΙΣΗ ΚΑΙ ΛΟΓΙΣΤΙΚΟ '213': ΕΞΗΛΕΚΤΡΙΣΜΟΣ ΝΗΣΩΝ '214': ΕΚΠΑΙΔΕΥΣΗ ΣΤΕΛΕΧΩΝ '215': ΩΡΕΣ ΕΡΓΑΣΙΑΣ ΚΑΤΑΣΤΗΜΑΤΩΝ ΚΑΙ ΓΡΑΦΕΙΩΝ '216': ΗΜΕΡΟΛΟΓΙΟ ΓΕΦΥΡΑΣ '217': ΠΡΟΣΤΑΣΙΑ ΤΗΣ ΣΤΑΦΙΔΑΣ '218': ΠΑΛΑΙΟΙ ΔΙΚΟΝΟΜΙΚΟΙ ΝΟΜΟΙ '219': ΤΑΜΕΙΟ ΕΠΙΚ. ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΟΡΓΑΝΙΣΜΩΝ ΚΟΙΝΩΝΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ (Τ.Ε.Α.Π.Ο.Κ.Α.) '220': ΠΑΡΟΧΕΣ ΥΓΕΙΑΣ ΑΣΦΑΛΙΣΤΙΚΩΝ ΟΡΓΑΝΙΣΜΩΝ '221': ΠΛΑΝΟΔΙΟΙ ΙΧΘΥΟΠΩΛΕΣ '222': ΔΙΑΦΟΡΟΙ ΝΟΜΟΙ ΠΕΡΙ ΑΞΙΩΜΑΤΙΚΩΝ Π.Ν '223': ΥΠΟΧΡΕΩΣΕΙΣ ΕΦΟΠΛΙΣΤΩΝ ΣΕ ΑΣΘΕΝΕΙΑ Η ΘΑΝΑΤΟ ΝΑΥΤΙΚΩΝ '224': ΠΡΟΣΤΑΣΙΑ ΚΑΤΑ ΤΗΣ ΑΣΘΕΝΕΙΑΣ '225': ΓΕΝΙΚΑ ΠΕΡΙ ΣΧΕΔΙΩΝ ΠΟΛΕΩΝ '226': ΕΞΑΙΡΕΣΕΙΣ ΑΠΟ ΤΗΝ ΕΡΓΑΤΙΚΗ ΝΟΜΟΘΕΣΙΑ '227': ΑΓΡΟΤΙΚΟ ΚΤΗΜΑΤΟΛΟΓΙΟ '228': ΣΥΝΤΑΓΜΑΤΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΕΚΚΛΗΣΙΑΣ ΤΗΣ ΕΛΛΑΔΟΣ '229': ΠΑΝΑΓΙΟΣ ΤΑΦΟΣ '230': ΣΥΝΕΡΓΕΙΑ Π. ΝΑΥΤΙΚΟΥ '231': ΕΠΙΘΕΩΡΗΣΙΣ ΣΤΡΑΤΟΥ '232': ΣΥΝΘΕΣΗ ΠΛΗΡΩΜΑΤΩΝ '233': ΟΡΓΑΝΙΣΜΟΣ ΕΡΓΑΤΙΚΗΣ ΕΣΤΙΑΣ '234': ΔΙΑΦΟΡΑ ΥΔΡΑΥΛΙΚΑ ΕΡΓΑ '235': ΔΙΚΑΙΩΜΑ ΤΟΥ ΣΥΝΕΡΧΕΣΘΑΙ '236': ΚΟΙΝΩΝΙΚΟΠΟΙΗΣΗ - ΑΠΟΚΡΑΤΙΚΟΠΟΙΗΣΗ ΕΠΙΧΕΙΡΗΣΕΩΝ ΔΗΜΟΣΙΟΥ ΧΑΡΑΚΤΗΡΑ '237': ΛΑΙΚΗ ΚΑΤΟΙΚΙΑ '238': ΦΟΡΟΛΟΓΙΑ ΚΕΡΔΩΝ '239': ΤΕΧΝΙΚΗ ΥΠΗΡΕΣΙΑ '240': ΜΕΤΕΚΠΑΙΔΕΥΣΗ ΔΗΜΟΔΙΔΑΣΚΑΛΩΝ '241': ΣΥΝΤΑΞΕΙΣ ΥΠΟΥΡΓΩΝ ΚΑΙ ΒΟΥΛΕΥΤΩΝ '242': ΟΡΙΟ ΗΛΙΚΙΑΣ '243': ΣΤΡΑΤΙΩΤΙΚΕΣ ΠΡΟΜΗΘΕΙΕΣ '244': ΑΠΟΣΤΟΛΑΙ ΕΞΩΤΕΡΙΚΟΥ '245': ΦΟΡΟΛΟΓΙΑ ΑΚΙΝΗΤΗΣ ΠΕΡΙΟΥΣΙΑΣ '246': ΧΡΟΝΟΣ ΕΡΓΑΣΙΑΣ - ΑΔΕΙΕΣ ΠΡΟΣΩΠΙΚΟΥ ΕΛΛΗΝΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ '247': ΝΑΥΤΙΚΑ ΕΡΓΑ ΚΑΙ ΠΡΟΜΗΘΕΙΕΣ '248': ΟΙΚΟΝΟΜΙΚΗ ΔΙΟΙΚΗΣΗ ΚΑΙ ΛΟΓΙΣΤΙΚΟ '249': ΔΑΣΜΟΛΟΓΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '250': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΧΡΗΜΑΤΙΣΤΩΝ ,ΜΕΣΙΤΩΝ,ΑΝΤΙΚΡΥΣΤΩΝ ΚΑΙ ΥΠΑΛΛΗΛΩΝ ΧΡΗΜΑΤΙΣΤΗΡΙΟΥ ΑΘΗΝΩΝ (Τ.Α.Χ.Μ.Α.) '251': ΚΡΑΤΙΚΗ ΣΧΟΛΗ ΟΡΧΗΣΤΙΚΗΣ ΤΕΧΝΗΣ '252': ΕΘΝΙΚΗ ΛΥΡΙΚΗ ΣΚΗΝΗ '253': ΑΕΡΟΝΑΥΤΙΚΕΣ ΤΗΛΕΠΙΚΟΙΝΩΝΙΕΣ '254': ΚΕΝΤΡΟ ΒΙΟΤΕΧΝΙΚΗΣ ΑΝΑΠΤΥΞΗΣ '255': ΑΡΧΑΙΟΛΟΓΙΚΟ ΜΟΥΣΕΙΟ '256': ΥΠΕΡΩΚΕΑΝΕΙΑ '257': ΔΑΣΗ '258': ΑΣΚΗΣΗ ΚΤΗΝΙΑΤΡΙΚΟΥ ΕΠΑΓΓΕΛΜΑΤΟΣ '259': ΚΤΗΣΗ ΚΑΙ ΑΠΩΛΕΙΑ '260': ΡΑΔΙΟΤΗΛΕΓΡΑΦΙΚΗ ΥΠΗΡΕΣΙΑ '261': ΑΕΡΟΛΙΜΕΝΑΣ ΑΘΗΝΩΝ '262': ΠΡΩΤΟΒΑΘΜΙΑ ΕΚΠΑΙΔΕΥΣΗ '263': ΣΤΕΛΕΧΟΣ ΕΦΕΔΡΩΝ ΑΞΙΩΜΑΤΙΚΩΝ '264': ΠΤΩΧΕΥΣΗ ΚΑΙ ΣΥΜΒΙΒΑΣΜΟΣ '265': ΠΟΛΙΤΙΚΟΣ ΓΑΜΟΣ '266': ΙΔΙΩΤΙΚΗ ΕΠΙΧΕΙΡΗΣΗ ΑΣΦΑΛΙΣΕΩΣ '267': ΠΛΟΙΑ ΠΟΛΕΜΙΚΟΥ ΝΑΥΤΙΚΟΥ '268': ΙΑΤΡΙΚΕΣ ΑΜΟΙΒΕΣ '269': ΕΛΛΗΝΙΚΟΣ ΕΡΥΘΡΟΣ ΣΤΑΥΡΟΣ '270': ΑΝΩΜΑΛΕΣ ΚΑΤΑΘΕΣΕΙΣ ΣΕ ΧΡΥΣΟ '271': ΣΥΜΒΟΥΛΙΟ ΤΙΜΗΣ ΑΞΙΩΜΑΤΙΚΩΝ Π.Ν '272': ΔΙΑΦΟΡΟΙ ΑΡΔΕΥΤΙΚΟΙ ΝΟΜΟΙ '273': ΚΥΒΕΡΝΗΤΙΚΟΣ ΕΠΙΤΡΟΠΟΣ '274': ΕΚΤΕΛΕΣΗ ΣΥΓΚΟΙΝΩΝΙΑΚΩΝ ΕΡΓΩΝ '275': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΚΑΙ ΑΡΩΓΗΣ '276': ΔΑΣΙΚΕΣ ΜΕΤΑΦΟΡΕΣ '277': ΜΕ ΤΗ ΔΗΜΟΚΡΑΤΙΑ ΤΟΥ ΚΕΜΠΕΚ '278': ΕΠΑΝΕΞΑΓΟΜΕΝΑ ΜΕ ΕΓΓΥΗΣΗ '279': ΔΙΑΝΟΜΗ ΗΛΕΚΤΡΙΚΗΣ ΕΝΕΡΓΕΙΑΣ '280': ΑΡΣΗ ΣΥΓΚΡΟΥΣΕΩΣ ΚΑΘΗΚΟΝΤΩΝ '281': ΕΚΠΑΙΔΕΥΤΙΚΑ ΠΛΟΙΑ '282': ΚΕΝΤΡΟ ΜΕΤΑΦΡΑΣΗΣ '283': ΕΙΣΦΟΡΕΣ ΚΑΙ ΝΑΥΛΩΣΕΙΣ '284': ΜΕΤΕΓΓΡΑΦΕΣ ΦΟΙΤΗΤΩΝ ΑΝΩΤ. ΕΚΠΑΙΔΕΥΤΙΚΩΝ ΙΔΡΥΜΑΤΩΝ '285': ΤΜΗΜΑΤΑ ΕΠΙΣΤΗΜΗΣ ΦΥΣΙΚΗΣ ΑΓΩΓΗΣ - ΑΘΛΗΤΙΣΜΟΥ '286': ΨΥΧΙΑΤΡΕΙΑ '287': ΦΟΡΟΛΟΓΙΑ ΚΕΦΑΛΑΙΟΥ ΑΝΩΝ. ΕΤΑΙΡΕΙΩΝ '288': ΤΥΠΟΙ ΣΥΜΒΟΛΑΙΩΝ '289': ΚΑΝΟΝΙΣΜΟΣ ΕΠΙΘΕΩΡΗΣΕΩΣ '290': ΜΟΥΣΕΙΟ ΕΛΛΗΝΙΚΗΣ ΛΑΙΚΗΣ ΤΕΧΝΗΣ '291': ΠΑΝΕΠΙΣΤΗΜΙΟ ΠΕΛΟΠΟΝΝΗΣΟΥ '292': ΟΡΓΑΝΙΣΜΟΣ ΕΡΓΑΤΙΚΗΣ ΚΑΤΟΙΚΙΑΣ '293': ΑΣΦΑΛΕΙΑ ΕΡΓΑΖΟΜΕΝΩΝ ΣΕ ΟΙΚΟΔΟΜΕΣ '294': ΣΤΕΓΑΝΗ ΥΠΟΔΙΑΙΡΕΣΗ ΠΛΟΙΩΝ '295': ΔΙΟΙΚΗΣΗ ΠΡΩΤΕΥΟΥΣΗΣ '296': ΔΙΔΑΚΤΟΡΙΚΕΣ - ΜΕΤΑΠΤΥΧΙΑΚΕΣ ΣΠΟΥΔΕΣ ΕΘΝΙΚΟΥ ΜΕΤΣΟΒΙΟΥ '297': ΕΙΣΦΟΡΑ ΚΑΤΟΧΩΝ ΕΙΔΩΝ ΠΡΩΤΗΣ ΑΝΑΓΚΗΣ '298': ΔΙΑΦΟΡΟΙ ΔΙΚΟΝΟΜΙΚΟΙ ΝΟΜΟΙ '299': ΔΙΕΘΝΕΙΣ ΛΙΜΕΝΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '300': ΥΓΕΙΟΝΟΜΙΚΗ ΥΠΗΡΕΣΙΑ ΕΛ.ΑΣ '301': ΕΛΛΗΝΙΚΑ ΤΑΧΥΔΡΟΜΕΙΑ (ΕΛ.ΤΑ) '302': ΜΙΣΘΟΙ ΚΑΙ ΕΠΙΔΟΜΑΤΑ Π. ΝΑΥΤΙΚΟΥ '303': ΓΕΩΡΓΙΚΑ ΤΑΜΕΙΑ '304': ΑΤΕΛΕΙΕΣ ΥΠΕΡ ΜΕΤΑΛΛΕΥΤΙΚΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ '305': ΑΠΟΒΑΡΟ '306': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΕΚΠΡΟΣΩΠΩΝ ΚΑΙ ΥΠΑΛΛΗΛΩΝ '307': ΚΩΔΙΚΑΣ ΠΕΡΙ ΔΙΚΗΓΟΡΩΝ '308': ΙΕΡΑΡΧΙΑ ΚΑΙ ΠΡΟΒΙΒΑΣΜΟΙ '309': ΙΣΡΑΗΛΙΤΕΣ '310': ΣΩΜΑ ΚΤΗΝΙΑΤΡΙΚΟ '311': ΝΟΡΒΗΓΙΑ - ΝΕΑ ΖΗΛΑΝΔΙΑ – ΝΙΓΗΡΙΑ Κ.ΛΠ '312': ΕΝΤΥΠΑ ΚΑΙ ΒΙΒΛΙΟΘΗΚΕΣ ΝΑΥΤΙΚΟΥ '313': ΥΠΟΥΡΓΕΙΟ ΤΥΠΟΥ ΚΑΙ ΜΕΣΩΝ ΜΑΖΙΚΗΣ ΕΝΗΜΕΡΩΣΗΣ '314': ΝΑΥΤΙΚΕΣ ΠΕΙΘΑΡΧΙΚΕΣ ΠΟΙΝΕΣ '315': ΜΙΣΘΩΣΕΙΣ ΑΓΡΟΤΙΚΩΝ ΑΚΙΝΗΤΩΝ '316': ΔΙΑΦΟΡΟΙ ΣΥΝΕΤΑΙΡΙΣΜΟΙ '317': ΑΓΡΟΤΙΚΗ ΠΙΣΤΗ '318': ΛΑΙΚΕΣ ΑΓΟΡΕΣ-ΤΑΜΕΙΟ ΛΑΙΚΩΝ ΑΓΟΡΩΝ '319': ΚΑΝΟΝΙΣΜΟΣ ΠΕΙΘΑΡΧΙΑΣ ΧΩΡΟΦΥΛΑΚΗΣ '320': ΑΔΙΚΗΜΑΤΑ ΚΑΤΑ ΤΗΣ ΔΗΜΟΣΙΑΣ ΑΣΦΑΛΕΙΑΣ '321': ΕΝΟΙΚΙΑΣΗ ΦΟΡΟΥ ΔΗΜΟΣΙΩΝ ΘΕΑΜΑΤΩΝ '322': ΕΥΡΩΠΑΙΚΗ ΣΥΜΒΑΣΗ ΚΟΙΝΩΝΙΚΗΣ ΚΑΙ ΙΑΤΡΙΚΗΣ ΑΝΤΙΛΗΨΕΩΣ '323': ΕΠΙΒΑΤΗΓΑ ΑΕΡΟΣΤΡΩΜΝΑ ΟΧΗΜΑΤΑ '324': ΕΦΕΔΡΟΙ '325': ΣΤΡΑΤΙΩΤΙΚΕΣ ΛΕΣΧΕΣ '326': ΠΡΟΣΩΠΙΚΟ ΦΥΛΑΚΩΝ '327': ΑΝΑΘΕΩΡΗΣΗ ΤΙΜΩΝ '328': ΜΑΛΑΚΙΑ ΚΑΙ ΜΑΛΑΚΟΣΤΡΑΚΑ '329': ΚΩΔΙΚΑΣ ΔΗΜΟΣΙΟΥ ΝΑΥΤΙΚΟΥ ΔΙΚΑΙΟΥ '330': ΔΙΑΦΟΡΑ ΣΩΜΑΤΕΙΑ '331': ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ '332': ΚΩΔΙΚΟΠΟΙΗΣΗ ΑΓΟΡΑΝΟΜΙΚΩΝ ΔΙΑΤΑΞΕΩΝ '333': ΕΚΠΑΙΔΕΥΣΗ ΣΤΗΝ ΑΛΛΟΔΑΠΗ '334': ΔΙΔΑΚΤΙΚΑ ΒΙΒΛΙΑ '335': ΣΥΝΤΑΞΙΟΔΟΤΙΚΑ ΚΑΙ ΑΣΦΑΛΙΣΤΙΚΑ ΘΕΜΑΤΑ ΠΡΟΣΩΠΙΚΟΥ Ν.Π.Δ.Δ '336': ΕΠΙΔΟΜΑ ΟΙΚΟΓΕΝΕΙΩΝ ΣΤΡΑΤΙΩΤΙΚΩΝ ΕΞΑΦΑΝΙΣΘΕΝΤΩΝ ΚΑΙ ΑΙΧΜΑΛΩΤΩΝ '337': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ '338': ΚΕΝΤΡΟ ΔΙΠΛΩΜΑΤΙΚΩΝ ΣΠΟΥΔΩΝ '339': ΓΕΝ. ΔΙΕΥΘΥΝΣΗ ΤΥΠΟΥ ΚΑΙ ΠΛΗΡΟΦΟΡΙΩΝ '340': ΑΡΧΕΙΑ ΤΕΛΩΝΕΙΑΚΩΝ ΑΡΧΩΝ '341': ΕΙΔΙΚΕΣ ΤΙΜΕΣ ΚΑΥΣΙΜΩΝ '342': ΣΤΕΓΗ ΥΓΕΙΟΝΟΜΙΚΩΝ '343': ΓΕΝΙΚΑ ΠΕΡΙ ΣΥΜΒΟΛΑΙΟΓΡΑΦΩΝ '344': ΒΟΥΛΗ '345': ΕΠΙΛΟΓΗ & ΑΞΙΟΛΟΓΗΣΗ ΑΣΤΥΝΟΜΙΚΟΥ ΠΡΟΣΩΠΙΚΟΥ ΕΛ.ΑΣ '346': ΧΟΙΡΟΤΡΟΦΙΑ '347': ΦΟΡΟΣ ΚΑΤΑΝΑΛΩΣΕΩΣ ΠΕΤΡΕΛΑΙΟΕΙΔΩΝ '348': ΕΠΙΒΟΛΗ ΤΕΛΩΝΙΑΚΩΝ ΔΑΣΜΩΝ '349': ΑΕΡΟΠΟΡΙΚΗ ΣΤΡΑΤΟΛΟΓΙΑ '350': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ ΓΙΑ ΤΑ ΝΑΡΚΩΤΙΚΑ '351': ΔΙΑΦΟΡΕΣ ΤΡΑΠΕΖΕΣ '352': ΟΙΝΟΛΟΓΟΙ '353': ΤΕΛΩΝΟΦΥΛΑΚΗ '354': ΤΑΜΕΙΟ ΕΘΝΙΚΗΣ ΑΜΥΝΑΣ (T.EΘ.A.) - ΕΘΝΙΚΗ ΕΠΙΤΡΟΠΗ ΕΞΟΠΛΙΣΜΟΥ ΕΝΟΠΛΩΝ ΔΥΝΑΜΕΩΝ (Ε.Ε.Ε.Ε.Δ.) '355': ΕΚΤΕΛΕΣΗ ΤΗΣ ΠΟΙΝΗΣ '356': ΙΣΟΛΟΓΙΣΜΟΙ ΑΝΩΝΥΜΩΝ ΕΤΑΙΡΕΙΩΝ '357': ΑΡΧΙΤΕΚΤΟΝΙΚΟΙ ΔΙΑΓΩΝΙΣΜΟΙ '358': ΚΑΤΑΡΓΗΣΗ ΦΥΛΕΤΙΚΩΝ ΔΙΑΚΡΙΣΕΩΝ '359': ΕΠΑΓΓΕΛΜΑΤΙΚΑ ΔΙΚΑΙΩΜΑΤΑ ΑΠΟΦΟΙΤΩΝ '360': ΜΟΝΑΣΤΗΡΙΑΚΗ ΠΕΡΙΟΥΣΙΑ ΣΑΜΟΥ '361': ΣΥΝΤΑΞΗ ΔΗΜΟΤΙΚΩΝ ΚΑΙ ΚΟΙΝΟΤΙΚΩΝ ΥΠΑΛΛΗΛΩΝ '362': ΟΙΚΟΝΟΜΙΚΕΣ ΕΦΟΡΙΕΣ '363': ΦΡΟΝΤΙΣΤΗΡΙΑ ΕΦΑΡΜΟΓΩΝ '364': ΝΟΜΑΡΧΙΕΣ ΑΤΤΙΚΗΣ '365': ΦΥΜΑΤΙΩΣΗ '366': ΕΛΕΓΧΟΣ ΑΝΑΤΙΜΗΣΕΩΝ '367': ΑΤΕΛΕΙΕΣ ΥΠΕΡ ΤΗΣ ΝΑΥΤΙΛΙΑΣ '368': ΚΩΦΑΛΑΛΟΙ '369': ΙΑΤΡΙΚΗ ΔΕΟΝΤΟΛΟΓΙΑ '370': ΕΞΟΔΑ ΔΗΜΟΣΙΑΣ ΑΣΦΑΛΕΙΑΣ '371': ΜΕ ΤΗΝ ΑΡΓΕΝΤΙΝΗ '372': ΚΛΑΔΟΣ ΥΓΕΙΟΝΟΜΙΚΗΣ ΠΕΡΙΘΑΛΨΗΣ Τ.Α.Ε '373': ΥΠΗΡΕΣΙΑ ΕΚΚΑΘΑΡΙΣΕΩΣ ΝΑΡΚΟΠΕΔΙΩΝ '374': ΤΑΜΕΙΟ ΑΡΩΓΗΣ ΥΠΑΛΛΗΛΩΝ ΑΣΤΥΝΟΜΙΑΣ ΠΟΛΕΩΝ Τ.Α.Υ.Α.Π '375': ΠΡΟΣΤΑΣΙΑ ΔΗΜΟΣΙΩΝ ΚΤΗΜΑΤΩΝ '376': ΒΙΒΛΙΑ ΕΝΔΙΚΩΝ ΜΕΣΩΝ '377': ΕΛΛΗΝΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ ΜΙΚΡΟΜΕΣΑΙΩΝ ΜΕΤΑΠΟΙΗΤΙΚΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ ΚΑΙ ΧΕΙΡΟΤΕΧΝΙΑΣ '378': ΔΗΜΟΣΙΟΓΡΑΦΙΚΟΣ ΧΑΡΤΗΣ '379': ΦΟΡΟΣ ΓΑΜΙΚΩΝ ΣΥΜΦΩΝΩΝ ΙΣΡΑΗΛΙΤΩΝ '380': ΥΠΟΤΡΟΦΙΑΙ ΚΤΗΝΙΑΤΡΙΚΗΣ '381': ΑΠΟΔΟΧΕΣ ΠΡΟΣΩΠΙΚΟΥ ΙΔΙΩΤΙΚΟΥ ΔΙΚΑΙΟΥ '382': ΕΠΙΒΑΤΗΓΑ ΑΚΤΟΠΛΟΙΚΑ ΠΛΟΙΑ '383': ΠΑΛΑΙΟΙ ΔΗΜΟΣΙΟΥΠΑΛΛΗΛΙΚΟΙ ΝΟΜΟΙ '384': ΚΩΔΙΚΑΣ ΠΕΡΙ ΚΛΗΡΟΔΟΤΗΜΑΤΩΝ '385': ΟΙΚΟΝΟΜΙΚΗ ΕΠΙΘΕΩΡΗΣΗ '386': ΚΤΗΜΑΤΟΓΡΑΦΗΣΗ ΔΑΣΩΝ '387': ΟΡΓΑΝΙΚΕΣ ΘΕΣΕΙΣ '388': ΠΕΡΙΟΡΙΣΜΟΣ ΧΡΗΣΗΣ ΟΡΙΣΜΕΝΩΝ ΣΥΜΒΑΤΙΚΩΝ ΟΠΛΩΝ '389': ΑΓΙΟΝ ΟΡΟΣ '390': ΚΥΡΩΣΕΙΣ ΦΟΡΟΛΟΓΙΚΩΝ ΠΑΡΑΒΑΣΕΩΝ '391': ΚΑΤΑΣΤΑΣΗ ΠΡΟΣΩΠΙΚΟΥ Ο.Γ.Α '392': ΕΠΑΝΑΠΑΤΡΙΣΜΟΣ ΚΕΦΑΛΑΙΩΝ '393': ΜΑΘΗΤΕΣ ΤΕΧΝΙΤΕΣ '394': ΔΙΑΒΙΒΑΣΕΙΣ '395': ΕΜΜΙΣΘΟΙ ΚΑΙ ΠΟΙΝΙΚΟΙ ΔΙΚ. ΕΠΙΜΕΛΗΤΕΣ '396': ΣΥΜΒΑΣΕΙΣ ΔΙΚΑΣΤΙΚΗΣ ΣΥΝΔΡΟΜΗΣ '397': ΔΗΜΟΣΙΑ ΕΠΙΧΕΙΡΗΣΗ ΠΕΤΡΕΛΑΙΟΥ '398': ΕΛΛΗΝΙΚΗ ΤΡΑΠΕΖΑ ΒΙΟΜΗΧΑΝΙΚΗΣ ΑΝΑΠΤΥΞΕΩΣ ΑΝΩΝΥΜΟΣ ΕΤΑΙΡΕΙΑ (Ε.Τ.Β.Α. Α.Ε.) '399': ΕΙΔΙΚΟΤΗΤΕΣ ΚΑΙ ΤΡΟΠΟΣ ΕΙΣΟΔΟΥ ΣΤΕΛΕΧΩΝ '400': ΠΡΟΣΤΑΣΙΑ ΕΡΓΑΖΟΜΕΝΩΝ ΣΤΗΝ ΗΜΕΔΑΠΗ - ΣΩΜΑ ΕΠΙΘΕΩΡΗΣΗΣ ΕΡΓΑΣΙΑΣ '401': ΙΝΣΤΙΤΟΥΤΟ ΩΚΕΑΝΟΓΡΑΦΙΚΩΝ ΚΑΙ ΑΛΙΕΥΤΙΚΩΝ ΕΡΕΥΝΩΝ '402': ΕΛΕΓΧΟΣ ΑΠΟΛΥΣΕΩΝ ΜΙΣΘΩΤΩΝ '403': ΠΑΝΕΛΛΗΝΙΑ ΕΚΘΕΣΗ ΛΑΜΙΑΣ '404': ΚΥΡΙΑΚΗ ΑΡΓΙΑ ΚΑΙ ΑΛΛΕΣ ΥΠΟΧΡΕΩΤΙΚΕΣ ΑΡΓΙΕΣ '405': ΚΛΑΔΟΣ ΥΓΕΙΑΣ Ο.Α.Ε.Ε '406': ΟΡΚΟΣ ΣΤΡΑΤΙΩΤΙΚΩΝ '407': ΕΜΠΟΡΙΚΑ ΒΙΒΛΙΑ '408': ΥΓΕΙΟΝΟΜΙΚΕΣ ΕΠΙΤΡΟΠΕΣ ΕΝΟΠΛΩΝ ΔΥΝΑΜΕΩΝ '409': ΑΓΙΟΣ ΒΙΚΕΝΤΙΟΣ-ΓΡΕΝΑΔΙΝΟΙ, ΑΓΙΟΣ ΜΑΡΙΝΟΣ Κ.ΛΠ '410': ΑΠΟΖΗΜΙΩΣΗ ΔΙΑΤΕΛΕΣΑΝΤΩΝ ΠΡΩΘΥΠΟΥΡΓΩΝ '411': ΑΣΦΑΛΙΣΗ ΛΟΓΟΤΕΧΝΩΝ ΚΑΙ ΚΑΛΛΙΤΕΧΝΩΝ '412': ΠΕΙΘΑΡΧΙΚΑ ΣΥΜΒΟΥΛΙΑ '413': ΕΤΑΙΡΙΕΣ ΧΡΗΜΑΤΟΔΟΤΙΚΗΣ ΜΙΣΘΩΣΗΣ '414': ΚΟΙΝΩΝΙΚΗ ΥΠΗΡΕΣΙΑ ΦΥΛΑΚΩΝ '415': ΚΑΝΟΝΙΣΜΟΣ ΥΠΗΡΕΣΙΩΝ ΑΓΡΟΦΥΛΑΚΗΣ '416': ΑΣΦΑΛΙΣΗ ΣΤΟ ΙΚΑ '417': ΕΜΠΟΡΙΚΟΙ ΣΥΜΒΟΥΛΟΙ ΚΑΙ ΑΚΟΛΟΥΘΟΙ '418': ΕΠΙΚΟΥΡΟΙ ΠΑΡΑΤΗΡΗΤΕΣ '419': ΥΠΟΤΡΟΦΙΕΣ '420': ΚΕΝΤΡΟ ΠΡΟΓΡΑΜΜΑΤΙΣΜΟΥ '421': ΠΡΩΤΕΣ ΥΛΕΣ ΣΟΚΟΛΑΤΟΠΟΙΙΑΣ '422': ΕΠΙΤΡΟΠΗ ΚΗΠΩΝ ΚΑΙ ΔΕΝΔΡΟΣΤΟΙΧΙΩΝ '423': ΚΙΝΗΤΟ ΕΠΙΣΗΜΑ '424': ΣΥΝΔΙΚΑΛΙΣΜΟΣ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ '425': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ Π.Ν '426': ΟΡΓΑΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΤΑΜΕΙΟΥ ΠΑΡΑΚΑΤΑΘΗΚΩΝ ΚΑΙ ΔΑΝΕΙΩΝ '427': ΑΔΕΙΕΣ ΗΝΙΟΧΙΑΣ '428': ΥΠΗΡΕΣΙΑ ΠΡΟΓΡΑΜΜΑΤΙΣΜΟΥ ΚΑΙ ΜΕΛΕΤΩΝ '429': ΚΡΑΤΙΚΑ ΑΥΤΟΚΙΝΗΤΑ '430': ΑΤΟΜΙΚΗ ΚΑΤΑΓΓΕΛΙΑ ΣΥΜΒΑΣΕΩΣ ΕΡΓΑΣΙΑΣ '431': ΠΟΛΥΤΕΚΝΟΙ '432': ΙΣΤΟΡΙΚΟ ΑΡΧΕΙΟ ΜΑΚΕΔΟΝΙΑΣ '433': ΑΣΦΑΛΙΣΗ ΑΥΤΟΚΙΝΗΤΙΚΩΝ ΑΤΥΧΗΜΑΤΩΝ '434': ΔΑΝΕΙΑ ΕΣΩΤΕΡΙΚΑ '435': ΕΚΚΛΗΣΙΑ ΚΡΗΤΗΣ '436': ΦΟΡΟΛΟΓΙΑ ΣΤΑΦΙΔΑΣ '437': ΕΚΠΑΙΔΕΥΤΙΚΕΣ ΑΔΕΙΕΣ '438': ΑΕΡΟΔΙΚΕΙΑ '439': ΕΠΙΔΟΜΑ ΑΣΘΕΝΕΙΑΣ '440': ΘΕΣΕΙΣ ΣΥΜΒΟΛΑΙΟΓΡΑΦΩΝ '441': ΑΓΟΡΑ ΣΥΝΑΛΛΑΓΜΑΤΟΣ '442': ΝΟΜΙΚΟ ΣΥΜΒΟΥΛΙΟ ΤΟΥ ΚΡΑΤΟΥΣ (Ν.Σ.Κ.) '443': ΦΟΡΟΛΟΓΙΑ ΜΕΤΑΒΙΒΑΣΗΣ '444': ΣΥΜΒΟΥΛΙΑ - ΕΠΙΤΡΟΠΕΣ - ΙΝΣΤΙΤΟΥΤΑ ΕΡΓΑΣΙΑΣ ΚΑΙ ΚΟΙΝΩΝΙΚΩΝ ΑΣΦΑΛΙΣΕΩΝ '445': ΤΕΛΗ ΕΙΣΙΤΗΡΙΩΝ ΚΑΙ ΚΟΜΙΣΤΡΩΝ '446': ΟΙΚΟΝΟΜΙΚΗ ΥΠΗΡΕΣΙΑ ΥΓΕΙΟΝΟΜΙΚΟΥ ΣΩΜΑΤΟΣ '447': ΠΡΟΣΩΠΙΚΟ ΣΩΜΑΤΩΝ ΑΣΦΑΛΕΙΑΣ ΜΕ ΣΧΕΣΗ ΙΔΙΩΤΙΚΟΥ ΔΙΚΑΙΟΥ '448': ΑΡΤΕΡΓΑΤΕΣ '449': ΕΥΚΟΛΙΕΣ ΣΕ ΦΟΙΤΗΤΕΣ '450': ΣΥΝΕΤΑΙΡΙΣΜΟΙ ΚΟΙΝΗΣ ΧΟΡΤΟΝΟΜΗΣ ΚΑΙ ΣΥΝΙΔΙΟΚΤΗΣΙΑΣ '451': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΠΕΡΙΦΕΡΕΙΑΚΟΥ ΓΕΝΙΚΟΥ ΝΟΣΟΚΟΜΕΙΟΥ Ο ΕΥΑΓΓΕΛΙΣΜΟΣ '452': ΠΡΟΣΚΟΠΙΣΜΟΣ '453': ΣΥΜΒΟΥΛΙΑ ΕΠΑΓΓΕΛΜΑΤΙΚΗΣ ΚΑΙ ΤΕΧΝΙΚΗΣ ΕΚΠΑΙΔΕΥΣΕΩΣ '454': ΚΡΑΤΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ ΜΗΧΑΝΗΜΑΤΩΝ ΔΗΜΟΣΙΩΝ ΕΡΓΩΝ '455': ΑΤΟΜΙΚΑ ΕΓΓΡΑΦΑ ΑΝΘΥΠΑΣΠΙΣΤΩΝ-ΥΠΑΞΙΩΜΑΤΙΚΩΝ '456': ΔΙΑΦΟΡΕΣ ΣΧΟΛΕΣ '457': ΒΙΒΛΙΑ ΔΗΜΟΣΙΕΥΣΕΩΣ ΔΙΑΘΗΚΩΝ '458': ΚΑΝΟΝΙΣΜΟΙ ΠΡΟΣΩΠΙΚΟΥ ΣΥΓΚΟΙΝΩΝΙΑΚΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ '459': ΤΟΥΡΙΣΤΙΚΟΙ ΤΟΠΟΙ '460': ΙΝΣΤΙΤΟΥΤΟ ΞΕΝΩΝ ΓΛΩΣΣΩΝ ΚΑΙ ΦΙΛΟΛΟΓΙΩΝ '461': ΚΑΠΝΟΠΩΛΕΣ '462': ΑΓΩΓΕΣ ΓΙΑΤΡΩΝ '463': ΣΥΣΤΑΣΗ ΚΑΙ ΑΠΟΔΟΣΗ ΠΑΡΑΚΑΤΑΘΗΚΩΝ ΑΠΟ Τ.Π. ΚΑΙ Δ '464': ΑΔΙΚΗΜΑΤΑ ΔΙΑΠΡΑΤΤΟΜΕΝΑ ΣΤΑ ΚΡΑΤΗ-ΜΕΛΗ '465': ΑΝΑΣΤΟΛΕΣ ΤΟΥ ΣΥΝΤΑΓΜΑΤΟΣ - ΚΑΤΑΣΤΑΣΗ ΠΟΛΙΟΡΚΙΑΣ '466': ΣΥΜΒΑΣΕΙΣ ΠΑΡΟΧΗΣ ΑΣΦΑΛΕΙΑΣ (ΕΝΕΧΥΡΟ, ΥΠΟΘΗΚΗ Κ.ΛΠ.) '467': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣΝΑΥΤΙΚΩΝ ΠΡΑΚΤΟΡΩΝ ΚΑΙ ΥΠΑΛΛΗΛΩΝ (Τ.Α.Ν.Π.Υ.) '468': ΑΝΩΤΑΤΟ ΣΥΓΚΟΙΝΩΝΙΑΚΟ ΣΥΜΒΟΥΛΙΟ '469': ΠΡΕΒΕΝΤΟΡΙΑ '470': ΑΝΑΒΟΛΗ ΣΤΡΑΤΕΥΣΕΩΣ '471': ΕΙΔΙΚΑ ΛΗΞΙΑΡΧΕΙΑ '472': ΓΕΩΤΕΧΝΙΚΟ ΕΠΙΜΕΛΗΤΗΡΙΟ '473': ΥΓΕΙΟΝΟΜΙΚΑ ΔΙΚΑΙΩΜΑΤΑ '474': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΗΣ ΕΚΠΑΙΔΕΥΤΙΚΩΝ '475': ΚΑΖΑΚΣΤΑΝ – ΚΑΜΕΡΟΥΝ – ΚΑΝΑΔΑΣ Κ.ΛΠ '476': ΣΥΝΤΑΞΕΙΣ ΘΥΜΑΤΩΝ ΑΠΟ ΤΟΝ ΑΜΑΧΟ ΠΛΗΘΥΣΜΟ '477': ΦΙΛΟΣΟΦΙΚΗ ΣΧΟΛΗ '478': ΕΚΤΕΛΩΝΙΣΜΟΣ ΤΑΧΥΔΡΟΜΙΚΩΝ ΔΕΜΑΤΩΝ '479': ΥΔΡΕΥΣΗ ΘΕΣΣΑΛΟΝΙΚΗΣ '480': ΣΥΜΦΩΝΙΕΣ ΠΕΡΙ ΠΛΩΤΩΝ ΟΔΩΝ '481': ΑΝΑΚΗΡΥΞΗ ΤΗΣ ΑΝΕΞΑΡΤΗΣΙΑΣ '482': ΕΠΙΤΡΟΠΗ ΟΛΥΜΠΙΑΚΩΝ ΑΓΩΝΩΝ '483': ΟΙΝΟΠΑΡΑΓΩΓΗ ΑΤΤΙΚΟΒΟΙΩΤΙΑΣ '484': ΕΚΠΤΩΣΕΙΣ ΥΠΕΡ ΕΞΑΓΩΓΕΩΝ '485': ΦΟΡΟΛΟΓΙΑ ΚΛΗΡΟΝΟΜΙΩΝ, ΔΩΡΕΩΝ, ΓΟΝΙΚΩΝ ΠΑΡΟΧΩΝ '486': ΟΡΦΑΝΟΤΡΟΦΕΙΑ ΚΑΙ ΟΙΚΟΤΡΟΦΕΙΑ '487': ΜΕ ΤΗΝ ΟΥΡΑΓΟΥΑΗ '488': ΜΕ ΤΗΝ ΑΥΣΤΡΙΑΚΗ '489': ΔΙΑΦΟΡΟΙ ΦΟΡΟΙ ΚΑΤΑΝΑΛΩΣΕΩΣ '490': ΔΙΕΥΘΥΝΣΗ ΕΦΕΔΡΩΝ - ΠΟΛΕΜΙΣΤΩΝ - ΑΓΩΝΙΣΤΩΝ '491': ΑΓΡΟΤΙΚΕΣ ΟΙΚΟΚΥΡΙΚΕΣ ΣΧΟΛΕΣ '492': ΞΥΛΕΙΑ '493': ΒΙΒΛΙΑΡΙΑ ΥΓΕΙΑΣ ΕΡΓΑΤΩΝ '494': ΣΧΟΛΗ ΑΞΙΩΜΑΤΙΚΩΝ ΣΤΡΑΤΙΩΤΙΚΩΝ ΥΠΗΡΕΣΙΩΝ '495': ΝΟΜΑΡΧΙΑΚΕΣ ΚΑΙ ΔΗΜΟΤΙΚΕΣ ΕΚΛΟΓΕΣ '496': ΕΓΓΥΗΣΕΙΣ ΚΑΙ ΔΑΝΕΙΑ ΤΟΥ ΔΗΜΟΣΙΟΥ '497': ΥΠΟΥΡΓΕΙΟ ΑΝΑΠΤΥΞΗΣ '498': ΤΑΚΤΙΚΑ ΔΙΟΙΚΗΤΙΚΑ ΔΙΚΑΣΤΗΡΙΑ - ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ '499': ΤΡΟΦΟΔΟΣΙΑ ΠΛΗΡΩΜΑΤΩΝ ΠΛΟΙΩΝ '500': ΔΙΑΦΟΡΟΙ ΛΙΜΕΝΕΣ ΚΑΙ ΛΙΜΕΝΙΚΑ ΤΑΜΕΙΑ '501': ΗΛΕΚΤΡΙΚΕΣ ΕΚΜΕΤΑΛΛΕΥΣΕΙΣ '502': ΠΡΟΥΠΟΘΕΣΕΙΣ ΑΣΚΗΣΗΣ ΔΙΑΦΟΡΩΝ ΕΠΑΓΓΕΛΜΑΤΩΝ '503': ΤΕΛΩΝΕΙΑΚΗ ΥΠΗΡΕΣΙΑ ΑΕΡΟΣΚΑΦΩΝ '504': ΕΠΙΤΡΟΠΗ ΔΑΣΜΟΛΟΓΙΟΥ '505': ΝΑΥΠΗΓΕΙΑ Π. ΝΑΥΤΙΚΟΥ '506': ΒΙΟΜΗΧΑΝΙΚΕΣ ΚΑΙ ΕΠΙΧΕΙΡΗΜΑΤΙΚΕΣ ΠΕΡΙΟΧΕΣ '507': ΙΑΤΡΟΔΙΚΑΣΤΕΣ '508': ΑΘΛΗΤΙΣΜΟΣ ΕΝΟΠΛΩΝ ΔΥΝΑΜΕΩΝ '509': ΟΡΓΑΝΙΣΜΟΣ ΣΥΚΩΝ '510': ΚΑΝΟΝΙΣΜΟΣ ΑΣΘΕΝΕΙΑΣ ΤΑΜΕΙΟΥ ΣΥΝΤΑΞΕΩΝ ΕΦΗΜΕΡΙΔΟΠΩΛΩΝ ΚΑΙ ΥΠΑΛΛΗΛΩΝ ΠΡΑΚΤΟΡΕΙΩΝ (Τ.Σ.Ε.Υ.Π.) '511': ΑΔΕΙΕΣ ΜΙΣΘΩΤΩΝ '512': ΠΡΟΣΤΑΣΙΑ ΚΕΦΑΛΑΙΩΝ ΕΞΩΤΕΡΙΚΟΥ '513': ΑΠΟΔΕΙΚΤΙΚΑ ΦΟΡΟΛΟΓΙΚΗΣ ΕΝΗΜΕΡΟΤΗΤΑΣ '514': ΟΡΓΑΝΩΣΗ ΚΑΙ ΛΕΙΤΟΥΡΓΙΑ ΤΩΝ ΤΗΛΕΠΙΚΟΙΝΩΝΙΩΝ ΕΘΝΙΚΗ ΕΠΙΤΡΟΠΗ ΤΗΛΕΠΙΚΟΙΝΩΝΙΩΝ ΚΑΙ ΤΑΧΥΔΡΟΜΕΙΩΝ (Ε.Ε.Τ.Τ.) '515': ΠΡΟΣΩΠΙΚΟ Ο.Τ.Ε '516': ΒΑΣΙΛΙΚΑ ΙΔΡΥΜΑΤΑ '517': ΑΠΟΚΑΤΑΣΤΑΣΗ ΠΛΗΓΕΝΤΩΝ ΑΠΟ ΕΚΡΗΞΗ ΠΛΟΙΟΥ ΣΤΗΝ ΚΡΗΤΗ '518': ΕΚΜΕΤΑΛΛΕΥΣΗ ΔΥΝΑΜΕΩΣ ΡΕΟΝΤΩΝ ΥΔΑΤΩΝ '519': ΚΑΚΟΥΡΓΙΟΔΙΚΕΙΑ '520': ΚΕΝΤΡΙΚΕΣ ΑΓΟΡΕΣ ΑΛΛΩΝ ΠΟΛΕΩΝ '521': ΤΑΜΕΙΟ ΑΛΛΗΛΟΒΟΗΘΕΙΑΣ Π.Ν '522': ΕΚΛΟΓΙΚΟΙ ΚΑΤΑΛΟΓΟΙ ΚΑΙ ΒΙΒΛΙΑΡΙΑ '523': ΥΠΗΡΕΣΙΑ ΕΓΓΕΙΩΝ ΒΕΛΤΙΩΣΕΩΝ '524': ΤΟΥΡΙΣΤΙΚΗ ΑΝΑΠΤΥΞΗ '525': ΝΟΜΟΘΕΣΙΑ ΠΕΡΙ ΣΥΜΒΑΣΕΩΣ ΕΡΓΑΣΙΑΣ '526': ΕΛΕΓΧΟΣ ΕΚΡΗΚΤΙΚΩΝ ΥΛΩΝ '527': ΜΑΚΕΔΟΝΙΚΟΙ ΣΙΔΗΡΟΔΡΟΜΟΙ '528': ΔΙΕΥΚΟΛΥΝΣΕΙΣ ΣΕ ΔΗΜΟΣΙΟΥΣ ΥΠΑΛΛΗΛΟΥΣ '529': ΣΤΡΑΤΙΩΤΙΚΕΣ ΥΠΟΧΡΕΩΣΕΙΣ ΕΠΑΝΑΠΑΤΡΙΖΟΜΕΝΩΝ '530': ΔΙΑΚΡΙΣΗ ΕΜΠΟΡΙΚΩΝ ΠΡΑΞΕΩΝ '531': ΟΡΓΑΝΙΣΜΟΣ ΕΛΛΗΝΙΚΩΝ ΓΕΩΡΓΙΚΩΝ ΑΣΦΑΛΙΣΕΩΝ (Ε.Λ.Γ.Α.) '532': ΕΞΩΣΧΟΛΙΚΗ ΣΩΜΑΤΙΚΗ ΑΓΩΓΗ '533': ΔΡΑΧΜΟΠΟΙΗΣΗ '534': ΜΕ ΤΗ ΒΡΑΖΙΛΙΑ '535': ΕΚΚΛΗΣΙΑΣΤΙΚΗ ΑΚΑΔΗΜΙΑ '536': ΑΝΤΑΛΛΑΓΗ ΘΕΡΑΠΕΥΤΙΚΩΝ ΟΥΣΙΩΝ '537': ΓΑΛΛΙΑ, ΓΕΡΜΑΝΙΑ Κ.ΛΠ '538': ΝΟΜΟΠΑΡΑΣΚΕΥΑΣΤΙΚΕΣ ΕΠΙΤΡΟΠΕΣ '539': ΚΥΒΕΡΝΕΙΟ ΘΕΣΣΑΛΟΝΙΚΗΣ '540': ΣΤΡΑΤΙΩΤΙΚΟΙ ΑΚΟΛΟΥΘΟΙ '541': ΔΙΑΘΕΣΗ ΑΠΟΣΤΡΑΓΓΙΖΟΜΕΝΩΝ ΓΑΙΩΝ '542': ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΓΙΑ ΡΑΔΙΟΦΩΝΙΑ – ΤΗΛΕΟΡΑΣΗ '543': ΓΝΩΜΟΔΟΤΙΚΟ ΣΥΜΒΟΥΛΙΟ ΦΑΡΜΑΚΩΝ '544': ΣΥΜΒΑΣΕΙΣ ΔΙΑΦΟΡΕΣ '545': ΠΡΑΞΕΙΣ ΚΑΤΑ ΤΗΣ ΑΣΦΑΛΕΙΑΣ ΤΗΣ ΑΕΡΟΠΟΡΙΑΣ '546': ΙΑΤΡΟΙ ΙΑΜΑΤΙΚΩΝ ΠΗΓΩΝ '547': ΚΕΝΤΡΙΚΟ ΣΥΜΒΟΥΛΙΟ ΥΓΕΙΑΣ (ΚΕ.Σ.Υ.) '548': ΑΝΩΤΑΤΟ ΣΥΜΒΟΥΛΙΟ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ '549': ΥΠΟΥΡΓΕΙΟ ΕΝΕΡΓΕΙΑΣ ΚΑΙ ΦΥΣΙΚΩΝ ΠΟΡΩΝ '550': ΤΕΧΝΙΚΗ ΕΚΜΕΤΑΛΛΕΥΣΗ ΕΛΑΦΡΩΝ ΑΕΡΟΠΛΑΝΩΝ Δ.Χ '551': ΠΟΛΥΕΘΝΕΙΣ ΜΟΡΦΩΤΙΚΕΣ ΣΥΜΦΩΝΙΕΣ '552': ΕΚΠΑΙΔΕΥΣΗ Λ.Σ '553': ΠΡΟΣΤΑΣΙΑ ΕΛΕΥΘΕΡΟΥ ΑΝΤΑΓΩΝΙΣΜΟΥ '554': ΕΘΝΙΚΗ ΕΠΙΤΡΟΠΗ ΔΙΕΘΝΟΥΣ ΕΜΠΟΡΙΚΟΥ ΕΠΙΜΕΛΗΤΗΡΙΟΥ '555': ΟΡΓΑΝΙΣΜΟΣ '556': ΤΕΛΩΝΕΙΑΚΕΣ ΠΑΡΑΚΑΤΑΘΗΚΕΣ '557': ΕΛΕΓΧΟΣ ΟΡΓΑΝΙΣΜΩΝ ΚΟΙΝΩΝΙΚΗΣ ΠΟΛΙΤΙΚΗΣ '558': ΕΝΩΣΕΙΣ ΑΠΟΣΤΡΑΤΩΝ ΑΞΙΩΜΑΤΙΚΩΝ ΕΝΟΠΛΩΝ ΔΥΝΑΜΕΩΝ '559': ΦΥΛΛΑ ΠΟΙΟΤΗΤΑΣ ΑΞΙΩΜΑΤΙΚΩΝ Π.Ν '560': ΙΝΣΤΙΤΟΥΤΟ ΓΕΩΛΟΓΙΚΩΝ ΚΑΙ ΜΕΤΑΛΛΕΥΤΙΚΩΝ ΕΡΕΥΝΩΝ '561': ΛΑΟΓΡΑΦΙΚΟ ΚΑΙ ΕΘΝΟΛΟΓΙΚΟ ΜΟΥΣΕΙΟ ΜΑΚΕΔΟΝΙΑΣ - ΘΡΑΚΗΣ '562': ΠΡΩΤΕΣ ΥΛΕΣ ΤΑΠΗΤΟΥΡΓΙΑΣ '563': ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΡΗΤΗΣ '564': ΚΩΔΙΚΑΣ ΟΔΙΚΗΣ ΚΥΚΛΟΦΟΡΙΑΣ '565': ΦΑΡΜΑΚΕΥΤΙΚΗ ΠΕΡΙΘΑΛΨΗ '566': ΜΕΛΕΤΕΣ ΠΡΟΓΡΑΜΜΑΤΟΣ ΔΗΜΟΣΙΩΝ ΕΠΕΝΔΥΣΕΩΝ '567': ΕΠΙΔΟΣΗ ΔΙΑ ΤΟΥ ΤΑΧΥΔΡΟΜΕΙΟΥ '568': ΠΑΝΕΠΙΣΤΗΜΙΟ ΘΡΑΚΗΣ '569': ΗΘΙΚΕΣ ΑΜΟΙΒΕΣ '570': ΔΗΜΟΣΙΑ ΚΤΗΜΑΤΑ ΣΤΗ ΔΩΔΕΚΑΝΗΣΟ '571': ΣΥΜΒΑΣΕΙΣ ΔΙΚΑΣΤΙΚΗΣ ΑΝΤΙΛΗΨΕΩΣ '572': ΠΕΡΙΟΡΙΣΜΟΙ ΑΛΙΕΙΑΣ '573': ΠΥΡΗΝΙΚΕΣ ΕΓΚΑΤΑΣΤΑΣΕΙΣ '574': ΩΡΕΣ ΕΡΓΑΣΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΑΥΤΟΚΙΝΗΤΩΝ '575': ΕΓΓΡΑΦΕΣ, ΕΞΕΤΑΣΕΙΣ, ΑΝΑΛΥΤΙΚΑ ΠΡΟΓΡΑΜΜΑΤΑ '576': ΔΙΚΑΙΩΜΑΤΑ ΤΕΛΩΝΕΙΑΚΩΝ ΕΡΓΑΣΙΩΝ '577': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΑΥΤΟΚΙΝΗΤΙΣΤΩΝ (Τ.Σ.Α.) '578': ΤΗΛΕΦΩΝΙΚΟΣ ΚΑΝΟΝΙΣΜΟΣ '579': ΦΟΡΟΛΟΓΙΑ ΑΣΦΑΛΙΣΤΡΩΝ '580': ΔΙΕΘΝΗΣ ΥΔΡΟΓΡΑΦΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ '581': ΕΠΑΡΧΙΕΣ '582': ΑΓΡΟΤ. ΑΠΟΚΑΤΑΣΤΑΣΗ ΠΡΟΣΦΥΓΩΝ '583': ΓΕΝΙΚΑ ΓΙΑ ΤΑ ΘΕΑΤΡΑ '584': ΣΥΜΒΑΣΕΙΣ ΔΙΩΞΕΩΣ ΛΑΘΡΕΜΠΟΡΙΟΥ '585': ΜΗΧΑΝΕΣ ΠΡΟΠΛΗΡΩΜΗΣ ΤΕΛΩΝ '586': ΟΡΓΑΝΙΣΜΟΣ ΚΡΑΤΙΚΩΝ ΘΕΑΤΡΩΝ '587': ΚΕΝΤΡΟ ΗΛΕΚΤΡΟΝΙΚΟΥ ΥΠΟΛΟΓΙΣΤΟΥ ΚΟΙΝΩΝΙΚΩΝ ΥΠΗΡΕΣΙΩΝ '588': ΦΟΡΟΣ ΠΡΟΣΤΙΘΕΜΕΝΗΣ ΑΞΙΑΣ '589': ΤΑΜΕΙΑ ΑΡΩΓΗΣ ΤΤΤ. ΥΠΑΛΛΗΛΩΝ '590': ΣΩΜΑ ΟΡΚΩΤΩΝ ΕΛΕΓΚΤΩΝ ΛΟΓΙΣΤΩΝ (Σ.Ο.Ε.Λ.), ΕΠΙΤΡΟΠΗ ΛΟΓΙΣΤΙΚΗΣ ΤΥΠΟΠΟΙΗΣΗΣ ΚΑΙ ΕΛΕΓΧΩΝ (Ε.Λ.Τ.Ε.) '591': ΑΓΡΟΤΙΚΑ ΝΗΠΙΟΤΡΟΦΕΙΑ '592': ΣΧΕΔΙΟ ΠΟΛΕΩΣ ΑΘΗΝΩΝ ΠΕΙΡΑΙΩΣ '593': ΜΙΣΘΩΣΕΙΣ ΑΚΙΝΗΤΩΝ Ο.Δ.Ε.Π '594': ΕΛΕΓΧΟΣ ΣΠΟΡΟΠΑΡΑΓΩΓΗΣ '595': ΑΜΥΝΤΙΚΕΣ ΠΕΡΙΟΧΕΣ ΚΑΙ Ν. ΟΧΥΡΑ '596': ΟΔΟΙΠΟΡΙΚΑ '597': ΠΟΡΟΙ ΟΡΓΑΝΙΣΜΩΝ ΤΟΥΡΙΣΜΟΥ '598': ΔΙΕΘΝΕΣ ΔΙΚΑΣΤΗΡΙΟ '599': ΟΙΚΟΝΟΜΙΚΗ ΜΕΡΙΜΝΑ ΕΝΟΠΛΩΝ ΔΥΝΑΜΕΩΝ '600': ΓΕΝΙΚΟ ΝΟΣΟΚΟΜΕΙΟ ΕΜΠΟΡΙΚΟΥ ΝΑΥΤΙΚΟΥ '601': ΝΟΜΙΚΗ ΒΟΗΘΕΙΑ ΣΕ ΠΟΛΙΤΕΣ ΧΑΜΗΛΟΥ ΕΙΣΟΔΗΜΑΤΟΣ '602': ΣΥΜΒΟΛΑΙΟΓΡΑΦΙΚΟΙ ΣΥΛΛΟΓΟΙ '603': ΥΠΟΥΡΓΕΙΟ ΣΤΡΑΤΙΩΤΙΚΩΝ '604': ΠΡΟΣΩΠΙΚΟ Ε.Μ.Π '605': ΥΠΟΥΡΓΕΙΟ ΕΡΓΑΣΙΑΣ '606': ΑΓΟΝΕΣ ΓΡΑΜΜΕΣ '607': ΜΟΝΟΠΩΛΙΟ ΠΕΤΡΕΛΑΙΟΥ '608': ΠΡΟΛΗΨΗ ΡΥΠΑΝΣΗΣ ΤΗΣ ΘΑΛΑΣΣΑΣ '609': ΧΩΡΙΚΗ ΔΙΚΑΙΟΔΟΣΙΑ ΤΕΛΩΝΕΙΑΚΩΝ ΑΡΧΩΝ '610': ΕΠΑΓΓΕΛΜΑΤΙΚΑ ΣΩΜΑΤΕΙΑ '611': ΥΠΗΡΕΣΙΑ ΑΓΡΟΤΙΚΗΣ ΑΣΦΑΛΕΙΑΣ '612': ΑΞΙΟΠΟΙΗΣΗ ΕΚΚΛΗΣΙΑΣΤΙΚΗΣ ΠΕΡΙΟΥΣΙΑΣ '613': ΕΜΠΟΡΙΚΟΙ ΑΝΤΙΠΡΟΣΩΠΟΙ '614': ΕΝΩΣΕΙΣ ΕΦΕΔΡΩΝ ΑΞΙΩΜΑΤΙΚΩΝ '615': ΑΤΕΛΕΙΕΣ ΥΠΕΡ ΤΗΣ ΒΙΟΜΗΧΑΝΙΑΣ '616': ΛΟΓΙΣΤΙΚΟ ΕΙΔΙΚΩΝ ΤΑΜΕΙΩΝ Ν.Π.Δ.Δ '617': ΣΥΜΒΑΣΗ ΓΙΑ ΔΕΙΓΜΑΤΑ ΚΛΠ '618': ΕΡΓΟΛΗΠΤΕΣ ΔΗΜΟΣΙΩΝ ΕΡΓΩΝ '619': ΕΠΑΝΕΠΟΙΚΙΣΜΟΣ ΠΑΡΑΜΕΘΟΡΙΩΝ ΠΕΡΙΟΧΩΝ '620': ΦΑΡΙΚΑ ΤΕΛΗ '621': ΛΑΤΟΜΕΙΑ ΜΑΡΜΑΡΩΝ '622': ΠΟΣΟΣΤΟ ΣΥΜΜΕΤΟΧΗΣ ΑΣΦΑΛΙΣΜΕΝΩΝ '623': ΑΣΦΑΛΕΙΑ ΑΝΘΡΩΠΙΝΗΣ ΖΩΗΣ ΣΤΗ ΘΑΛΑΣΣΑ '624': ΟΡΓΑΝΙΚΟΙ ΝΟΜΟΙ ΠΕΡΙ ΦΥΛΑΚΩΝ '625': ΛΑΘΡΕΜΠΟΡΙΑ '626': ΑΣΦΑΛΙΣΤΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΓΕΝΙΚΑ '627': ΕΙΣΑΓΩΓΗ ΧΛΩΡΙΚΟΥ ΚΑΛΙΟΥ '628': ΙΝΣΤΙΤΟΥΤΟ ΓΕΩΠΟΝΙΚΩΝ ΕΠΙΣΤΗΜΩΝ '629': ΕΠΙΔΟΜΑ ΠΑΣΧΑ - ΧΡΙΣΤΟΥΓΕΝΝΩΝ '630': ΓΕΩΡΓΙΚΟΙ ΣΥΝΕΤΑΙΡΙΣΜΟΙ ΑΛΛΗΛΑΣΦΑΛΕΙΑΣ '631': ΟΡΓΑΝΙΣΜΟΣ ΦΟΡΟΛΟΓΙΚΩΝ ΔΙΚΑΣΤΗΡΙΩΝ '632': ΕΠΙΔΟΣΗ '633': ΙΔΡΥΜΑ ΚΡΑΤΙΚΩΝ ΥΠΟΤΡΟΦΙΩΝ '634': ΥΓΕΙΟΝΟΜΙΚΟΣ ΚΑΝΟΝΙΣΜΟΣ ΑΕΡΟΥΓΕΙΟΝΟΜΕΙΩΝ '635': ΟΦΕΙΛΕΣ ΠΡΟΣ ΤΟ ΔΗΜΟΣΙΟ '636': ΠΡΑΚΤΟΡΕΙΑ ΕΙΔΗΣΕΩΝ '637': ΕΛΕΓΧΟΣ ΚΑΙ ΕΠΟΠΤΕΙΑ ΞΕΝΟΔΟΧΕΙΩΝ ΚΛΠ '638': ΚΟΙΝΑ ΤΑΜΕΙΑ ΕΚΜΕΤΑΛΛΕΥΣΕΩΣ ΛΕΩΦΟΡΕΙΩΝ (Κ.Τ.Ε.Λ.) '639': ΚΑΤΩΤΑΤΑ ΟΡΙΑ ΜΙΣΘΩΝ ΚΑΙ ΗΜΕΡΟΜΙΣΘΙΩΝ '640': ΣΥΝΤΗΡΗΤΙΚΗ ΚΑΤΑΣΧΕΣΗ ΠΛΟΙΩΝ '641': ΥΠΗΡΕΣΙΑ ΠΡΟΣΤΑΣΙΑΣ ΕΡΓΑΖΟΜΕΝΩΝ ΣΤΗΝ ΑΛΛΟΔΑΠΗ '642': ΕΥΡΩΠΑΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ ΠΥΡΗΝΙΚΩΝ ΕΡΕΥΝΩΝ '643': ΒΙΒΛΙΑ ΓΕΩΡΓΙΚΩΝ ΣΥΝΕΤΑΙΡΙΣΜΩΝ '644': ΠΟΛΙΤΙΚΕΣ ΚΑΙ ΣΤΡΑΤΙΩΤΙΚΕΣ ΣΥΝΤΑΞΕΙΣ '645': ΜΕΤΑΤΡΟΠΗ ΜΕΤΟΧΩΝ ΣΕ ΟΝΟΜΑΣΤΙΚΕΣ '646': ΕΙΔΙΚΟΙ ΦΡΟΥΡΟΙ '647': ΥΠΗΡΕΣΙΑ ΕΘΝΙΚΗΣ ΑΣΦΑΛΕΙΑΣ '648': ΡΥΘΜΙΣΤΙΚΟΣ ΦΟΡΟΣ '649': ΛΙΜΑΝΙ ΗΡΑΚΛΕΙΟΥ ΚΡΗΤΗΣ ΚΑΙ '650': ΕΚΚΛΗΣΙΑΣΤΙΚΕΣ ΥΠΟΤΡΟΦΙΕΣ '651': ΦΟΡΟΛΟΓΙΑ ΟΙΝΟΥ '652': ΔΙΕΘΝΗΣ ΥΓΕΙΟΝΟΜΙΚΗ ΣΥΜΒΑΣΗ ΑΕΡΟΝΑΥΤΙΛΙΑΣ '653': ΤΑΜΕΙΟ ΑΡΩΓΗΣ ΥΠΑΛΛΗΛΩΝ '654': ΚΟΙΝΩΝΙΚΗ ΑΣΦΑΛΙΣΗ ΑΓΡΟΤΩΝ '655': ΚΥΡΟΣ ΣΥΜΒΟΛΑΙΟΓΡΑΦΙΚΩΝ ΠΡΑΞΕΩΝ '656': ΦΟΡΟΛΟΓΙΑ ΥΠΕΡΑΞΙΑΣ ΑΚΙΝΗΤΩΝ '657': ΝΗΠΙΑΓΩΓΕΙΑ '658': ΕΚΘΕΜΑΤΑ ΚΑΙ ΔΕΙΓΜΑΤΑ '659': ΥΓΕΙΟΝΟΜΙΚΟ ΣΩΜΑ ΑΕΡΟΠΟΡΙΑΣ '660': ΠΛΗΡΩΜΗ ΜΙΣΘΩΝ ΚΑΙ ΗΜΕΡΟΜΙΣΘΙΩΝ '661': ΚΩΔΙΚΑΣ ΦΟΡΟΛΟΓΙΑΣ ΚΑΠΝΟΥ '662': ΟΡΙΑ '663': ΔΙΚΑΙΟΣΤΑΣΙΑ ΣΕΙΣΜΟΠΑΘΩΝ, ΠΥΡΟΠΑΘΩΝ, ΠΡΟΣΦΥΓΩΝ ΚΛΠ '664': ΧΡΕΗ ΚΛΗΡΟΝΟΜΙΩΝ '665': ΠΡΟΣΩΠΙΚΟΝ ΙΔΡΥΜΑΤΩΝ ΠΑΙΔΙΚΗΣ ΠΡΟΣΤΑΣΙΑΣ '666': ΜΙΣΘΩΣΕΙΣ ΚΑΙ ΑΓΟΡΕΣ '667': ΠΑΛΑΙΟΤΕΡΑΙ ΕΚΚΑΘΑΡΙΣΕΙΣ '668': ΟΙΚΟΝΟΜΙΚΗ ΑΠΟΚΑΤΑΣΤΑΣΗ ΑΓΡΟΤΩΝ '669': ΑΠΑΛΛΟΤΡΙΩΣΕΙΣ ΓΙΑ ΔΗΜΟΤΙΚΑ ΚΑΙ ΚΟΙΝΟΤΙΚΑ ΕΡΓΑ '670': ΜΗΤΡΩΟ ΑΓΡΟΤΩΝ '671': ΚΑΝΟΝΙΣΜΟΣ ΔΙΕΥΚΟΛΥΝΣΕΩΝ '672': ΚΡΑΤΙΚΟ ΕΡΓΟΣΤΑΣΙΟ ΑΕΡΟΠΛΑΝΩΝ '673': ΕΠΑΓΓΕΛΜΑΤΙΚΑ ΕΝΔΕΙΚΤΙΚΑ '674': ΑΥΘΑΙΡΕΤΕΣ ΚΑΤΑΣΚΕΥΕΣ '675': ΕΓΚΑΤΑΛΕΛΕΙΜΜΕΝΕΣ ΕΚΤΑΣΕΙΣ '676': ΥΠΟΥΡΓΕΙΟ ΔΗΜΟΣΙΩΝ ΄ΕΡΓΩΝ '677': ΠΡΟΝΟΙΑ Β. ΕΛΛΑΔΟΣ '678': ΔΙΚΑΣΤΙΚΟ ΕΝΣΗΜΟ - ΑΓΩΓΟΣΗΜΟ '679': ΤΑΧΥΔΡΟΜΙΚΗ ΑΝΤΑΠΟΚΡΙΣΗ '680': ΕΣΩΤΕΡΙΚΗ ΝΟΜΟΘΕΣΙΑ '681': ΦΟΡΟΛΟΓΙΑ ΤΣΙΓΑΡΟΧΑΡΤΟΥ '682': ΟΡΓΑΝΙΚΕΣ ΘΕΣΕΙΣ ΑΞΙΩΜΑΤΙΚΩΝ '683': ΜΑΙΕΥΤΙΚΗ ΠΕΡΙΘΑΛΨΗ '684': ΑΔΕΙΕΣ ΣΤΡΑΤΙΩΤΙΚΩΝ '685': ΟΡΓΑΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΕΛΛΗΝΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ '686': ΠΟΙΝΙΚΟΣ ΚΑΙ ΠΕΙΘΑΡΧΙΚΟΣ ΚΩΔΙΚΑΣ '687': ΑΝΥΠΟΤΑΚΤΟΙ '688': ΔΙΕΥΘΥΝΣΗ ΤΕΛΩΝΕΙΩΝ ΘΕΣΣΑΛΟΝΙΚΗΣ '689': ΠΕΡΙΦΕΡΕΙΕΣ ΛΙΜΕΝΙΚΩΝ ΑΡΧΩΝ '690': ΑΣΦΑΛΙΣΗ ΚΑΙ ΕΙΣΠΡΑΞΗ ΠΟΡΩΝ Τ.Ε.Β.Ε '691': ΣΙΔΗΡΟΣ '692': ΓΕΝΙΚΗ ΓΡΑΜΜΑΤΕΙΑ ΕΜΠΟΡΙΟΥ '693': ΔΙΑΧΕΙΡΙΣΗ ΙΣΡΑΗΛΙΤΙΚΩΝ ΠΕΡΟΥΣΙΩΝ '694': ΛΙΠΟΤΑΞΙΑ '695': ΒΑΡΕΑ ΚΑΙ ΑΝΘΥΓΙΕΙΝΑ ΕΠΑΓΓΕΛΜΑΤΑ '696': ΕΙΔΙΚΟ ΤΑΜΕΙΟ ΜΗΧΑΝΗΜΑΤΩΝ '697': ΛΕΩΦΟΡΕΙΑ ΠΕΡΙΟΧΗΣ ΠΡΩΤΕΥΟΥΣΑΣ '698': ΑΝΑΜΟΡΦΩΤΙΚΑ ΚΑΤΑΣΤΗΜΑΤΑ '699': ΥΓΕΙΟΝΟΜΙΚΟ ΣΩΜΑ '700': ΟΡΓΑΝΙΣΜΟΣ ΥΠΟΥΡΓΕΙΟΥ ΕΡΓΑΣΙΑΣ '701': ΔΙΩΡΥΓΑ ΚΟΡΙΝΘΟΥ '702': ΠΕΡΙΘΑΛΨΗ ΦΥΜΑΤΙΚΩΝ ΑΣΦΑΛΙΣΜΕΝΩΝ '703': ΚΟΙΝΩΝΙΚΟΣ ΕΛΕΓΧΟΣ ΔΙΟΙΚΗΣΗΣ - ΑΝΤΙΓΡΑΦΕΙΟΚΡΑΤΙΚΑ ΜΕΤΡΑ -ΕΚΚΑΘΑΡΙΣΗ ΑΡΧΕΙΩΝ '704': ΒΙΒΛΙΑ ΥΠΟΘΕΣΕΩΝ ΕΚΟΥΣΙΑΣ ΔΙΚΑΙΟΔΟΣΙΑΣ '705': ΖΑΧΑΡΗ '706': ΒΟΡΕΙΟΑΤΛΑΝΤΙΚΗ ΑΜΥΝΤΙΚΗ ΟΡΓΑΝΩΣΗ (Ν.Α.Τ.Ο) '707': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΠΡΟΣΩΠΙΚΟΥ ΕΤΑΙΡΕΙΑΣ ΓΕΝΙΚΩΝ ΑΠΟΘΗΚΩΝ '708': ΝΟΜΙΚΗ ΚΑΤΑΣΤΑΣΗ ΠΡΟΣΦΥΓΩΝ '709': ΔΙΚΑΣΤΗΡΙΟ ΛΕΙΩΝ '710': ΔΙΕΘΝΗΣ ΟΡΓΑΝΩΣΗ ΕΡΓΑΣΙΑΣ '711': ΠΡΟΜΗΘΕΙΕΣ–ΜΙΣΘΩΣΕΙΣ–ΕΡΓΑ Ο.Γ.Α '712': ΠΕΡΙΘΑΛΨΗ ΠΡΟΣΩΠΙΚΟΥ Ο.Γ.Α '713': ΧΟΡΗΓΗΣΗ ΔΑΝΕΙΩΝ ΑΠΟ Τ.Π. ΚΑΙ ΔΑΝΕΙΩΝ '714': ΤΕΛΟΣ ΕΠΙΤΗΔΕΥΜΑΤΟΣ '715': ΕΛΕΥΘΕΡΑ ΤΕΛΩΝΕΙΑΚΑ ΣΥΓΚΡΟΤΗΜΑΤΑ '716': ΦΟΡΟΛΟΓΙΚΑ ΚΙΝΗΤΡΑ ΣΥΓΧΩΝΕΥΣΕΩΣ Η ΜΕΤΑΤΡΟΠΗΣ ΕΠΙΧΕΙΡΗΣΕΩΝ '717': ΚΑΤΑΣΤΑΤΙΚΕΣ ΔΙΑΤΑΞΕΙΣ T.E.B.E '718': ΝΑΥΤΙΚΟ ΕΠΙΜΕΛΗΤΗΡΙΟ '719': ΠΡΟΣΩΠΙΚΟ Υ.Ε.Ν '720': ΛΕΙΤΟΥΡΓΟΙ ΜΕΣΗΣ ΕΚΠΑΙΔΕΥΣΗΣ '721': ΚΟΙΝΟΠΡΑΞΙΑ ΓΕΩΡΓΙΚΩΝ ΣΥΝΕΤΑΙΡΙΣΜΩΝ '722': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΕΠΙΧΕΙΡΗΜΑΤΙΩΝ ΚΙΝΗΜΑΤΟΓΡΑΦΟΥ '723': ΒΟΣΚΟΤΟΠΟΙ '724': ΕΠΙΤΟΚΙΑ ΤΡΑΠΕΖΩΝ '725': ΚΑΠΝΙΚΟΙ ΟΡΓΑΝΙΣΜΟΙ '726': ΣΤΑΘΜΟΙ ΑΥΤΟΚΙΝΗΤΩΝ '727': ΕΥΛΟΓΙΑ '728': ΠΕΡΙΦΕΡΕΙΑΚΕΣ ΥΠΗΡΕΣΙΕΣ ΥΠΟΥΡΓΕΙΟΥ ΒΙΟΜΗΧΑΝΙΑΣ '729': ΤΑΜΕΙΟ ΑΕΡΟΠΟΡΙΚΗΣ ΑΜΥΝΑΣ '730': ΟΡΓΑΝΙΣΜΟΣ ΚΕΝΤΡΙΚΗΣ ΥΠΗΡΕΣΙΑΣ '731': ΤΑΜΕΙΟ ΕΡΓΑΣΙΑΣ ΗΘΟΠΟΙΩΝ '732': ΤΕΛΩΝΙΣΜΟΣ ΕΙΔΩΝ ΑΤΟΜΙΚΗΣ ΧΡΗΣΕΩΣ '733': ΦΟΡΟΛΟΓΙΑ ΠΡΟΣΟΔΟΥ ΑΠΟ ΠΛΟΙΑ '734': ΔΙΟΙΚΗΤΙΚΗ ΔΙΑΙΡΕΣΗΣ '735': ΟΡΓΑΝΙΣΜΟΣ ΑΥΤΟΚΙΝΗΤΟΔΡΟΜΙΩΝ ΕΛΛΑΔΟΣ (Ο.Α.Ε.) '736': ΕΘΝΙΚΟ ΚΕΝΤΡΟ ΑΜΕΣΗΣ ΒΟΗΘΕΙΑΣ (Ε.Κ.Α.Β.) '737': ΓΝΩΜΟΔΟΤΙΚΟ ΣΥΜΒΟΥΛΙΟ ΟΙΚΟΝΟΜΙΚΗΣ ΑΝΑΠΤΥΞΗΣ '738': ΔΙΑΘΗΚΗ '739': ΑΓΩΓΕΣ ΔΙΑΤΡΟΦΗΣ '740': ΦΑΡΜΑΚΕΥΤΙΚΟΙ ΣΥΛΛΟΓΟΙ '741': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΚΑΙ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΓΕΩΡΓΙΚΩΝ ΣΥΝΕΤΑΙΡΙΣΤΙΚΩΝ ΟΡΓΑΝΩΣΕΩΝ (Τ.Σ.Ε.Α.Π.Γ.Σ.Ο) '742': ΕΠΙΔΟΜΑΤΑ ΔΙΑΦΟΡΑ '743': ΠΕΙΘΑΡΧΙΚΟ ΔΙΚΑΙΟ '744': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΧΗΜΙΚΩΝ (Τ.Ε.Α.Χ) '745': ΠΡΟΑΓΩΓΕΣ ΚΑΙ ΠΡΟΣΟΝΤΑ ΠΥΡΟΣΒΕΣΤΙΚΟΥ ΠΡΟΣΩΠΙΚΟΥ '746': ΟΔΟΙΠΟΡΙΚΑ ΕΞΟΔΑ ΠΡΟΣΩΠΙΚΟΥ ΣΩΜΑΤΩΝ ΑΣΦΑΛΕΙΑΣ '747': ΝΟΣΗΛΕΥΤΙΚΑ ΙΔΡΥΜΑΤΑ ΚΑΤ’ ΙΔΙΑΝ '748': ΠΡΟΣΤΑΣΙΑ ΚΑΤΑ ΤΗΣ ΦΥΛΛΟΞΗΡΑΣ '749': ΟΡΓΑΝΙΣΜΟΣ ΤΑΜΕΙΟΥ ΝΟΜΙΚΩΝ '750': ΠΡΑΤΗΡΙΑ ΥΓΡΩΝ ΚΑΥΣΙΜΩΝ '751': ΘΡΗΣΚΕΥΤΙΚΟ ΣΩΜΑ ΕΝΟΠΛΩΝ ΔΥΝΑΜΕΩΝ '752': ΔΙΑΔΙΚΑΣΙΑ ΑΝΑΓΚΑΣΤΙΚΩΝ ΑΠΑΛΛΟΤΡΙΩΣΕΩΝ ΑΚΙΝΗΤΩΝ '753': ΔΙΕΡΜΗΝΕΙΣ '754': ΣΧΕΔΙΑ ΑΛΛΩΝ ΠΟΛΕΩΝ '755': ΤΑΜΕΙΟ ΑΛΛΗΛΟΒΟΗΘΕΙΑΣ ΣΤΡΑΤΙΩΤΙΚΩΝ ΑΕΡΟΠΟΡΙΑΣ '756': ΗΜΕΡΟΛΟΓΙΟ ΜΗΧΑΝΗΣ '757': ΚΕΝΤΡΟ ΕΛΛΗΝΙΚΗΣ ΓΛΩΣΣΑΣ '758': ΩΡΕΣ ΕΡΓΑΣΙΑΣ ΣΕ ΑΡΤΟΠΟΙΕΙΑ '759': ΓΕΝΙΚΗ ΓΡΑΜΜΑΤΕΙΑ '760': ΜΕΤΑΦΡΑΣΤΙΚΑ ΓΡΑΦΕΙΑ '761': ΠΡΟΔΙΑΓΡΑΦΕΣ ΜΕΛΕΤΩΝ '762': ΣΥΝΤΑΞΕΙΣ ΘΥΜΑΤΩΝ ΕΘΝΙΚΗΣ '763': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΣΥΜΒΟΛΑΙΟΓΡΑΦΩΝ '764': ΙΑΤΡΟΔΙΚΑΣΤΙΚΗ ΑΜΟΙΒΗ '765': ΕΦΟΡΙΕΣ ΚΑΠΝΟΥ – ΚΑΠΝΕΡΓΟΣΤΑΣΙΑ '766': ΠΟΙΜΝΙΟΣΤΑΣΙΑ '767': ΚΕΝΤΡΑ ΕΡΕΥΝΑΣ - ΕΡΕΥΝΗΤΙΚΑ ΙΝΣΤΙΤΟΥΤΑ '768': ΤΑΜΕΙΑ ΠΡΟΝΟΙΑΣ ΔΙΚΗΓΟΡΩΝ '769': ΟΙΝΟΠΑΡΑΓΩΓΗ ΣΑΜΟΥ '770': ΙΜΑΤΙΣΜΟΣ Π. ΝΑΥΤΙΚΟΥ '771': ΜΗΧΑΝΙΚΟΙ,ΑΡΧΙΤΕΚΤΟΝΕΣ,ΤΟΠΟΓΡΑΦΟΙ '772': ΠΑΝΤΕΙΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΟΙΝΩΝΙΚΩΝ ΚΑΙ ΠΟΛΙΤΙΚΩΝ ΕΠΙΣΤΗΜΩΝ '773': ΝΕΟΙ ΧΡΗΜΑΤΟΠΙΣΤΩΤΙΚΟΙ ΘΕΣΜΟΙ '774': ΥΠΗΡΕΣΙΑ ΠΟΛΙΤΙΚΗΣ ΑΕΡΟΠΟΡΙΑΣ '775': ΟΡΓΑΝΙΣΜΟΣ ΥΠΟΘΗΚΟΦΥΛΑΚΕΙΩΝ '776': ΑΤΥΧΗΜΑΤΑ ΣΕ ΔΗΜΟΣΙΑ ΕΡΓΑ '777': ΑΡΕΙΟΣ ΠΑΓΟΣ '778': ΥΠΑΓΩΓΗ ΣΕ ΑΣΦΑΛΙΣΗ ΚΑΙ '779': ΔΙΕΘΝΕΙΣ ΣΙΔΗΡΟΔΡΟΜΙΚΕΣ ΜΕΤΑΦΟΡΕΣΔΙΕΥΡΩΠΑΙΚΟ ΣΙΔΗΡΟΔΡΟΜΙΚΟ ΣΥΣΤΗΜΑ '780': ΟΙΚΟΝΟΜΙΚΗ ΕΠΙΘΕΩΡΗΣΗ Π. ΝΑΥΤΙΚΟΥ '781': ΑΝΑΠΤΥΞΙΑΚΗ ΚΑΙ ΒΙΟΜΗΧΑΝΙΚΗ ΠΟΛΙΤΙΚΗ '782': ΒΕΒΑΙΩΣΗ ΚΑΙ ΕΙΣΠΡΑΞΗ ΠΟΙΝΙΚΩΝ ΕΞΟΔΩΝ '783': ΝΑΥΤΙΚΟ ΧΗΜΕΙΟ '784': ΛΑΧΕΙΑ '785': ΤΡΟΧΙΟΔΡΟΜΟΙ ΑΘΗΝΩΝ – ΠΕΙΡΑΙΩΣ '786': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΕΤΑΙΡΕΙΩΝ ΛΙΠΑΣΜΑΤΩΝ ΤΑ.Π.Π.Ε.Λ '787': ΔΙΕΥΚΟΛΥΝΣΕΙΣ ΓΙΑ ΑΝΟΙΚΟΔΟΜΗΣΗ '788': ΑΓΟΡΑΠΩΛΗΣΙΑ ΚΑΠΝΟΥ '789': ΠΕΡΙ ΟΡΩΝ ΕΡΓΑΣΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΔΙΕΘΝΩΝ ΜΕΤΑΦΟΡΩΝ '790': ΑΛΙΕΥΤΙΚΟΣ ΚΩΔΙΚΑΣ '791': ΣΥΜΒΟΥΛΙΑ ΚΑΙ ΕΠΙΤΡΟΠΕΣ '792': ΠΕΡΙΦΕΡΕΙΑΚΕΣ ΥΠΗΡΕΣΙΕΣ ΥΠΟΥΡΓΕΙΟΥ ΟΙΚΟΝΟΜΙΚΩΝ '793': ΣΥΜΒΑΣΕΙΣ ΠΕΡΙ ΑΣΕΜΝΩΝ ΔΗΜΟΣΙΕΥΜΑΤΩΝ '794': ΓΕΩΡΓΙΚΟΙ ΣΤΑΘΜΟΙ '795': ΝΑΞΙΩΤΙΚΗ ΣΜΥΡΙΔΑ '796': ΑΝΑΣΤΟΛΗ ΠΡΟΣΕΛΕΥΣΕΩΣ ΕΦΕΔΡΩΝ '797': ΕΚΠΑΙΔΕΥΣΗ ΧΩΡΟΦΥΛΑΚΗΣ '798': ΑΣΦΑΛΙΣΗ ΕΞΑΓΩΓΙΚΩΝ ΠΙΣΤΩΣΕΩΝ '799': ΘΕΡΑΠΑΙΝΙΔΕΣ ΑΝΑΠΗΡΩΝ ΠΟΛΕΜΟΥ '800': ΕΠΙΤΡΟΠΗ ΑΤΟΜΙΚΗΣ ΕΝΕΡΓΕΙΑΣ '801': ΚΑΝΟΝΙΣΜΟΣ ΑΣΤΥΝΟΜΙΑΣ ΠΟΛΕΩΝ '802': ΦΥΛΛΑ ΠΟΙΟΤΗΤΑΣ ΥΠΑΞΙΩΜΑΤΙΚΩΝ Π.Ν '803': ΕΠΙΘΕΩΡΗΣΕΙΣ ΚΤΗΝΙΑΤΡΙΚΗΣ '804': ΜΕΡΙΚΗ ΑΠΑΣΧΟΛΗΣΗ - ΦΑΣΟΝ - ΤΗΛΕΡΓΑΣΙΑ ΚΑΤ’ ΟΙΚΟΝ ΑΠΑΣΧΟΛΗΣΗ '805': ΗΛΕΚΤΡΙΚΗ ΕΤΑΙΡΕΙΑ ΑΘΗΝΩΝ - ΠΕΙΡΑΙΩΣ '806': ΠΡΟΚΑΤΑΣΚΕΥΑΣΜΕΝΑΙ ΟΙΚΙΑΙ '807': ΤΡΑΠΕΖΑ ΤΗΣ ΕΛΛΑΔΟΣ '808': ΣΥΜΦΩΝΙΕΣ ΠΡΟΣΤΑΣΙΑΣ ΤΟΥ ΠΕΡΙΒΑΛΛΟΝΤΟΣ '809': ΛΙΓΝΙΤΗΣ '810': ΤΑΜΕΙΟ ΕΠΑΓΓΕΛΜΑΤΙΚΗΣ ΑΣΦΑΛΙΣΗΣ ΠΡΟΣΩΠΙΚΟΥ ΕΛΤΑ '811': ΜΕΛΕΤΕΣ ΤΕΧΝΙΚΩΝ ΕΡΓΩΝ '812': ΠΛΗΡΩΜΑΤΑ ΑΕΡΟΣΚΑΦΩΝ '813': ΕΞΑΓΩΓΗ ΣΤΑΦΙΔΑΣ '814': ΤΑΜΕΙΟΝ ΠΡΟΝΟΙΑΣ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ '815': ΔΙΑΧΕΙΡΙΣΗ ΠΕΡΙΟΥΣΙΑΣ '816': ΟΡΓΑΝΙΚΟΙ ΝΟΜΟΙ '817': ΥΠΗΡΕΣΙΕΣ ΑΙΜΟΔΟΣΙΑΣ '818': ΣΩΜΑΤΕΙΑ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ '819': ΠΕΖΟΔΡΟΜΙΑ '820': ΔΙΑΘΕΣΗ ΑΠΟΡΡΙΜΜΑΤΩΝ '821': ΤΡΟΧΙΟΔΡΟΜΟΙ ΘΕΣΣΑΛΟΝΙΚΗΣ '822': ΓΕΝΙΚΗ ΔΙΕΥΘΥΝΣΗ ΔΗΜΟΣΙΟΥ ΛΟΓΙΣΤΙΚΟΥ '823': ΡΥΜΟΥΛΚΑ - ΛΑΝΤΖΕΣ '824': ΠΕΤΡΕΛΑΙΟΕΙΔΗ '825': ΓΕΝΙΚΑ ΑΡΧΕΙΑ ΤΟΥ ΚΡΑΤΟΥΣ '826': ΚΑΤΑΣΤΑΤΙΚΕΣ ΔΙΑΤΑΞΕΙΣ Ο.Τ.Ε. - ΣΧΕΣΕΙΣ Ο.Τ.Ε. ΜΕ ΑΛΛΟΥΣ ΠΑΡΟΧΟΥΣ '827': ΥΠΗΡΕΣΙΑ ΑΥΤΟΚΙΝΗΤΩΝ '828': ΑΚΑΔΗΜΙΑ ΑΘΗΝΩΝ '829': ΜΟΝΟΠΩΛΙΟ ΖΑΧΑΡΙΝΗΣ '830': ΟΙΚΙΣΤΙΚΕΣ ΠΕΡΙΟΧΕΣ '831': ΑΤΕΛΕΙΕΣ ΥΠΕΡ ΤΗΣ ΑΛΙΕΙΑΣ '832': ΔΙΑΦΟΡΕΣ ΕΚΤΑΚΤΕΣ ΦΟΡΟΛΟΓΙΕΣ '833': ΒΙΒΛΙΑ ΔΗΜΩΝ ΚΑΙ ΚΟΙΝΟΤΗΤΩΝ '834': ΕΡΓΑΤΙΚΑ ΑΤΥΧΗΜΑΤΑ '835': ΝΟΣΗΛΕΥΤΕΣ '836': ΣΥΝΔΙΚΑΛΙΣΤΙΚΕΣ ΕΛΕΥΘΕΡΙΕΣ '837': ΕΘΝΙΚΟ ΣΥΜΒΟΥΛΙΟ ΕΝΕΡΓΕΙΑΣ '838': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΕΡΓΑΤΟΤΕΧΝΙΤΩΝ ΥΑΛΟΥΡΓΩΝ '839': ΑΓΩΓΕΣ ΑΣΦΑΛΙΣΤΡΩΝ '840': ΣΩΜΑΤΕΜΠΟΡΙΑ ΓΥΝΑΙΚΩΝ '841': ΑΤΕΛΕΙΕΣ ΕΡΓΩΝ ΑΜΥΝΤΙΚΟΥ ΠΡΟΓΡΑΜΜΑΤΟΣ '842': ΤΕΧΝΙΚΗ ΕΚΠΑΙΔΕΥΣΗ ΑΞΙΩΜΑΤΙΚΩΝ ΣΕ ΑΝΩΤΑΤΕΣ ΣΧΟΛΕΣ '843': ΔΙΚΑΙΩΜΑΤΑ ΚΗΡΥΚΩΝ ΚΛΠ '844': ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΤΑΜΕΙΟΥ ΝΟΜΙΚΩΝ '845': ΝΑΥΤΕΣ ΚΑΙ ΛΙΜΕΝΟΦΥΛΑΚΕΣ '846': ΠΑΝΕΠΙΣΤΗΜΙΑΚΗ ΣΧΟΛΗ ΑΓΡΙΝΙΟΥ '847': ΠΟΛΥΤΕΧΝΙΚΗ ΣΧΟΛΗ '848': ΜΕΙΩΣΗ ΕΙΣΦΟΡΩΝ '849': ΚΕΝΤΡΑ ΛΗΨΕΩΣ ΤΙΜΩΝ ΣΦΑΓΕΙΩΝ '850': ΑΠΟΔΗΜΙΑ ΣΤΡΑΤΕΥΣΙΜΩΝ '851': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΝΟΙΑΣ ΚΑΙ ΚΟΙΝΗΣ ΔΙΑΝΟΜΗΣ ΠΩΛΗΤΩΝ ΒΕΝΖΙΝΗΣ ΑΘΗΝΩΝ - ΠΕΙΡΑΙΩΣ ΚΑΙ ΠΕΡΙΧΩΡΩΝ '852': ΙΑΤΡΟΦΑΡΜΑΚΕΥΤΙΚΗ ΠΕΡΙΘΑΛΨΗ '853': ΝΟΣΗΛΕΥΤΙΚΑ ΙΔΡΥΜΑΤΑ '854': ΓΕΝΙΚΑ ΠΕΡΙ ΜΟΥΣΕΙΩΝ '855': ΑΣΦΑΛΕΙΑ ΟΧΥΡΩΝ ΘΕΣΕΩΝ '856': ΓΕΩΡΓΙΚΑ ΜΗΧΑΝΗΜΑΤΑ '857': ΤΑΜΕΙΑ ΣΥΝΕΡΓΑΣΙΑΣ '858': ΙΔΙΩΤΙΚΕΣ ΚΛΙΝΙΚΕΣ ΚΑΙ ΕΡΓΑΣΤΗΡΙΑ '859': ΥΓΕΙΟΝΟΜΙΚΗ ΕΞΕΤΑΣΗ ΙΠΤΑΜΕΝΩΝ '860': ΔΙΑΦΟΡΕΣ ΑΕΡΟΠΟΡΙΚΕΣ ΣΧΟΛΕΣ '861': ΓΥΝΑΙΚΕΣ ΝΟΣΟΚΟΜΟΙ '862': ΦΟΙΤΗΣΗ, ΒΑΘΜΟΛΟΓΙΑ, ΕΞΕΤΑΣΕΙΣ ΚΛΠ. Α.Σ.Κ.Τ '863': ΣΕΙΣΜΟΠΛΗΚΤΟΙ ΔΙΑΦΟΡΟΙ '864': ΟΡΓΑΝΙΣΜΟΣ ΥΠΟΥΡΓΕΙΟΥ ΓΕΩΡΓΙΑΣ '865': ΚΩΔΙΚΟΠΟΙΗΣΗ ΤΗΣ ΝΟΜΟΘΕΣΙΑΣ '866': ΜΕΤΑ ΤΗΣ ΓΑΛΛΙΑΣ '867': ΓΕΩΓΡΑΦΙΚΗ ΥΠΗΡΕΣΙΑ ΣΤΡΑΤΟΥ '868': ΕΙΔΗ ΠΑΡΑΔΙΔΟΜΕΝΑ ΣΤΗΝ ΕΛΕΥΘΕΡΗ ΧΡΗΣΗ '869': ΜΟΝΟΠΩΛΙΟ ΣΠΙΡΤΩΝ '870': ΚΑΤΑΣΤΑΤΙΚΟΝ Τ.Α.Κ.Ε '871': ΕΠΙΚΟΥΡΙΚΟ ΤΑΜΕΙΟ ΥΠΑΛΛΗΛΩΝ ΑΣΤΥΝΟΜΙΑΣ ΠΟΛΕΩΝ (Ε.Τ.Υ.Α.Π.) '872': ΜΙΣΘΟΔΟΣΙΑ ΙΕΡΕΩΝ – ΕΝΟΡΙΑΚΗ ΕΙΣΦΟΡΑ '873': ΥΓΕΙΟΝΟΜΙΚΟΣ ΚΑΝΟΝΙΣΜΟΣ '874': ΝΟΜΟΣ ΠΕΡΙ ΚΤΗΜΑΤΙΚΩΝ ΤΡΑΠΕΖΩΝ '875': ΔΙΕΘΝΗΣ ΣΥΜΒΑΣΗ ΠΕΡΙ ΥΔΡΑΥΛΙΚΩΝ ΔΥΝΑΜΕΩΝ '876': ΑΝΑΠΗΡΟΙ ΑΞΙΩΜΑΤΙΚΟΙ ΚΑΙ ΟΠΛΙΤΕΣ ΕΙΡΗΝΙΚΗΣ ΠΕΡΙΟΔΟΥ '877': ΠΟΙΝΙΚΗ ΚΑΙ ΠΕΙΘΑΡΧΙΚΗ ΔΩΣΙΔΙΚΙΑ Λ.Σ '878': ΔΑΣΙΚΟ ΠΡΟΣΩΠΙΚΟ '879': ΑΟΠΛΗ ΘΗΤΕΙΑ-ΑΝΤΙΡΡΗΣΙΕΣ ΣΥΝΕΙΔΗΣΗΣ '880': ΝΕΟΙ ΠΡΟΣΦΥΓΕΣ '881': ΤΕΧΝΙΚΕΣ ΥΠΗΡΕΣΙΕΣ ΣΤΡΑΤΟΥ '882': ΜΕΤΟΧΙΚΟ ΤΑΜΕΙΟ ΠΟΛΙΤΙΚΩΝ ΥΠΑΛΛΗΛΩΝ '883': ΠΡΟΣΩΠΙΚΟ ΙΔΙΩΤΙΚΟΥ ΔΙΚΑΙΟΥ '884': ΚΩΔΙΚΑΣ ΑΓΡΟΤΙΚΗΣ ΑΣΦΑΛΕΙΑΣ '885': ΟΡΓΑΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΑΠΟΣΤΟΛΙΚΗΣ ΔΙΑΚΟΝΙΑΣ '886': ΥΠΟΥΡΓΕΙΟ ΑΙΓΑΙΟΥ '887': ΓΑΜΟΙ ΔΩΔΕΚΑΝΗΣΟΥ '888': ΩΡΕΣ ΕΡΓΑΣΙΑΣ ΚΡΕΟΠΩΛΕΙΩΝ '889': ΚΩΔΙΚΑΣ ΤΕΛΩΝ ΧΑΡΤΟΣΗΜΟΥ '890': ΔΕΛΤΙΟ ΑΝΩΝΥΜΩΝ ΕΤΑΙΡΕΙΩΝ '891': ΑΡΜΟΔΙΟΤΗΤΑ ΝΟΜΑΡΧΗ ΣΕ ΕΡΓΑΤΙΚΑ ΖΗΤΗΜΑΤΑ '892': ΤΡΟΦΟΔΟΣΙΑ Π. ΝΑΥΤΙΚΟΥ '893': ΣΥΜΦΩΝΙΑ ΠΕΡΙ ΔΙΠΛΩΜΑΤΙΚΩΝ ΣΧΕΣΕΩΝ '894': ΕΦΕΔΡΟΙ ΚΑΙ ΕΠΙΚΟΥΡΟΙ ΑΞΙΩΜΑΤΙΚΟΙ Π.Ν '895': ΤΟΥΡΙΣΤΙΚΕΣ ΕΠΙΧΕΙΡΗΣΕΙΣ '896': ΔΙΕΘΝΕΣ ΠΟΙΝΙΚΟ ΔΙΚΑΣΤΗΡΙΟ '897': ΔΙΟΙΚΗΤΙΚΕΣ ΠΡΑΞΕΙΣ '898': ΝΟΣΟΚΟΜΕΙΑ ΠΟΛΕΜΙΚΟΥ ΝΑΥΤΙΚΟΥ '899': ΣΥΜΒΟΥΛΙΟ ΧΑΛΥΒΑ '900': ΤΕΜΑΧΙΣΜΟΣ ΚΡΕΑΤΩΝ '901': ΕΛΕΓΧΟΣ ΚΑΤΟΧΗΣ ΟΠΛΩΝ '902': ΑΝΑΠΡΟΣΑΡΜΟΓΕΣ ΤΗΣ ΔΡΑΧΜΗΣ '903': ΕΦΟΔΙΑΣΜΟΣ ΠΛΟΙΩΝ '904': ΣΕΙΣΜΟΠΛΗΚΤΟΙ ΙΟΝΙΩΝ ΝΗΣΩΝ '905': ΔΗΜΟΣΙΑ ΕΠΙΧΕΙΡΗΣΗ ΚΙΝΗΤΩΝ ΑΞΙΩΝ ΑΝΩΝΥΜΗ ΕΤΑΙΡΕΙΑ (Δ.Ε.Κ.Α. Α.Ε.) '906': ΕΤΑΙΡΕΙΑ – ΕΥΡΩΠΑΙΚΟΣ ΟΜΙΛΟΣ '907': ΔΙΕΥΘΥΝΣΗ ΑΛΙΕΙΑΣ '908': ΕΠΙΜΕΛΗΤΗΡΙΟ ΤΟΥΡΙΣΤΙΚΩΝ ΚΑΤΑΣΤΗΜΑΤΩΝ '909': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ ΕΛΑΙΟΛΑΔΟΥ '910': ΠΤΗΤΙΚΗ ΙΚΑΝΟΤΗΤΑ '911': ΕΚΚΛΗΣΙΑΣΤΙΚΕΣ ΣΧΟΛΕΣ '912': ΔΙΑΤΙΜΗΣΗ ΙΑΤΡΙΚΩΝ ΠΡΑΞΕΩΝ '913': ΑΔΙΚΗΜΑΤΑ ΤΥΠΟΥ '914': ΕΞΑΝΘΗΜΑΤΙΚΟΣ ΤΥΦΟΣ '915': ΟΙΚΟΣ ΝΑΥΤΟΥ '916': ΜΑΣΤΙΧΑ '917': ΣΥΛΛΟΓΟΙ ΚΑΙ ΟΜΟΣΠΟΝΔΙΑ ΔΙΚΑΣΤΙΚΩΝ ΕΠΙΜΕΛΗΤΩΝ '918': ΕΜΠΟΡΙΚΑ ΚΑΙ ΒΙΟΜΗΧΑΝΙΚΑ ΣΗΜΑΤΑ '919': ΟΡΓΑΝΩΣΗ ΚΑΙ ΛΕΙΤΟΥΡΓΙΑ ΑΝΩΤΑΤΩΝ ΕΚΠΑΙΔΕΥΤΙΚΩΝ ΙΔΡΥΜΑΤΩΝ '920': ΥΓΕΙΟΝΟΜΙΚΗ ΑΠΟΘΗΚΗ '921': ΓΕΝ. ΔΙΕΥΘΥΝΣΗ ΠΟΙΝΙΚΗΣ ΔΙΚΑΙΟΣΥΝΗΣ '922': ΑΕΡΟΠΟΡΙΚΟ ΔΙΚΑΙΟ '923': ΜΕΛΕΤΗ ΚΑΙ ΕΠΙΒΛΕΨΗ ΜΗΧΑΝΟΛΟΓΙΚΩΝ ΕΓΚΑΤΑΣΤΑΣΕΩΝ '924': ΑΘΕΜΙΤΟΣ ΑΝΤΑΓΩΝΙΣΜΟΣ '925': ΠΟΛΕΜΙΚΗ ΔΙΑΘΕΣΙΜΟΤΗΤΑ '926': ΛΕΣΧΕΣ ΚΑΙ ΠΡΑΤΗΡΙΑ ΕΛ.ΑΣ '927': ΚΑΥΣΙΜΑ '928': ΥΓΕΙΟΝΟΜΙΚΑ ΜΕΤΡΑ '929': ΚΑΤΑΣΤΑΣΗ ΑΞΙΩΜΑΤΙΚΩΝ '930': ΕΙΣΠΡΑΞΗ ΠΟΡΩΝ ΤΑΜΕΙΟΥ ΝΟΜΙΚΩΝ '931': ΔΙΟΙΚΗΤΙΚΗ ΡΥΘΜΙΣΗ ΑΠΟΔΟΧΩΝ ΚΑΙ ΟΡΩΝ ΕΡΓΑΣΙΑΣ '932': ΓΕΝΙΚΗ ΔΙΕΥΘΥΝΣΗ ΤΑΧΥΔΡΟΜΕΙΩΝ '933': ΟΡΓΑΝΙΣΜΟΣ ΛΙΜΕΝΟΣ ΘΕΣΣΑΛΟΝΙΚΗΣ ΑΝΩΝΥΜΗ ΕΤΑΙΡΙΑ (Ο.Λ.Θ. Α.Ε.) '934': ΣΧΟΛΗ ΕΘΝΙΚΗΣ ΑΜΥΝΑΣ '935': ΚΑΘΟΛΙΚΟΙ '936': ΕΚΚΛΗΣΙΑΣΤΙΚΑ ΜΟΥΣΕΙΑ '937': ΔΙΕΘΝΗΣ ΕΚΘΕΣΗ ΘΕΣΣΑΛΟΝΙΚΗΣ Α.Ε. – XELEXPO Α.Ε '938': ΕΥΕΡΓΕΤΙΚΟΣ ΥΠΟΛΟΓΙΣΜΟΣ ΗΜΕΡΩΝ ΕΡΓΑΣΙΑΣ '939': ΕΙΣΦΟΡΑ ΕΠΑΓΓΕΛΜΑΤΙΚΟΥ ΚΙΝΔΥΝΟΥ '940': ΑΠΑΛΛΟΤΡΙΩΣΕΙΣ ΓΙΑ ΤΟΥΡΙΣΤΙΚΟΥΣ ΣΚΟΠΟΥΣ '941': ΑΠΟΛΥΜΑΝΤΗΡΙΑ '942': ΕΚΠΟΙΗΣΗ ΠΛΟΙΩΝ ΔΗΜΟΣΙΟΥ '943': ΔΙΑΚΟΝΟΙ '944': ΥΔΡΕΥΣΗ ΔΙΑΦΟΡΩΝ ΠΟΛΕΩΝ '945': ΠΡΩΤΕΣ ΥΛΕΣ ΚΛΩΣΤΟΥΦΑΝΤΟΥΡΓΙΑΣ '946': ΨΕΥΔΗΣ ΒΕΒΑΙΩΣΗ ΕΝΩΠΙΟΝ ΑΡΧΗΣ '947': ΑΠΩΛΕΣΘΕΙΣΕΣ ΚΑΙ ΠΑΡΑΓΡΑΦΕΙΣΕΣ ΑΞΙΕΣ '948': ΦΟΙΤΗΤΙΚΗ ΛΕΣΧΗ '949': ΤΑΜΕΙΟ ΥΓΕΙΑΣ ΤΑΧΥΔΡΟΜΙΚΟΥ ΠΡΟΣΩΠΙΚΟΥ '950': ΕΛΕΓΧΟΣ ΔΕΝΔΡΩΔΩΝ ΚΑΛΛΙΕΡΓΕΙΩΝ '951': ΚΑΤΑΠΟΛΕΜΗΣΗ ΑΝΑΛΦΑΒΗΤΙΣΜΟΥΛΑΙΚΗ ΕΠΙΜΟΡΦΩΣΗ '952': ΕΠΙΚΟΥΡΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ ΜΕΤΑΦΟΡΩΝ '953': ΦΟΙΤΗΤΙΚΕΣ ΛΕΣΧΕΣ '954': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ ΓΙΑ ΤΗΝ ΠΡΟΣΤΑΣΙΑ ΤΩΝ ΕΡΓΑΖΟΜΕΝΩΝ ΓΥΝΑΙΚΩΝ '955': ΛΗΣΤΕΙΑ '956': ΑΓΩΓΕΣ ΑΠΟ ΣΥΝΑΛΛΑΓΜΑΤΙΚΕΣ ΚΑΙ ΓΡΑΜΜΑΤΙΑ '957': ΕΚΜΙΣΘΩΣΗ ΔΗΜΟΣΙΩΝ ΜΕΤΑΛΛΕΙΩΝ '958': ΚΟΛΥΜΒΗΤΙΚΕΣ ΔΕΞΑΜΕΝΕΣ '959': ΕΡΑΝΟΙ ΚΑΙ ΛΑΧΕΙΟΦΟΡΟΙ Η ΦΙΛΑΝΘΡΩΠΙΚΕΣ ΑΓΟΡΕΣ '960': ΠΡΟΣΤΑΣΙΑ ΕΠΙΒΑΤΗΓΟΥ ΝΑΥΤΙΛΙΑΣ '961': ΓΕΝΙΚΟΙ ΝΟΜΟΙ ΠΕΡΙ ΞΕΝΟΔΟΧΕΙΩΝ-ΕΠΙΠΛ. ΔΩΜΑΤΙΩΝ ΚΛΠ '962': ΙΕΡΑΡΧΙΑ ΚΑΙ ΠΡΟΑΓΩΓΕΣ ΑΞΙΩΜΑΤΙΚΩΝ '963': ΣΥΝΕΡΓΑΤΕΣ (ΓΡΑΜΜΑΤΕΙΣ) ΒΟΥΛΕΥΤΩΝ-ΕΥΡΩΒΟΥΛΕΥΤΩΝ '964': ΣΧΟΛΗ ΙΚΑΡΩΝ '965': ΟΡΓΑΝΙΣΜΟΣ ΣΙΔΗΡΟΔΡΟΜΩΝ ΕΛΛΑΔΟΣ (Ο.Σ.Ε.)ΣΙΔΗΡΟΔΡΟΜΙΚΕΣ ΕΠΙΧΕΙΡΗΣΕΙΣ '966': ΥΓΕΙΟΝΟΜΙΚΟΣ ΚΑΝΟΝΙΣΜΟΣ ΚΑΤΑ ΘΑΛΑΣΣΑΝ ΚΑΙ ΚΑΤΑ ΞΗΡΑΝ '967': ΚΑΝΟΝΙΣΜΟΣ ΜΕΤΑΛΛΕΥΤΙΚΩΝ ΕΡΓΑΣΙΩΝ '968': ΑΠΟΦΥΓΗ ΣΥΓΚΡΟΥΣΕΩΝ '969': ΤΟΜΑΤΟΠΑΡΑΓΩΓΗ '970': ΔΙΑΦΟΡΕΣ ΔΙΑΤΑΞΕΙΣ ΓΙΑ ΤΑ ΑΥΤΟΚΙΝΗΤΑ '971': ΚΑΤΑΤΑΞΗ ΓΥΝΑΙΚΩΝ ΣΤΟ Λ.Σ '972': ΕΤΑΙΡΕΙΕΣ ΔΙΟΙΚΟΥΜΕΝΕΣ ΑΠΟ ΤΟΥΣ ΠΙΣΤΩΤΕΣ '973': ΒΑΛΚΑΝΙΚΕΣ ΣΥΜΦΩΝΙΕΣ '974': ΜΕΤΑΦΟΡΑ ΣΥΝΤΕΛΕΣΤΗ ΔΟΜΗΣΗΣ '975': ΠΡΟΜΗΘΕΥΤΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ Π.Ν '976': ΠΡΟΣΩΠΙΚΟ ΦΑΡΜΑΚΕΙΩΝ '977': ΔΙΔΑΣΚΟΜΕΝΑ ΜΑΘΗΜΑΤΑ '978': ΕΚΛΟΓΗ ΒΟΥΛΕΥΤΩΝ - ΕΥΡΩΒΟΥΛΕΥΤΩΝ '979': ΦΑΡΜΑΚΟΠΟΙΟΙ '980': ΣΤΡΑΤΙΩΤΙΚΑ ΠΡΑΤΗΡΙΑ '981': ΚΑΡΚΙΝΟΣ '982': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ Α.Ε. ΟΙΝΟΠΟΙΙΑΣ, ΖΥΘΟΠΟΙΙΑΣ ΚΑΙ ΟΙΝΟΠΝΕΥΜΑΤΟΠΟΙΙΑΣ '983': ΧΕΙΡΙΣΤΕΣ ΑΣΥΡΜΑΤΟΥ '984': ΠΟΛΙΤΙΚΗ ΕΠΙΣΤΡΑΤΕΥΣΗ-ΠΑΛΛΑΙΚΗ ΑΜΥΝΑ '985': ΟΡΓΑΝΙΣΜΟΙ ΕΓΓΕΙΩΝ ΒΕΛΤΙΩΣΕΩΝ '986': ΟΜΟΓΕΝΕΙΣ ΠΑΛΛΙΝΟΣΤΟΥΝΤΕΣ '987': ΕΥΡΩΠΑΙΚΟΣ ΚΟΙΝΩΝΙΚΟΣ ΧΑΡΤΗΣ '988': ΟΡΓΑΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ '989': ΕΞΑΙΡΕΣΗ ΔΙΚΑΣΤΩΝ '990': ΓΕΝΙΚΕΣ ΕΠΙΘΕΩΡΗΣΕΙΣ – ΔΙΕΥΘΥΝΣΕΙΣ ΣΤΟΙΧΕΙΩΔΟΥΣ ΕΚΠΑΙΔΕΥΣΗΣ '991': ΚΑΝΟΝΙΣΜΟΣ ΕΠΙΘΕΩΡΗΣΕΩΣ ΚΑΙ ΑΣΦΑΛΕΙΑΣ '992': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΑΥΤΟΝΟΜΟΥ ΣΤΑΦΙΔΙΚΟΥ ΟΡΓΑΝΙΣΜΟΥ (Τ.Α.Π.Α.Σ.Ο) '993': ΤΑΜΕΙΟΝ ΠΡΟΝΟΙΑΣ ΟΡΘΟΔΟΞΟΥ ΕΦΗΜΕΡΙΑΚΟΥ '994': ΣΧΟΛΙΚΗ ΣΩΜΑΤΙΚΗ ΑΓΩΓΗ '995': ΚΕΝΤΡΟ ΠΑΡΑΓΩΓΙΚΟΤΗΤΑΣ '996': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΙΔΙΟΚΤΗΤΩΝ '997': ΒΟΣΚΗ ΕΝΤΟΣ ΔΑΣΩΝ '998': ΕΛΕΓΧΟΣ ΕΞΑΓΟΜΕΝΩΝ ΓΕΩΡΓΙΚΩΝ ΠΡΟΙΟΝΤΩΝ '999': ΠΑΙΔΑΓΩΓΙΚΑ ΤΜΗΜΑΤΑ Α.Ε.Ι '1000': ΥΠΟΤΡΟΦΙΕΣ ΚΛΗΡΟΔΟΤΗΜΑΤΟΣ Π. ΒΑΣΣΑΝΗ '1001': ΑΤΥΧΗΜΑ ΑΠΟ ΔΟΛΟ ΤΟΥ ΕΡΓΟΔΟΤΗ '1002': ΒΥΖΑΝΤΙΝΟ ΚΑΙ ΧΡΙΣΤΙΑΝΙΚΟ ΜΟΥΣΕΙΟ '1003': ΕΙΡΗΝΕΥΤΙΚΕΣ ΑΠΟΣΤΟΛΕΣ '1004': ΥΓΕΙΟΝΟΜΙΚΟΣ ΄ΕΛΕΓΧΟΣ ΕΙΣΕΡΧΟΜΕΝΩΝ '1005': ΟΡΚΟΣ ΤΟΥ ΠΟΛΙΤΗ '1006': ΥΓΕΙΟΝΟΜΙΚΗ ΠΕΡΙΘΑΛΨΗ ΣΠΟΥΔΑΣΤΩΝ '1007': ΠΑΡΑΧΑΡΑΞΗ ΚΑΙ ΚΙΒΔΗΛΙΑ '1008': ΔΙΑΜΕΡΙΣΜΑΤΑ ΠΛΟΙΑΡΧΩΝ ΚΑΙ ΠΛΗΡΩΜΑΤΩΝ '1009': ΚΛΑΔΟΣ ΑΡΩΓΗΣ Τ.Α.Κ.Ε '1010': ΟΡΓΑΝΙΣΜΟΣ ΒΑΜΒΑΚΟΣ '1011': ΝΟΣΗΛΕΙΑ ΣΤΡΑΤΙΩΤΙΚΩΝ '1012': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ '1013': ΠΟΛΥΕΘΝΕΙΣ ΑΕΡΟΠΟΡΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '1014': ΝΑΥΤΙΚΟ ΑΠΟΜΑΧΙΚΟ ΤΑΜΕΙΟ '1015': ΥΓΙΕΙΝΗ ΑΡΤΟΠΟΙΕΙΩΝ '1016': ΝΟΜΑΡΧΙΑΚΑ ΣΥΜΒΟΥΛΙΑ '1017': ΛΕΣΧΗ ΑΞΙΩΜΑΤΙΚΩΝ Π.Ν '1018': ΚΑΤΩΤΕΡΟ ΔΙΔΑΚΤΙΚΟ ΠΡΟΣΩΠΙΚΟ '1019': ΓΕΝΙΚΑ ΠΕΡΙ ΚΥΚΛΟΦΟΡΙΑΣ ΑΥΤΟΚΙΝΗΤΩΝ '1020': ΤΑΜΕΙΟ ΝΟΣΗΛΕΙΑΣ ΣΠΟΥΔΑΣΤΩΝ '1021': ΕΠΑΓΓΕΛΜΑΤΙΚΑ ΚΑΙ ΒΙΟΤΕΧΝΙΚΑ ΕΠΙΜΕΛΗΤΗΡΙΑ '1022': ΑΚΤΟΠΛΟΙΑ '1023': ΠΡΟΣΤΑΣΙΑ ΑΛΙΕΙΑΣ '1024': ΜΕ ΤΗ ΝΟΡΒΗΓΙΑ '1025': ΗΘΙΚΕΣ ΑΜΟΙΒΕΣ ΠΡΟΣΩΠΙΚΟΥ (΄ΕΝΟΠΛΟΥ-ΠΟΛΙΤΙΚΟΥ) ΥΠΟΥΡΓΕΙΟΥ ΔΗΜΟΣΙΑΣ ΤΑΞΗΣ '1026': ΛΕΩΦΟΡΕΙΑ ΙΔΙΩΤΙΚΗΣ ΧΡΗΣΕΩΣ '1027': ΕΡΓΑΤΙΚΕΣ ΔΙΑΦΟΡΕΣ '1028': ΡΑΔΙΟΗΛΕΚΤΡΟΛΟΓΟΙ-ΡΑΔΙΟΤΕΧΝΙΤΕΣ '1029': ΠΡΟΓΝΩΣΤΙΚΑ ΠΟΔΟΣΦΑΙΡΟΥ '1030': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΚΑΙ ΠΡΟΝΟΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΤΗΣ ΑΓΡΟΤΙΚΗΣ ΤΡΑΠΕΖΑΣ ΤΗΣ ΕΛΛΑΔΑΣ (Τ.Σ.Π. – Α.Τ.Ε.) '1031': ΥΔΡΕΥΣΗ ΛΕΚΑΝΟΠΕΔΙΟΥ ΑΘΗΝΩΝ '1032': ΤΡΑΠΕΖΑ ΟΦΘΑΛΜΩΝ '1033': ΕΘΝΙΚΟ ΚΕΝΤΡΟ ΧΑΡΤΩΝ ΚΑΙ ΧΑΡΤΟΓΡΑΦΙΚΗΣ ΚΛΗΡΟΝΟΜΙΑΣ - ΕΘΝΙΚΗ ΧΑΡΤΟΘΗΚΗ '1034': ΚΑΝΟΝΙΣΜΟΙ ΑΠΟΦΥΓΗΣ ΣΥΓΚΡΟΥΣΕΩΝ '1035': ΓΡΑΦΕΙΟ ΕΓΚΛΗΜΑΤΙΩΝ ΠΟΛΕΜΟΥ '1036': ΑΓΡΟΤΙΚΕΣ ΣΥΝΔΙΚΑΛΙΣΤΙΚΕΣ ΟΡΓΑΝΩΣΕΙΣ '1037': ΤΑΥΤΟΤΗΤΕΣ '1038': ΔΑΣΙΚΟΙ ΣΥΝΕΤΑΙΡΙΣΜΟΙ '1039': ΣΥΜΒΟΛΑΙΟΓΡΑΦΙΚΑ ΔΙΚΑΙΩΜΑΤΑ '1040': ΙΔΙΟΚΤΗΣΙΑ ΚΑΤ’ ΟΡΟΦΟ '1041': ΣΧΟΛΙΚΑ ΤΑΜΕΙΑ '1042': ΑΡΧΕΙΟΦΥΛΑΚΕΙΑ ΔΙΑΦΟΡΑ '1043': ΑΠΟΖΗΜΙΩΣΗ ΑΝΤΑΛΛΑΞΙΜΩΝ '1044': ΣΧΟΛΙΚΑ ΚΤΙΡΙΑ '1045': ΦΟΡΟΛΟΓΙΑ ΟΙΚΟΔΟΜΩΝ '1046': ΠΡΟΤΥΠΑ ΔΗΜΟΤΙΚΑ '1047': ΠΡΩΤΕΣ ΥΛΕΣ ΒΥΡΣΟΔΕΨΙΑΣ - ΔΕΡΜΑΤΑ '1048': ΣΥΜΒΙΒΑΣΜΟΣ ΚΑΙ ΔΙΑΙΤΗΣΙΑ '1049': ΚΑΤΑΣΤΑΣΗ ΔΗΜΟΤΙΚΩΝ ΚΑΙ ΚΟΙΝΟΤΙΚΩΝ ΥΠΑΛΛΗΛΩΝ '1050': ΕΣΟΔΑ ΔΗΜΩΝ ΚΑΙ ΚΟΙΝΟΤΗΤΩΝ '1051': ΣΤΑΔΙΑ ΚΑΙ ΓΥΜΝΑΣΤΗΡΙΑ '1052': ΚΟΙΝΗ ΑΓΡΟΤΙΚΗ ΠΟΛΙΤΙΚΗ '1053': ΑΤΟΜΑ ΜΕ ΕΙΔΙΚΕΣ ΑΝΑΓΚΕΣ - ΥΠΕΡΗΛΙΚΕΣ - ΧΡΟΝΙΑ ΠΑΣΧΟΝΤΕΣ '1054': ΕΚΚΛΗΣΙΑΣΤΙΚΑ ΔΙΚΑΣΤΗΡΙΑ '1055': ΣΥΜΒΑΣΕΙΣ ΓΙΑ ΤΗΝ ΑΠΟΦΥΓΗ ΔΙΠΛΗΣ ΦΟΡΟΛΟΓΙΑΣ '1056': ΠΡΟΣΤΑΣΙΑ ΒΑΜΒΑΚΟΠΑΡΑΓΩΓΗΣ '1057': ΝΑΥΤΙΚΗ ΣΤΡΑΤΟΛΟΓΙΑ '1058': ΝΟΣΟΚΟΜΕΙΑΚΗ ΠΕΡΙΘΑΛΨΗ ΑΣΦΑΛΙΣΜΕΝΩΝ Ο.Γ.Α '1059': ΦΥΣΙΚΑ ΟΡΓΑΝΙΚΑ ΛΙΠΑΣΜΑΤΑ '1060': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΜΙΣΘΩΤΩΝ ΕΣΤΙΑΤΟΡΙΩΝ, ΖΑΧΑΡΟΠΛΑΣΤΕΙΩΝ, ΚΑΦΕΝΕΙΩΝ Κ.ΛΠ. (Τ.Ε.Α.Μ.Ε.Ζ.) '1061': ΤΕΧΝΙΚΑΙ ΥΠΗΡΕΣΙΑΙ '1062': ΣΥΓΚΕΝΤΡΩΣΗ ΠΡΟΙΟΝΤΩΝ '1063': ΥΔΡΟΓΡΑΦΙΚΗ ΥΠΗΡΕΣΙΑ '1064': ΥΠΗΡΕΣΙΑ ΕΛΕΓΧΟΥ ΚΑΤΑΣΚΕΥΗΣ ΑΞΙΩΝ ΤΟΥ ΔΗΜΟΣΙΟΥ '1065': ΕΠΙΣΚΟΠΙΚΑ ΓΡΑΦΕΙΑ '1066': ΒΕΛΓΙΟ, ΒΕΝΕΖΟΥΕΛΑ Κ.ΛΠ '1067': ΔΗΜΟΤΙΚΟΣ ΚΑΙ ΚΟΙΝΟΤΙΚΟΣ ΚΩΔΙΚΑΣ '1068': ΠΡΟΔΟΣΙΑ '1069': ΜΙΣΘΟΣ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ '1070': ΠΟΛΙΤΙΚΟ ΠΡΟΣΩΠΙΚΟ ΝΑΥΤΙΚΟΥ '1071': ΑΝΑΖΗΤΗΣΗ ΚΑΙ ΔΙΑΦΥΛΑΞΗ ΑΡΧΑΙΟΤΗΤΩΝ '1072': ΑΔΕΙΕΣ ΛΙΑΝΙΚΗΣ ΠΩΛΗΣΗΣ ΤΣΙΓΑΡΩΝ ΚΑΙ ΕΙΔΩΝ ΜΟΝΟΠΩΛΙΟΥ '1073': ΕΠΟΠΤΙΚΑ ΜΕΣΑ ΔΙΔΑΣΚΑΛΙΑΣ '1074': ΕΚΛΟΓΟΔΙΚΕΙΑ '1075': Ο.Γ.Α ΚΑΤΑΣΤΑΤΙΚΕΣ ΔΙΑΤΑΞΕΙΣ '1076': ΙΝΣΤΙΤΟΥΤΟ ΥΓΕΙΑΣ ΤΟΥ ΠΑΙΔΙΟΥ '1077': ΣΧΟΛΗ ΘΕΤΙΚΩΝ ΕΠΙΣΤΗΜΩΝ ΠΑΝΕΠΙΣΤΗΜΙΟΥ ΠΑΤΡΩΝ '1078': ΕΣΠΕΡΙΔΟΕΙΔΗ-ΟΠΩΡΟΚΗΠΕΥΤΙΚΑ '1079': ΕΠΙΔΟΜΑΤΑ ΣΤΡΑΤΕΥΟΜΕΝΩΝ '1080': ΠΡΟΛΗΨΗ ΕΡΓΑΤΙΚΩΝ ΑΤΥΧΗΜΑΤΩΝ ΤΩΝ ΝΑΥΤΙΚΩΝ '1081': ΥΠΗΡΕΣΙΑ ΑΠΟΜΑΓΝΗΤΙΣΕΩΣ ΠΛΟΙΩΝ '1082': ΔΙΑΦΟΡΕΣ ΕΙΔΙΚΕΣ ΔΙΑΔΙΚΑΣΙΕΣ '1083': ΓΕΝΙΚΗ ΔΙΕΥΘΥΝΣΗ ΤΗΛΕΠΙΚΟΙΝΩΝΙΩΝ '1084': ΕΘΝΙΚΗ ΥΠΗΡΕΣΙΑ ΠΛΗΡΟΦΟΡΙΩΝ (Ε.Υ.Π.) '1085': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΜΙΣΘΩΤΩΝ (T.E.A.M) '1086': ΑΣΦΑΛΙΣΗ ΚΑΤΑ ΤΗΣ ΑΝΕΡΓΙΑΣ - ΟΡΓΑΝΙΣΜΟΣ ΑΠΑΣΧΟΛΗΣΗΣ ΕΡΓΑΤΙΚΟΥ ΔΥΝΑΜΙΚΟΥ '1087': ΣΩΜΑΤΙΚΗ ΙΚΑΝΟΤΗΤΑ ΠΡΟΣΩΠΙΚΟΥ ΣΤΡΑΤΕΥΜΑΤΟΣ '1088': ΟΙΚΟΝΟΜΙΚΗ ΥΠΗΡΕΣΙΑ Π. ΝΑΥΤΙΚΟΥ '1089': ΔΑΣΙΚΗ ΦΟΡΟΛΟΓΙΑ '1090': ΑΤΕΛΕΙΕΣ ΥΠΕΡ ΤΗΣ ΚΤΗΝΟΤΡΟΦΙΑΣ, ΜΕΛΙΣΣΟΚΟΜΙΑΣ Κ.Λ.Π '1091': ΠΟΛΙΤΙΚΑ ΔΙΚΑΙΩΜΑΤΑ ΤΩΝ ΓΥΝΑΙΚΩΝ '1092': ΜΕΤΑΘΕΣΕΙΣ ΕΚΠΑΙΔΕΥΤΙΚΩΝ '1093': ΔΙΕΘΝΕΣ ΚΕΝΤΡΟ ΥΠΟΛΟΓΙΣΜΟΥ '1094': ΔΙΑΧΕΙΡΙΣΗ ΔΑΣΩΝ '1095': ΔΟΥΛΕΙΑ '1096': ΜΕ ΤΗ ΠΟΛΩΝΙΑ '1097': ΑΝΑΔΙΑΝΟΜΗ ΚΤΗΜΑΤΩΝ '1098': ΥΠΟΑΠΑΣΧΟΛΟΥΜΕΝΟΙ ΜΙΣΘΩΤΟΙ '1099': ΟΡΓΑΝΙΣΜΟΙ ΠΡΩΗΝ Υ.Β.Ε.Τ. - Γ.Γ.Β. - Γ.Γ.Ε.Τ '1100': ΠΑΝΕΠΙΣΤΗΜΙΑΚΗ ΒΙΒΛΙΟΘΗΚΗ ΑΘΗΝΩΝ '1101': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΑΣΦΑΛΙΣΤ.ΕΤΑΙΡΕΙΑΣ Η ΕΘΝΙΚΗ (Τ.Α.Π.Α.Ε. Η ΕΘΝΙΚΗ) '1102': ΤΕΛΗ ΣΧΟΛΑΖΟΥΣΩΝ ΚΛΗΡΟΝΟΜΙΩΝ '1103': ΞΕΝΕΣ ΓΛΩΣΣΕΣ '1104': ΚΑΤΑΣΚΗΝΩΣΕΙΣ - ΠΑΙΔΙΚΕΣ ΕΞΟΧΕΣ '1105': ΔΙΚΑΣΤΗΡΙΑ ΑΝΗΛΙΚΩΝ '1106': ΣΥΜΒΑΣΕΙΣ ΕΚΤΕΛΕΣΕΩΣ ΑΛΛΟΔΑΠΩΝ ΑΠΟΦΑΣΕΩΝ '1107': ΦΟΡΟΣ ΕΙΣΟΔΗΜΑΤΟΣ ΝΟΜΙΚΩΝ ΠΡΟΣΩΠΩΝ '1108': ΘΕΩΡΗΤΙΚΑ ΚΑΙ ΙΣΤΟΡΙΚΑ ΜΑΘΗΜΑΤΑ '1109': ΑΦΡΟΔΙΣΙΑ '1110': ΦΑΡΟΙ '1111': ΔΗΜΟΣΙΟΓΡΑΦΙΚΟ ΕΠΑΓΓΕΛΜΑ '1112': ΚΑΤΑΣΤΑΤΙΚΟΣ ΝΟΜΟΣ ΕΚΚΛΗΣΙΑΣ ΤΗΣ ΕΛΛΑΔΟΣ '1113': ΕΛΕΓΧΟΣ ΣΚΟΠΙΜΟΤΗΤΑΣ ΙΔΡΥΣΕΩΣ ΒΙΟΜΗΧΑΝΙΩΝ '1114': ΓΥΜΝΑΣΙΑ ΚΑΙ ΛΥΚΕΙΑ '1115': ΑΕΡΟΝΑΥΤΙΚΕΣ ΠΛΗΡΟΦΟΡΙΕΣ '1116': ΚΑΤΑΣΤΑΣΗ ΥΠΑΞΙΩΜΑΤΙΚΩΝ Π.Ν '1117': ΥΠΟΥΡΓΕΙΟ ΧΩΡΟΤΑΞΙΑΣ '1118': ΕΚΤΕΛΕΣΗ ΄ΕΡΓΩΝ '1119': ΜΙΣΘΟΔΟΣΙΑ ΥΠΑΛΛΗΛΩΝ ΣΕ ΕΠΙΣΤΡΑΤΕΥΣΗ '1120': ΚΟΙΜΗΤΗΡΙΑ '1121': ΑΣΦΑΛΙΣΤΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ ΚΙΝΔΥΝΩΝ ΠΟΛΕΜΟΥ '1122': ΣΥΜΦΩΝΙΑ ΓΙΑ ΑΝΙΘΑΓΕΝΕΙΣ '1123': ΝΟΜΑΡΧΙΑΚΗ ΑΥΤΟΔΙΟΙΚΗΣΗ '1124': ΣΧΟΛΗ ΤΟΥΡΙΣΤΙΚΩΝ ΕΠΑΓΓΕΛΜΑΤΩΝ '1125': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΜΙΣΘΩΤΩΝ ΠΑΡΑΓΩΓΗΣ ΚΑΙ ΕΜΠΟΡΙΑΣ ΟΠΩΡΟΚΗΠΕΥΤΙΚΩΝ '1126': ΑΠΟΛΥΜΑΝΣΗ ΥΔΑΤΩΝ '1127': ΠΟΛΕΟΔΟΜΙΚΕΣ ΕΠΙΤΡΟΠΕΣ '1128': ΟΡΓΑΝΙΣΜΟΣ ΕΚΔΟΣΕΩΣ ΣΧΟΛΙΚΩΝ ΒΙΒΛΙΩΝ '1129': ΥΠΑΛΛΗΛΟΙ ΝΟΜ. ΠΡΟΣΩΠΩΝ ΔΗΜΟΣΙΟΥ ΔΙΚΑΙΟΥ '1130': ΑΝΤΙΣΤΑΘΜΙΣΤΙΚΗ ΕΙΣΦΟΡΑ '1131': ΠΡΟΣΩΠΙΚΟ ΙΔΙΩΤΙΚΩΝ ΕΚΠΑΙΔΕΥΤΗΡΙΩΝ '1132': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ ΓΙΑ ΤΑ ΑΥΤΟΚΙΝΗΤΑ '1133': ΕΞΩΣΧΟΛΙΚΗ ΑΓΩΓΗ '1134': ΑΣΦΑΛΙΣΤΙΚΗ ΑΡΜΟΔΙΟΤΗΤΑ '1135': ΕΛΙΕΣ ΚΑΙ ΕΛΑΙΑ '1136': ΓΑΜΟΙ ΙΣΡΑΗΛΙΤΩΝ '1137': ΤΑΜΕΙΟ ΑΡΤΟΥ '1138': ΚΑΝΟΝΙΣΜΟΣ ΕΠΙΤΡΟΠΩΝ '1139': ΣΥΜΒΑΣΗ ΚΑΤΑ ΔΑΓΚΕΙΟΥ '1140': ΕΘΝΙΚΟΙ ΔΡΥΜΟΙ '1141': ΑΠΑΛΛΑΓΕΣ ΤΕΛΩΝ ΧΑΡΤΟΣΗΜΟΥ '1142': ΔΙΕΘΝΗΣ ΟΡΓΑΝΙΣΜΟΣ ΑΝΑΠΤΥΞΕΩΣ '1143': ΚΑΝΟΝΙΣΜΟΣ ΕΡΓΑΣΙΑΣ ΕΠΙ ΦΟΡΤΗΓΩΝ ΠΛΟΙΩΝ '1144': ΛΥΣΣΑ '1145': ΑΓΡΟΚΤΗΜΑ '1146': ΚΑΘΗΓΗΤΕΣ ΚΑΙ ΥΦΗΓΗΤΕΣ '1147': ΠΑΙΔΙΚΟΙ - ΒΡΕΦΟΝΗΠΙΑΚΟΙ ΣΤΑΘΜΟΙ '1148': ΚΕΝΤΡΟ ΒΥΖΑΝΤΙΝΩΝ ΕΡΕΥΝΩΝ '1149': ΙΔΡΥΣΗ ΕΛΕΥΘΕΡΗΣ ΖΩΝΗΣ ΣΕ ΔΙΑΦΟΡΑ ΛΙΜΑΝΙΑ ΤΗΣ ΧΩΡΑΣ '1150': ΣΧΟΛΙΚΑ ΛΕΩΦΟΡΕΙΑ '1151': ΣΦΑΓΕΙΑ '1152': ΕΠΙΚΥΡΩΣΗ ΝΟΜΟΘΕΤΗΜΑΤΩΝ '1153': ΕΓΓΡΑΦΑ ΤΑΥΤΟΤΗΤΑΣ ΝΑΥΤΙΚΩΝ '1154': ΑΤΟΜΙΚΑ ΔΙΚΑΙΩΜΑΤΑ - ΔΕΔΟΜΕΝΑ ΠΡΟΣΩΠΙΚΟΥ ΧΑΡΑΚΤΗΡΑ '1155': ΙΑΤΡΟΦΑΡΜΑΚΕΥΤΙΚΗ - ΝΟΣΟΚΟΜΕΙΑΚΗ ΠΕΡΙΘΑΛΨΗ - ΕΞΟΔΑ ΚΗΔΕΙΑΣ '1156': ΥΠΗΡΕΣΙΑ ΔΙΑΧΕΙΡΙΣΕΩΣ ΑΝΤΑΛΛΑΞΙΜΩΝ ΚΤΗΜΑΤΩΝ '1157': ΣΤΟΛΕΣ ΠΡΟΣΩΠΙΚΟΥ Λ.Σ '1158': ΠΕΡΙΦΡΑΞΗ ΟΙΚΟΠΕΔΩΝ '1159': ΣΙΔΗΡΟΔΡΟΜΟΙ ΑΤΤΙΚΗΣ '1160': ΤΡΑΧΩΜΑΤΑ '1161': ΝΑΥΑΓΙΑ-ΝΑΥΑΓΙΑΙΡΕΣΗ '1162': ΥΠΟΜΗΧΑΝΙΚΟΙ '1163': ΤΑΙΝΙΟΘΗΚΗ ΤΗΣ ΕΛΛΑΔΟΣ '1164': ΚΑΝΟΝΙΣΜΟΣ ΤΗΛΕΓΡΑΦΙΚΗΣ ΥΠΗΡΕΣΙΑΣ '1165': ΣΥΝΤΑΞΕΙΣ ΘΥΜΑΤΩΝ ΤΡΟΜΟΚΡΑΤΙΑΣ '1166': ΚΑΝΟΝΙΣΜΟΣ ΠΥΡΙΜΑΧΟΥ ΠΡΟΣΤΑΣΙΑΣ ΕΠΙΒΑΤΗΓΩΝ ΠΛΟΙΩΝ '1167': ΑΤΟΜΙΚΑ ΒΙΒΛΙΑΡΙΑ '1168': ΕΠΑΓΓΕΛΜΑΤΙΚΑ ΒΙΒΛΙΑΡΙΑ ΑΡΤΕΡΓΑΤΩΝ ΚΛΠ '1169': ΦΟΡΟΛΟΓΙΑ ΑΜΥΛΟΣΙΡΟΠΙΟΥ, ΣΤΑΦΙΔΙΝΗΣ ΚΛΠ '1170': ΜΟΥΣΕΙΟ ΕΛΛΗΝΙΚΩΝ ΛΑΙΚΩΝ ΟΡΓΑΝΩΝ '1171': ΕΠΙΚΟΥΡΙΚΟ ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΚΑΙ ΠΕΡΙΘΑΛΨΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΕΛΛΗΝ. ΗΛΕΚΤΡ. ΕΤΑΙΡΙΑΣ (Ε.Η.Ε.) '1172': ΤΑΜΕΙΑ ΜΟΝΙΜΩΝ ΟΔΟΣΤΡΩΜΑΤΩΝ '1173': ΟΡΓΑΝΙΚΕΣ ΘΕΣΕΙΣ ΑΞΙΩΜΑΤΙΚΩΝ Π.Ν '1174': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΠΡΟΣΩΠΙΚΟΥ ΤΡΑΠΕΖΑΣ ΑΘΗΝΩΝ '1175': ΠΟΛΙΟΜΥΕΛΙΤΙΔΑ '1176': ΠΡΟΑΓΩΓΑΙ ΑΞΙΩΜΑΤΙΚΩΝ ΧΩΡΟΦΥΛΑΚΗΣ '1177': ΕΠΙΔΟΜΑ ΑΔΕΙΑΣ '1178': ΕΞΕΤΑΣΕΙΣ ΓΙΑ ΤΗΝ ΠΡΟΣΛΗΨΗ ΠΡΟΣΩΠΙΚΟΥ '1179': ΕΛΕΓΧΟΣ ΕΞΑΓΩΓΙΚΟΥ ΕΜΠΟΡΙΟΥ '1180': ΡΑΔΙΟΦΩΝΙΚΟΙ ΣΤΑΘΜΟΙ '1181': ΚΑΝΟΝΙΣΜΟΣ ΔΙΟΙΚΗΤΙΚΗΣ ΟΡΓΑΝΩΣΕΩΣ Τ.Σ.Α.Υ '1182': Φ.Κ.Π. ΑΝΩΝΥΜΩΝ ΕΤΑΙΡΕΙΩΝ '1183': ΔΙΑΦΟΡΟΙ ΠΟΛΥΕΘΝΕΙΣ ΟΡΓΑΝΙΣΜΟΙ '1184': ΧΟΛΕΡΑ '1185': EΝΙΑΙΟΣ ΔΗΜΟΣΙΟΓΡΑΦΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ '1186': ΑΤΕΛΕΙΕΣ ΔΗΜΟΣΙΩΝ ΥΠΗΡΕΣΙΩΝ '1187': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΜΗΧΑΝΟΔΗΓΩΝ ΟΔΟΣΤΡΩΤΗΡΩΝ ΚΛΠ '1188': ΝΟΣΟΚΟΜΟΙ '1189': ΝΟΣΟΚΟΜΕΙΑ ΦΥΛΑΚΩΝ '1190': ΑΠΟΚΑΤΑΣΤΑΣΗ ΚΤΗΝΟΤΡΟΦΩΝ '1191': ΤΕΛΗ ΚΑΙ ΕΙΣΦΟΡΕΣ '1192': ΑΚΑΤΑΣΧΕΤΑ '1193': ΞΕΝΟΔΟΧΕΙΑΚΟ ΕΠΙΜΕΛΗΤΗΡΙΟ ΤΗΣ ΕΛΛΑΔΑΣ '1194': ΔΗΜΟΤΟΛΟΓΙΑ '1195': ΣΤΑΤΙΣΤΙΚΗ ΥΠΗΡΕΣΙΑ '1196': ΚΡΑΤΙΚΟ ΕΡΓΑΣΤΗΡΙΟ ΕΛΕΓΧΟΥ ΦΑΡΜΑΚΩΝ '1197': ΑΕΡΟΠΟΡΙΚΗ ΑΣΤΥΝΟΜΙΑ '1198': ΕΚΤΑΚΤΕΣ ΕΙΣΦΟΡΕΣ '1199': ΣΥΝΤΑΞΕΙΣ ΥΠΑΛΛΗΛΩΝ Τ.Τ.Τ '1200': ΜΕΤΡΑ ΚΑΤΑ ΤΗΣ ΦΟΡΟΔΙΑΦΥΓΗΣ '1201': ΕΔΑΦΙΚΗ ΕΠΕΚΤΑΣΗ ΝΟΜΟΘΕΣΙΑΣ '1202': ΜΙΚΡΟΔΙΑΦΟΡΕΣ '1203': ΤΑΤΖΙΚΙΣΤΑΝ – ΤΑΥΛΑΝΔΗ – ΤΟΥΡΚΙΑ Κ.ΛΠ '1204': ΣΥΜΒΑΣΗ ΔΙΕΘΝΟΥΣ ΜΕΤΑΦΟΡΑΣ ΕΜΠΟΡΕΥΜΑΤΩΝ ΟΔΙΚΩΣ '1205': ΚΩΔΙΚΑΣ ΙΔΙΩΤΙΚΟΥ ΝΑΥΤΙΚΟΥ ΔΙΚΑΙΟΥ '1206': ΚΕΝΤΡΑ ΓΕΩΡΓΙΚΗΣ ΕΚΠΑΙΔΕΥΣΗΣ-Ο.Γ.Ε.Ε.Κ.Α '1207': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΙΔΡΥΜΑΤΩΝ ΕΜΠΟΡΙΚΟΥ ΝΑΥΤΙΚΟΥ '1208': ΓΡΑΦΕΙΟ ΔΙΑΡΚΗ ΚΩΔΙΚΑ ΝΟΜΟΘΕΣΙΑΣ '1209': ΕΡΕΥΝΑ ΙΔΙΩΤΙΚΩΝ ΜΕΤΑΛΛΕΙΩΝ '1210': ΔΙΕΥΘΥΝΣΗ ΔΗΜΟΣΙΩΝ ΕΡΓΩΝ ΑΕΡΟΠΟΡΙΑΣ '1211': ΠΕΡΙ ΝΟΜΑΡΧΩΝ '1212': ΣΥΝΤΑΞΕΙΣ ΘΥΜΑΤΩΝ ΑΠΟ ΕΣΩΤΕΡΙΚΕΣ ΔΙΑΜΑΧΕΣ '1213': ΔΙΑΧΕΙΡΙΣΗ ΕΦΟΔΙΩΝ ΕΞΩΤΕΡΙΚΟΥ '1214': ΟΡΓΑΝΩΣΗ ΥΠΗΡΕΣΙΩΝ ΠΟΛΕΜΙΚΟΥ ΝΑΥΤΙΚΟΥ '1215': ΦΟΡΤΗΓΑ ΠΛΟΙΑ ΑΝΩ ΤΩΝ 4.500 ΤΟΝΝΩΝ '1216': ΡΑΔΙΟΤΗΛΕΓΡΑΦΙΚΗ ΥΠΗΡΕΣΙΑ ΠΛΟΙΩΝ '1217': ΕΠΑΓΓΕΛΜΑΤΙΚΕΣ ΣΧΟΛΕΣ '1218': ΔΙΑΦΟΡΕΣ ΒΙΟΜΗΧΑΝΙΕΣ '1219': ΣΥΝΤΗΡΗΣΗ ΑΕΡΟΣΚΑΦΩΝ '1220': ΟΛΥΜΠΙΑΚΗ ΑΕΡΟΠΟΡΙΑ '1221': ΟΡΓΑΝΙΣΜΟΣ ΧΩΡΟΦΥΛΑΚΗΣ '1222': ΠΕΡΙΘΑΛΨΗ ΦΥΜΑΤΙΚΩΝ ΤΑΧΥΔΡΟΜΙΚΩΝ ΥΠΑΛΛΗΛΩΝ '1223': ΟΡΓΑΝΙΣΜΟΣ ΧΡΗΜΑΤΟΔΟΤΗΣΗΣ ΟΙΚΟΝΟΜΙΚΗΣ ΑΝΑΠΤΥΞΗΣ '1224': ΠΡΩΤΕΣ ΥΛΕΣ ΞΥΛΙΝΩΝ ΒΑΡΕΛΙΩΝ '1225': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΤΕΧΝΙΚΩΝ ΤΥΠΟΥ ΑΘΗΝΩΝ (Τ.Α.Τ.Τ.Α.) '1226': ΠΡΟΠΑΡΑΣΚΕΥΑΣΤΙΚΗ ΣΧΟΛΗ ΚΑΛΩΝ ΤΕΧΝΩΝ ΤΗΝΟΥ '1227': ΟΙΚΟΝΟΜΙΚΕΣ ΑΝΤΙΠΡΟΣΩΠΕΙΕΣ ΕΞΩΤΕΡΙΚΟΥ '1228': ΚΑΛΛΙΤΕΧΝΙΚΟΙ ΣΤΑΘΜΟΙ '1229': ΣΥΜΒΑΣΕΙΣ ΓΙΑ ΤΗ ΒΙΑ ΤΩΝ '1230': ΠΡΟΣΤΑΣΙΑ ΑΜΠΕΛΟΥΡΓΙΚΗΣ ΠΑΡΑΓΩΓΗΣ '1231': ΔΙΑΦΟΡΑ ΑΔΙΚΗΜΑΤΑ '1232': ΑΣΤΥΝΟΜΙΑ ΚΑΙ ΑΣΦΑΛΕΙΑ ΣΙΔΗΡΟΔΡΟΜΩΝ '1233': ΜΕΤΟΧΙΚΟ ΤΑΜΕΙΟ ΒΑΣΙΛΙΚΗΣ ΑΕΡΟΠΟΡΙΑΣ '1234': ΥΠΟΘΗΚΗ ΜΗΧΑΝΙΚΩΝ ΕΓΚΑΤΑΣΤΑΣΕΩΝ '1235': ΕΥΘΥΝΗ ΑΠΟ Τ’ΑΥΤΟΚΙΝΗΤΑ '1236': ΠΡΟΣΤΑΣΙΑ ΜΗΤΡΟΤΗΤΟΣ ΚΑΙ ΒΡΕΦΩΝ '1237': ΜΕ ΤΗ ΦΙΛΑΝΔΙΑ '1238': ΕΠΑΡΧΙΑΚΟΣ ΤΥΠΟΣ '1239': ΕΠΙΘΕΩΡΗΣΗ ΤΕΛΩΝΕΙΩΝ '1240': ΕΠΙΤΡΟΠΕΙΕΣ ΤΟΠΩΝΥΜΙΩΝ '1241': ΜΕΤΑΝΑΣΤΕΥΣΗ ΚΑΙ ΑΠΟΔΗΜΙΑ '1242': ΔΙΚΗΓΟΡΙΚΟΙ ΣΥΛΛΟΓΟΙ '1243': ΠΡΟΣΩΠΙΚΟ ΥΠΟΥΡΓΕΙΟΥ ΓΕΩΡΓΙΑΣ '1244': ΤΜΗΜΑ ΟΙΚΟΝΟΜΙΚΩΝ ΕΠΙΣΤΗΜΩΝ ΠΑΝΜΙΟΥ ΠΑΤΡΩΝ '1245': ΜΑΛΑΚΤΕΣ '1246': ΕΛΑΙΑ '1247': ΑΤΟΜΙΚΑ ΕΓΓΡΑΦΑ ΑΞΙΩΜΑΤΙΚΩΝ '1248': ΑΓΡΟΤΙΚΗ ΤΡΑΠΕΖΑ ΤΗΣ ΕΛΛΑΔΟΣ '1249': ΟΠΤΙΚΟΙ - ΚΑΤΑΣΤΗΜΑΤΑ ΟΠΤΙΚΩΝ ΕΙΔΩΝ '1250': ΔΗΜΟΣΙΕΣ ΕΠΕΝΔΥΣΕΙΣ '1251': ΚΡΑΤΙΚΗ ΟΡΧΗΣΤΡΑ ΘΕΣΣΑΛΟΝΙΚΗΣ '1252': ΝΗΟΛΟΓΙΑ-ΥΠΟΘΗΚΟΛΟΓΙΑ-ΣΗΜΑΤΟΛΟΓΗΣΗ '1253': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΠΡΟΣΩΠΙΚΟΥ ΕΤΑΙΡΕΙΑΣ ΔΙΑΧΕΙΡΙΣΕΩΣ ΕΙΔΩΝ ΜΟΝΟΠΩΛΙΟΥ (Τ.Α.Π.-Ε.Δ.Ε.Μ.Ε.) '1254': ΕΙΣΠΡΑΞΗ ΑΞΙΩΝ '1255': ΥΓΕΙΟΝΟΜΙΚΟΣ ΕΛΕΓΧΟΣ ΤΡΟΦΙΜΩΝ-ΠΟΤΩΝ-ΝΕΡΩΝ '1256': ΛΟΓΙΣΤΕΣ - ΦΟΡΟΤΕΧΝΙΚΟΙ '1257': ΕΙΔΙΚΕΣ ΔΙΚΟΝΟΜΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΓΙΑ ΤΟ ΔΗΜΟΣΙΟ '1258': ΣΧΟΛΕΣ ΣΩΜΑΤΩΝ ΑΣΦΑΛΕΙΑΣ '1259': ΤΑΜΕΙΟΝ ΚΟΙΝΩΦΕΛΩΝ ΕΡΓΩΝ ΛΕΥΚΑΔΟΣ '1260': ΕΙΔΙΚΗ ΑΓΩΓΗ, ΕΙΔΙΚΗ ΕΠΑΓΓΕΛΜΑΤΙΚΗ '1261': ΥΠΗΡΕΣΙΑ ΚΡΑΤΙΚΩΝ ΠΡΟΜΗΘΕΙΩΝ '1262': ΟΙΝΟΛΟΓΙΚΑ ΙΔΡΥΜΑΤΑ '1263': ΣΥΝΘΗΚΕΣ ΕΚΔΟΣΕΩΣ '1264': ΑΞΙΩΜΑΤΙΚΟΙ ΚΑΙ ΥΠΑΞΙΩΜΑΤΙΚΟΙ Λ.Σ '1265': ΥΓΕΙΟΝΟΜΙΚΗ ΕΞΕΤΑΣΗ ΠΡΟΣΩΠΙΚΟΥ '1266': ΞΕΝΑ ΣΧΟΛΕΙΑ ΗΜΕΔΑΠΗΣ '1267': Ε.Σ.Υ.-ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ '1268': ΤΑΜΕΙΑ ΕΦΑΡΜΟΓΗΣ ΣΧΕΔΙΩΝ ΠΟΛΕΩΝ '1269': ΣΥΝΕΤΑΙΡΙΣΜΟΙ ΣΤΡΑΤΙΩΤΙΚΩΝ ΕΙΔΩΝ '1270': ΣΥΝΘΗΚΗ ΠΕΡΙ ΔΙΑΣΤΗΜΑΤΟΣ '1271': ΔΙΑΧΕΙΡΙΣΗ ΑΝΤΑΛΛΑΞΙΜΩΝ ΚΤΗΜΑΤΩΝ '1272': ΠΡΟΣΩΠΙΚΟΝ ΔΙΟΙΚΗΣΕΩΣ '1273': ΣΧΟΛΗ ΕΚΠΤΙΚΩΝ ΛΕΙΤΟΥΡΓΩΝ '1274': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΞΕΝΟΔΟΧΟΥΠΑΛΛΗΛΩΝ (Τ.Α.Ξ.Υ.) '1275': ΣΩΜΑΤΙΚΗ ΙΚΑΝΟΤΗΤΑ ΑΞΙΩΜΑΤΙΚΩΝ '1276': ΒΕΒΑΙΩΣΗ ΕΣΟΔΩΝ ΔΗΜΟΣΙΟΥ ΑΠΟ ΜΕΤΑΛΛΕΙΑ ΚΑΙ ΛΑΤΟΜΕΙΑ '1277': ΔΙΑΦΟΡΟΙ ΕΠΟΙΚΙΣΤΙΚΟΙ ΝΟΜΟΙ '1278': ΕΠΙΚΟΥΡΙΚΟ ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΚΡΕΟΠΩΛΩΝ ΚΑΙ ΕΡΓΑΤΟΥΠΑΛΛΗΛΩΝ ΚΡΕΑΤΟΣ (Ε.Τ.Α.Κ.Ε.Κ) '1279': ΟΙΚΟΝΟΜΙΚΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΑΘΗΝΩΝ '1280': ΓΕΝΙΚΕΣ ΑΠΟΘΗΚΕΣ '1281': ΤΑΜΕΙΑΚΗ ΥΠΗΡΕΣΙΑ '1282': ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΠΕΡΙ ΑΝΩΝΥΜΩΝ ΕΤΑΙΡΕΙΩΝ '1283': ΤΟΜΕΑΣ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΜΙΣΘΩΤΩΝ (ΙΚΑ-ΤΕΑΜ)ΕΙΔΙΚΟΣ ΤΟΜΕΑΣ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΜΙΣΘΩΤΩΝ (ΙΚΑ-ΕΤΕΑΜ) '1284': ΒΑΡΒΑΚΕΙΟ ΛΥΚΕΙΟ '1285': ΚΩΔΙΚΑΣ ΔΙΚΩΝ ΤΟΥ ΔΗΜΟΣΙΟΥ '1286': ΔΙΕΘΝΕΣ ΤΑΜΕΙΟΝ ΠΕΡΙΘΑΛΨΕΩΣ ΤΟΥ ΠΑΙΔΙΟΥ '1287': ΣΙΔΗΡΟΔΡΟΜΟΙ ΕΛΛΗΝΙΚΟΥ ΚΡΑΤΟΥΣ '1288': ΑΡΔΕΥΣΕΙΣ '1289': ΤΑΜΕΙΟ ΑΡΧΑΙΟΛΟΓΙΚΩΝ ΠΟΡΩΝ ΚΑΙ ΑΠΑΛΛΟΤΡΙΩΣΕΩΝ '1290': ΙΔΡΥΜΑ ΒΥΖΑΝΤΙΝΗΣ ΜΟΥΣΙΚΟΛΟΓΙΑΣ '1291': ΚΥΒΕΡΝΗΤΙΚΟ ΣΥΜΒΟΥΛΙΟ ΕΛΕΓΧΟΥ ΤΙΜΩΝ '1292': ΕΙΔΙΚΟ ΤΑΜΕΙΟ ΕΠΟΙΚΙΣΜΟΥ '1293': ΚΤΗΜΑΤΟΛΟΓΙΑ ΔΗΜΩΝ ΚΑΙ ΚΟΙΝΟΤΗΤΩΝ '1294': ΚΑΤΑΣΚΕΥΗ ΣΤΑΦΙΔΙΝΗΣ '1295': ΔΙΕΘΝΗΣ ΥΓΕΙΟΝΟΜΙΚΟΣ ΚΑΝΟΝΙΣΜΟΣ '1296': ΕΠΕΤΗΡΙΔΑ '1297': ΠΑΓΚΟΣΜΙΟΣ ΟΡΓΑΝΙΣΜΟΣ ΤΟΥΡΙΣΜΟΥ '1298': ΕΝΙΣΧΥΣΗ ΑΠΡΟΣΤΑΤΕΥΤΩΝ ΠΑΙΔΙΩΝ '1299': ΔΙΑΦΟΡΟΙ ΕΠΙΣΙΤΙΣΤΙΚΟΙ ΝΟΜΟΙ '1300': ΔΙΠΛΩΜΑΤΙΚΕΣ ΑΤΕΛΕΙΕΣ '1301': ΜΕΤΑ ΤΟΥ ΒΕΛΓΙΟΥ '1302': ΚΑΝΝΑΒΙΣ '1303': ΕΚΤΕΛΕΣΗ '1304': ΤΟΥΡΙΣΤΙΚΕΣ ΕΓΚΑΤΑΣΤΑΣΕΙΣ ΡΟΔΟΥ '1305': ΠΟΙΝΙΚΟ ΜΗΤΡΩΟ '1306': ΑΝΩΜΑΛΕΣ ΔΙΚΑΙΟΠΡΑΞΙΕΣ ΔΩΔΕΚΑΝΗΣΟΥ '1307': ΕΜΠΟΡΙΚΑ ΚΑΙ ΒΙΟΜΗΧΑΝΙΚΑ ΕΠΙΜΕΛΗΤΗΡΙΑ '1308': ΣΥΝΤΟΝΙΣΜΟΣ ΠΡΟΓΡΑΜΜΑΤΩΝ ΚΑΙ ΕΡΓΑΣΙΩΝ ΟΔΩΝ ΚΑΙ ΕΡΓΩΝ ΚΟΙΝΗΣ ΩΦΕΛΕΙΑΣ '1309': ΠΡΟΣΩΠΙΚΟ ΞΕΝΟΔΟΧΕΙΩΝ '1310': ΙΝΣΤΙΤΟΥΤΟ ΦΥΣΙΚΗΣ ΤΟΥ ΣΤΕΡΕΟΥ ΦΛΟΙΟΥ ΤΗΣ ΓΗΣ '1311': ΕΠΙΚΙΝΔΥΝΕΣ ΟΙΚΟΔΟΜΕΣ '1312': ΑΡΧΕΙΑ ΔΙΚΑΣΤΗΡΙΩΝ '1313': ΣΚΟΠΟΒΟΛΗ '1314': ΑΠΟΝΟΜΗ ΣΥΝΤΑΞΕΩΝ ΤΑΜΕΙΟΥ ΝΟΜΙΚΩΝ '1315': ΣΗΡΟΤΡΟΦΙΑ '1316': ΕΣΩΤΕΡΙΚΟΣ ΚΑΝΟΝΙΣΜΟΣ '1317': ΠΡΟΣΤΑΣΙΑ ΤΗΣ ΚΤΗΝΟΤΡΟΦΙΑΣ '1318': ΧΑΡΤΗΣ '1319': ΥΠΗΡΕΣΙΑ ΕΓΚΛΗΜΑΤΟΛΟΓΙΚΩΝ ΑΝΑΖΗΤΗΣΕΩΝ '1320': ΥΓΕΙΟΝΟΜΙΚΗ ΠΕΡΙΘΑΛΨΗ ΒΟΥΛΕΥΤΩΝ '1321': ΔΙΚΑΙΟΣΤΑΣΙΟ ΠΟΛΕΜΟΥ 1940 '1322': ΧΗΜΕΙΟ ΣΤΡΑΤΟΥ '1323': ΕΠΑΡΧΙΑΚΕΣ ΓΕΝΙΚΕΣ ΣΥΝΕΛΕΥΣΕΙΣ '1324': ΛΟΓΑΡΙΑΣΜΟΣ ΑΡΩΓΗΣ ΟΙΚΟΓΕΝΕΙΩΝ ΣΤΡΑΤΙΩΤΙΚΩΝ ΕΝΟΠΛΩΝ ΔΥΝΑΜΕΩΝ '1325': ΚΑΤ’ ΙΔΙΑΝ ΝΑΟΙ '1326': ΠΛΗΡΩΜΗ ΜΕ ΕΠΙΤΑΓΕΣ '1327': ΕΘΝΙΚΕΣ ΣΥΛΛΟΓΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '1328': ΣΩΜΑ ΣΤΡΑΤΟΛΟΓΙΑΣ '1329': ΟΔΟΝΤΙΑΤΡΟΙ '1330': ΤΑΜΕΙΟ ΕΘΝΙΚΟΥ ΣΤΟΛΟΥ '1331': ΣΥΜΠΛΗΡΩΜΑΤΙΚΕΣ ΠΑΡΟΧΕΣ ΜΗΤΡΟΤΗΤΑΣ '1332': ΜΕΤΑΤΡΕΨΙΜΟΤΗΤΑ ΚΑΤΑΘΕΣΕΩΝ '1333': ΠΤΗΝΟΤΡΟΦΙΑ '1334': ΠΤΥΧΙΟΥΧΟΙ ΑΛΛΟΔΑΠΩΝ ΠΑΝΕΠΙΣΤΗΜΙΩΝ - ΔΙΑΠΑΝΕΠΙΣΤΗΜΙΑΚΟ ΚΕΝΤΡΟ ΑΝΑΓΝΩΡΙΣΕΩΣ '1335': ΦΟΡΤΗΓΑ ΑΥΤΟΚΙΝΗΤΑ '1336': ΥΠΗΡΕΣΙΑ ΜΗΧΑΝΙΚΗΣ ΚΑΛΛΙΕΡΓΕΙΑΣ '1337': ΕΛΕΓΧΟΣ ΚΙΝΗΜΑΤΟΓΡΑΦΩΝ '1338': ΔΗΜΟΣΙΟΓΡΑΦΙΚΕΣ ΟΡΓΑΝΩΣΕΙΣ '1339': ΝΑΥΤΙΛΙΑΚΕΣ ΤΡΑΠΕΖΕΣ '1340': ΛΕΙΤΟΥΡΓΙΑ ΥΔΡΟΘΕΡΑΠΕΥΤΗΡΙΩΝ '1341': ΣΥΜΒΟΥΛΙΟ ΕΜΠΟΡΙΚΗΣ ΝΑΥΤΙΛΙΑΣ '1342': ΕΓΓΕΙΟΣ ΦΟΡΟΛΟΓΙΑ ΚΑΠΝΟΥ '1343': ΤΕΛΟΣ ΑΔΕΙΩΝ ΟΙΚΟΔΟΜΩΝ '1344': ΕΘΝΙΚΟΤΗΤΑ ΠΛΟΙΩΝ '1345': ΠΟΛΙΤΙΚΑ ΚΟΜΜΑΤΑ '1346': ΣΧΟΛΗ ΘΕΤΙΚΩΝ ΕΠΙΣΤΗΜΩΝ '1347': ΝΗΟΓΝΩΜΟΝΕΣ '1348': ΔΙΑΦΟΡΟΙ ΠΟΙΝΙΚΟΙ ΝΟΜΟΙ '1349': ΠΡΟΣΩΡΙΝΗ ΑΠΟΛΥΣΗ '1350': ΤΑΜΕΙΟ ΑΛΛΗΛΟΒΟΗΘΕΙΑΣ ΣΤΡΑΤΟΥ ΞΗΡΑΣ '1351': ΥΠΑΞΙΩΜΑΤΙΚΟΙ ΑΕΡΟΠΟΡΙΑΣ '1352': ΦΟΡΟΛΟΓΙΑ ΧΡΗΜΑΤΙΣΤΗΡΙΑΚΩΝ ΣΥΜΒΑΣΕΩΝ '1353': ΠΤΥΧΙΑ ΙΠΤΑΜΕΝΟΥ ΠΡΟΣΩΠΙΚΟΥ '1354': ΚΡΕΑΤΑ ΣΕ ΠΑΚΕΤΑ '1355': ΕΛΕΓΧΟΣ ΟΠΛΟΦΟΡΙΑΣ '1356': ΑΝΑΣΤΟΛΕΣ ΔΗΜΟΣΙΟΥ ΧΡΕΟΥΣ '1357': ΗΛΕΚΤΡΙΚΟΙ ΣΙΔΗΡΟΔΡΟΜΟΙ ΑΘΗΝΩΝ-ΠΕΙΡΑΙΩΣ (Η.Σ.Α.Π) '1358': ΔΙΑΘΕΣΗ ΛΥΜΑΤΩΝ ΚΑΙ ΑΠΟΒΛΗΤΩΝ '1359': ΕΠΙΘΕΩΡΗΣΗ ΤΕΧΝΙΚΗΣ ΕΚΠΑΙΔΕΥΣΗΣ '1360': ΤΕΛΗ ΑΔΕΙΩΝ ΕΞΑΓΩΓΗΣ '1361': ΠΡΟΙΟΝΤΑ ΓΑΛΑΚΤΟΣ '1362': ΓΕΩΡΓΙΚΑ ΕΠΙΜΕΛΗΤΗΡΙΑ '1363': ΙΕΡΑΡΧΙΚΟΣ ΄ΕΛΕΓΧΟΣ '1364': ΣΤΡΑΤΙΩΤΙΚΕΣ ΦΥΛΑΚΕΣ '1365': ΤΑΜΕΙΟ ΕΠΙΚ. ΑΣΦΑΛΙΣΕΩΣ ΥΠΑΛΛΗΛΩΝ ΚΑΠΝΕΜΠΟΡΙΚΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ '1366': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΚΑΙ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΙΠΠΟΔΡΟΜΙΩΝ (Τ.Α.Π.Ε.Α.Π.Ι.) '1367': ΑΠΟΧΩΡΗΤΗΡΙΑ '1368': ΦΟΡΟΣ ΕΙΣΟΔΗΜΑΤΟΣ ΦΥΣΙΚΩΝ ΚΑΙ ΝΟΜΙΚΩΝ ΠΡΟΣΩΠΩΝ '1369': ΚΑΤΑΣΤΑΤΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΠΑΡΟΧΩΝ '1370': ΑΤΤΙΚΟ ΜΕΤΡΟ '1371': ΒΟΥΣΤΑΣΙΑ '1372': ΑΠΟΣΤΡΑΤΕΙΕΣ - ΕΠΑΝΑΦΟΡΕΣ '1373': ΤΡΑΠΕΖΙΤΙΚΑ ΔΑΝΕΙΑ ΣΕ ΧΡΥΣΟ ΚΛΠ '1374': ΔΙΚΑΙΟΣΤΑΣΙΟ ΠΟΛΕΜΩΝ '1375': ΕΘΝΙΚΟ ΑΣΤΕΡΟΣΚΟΠΕΙΟ '1376': ΙΔΙΩΤΙΚΕΣ ΕΠΙΧΕΙΡΗΣΕΙΣ ΠΑΡΟΧΗΣ ΥΠΗΡΕΣΙΩΝ ΑΣΦΑΛΕΙΑΣ '1377': ΔΑΝΕΙΑ ΕΞΩΤΕΡΙΚΑ '1378': ΠΝΕΥΜΑΤΙΚΟ ΚΕΝΤΡΟ ΑΘΗΝΩΝ '1379': ΑΠΟΣΒΕΣΕΙΣ '1380': ΔΙΑΦΟΡΟΙ ΟΙΝΙΚΟΙ ΚΑΙ ΣΤΑΦΙΔΙΚΟΙ ΝΟΜΟΙ '1381': ΑΚΑΔΗΜΙΑ ΣΩΜΑΤΙΚΗΣ ΑΓΩΓΗΣ '1382': ΑΜΜΟΛΗΨΙΑ '1383': ΠΡΟΣΩΠΙΚΟ ΠΛΟΗΓΙΚΗΣ ΥΠΗΡΕΣΙΑΣ '1384': ΗΘΙΚΕΣ ΑΜΟΙΒΕΣ ΑΕΡΟΠΟΡΙΑΣ '1385': ΚΩΔΙΚΑΣ ΦΟΡΟΛΟΓΙΑΣ ΟΙΝΟΠΝΕΥΜΑΤΟΣ '1386': ΛΙΜΕΝΙΚΑ ΤΑΜΕΙΑ – ΛΙΜΕΝΙΚΑ ΕΡΓΑ '1387': ΤΑΜΕΙΟ ΕΠΙΚ. ΑΣΦΑΛΙΣΕΩΣ ΥΠΑΛΛΗΛΩΝ ΕΘΝΙΚΟΥ ΟΡΓΑΝΙΣΜΟΥ ΚΑΠΝΟΥ (Τ.Ε.Α.ΥΕ.Ο.Κ) '1388': ΕΛΕΓΧΟΣ ΤΗΣ ΠΙΣΤΕΩΣ '1389': ΣΤΡΑΤΙΩΤΙΚΗ ΣΧΟΛΗ ΑΞΙΩΜΑΤΙΚΩΝ ΣΩΜΑΤΩΝ '1390': ΒΟΗΘΗΤΙΚΑ ΠΡΟΣΩΠΑ ΤΗΣ ΔΙΚΗΣ '1391': ΟΡΓΑΝΙΣΜΟΣ ΣΧΟΛΙΚΩΝ ΚΤΙΡΙΩΝ '1392': ΒΙΟΜΗΧΑΝΙΕΣ ΔΩΔΕΚΑΝΗΣΟΥ '1393': ΥΓΙΕΙΝΗ ΚΑΙ ΑΣΦΑΛΕΙΑ ΧΩΡΩΝ ΕΡΓΑΣΙΑΣ ΚΑΙ ΕΡΓΑΖΟΜΕΝΩΝ '1394': ΜΕΤΑΤΡΟΠΗ ΤΗΣ ΠΟΙΝΗΣ '1395': ΑΥΤΟΝΟΜΟΣ ΟΙΚΟΔΟΜΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ ΑΞΙΩΜΑΤΙΚΩΝ '1396': ΟΔΙΚΕΣ ΜΕΤΑΦΟΡΕΣ-ΜΕΤΑΦΟΡΕΙΣ '1397': ΑΡΜΑ ΘΕΣΠΙΔΟΣ '1398': ΔΗΜΟΤΙΚΑ & ΚΟΙΝΟΤΙΚΑ '1399': ΠΕΡΙΦΕΡΕΙΑΚΕΣ ΥΠΗΡΕΣΙΕΣ '1400': ΣΧΟΛΗ ΑΝΘΡΩΠΙΣΤΙΚΩΝ ΚΑΙ ΚΟΙΝΩΝΙΚΩΝ ΕΠΙΣΤΗΜΩΝ '1401': ΣΤΡΑΤΕΥΟΜΕΝΟΙ ΦΟΙΤΗΤΑΙ '1402': ΓΕΝΙΚΑ '1403': ΚΑΤΑΠΟΛΕΜΗΣΗ ΕΠΙΖΩΟΤΙΩΝ '1404': ΟΡΓΑΝΙΣΜΟΣ ΔΙΟΙΚΗΣΕΩΣ ΕΚΚΛΗΣΙΑΣΤΙΚΗΣ ΚΑΙ ΜΟΝΑΣΤΗΡΙΑΚΗΣ ΠΕΡΙΟΥΣΙΑΣ '1405': ΑΠΑΓΟΡΕΥΣΗ ΧΡΗΣΗΣ ΕΠΙΒΛΑΒΩΝ ΟΥΣΙΩΝ '1406': ΨΥΧΟΛΟΓΟΙ '1407': ΠΥΡΑΣΦΑΛΕΙΑ ΕΠΙΧΕΙΡΗΣΕΩΝ ΚΑΙ ΑΠΟΘΗΚΩΝ '1408': ΑΠΟΚΑΤΑΣΤΑΣΙΣ ΑΠΟΡΩΝ ΚΟΡΑΣΙΔΩΝ '1409': ΜΕ ΤΗ ΒΕΝΕΖΟΥΕΛΑ '1410': ΔΙΚΑΙΟ ΤΩΝ ΣΥΝΘΗΚΩΝ '1411': ΚΤΗΝΙΑΤΡΙΚΑ ΜΙΚΡΟΒΙΟΛΟΓΙΚΑ ΕΡΓΑΣΤΗΡΙΑ '1412': ΕΡΓΑΣΤΗΡΙΑ '1413': ΚΑΝΟΝΙΣΜΟΙ TELEX ΚΑΙ TELEFAX '1414': ΟΠΛΑ ΚΑΙ ΣΩΜΑΤΑ ΣΤΡΑΤΟΥ ΞΗΡΑΣ '1415': ΕΚΠΑΙΔΕΥΣΗ ΤΑΧΥΔΡΟΜΙΚΩΝ ΥΠΑΛΛΗΛΩΝ '1416': ΤΙΜΟΛΟΓΙΑ ΠΑΡΟΧΩΝ '1417': ΜΟΥΣΟΥΛΜΑΝΙΚΕΣ ΚΟΙΝΟΤΗΤΕΣ '1418': ΣΤΡΑΤΙΩΤΙΚΑ ΕΡΓΑ ΕΝ ΓΕΝΕΙ '1419': ΣΤΡΑΤΙΩΤΙΚΑ ΝΟΣΟΚΟΜΕΙΑ '1420': ΔΙΟΙΚΗΣΗ ΔΗΜΟΣΙΩΝ ΚΤΗΜΑΤΩΝ – '1421': ΕΙΔΙΚΕΣ ΤΙΜΕΣ ΚΑΥΣΙΜΩΝ ΚΑΙ ΗΛΕΚΤΡΙΚΗΣ ΕΝΕΡΓΕΙΑΣ '1422': ΕΓΓΡΑΦΗ ΣΠΟΥΔΑΣΤΩΝ '1423': ΔΗΜΟΤΙΚΑ-ΚΟΙΝΟΤΙΚΑ ΔΑΣΗ ΚΑΙ ΚΗΠΟΙ '1424': ΔΗΜΟΣΙΑ ΕΠΙΧΕΙΡΗΣΗ ΠΟΛΕΟΔΟΜΙΑΣ ΚΑΙ ΣΤΕΓΑΣΕΩΣ '1425': ΣΥΝΤΑΞΙΟΔΟΤΗΣΗ ΠΡΟΣΩΠΙΚΟΥ Ι.Κ.Α '1426': ΕΞΕΤΑΣΤΙΚΕΣ ΕΠΙΤΡΟΠΕΣ ΒΟΥΛΗΣ '1427': ΜΕΤΡΑ ΚΑΤΑ ΤΩΝ ΠΥΡΚΑΙΩΝ ΔΑΣΩΝ '1428': ΥΠΟΥΡΓΕΙΟ ΕΘΝΙΚΗΣ ΟΙΚΟΝΟΜΙΑΣ '1429': ΣΥΓΚΕΝΤΡΩΣΗ ΠΕΡΙΟΥΣΙΑΣ ΤΟΥ ΔΗΜΟΣΙΟΥ '1430': ΚΑΤΑΣΚΕΥΗ ΚΑΙ ΣΥΝΤΗΡΗΣΗ ΟΔΩΝ '1431': ΤΕΛΩΝΕΙΑΚΑ ΚΤΙΡΙΑ '1432': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΕΚΤΕΛΩΝΙΣΤΩΝ (Τ.Σ.Ε.) '1433': ΚΑΘΗΓΗΤΙΚΕΣ ΕΔΡΕΣ '1434': ΝΑΥΤΙΚΗ ΕΡΓΑΣΙΑ ΝΕΩΝ '1435': ΕΚΤΕΛΕΣΗ ΘΑΝΑΤΙΚΗΣ ΠΟΙΝΗΣ '1436': ΕΠΙΘΕΩΡΗΣΗ ΠΛΟΙΩΝ '1437': ΔΙΠΛΩΜΑΤΑ ΚΑΙ ΑΔΕΙΕΣ ΝΑΥΤΙΚΗΣ ΙΚΑΝΟΤΗΤΑΣ '1438': ΙΣΤΟΡΙΚΟ ΚΑΙ ΕΘΝΟΛΟΓΙΚΟ ΜΟΥΣΕΙΟ '1439': ΠΡΟΣΤΑΣΙΑ ΕΡΓΑΖΟΜΕΝΗΣ ΝΕΑΣ '1440': ΥΠΗΡΕΣΙΑ ΕΠΙΜΕΛΗΤΩΝ ΑΝΗΛΙΚΩΝ '1441': ΑΣΤΙΚΗ ΕΥΘΥΝΗ ΑΠΟ ΠΥΡΗΝΙΚΗ ΕΝΕΡΓΕΙΑ '1442': ΚΩΔΙΚΑΣ ΦΟΡΟΛΟΓΙΑΣ ΚΑΘΑΡΑΣ ΠΡΟΣΟΔΟΥ '1443': ΕΠΙΘΕΩΡΗΣΗ Υ.Ε.Ν '1444': ΚΑΤΑΓΓΕΛΙΑ ΣΥΜΒΑΣΕΩΣ ΕΡΓΑΣΙΑΣ ΣΥΝΔΙΚΑΛΙΣΤΙΚΩΝ ΣΤΕΛΕΧΩΝ '1445': ΥΓΕΙΟΝΟΜΙΚΕΣ ΔΙΑΤΑΞΕΙΣ '1446': ΔΙΔΑΣΚΑΛΕΙΟ ΜΕΣΗΣ ΕΚΠΑΙΔΕΥΣΗΣ '1447': ΥΠΟΒΡΥΧΙΑ '1448': ΥΠΗΡΕΣΙΑ ΑΠΩΛΕΙΩΝ, ΝΕΚΡΟΤΑΦΕΙΩΝ ΚΛΠ '1449': ΑΓΡΟΤ. ΑΠΟΚΑΤΑΣΤΑΣΗ ΣΤΑ ΔΩΔΕΚΑΝΗΣΑ '1450': ΕΙΔΙΚΕΣ ΑΠΑΛΛΟΤΡΙΩΣΕΙΣ '1451': ΣΤΕΓΑΣΗ ΤΑΧΥΔΡΟΜΙΚΩΝ ΥΠΗΡΕΣΙΩΝ '1452': ΔΙΑΜΕΤΑΚΟΜΙΣΗ ΝΑΡΚΩΤΙΚΩΝ '1453': ΜΕΤΑΜΟΣΧΕΥΣΗ ΒΙΟΛΟΓΙΚΩΝ ΟΥΣΙΩΝ '1454': ΒΡΑΒΕΙΑ ΚΑΙ ΧΟΡΗΓΙΕΣ '1455': ΕΥΡΩΠΑΙΚΗ ΜΟΡΦΩΤΙΚΗ ΣΥΜΒΑΣΗ '1456': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΕΛΛΗΝ. ΕΡΥΘΡΟΥ ΣΤΑΥΡΟΥ (Τ.Ε.Α.Π.Ε.Ε.Σ.) '1457': ΑΤΕΛΕΙΕΣ ΕΙΔΩΝ ΒΟΗΘΕΙΑΣ '1458': ΕΚΤΕΛΕΣΗ ΕΡΓΩΝ ΟΧΥΡΩΣΗΣ '1459': ΡΟΥΑΝΤΑ – ΡΟΥΜΑΝΙΑ Κ.ΛΠ '1460': ΜΟΝΙΜΕΣ ΑΝΤΙΠΡΟΣΩΠΕΙΕΣ '1461': ΠΡΟΣΤΑΣΙΑ ΕΦΕΔΡΩΝ ΙΠΤΑΜΕΝΩΝ '1462': ΤΡΑΠΕΖΕΣ ΕΞΩΤΕΡΙΚΟΥ ΕΜΠΟΡΙΟΥ '1463': ΙΑΤΡΙΚΟΝ ΠΡΟΣΩΠΙΚΟΝ ΔΗΜΟΣΙΟΥ ΚΑΙ Ν.Π.Δ.Δ '1464': ΔΙΑΦΟΡΑ ΜΟΝΑΣΤΗΡΙΑ '1465': ΕΤΑΙΡΕΙΕΣ ΕΠΕΝΔΥΣΕΩΝ - ΧΑΡΤΟΦΥΛΑΚΙΟΥ ΚΑΙ ΑΜΟΙΒΑΙΩΝ ΚΕΦΑΛΑΙΩΝ '1466': ΑΝΑΓΝΩΡΙΣΗ ΤΗΣ ΕΛΛΗΝΙΚΗΣ ΠΟΛΙΤΕΙΑΣ '1467': ΔΙΕΘΝΗΣ ΣΥΜΒΑΣΗ '1468': ΛΙΜΕΝΑΡΧΕΙΑ '1469': ΣΕΙΣΜΟΠΛΗΚΤΟΙ ΘΕΣΣΑΛΙΑΣ '1470': ΣΤΡΑΤΕΥΣΗ ΓΥΝΑΙΚΩΝ '1471': ΣΤΡΑΤΙΩΤΙΚΗ ΥΠΗΡΕΣΙΑ ΚΑΤΑΣΚΕΥΗΣ ΕΡΓΩΝ ΑΝΑΣΥΓΚΡΟΤΗΣΗΣ '1472': ΠΡΟΣΤΑΣΙΑ ΤΗΣ ΤΙΜΗΣ ΤΟΥ ΠΟΛΙΤΙΚΟΥ ΚΟΣΜΟΥ '1473': ΕΠΙΜΟΡΦΩΣΗ ΛΕΙΤΟΥΡΓΩΝ Μ.Ε '1474': ΕΝΙΣΧΥΣΗ ΕΞΑΓΩΓΗΣ '1475': ΗΛΕΚΤΡΟΦΩΤΙΣΜΟΣ ΔΙΑΦΟΡΩΝ ΠΟΛΕΩΝ '1476': ΜΕ ΤΙΣ ΚΑΤΩ ΧΩΡΕΣ '1477': ΝΑΥΠΗΓΟΥΜΕΝΑ ΠΛΟΙΑ-ΝΑΥΠΗΓΟΕΠΙΣΚΕΥΑΣΤΙΚΕΣ '1478': ΕΛΕΓΧΟΣ ΠΩΛΗΣΕΩΝ ΕΠΙ ΠΙΣΤΩΣΕΙ '1479': ΕΛΕΓΧΟΣ ΒΙΟΜΗΧΑΝΙΚΩΝ ΕΓΚΑΤΑΣΤΑΣΕΩΝ '1480': ΔΙΕΘΝΗΣ ΟΙΚΟΝΟΜΙΚΗ ΕΠΙΤΡΟΠΗ '1481': ΓΡΑΦΕΙΑ ΕΥΡΕΣΗΣ ΕΡΓΑΣΙΑΣ - ΣΥΜΒΟΥΛΟΙ ΕΡΓΑΣΙΑΣ '1482': ΜΟΝΟΠΩΛΙΟ ΝΑΡΚΩΤΙΚΩΝ '1483': ΑΠΑΛΛΑΓΕΣ ΦΟΡΟΛΟΓΙΑΣ ΚΛΗΡΟΝΟΜΙΩΝ '1484': ΠΑΓΚΟΣΜΙΑ ΟΡΓΑΝΩΣΗ ΥΓΕΙΑΣ '1485': ΕΘΝΙΚΟ ΙΔΡΥΜΑ ΕΡΕΥΝΩΝ '1486': ΝΟΜΟΘΕΣΙΑ ΠΕΡΙ ΣΥΛΛΟΓΙΚΗΣ ΣΥΜΒΑΣΕΩΣ '1487': ΕΘΝΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ ΦΑΡΜΑΚΩΝ '1488': ΔΙΑΦΟΡΑ ΓΥΜΝΑΣΙΑ & ΛΥΚΕΙΑ '1489': ΞΕΝΕΣ ΣΧΟΛΕΣ ΓΕΩΠΟΝΙΑΣ ΚΑΙ ΔΑΣΟΛΟΓΙΑΣ '1490': ΠΡΟΣΤΑΣΙΑ ΑΝΕΡΓΩΝ '1491': ΦΙΛΑΝΘΡΩΠΙΚΑ ΚΑΤΑΣΤΗΜΑΤΑ ΚΕΦΑΛΛΗΝΙΑΣ '1492': ΚΑΝΟΝΙΣΜΟΣ ΠΑΡΟΧΩΝ Τ.Ε.Β.Ε '1493': ΩΔΕΙΑ ΚΛΠ. ΜΟΥΣΙΚΑ ΙΔΡΥΜΑΤΑ '1494': ΠΡΟΣΚΥΝΗΜΑΤΙΚΑ ΙΔΡΥΜΑΤΑ '1495': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΑΝΩΝ. ΥΔΡΟΗΛΕΚΤΡ. ΕΤ. ΓΛΑΥΚΟΣ '1496': ΠΡΕΣΒΕΙΕΣ ΚΑΙ ΠΡΟΞΕΝΕΙΑ '1497': ΥΠΟΥΡΓΕΙΑ ΤΥΠΟΥ ΚΑΙ ΤΟΥΡΙΣΜΟΥ '1498': ΖΩΝΕΣ ΕΝΕΡΓΟΥ ΠΟΛΕΟΔΟΜΙΑΣ '1499': ΕΚΚΛΗΣΙΑ ΙΟΝΙΩΝ ΝΗΣΩΝ '1500': ΕΠΙΤΡΟΠΑΙ ΑΣΦΑΛΕΙΑΣ '1501': ΥΠΟΥΡΓΟΙ '1502': ΠΟΙΝΙΚΗ ΔΙΑΤΙΜΗΣΗ '1503': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΕΡΓΑΤΩΝ ΚΕΡΑΜΟΠΟΙΩΝ '1504': ΠΡΩΤΕΣ ΥΛΕΣ ΠΑΙΓΝΙΟΧΑΡΤΩΝ '1505': ΚΡΥΠΤΟΓΡΑΦΙΚΗ ΥΠΗΡΕΣΙΑ '1506': ΔΙΕΘΝΗΣ ΕΠΙΤΡΟΠΗ ΠΡΟΣΩΠΙΚΗΣ ΚΑΤΑΣΤΑΣΕΩΣ '1507': ΕΛΕΓΧΟΣ ΗΛΕΚΤΡΙΚΩΝ ΕΓΚΑΤΑΣΤΑΣΕΩΝ '1508': ΔΙΑΧΕΙΡΙΣΗ ΙΔΡΥΜΑΤΩΝ ΚΑΙ ΚΛΗΡΟΔΟΤΗΜΑΤΩΝ '1509': ΤΕΛΩΝΕΙΑΚΗ ΣΤΑΤΙΣΤΙΚΗ '1510': ΙΔΙΩΤΙΚΕΣ ΝΑΥΤΙΚΕΣ ΣΧΟΛΕΣ '1511': ΑΕΡΟΠΟΡΙΚΑ ΑΤΥΧΗΜΑΤΑ '1512': ΑΝΩΤΕΡΟ ΔΙΔΑΚΤΙΚΟ ΠΡΟΣΩΠΙΚΟ '1513': ΔΙΑΦΟΡΟΙ ΔΙΟΙΚΗΤΙΚΟΙ ΕΡΓΑΤΙΚΟΙ ΝΟΜΟΙ '1514': ΣΥΜΒΟΥΛΙΟ ΓΕΩΓΡΑΦΙΚΩΝ ΥΠΗΡΕΣΙΩΝ '1515': ΕΚΚΛΗΣΙΑΣΤΙΚΕΣ ΒΙΒΛΙΟΘΗΚΕΣ '1516': ΤΜΗΜΑ ΕΠΙΣΤΗΜΗΣ ΦΥΣΙΚΗΣ ΑΓΩΓΗΣ ΚΑΙ ΑΘΛΗΤΙΣΜΟΥ '1517': ΠΕΡΙΟΡΙΣΜΟΣ ΣΥΝΘΕΣΕΩΣ ΥΠΗΡΕΣΙΩΝ '1518': ΤΑΜΕΙΑ ΕΠΑΡΧΙΑΚΗΣ ΟΔΟΠΟΙΙΑΣ '1519': ΤΙΜΟΛΟΓΙΑ Ο.Τ.Ε - ΚΟΣΤΟΛΟΓΗΣΗ ΥΠΗΡΕΣΙΩΝ Ο.Τ.Ε '1520': ΕΘΝΙΚΗ ΒΙΒΛΙΟΘΗΚΗ '1521': ΔΗΜΟΣΙΕΣ ΣΧΟΛΕΣ ΥΠΟΜΗΧΑΝΙΚΩΝ '1522': ΑΝΑΦΟΡΕΣ ΠΡΟΣ ΤΙΣ ΑΡΧΕΣ '1523': ΚΡΑΤΙΚΗ ΕΚΜΕΤΑΛΛΕΥΣΗ ΛΕΩΦΟΡΕΙΑΚΩΝ ΓΡΑΜΜΩΝ '1524': ΔΙΑΦΟΡΑ ΕΠΙΔΟΜΑΤΑ '1525': ΙΔΙΩΤΙΚΗ ΑΕΡΟΠΟΡΙΑ – ΑΕΡΟΛΕΣΧΕΣ '1526': ΤΜΗΜΑ ΔΙΟΙΚΗΤΙΚΗΣ ΤΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ '1527': ΔΙΕΘΝΕΙΣ ΑΕΡΟΠΟΡΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '1528': ΠΡΟΙΚΟΔΟΤΗΣΕΙΣ ΕΞ ΕΘΝΙΚΩΝ ΓΑΙΩΝ '1529': ΔΙΟΡΘΩΣΗ ΑΣΥΜΦΩΝΙΩΝ '1530': ΕΠΙΤΡΟΠΗ ΔΙΟΙΚΗΣΕΩΣ '1531': ΜΕΤΑ ΤΗΣ ΓΕΡΜΑΝΙΑΣ '1532': ΟΙΚΟΔΟΜΙΚΟΙ ΣΥΝΕΤΑΙΡΙΣΜΟΙ '1533': ΚΑΤΑΣΤΑΤΙΚΟΙ ΝΟΜΟΙ '1534': ΑΞΙΩΜΑΤΙΚΟΙ ΓΡΑΦΕΙΟΥ '1535': ΚΑΝΟΝΙΣΜΟΣ ΕΝΑΕΡΙΟΥ ΚΥΚΛΟΦΟΡΙΑΣ '1536': ΔΙΑΧΕΙΡΙΣΗ ΚΑΥΣΙΜΩΝ '1537': ΟΜΟΛΟΓΙΑΚΑ ΔΑΝΕΙΑ '1538': ΕΡΓΑ '1539': ΣΧΟΛΗ ΝΑΥΤΙΚΩΝ ΔΟΚΙΜΩΝ '1540': ΠΩΛΗΣΗ ΦΑΡΜΑΚΩΝ ΑΠΟ ΙΑΤΡΟΥΣ '1541': ΣΗΜΑΤΑ ΕΘΝΙΚΟΤΗΤΑΣ ΚΑΙ ΝΗΟΛΟΓΗΣΕΩΣ '1542': ΛΕΙΤΟΥΡΓΟΙ ΣΤΟΙΧΕΙΩΔΟΥΣ '1543': ΕΦΕΤΕΙΑ ΚΑΙ ΠΡΩΤΟΔΙΚΕΙΑ '1544': ΥΠΟΥΡΓΕΙΟ ΠΡΟΕΔΡΙΑΣ ΚΥΒΕΡΝΗΣΕΩΣ '1545': ΜΟΡΦΩΤΙΚΟΣ – ΚΙΝΗΜΑΤΟΓΡΑΦΟΣ '1546': ΚΑΤΑΜΕΤΡΗΣΗ ΧΩΡΗΤΙΚΟΤΗΤΑΣ '1547': ΦΩΤΑΕΡΙΟ '1548': ΠΑΘΗΤΙΚΗ ΑΕΡΑΜΥΝΑ '1549': ΠΡΟΣΩΠΙΚΟ ΝΟΣΗΛΕΥΤΙΚΩΝ ΙΔΡΥΜΑΤΩΝ '1550': ΜΕ ΤΗΝ ΚΥΠΡΟ '1551': ΚΟΛΛΗΓΟΙ (ΕΠΙΜΟΡΤΟΙ ΚΑΛΛΙΕΡΓΗΤΕΣ) '1552': ΤΑΜΕΙΟ ΑΡΩΓΗΣ Λ.Σ '1553': ΙΧΘΥΟΣΚΑΛΕΣ '1554': ΣΧΗΜΑ ΚΑΙ ΤΙΜΗ ΠΩΛΗΣΗΣ ΕΦΗΜΕΡΙΔΩΝ '1555': ΥΙΟΘΕΣΙΑ '1556': ΕΚΤΕΛΕΣΗ ΕΡΓΩΝ ΑΡΜΟΔΙΟΤΗΤΑΣ ΕΚΚΛΗΣΙΑΣ '1557': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΠΡΟΣΩΠΙΚΟΥ '1558': ΔΙΑΦΟΡΕΣ ΕΥΡΩΠΑΙΚΕΣ ΣΥΜΦΩΝΙΕΣ '1559': ΕΓΓΕΙΟΣ ΦΟΡΟΛΟΓΙΑ '1560': ΠΑΙΔΑΓΩΓΙΚΕΣ ΑΚΑΔΗΜΙΕΣ '1561': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΕΡΓΑΤΟΥΠΑΛΛΗΛΩΝ ΜΕΤΑΛΛΟΥ (ΤΑ.Π.Ε.Μ.) '1562': ΤΕΧΝΙΚΗ ΕΚΜΕΤΑΛΛΕΥΣΗ ΑΕΡΟΣΚΑΦΩΝ '1563': ΕΝΩΣΗ ΑΠΟΣΤΡΑΤΩΝ ΑΞΙΩΜΑΤΙΚΩΝ Β.Α '1564': ΑΣΦΑΛΙΣΗ ΕΡΓΑΤΩΝ ΓΕΩΡΓΙΑΣ '1565': ΟΡΓΑΝΩΣΗ ΚΑΛΛΙΤΕΧΝΙΚΩΝ ΕΚΔΗΛΩΣΕΩΝ-ΦΕΣΤΙΒΑΛ '1566': ΠΕΡΙΟΥΣΙΑΚΕΣ ΣΥΝΕΠΕΙΕΣ ΤΗΣ ΠΟΙΝΗΣ '1567': ΤΗΛΕΓΡΑΦΙΚΗ ΑΝΤΑΠΟΚΡΙΣΗ '1568': ΕΠΙΘΕΩΡΗΣΗ ΔΗΜΟΣΙΩΝ ΥΠΟΛΟΓΩΝ '1569': ΜΕ ΤΟΝ ΚΑΝΑΔΑ '1570': ΑΛΛΗΛΟΓΡΑΦΙΑ Υ.Ε.Ν '1571': ΤΕΧΝΙΚΟ ΠΡΟΣΩΠΙΚΟ ΑΕΡΟΠΟΡΙΑΣ '1572': ΚΛΑΔΟΣ ΑΥΤΟΤΕΛΩΣ ΑΠΑΣΧΟΛΟΥΜΕΝΩΝ, ΕΛΕΥΘΕΡΩΝ ΚΑΙ ΑΝΕΞΑΡΤΗΤΩΝ '1573': ΣΧΟΛΕΙΑ ΒΑΡΥΚΟΩΝ Η ΚΩΦΩΝ '1574': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΚΑΤΩΤΕΡΩΝ ΠΛΗΡΩΜΑΤΩΝ Ε.Ν '1575': ΤΟΥΡΙΣΤΙΚΑ ΠΛΟΙΑ - ΣΚΑΦΗ ΑΝΑΨΥΧΗΣ - ΤΟΥΡΙΣΤΙΚΟΙ ΛΙΜΕΝΕΣ (ΜΑΡΙΝΕΣ) '1576': ΕΠΙΔΟΜΑΤΑ ΕΟΡΤΩΝ ΧΡΙΣΤΟΥΓΕΝΝΩΝ ΚΑΙ ΠΑΣΧΑ '1577': ΕΠΙΜΕΛΗΤΗΡΙΑ - ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ '1578': ΥΠΟΥΡΓΕΙΟ ΕΡΕΥΝΑΣ ΚΑΙ ΤΕΧΝΟΛΟΓΙΑΣ '1579': ΣΤΕΓΑΣΗ ΑΞΙΩΜΑΤΙΚΩΝ '1580': ΠΑΡΑΡΤΗΜΑΤΑ ΓΕΝΙΚΟΥ ΧΗΜΕΙΟΥ '1581': ΚΑΘΑΡΙΣΤΡΙΕΣ '1582': ΚΑΝΟΝΙΣΜΟΣ ΝΑΥΤΟΔΙΚΕΙΟΥ '1583': ΑΜΟΙΒΕΣ ΜΗΧΑΝΙΚΩΝ '1584': ΕΠΙΜΟΡΦΩΣΗ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ '1585': ΚΑΝΟΝΙΣΜΟΙ ΕΠΙΒΑΤΗΓΩΝ ΠΛΟΙΩΝ '1586': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΠΡΟΣΩΠΙΚΟΥ ΕΤΑΙΡΙΑΣ ΕΛΛ. ΚΑΛΥΚΟΠΟΙΕΙΟΥ-ΠΥΡΙΤΙΔΟΠΟΙΕΙΟΥ '1587': ΠΡΟΣΩΠΙΚΟ ΤΡΑΠΕΖΩΝ '1588': ΛΥΣΣΙΑΤΡΕΙΑ '1589': ΣΥΝΟΡΙΑΚΕΣ ΥΓΕΙΟΝΟΜΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '1590': ΠΟΛΕΜΙΚΟ ΜΟΥΣΕΙΟ '1591': ΚΑΘΗΚΟΝΤΑ ΤΕΛΩΝΕΙΑΚΩΝ ΥΠΑΛΛΗΛΩΝ '1592': ΕΠΕΚΤΑΣΗ ΤΗΣ ΑΣΦΑΛΙΣΕΩΣ '1593': ΦΟΡΟΛΟΓΙΚΕΣ ΑΠΑΛΛΑΓΕΣ '1594': ΕΠΙΔΟΜΑ ΣΤΡΑΤΕΥΣΗΣ '1595': ΔΙΑΡΚΗ ΣΤΡΑΤΟΔΙΚΕΙΑ '1596': ΣΥΝΤΑΞΙΟΔΟΤΗΣΗ ΠΡΟΣΩΠΙΚΟΥ Ο.Γ.Α '1597': ΑΣΤΥΝΟΜΙΑ ΕΜΠΟΡΙΚΗΣ ΝΑΥΤΙΛΙΑΣ '1598': ΦΡΟΝΤΙΣΤΕΣ ΜΟΝΑΔΩΝ '1599': ΑΡΑΒΟΣΙΤΟΣ '1600': ΜΗΤΡΟΠΟΛΕΙΣ '1601': ΦΙΛΑΝΘΡΩΠΙΚΑ ΣΩΜΑΤΕΙΑ '1602': ΔΙΑΦΟΡΟΙ ΠΟΛΥΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ '1603': ΕΞΥΓΙΑΝΤΙΚΑ ΕΡΓΑ '1604': ΦΥΛΛΑ ΠΟΙΟΤΗΤΑΣ ΝΑΥΤΩΝ '1605': ΦΙΛΑΝΘΡΩΠΙΚΑ ΙΔΡΥΜΑΤΑ ΚΑΙ ΣΩΜΑΤΕΙΑ '1606': ΕΣΤΙΑ ΝΑΥΤΙΚΩΝ '1607': ΓΛΥΚΑ ΚΑΙ ΚΟΝΣΕΡΒΕΣ '1608': ΠΡΟΣΤΑΣΙΑ ΥΠΟΒΡΥΧΙΩΝ ΚΑΛΩΔΙΩΝ '1609': ΕΠΕΞΕΡΓΑΣΙΑ ΚΑΙ ΕΜΠΟΡΙΑ ΣΥΚΩΝ '1610': ΧΑΡΟΚΟΠΕΙΟ '1611': ΔΙΑΜΕΤΑΚΟΜΙΣΗ ΣΤΗΝ ΑΛΒΑΝΙΑ '1612': ΕΠΙΘΕΩΡΗΣΗ ΦΥΛΑΚΩΝ '1613': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ ΠΕΡΙ ΚΥΡΙΑΚΗΣ ΑΡΓΙΑΣ '1614': ΚΙΝΗΜΑΤΟΓΡΑΦΙΚΗ ΒΙΟΜΗΧΑΝΙΑ '1615': ΠΙΣΤΟΠΟΙΗΤΙΚΑ ΠΡΟΕΛΕΥΣΕΩΣ '1616': ΤΟΥΡΙΣΤΙΚΗ ΠΡΟΠΑΓΑΝΔΑ '1617': ΕΙΣΦΟΡΑ ΕΙΣΑΓΩΓΕΩΝ '1618': ΚΑΖΙΝΟ '1619': ΜΕ ΤΗΝ ΕΛΒΕΤΙΑ '1620': ΔΙΚΑΣΤΙΚΟΙ ΕΠΙΜΕΛΗΤΕΣ '1621': ΚΩΔΙΚΑΣ ΠΟΙΝΙΚΗΣ ΔΙΚΟΝΟΜΙΑΣ '1622': ΤΟΠΙΚΕΣ ΔΙΟΙΚΗΤΙΚΕΣ ΕΠΙΤΡΟΠΕΣ '1623': ΕΤΑΙΡΕΙΕΣ ΚΕΦΑΛΑΙΟΠΟΙΗΣΕΩΣ '1624': ΟΡΥΖΑ '1625': ΔΙΟΙΚΗΤΙΚΟ ΣΥΜΒΟΥΛΙΟ Ο.Γ.Α '1626': ΕΚΠΑΙΔΕΥΤΙΚΟ ΠΡΟΣΩΠΙΚΟ ΣΧΟΛΩΝ Π.Ν '1627': ΒΑΣΙΛΕΙΑ ΚΑΙ ΑΝΤΙΒΑΣΙΛΕΙΑ '1628': ΥΠΗΡΕΣΙΑ ΣΤΙΣ ΕΠΑΡΧΙΕΣ Τ.Π. ΚΑΙ Δ '1629': ΓΕΩΡΓΙΚΕΣ ΒΙΟΜΗΧΑΝΙΕΣ '1630': ΒΟΥΛΕΥΤΗΡΙΟ '1631': ΠΟΡΘΜΕΙΑ '1632': ΕΚΤΕΛΕΣΗ ΥΔΡΑΥΛΙΚΩΝ ΕΡΓΩΝ '1633': ΙΝΣΤΙΤΟΥΤΑ ΚΡΗΤΙΚΟΥ ΔΙΚΑΙΟΥ - ΑΙΓΑΙΟΥ ΚΑΙ ΔΙΑΦΟΡΑ ΕΡΕΥΝΗΤΙΚΑ ΚΕΝΤΡΑ '1634': ΑΤΕΛΕΙΕΣ ΔΙΑΦΟΡΕΣ '1635': ΚΕΝΤΡΑ ΠΑΡΑΘΕΡΙΣΜΟΥ - '1636': ΣΧΟΛΕΣ ΑΕΡΟΠΟΡΙΑΣ '1637': ΛΕΠΡΑ '1638': ΑΙΣΘΗΤΙΚΟΙ '1639': ΕΚΚΑΘΑΡΙΣΗ ΠΟΙΝΙΚΩΝ ΕΞΟΔΩΝ '1640': ΓΕΝ. ΟΙΚΟΔΟΜΙΚΟΣ ΚΑΝΟΝΙΣΜΟΣ '1641': ΕΛΕΓΧΟΣ ΔΑΠΑΝΩΝ ΤΟΥ ΚΡΑΤΟΥΣ '1642': ΠΕΤΡΕΛΑΙΟΚΙΝΗΤΑ ΚΑΙ ΙΣΤΙΟΦΟΡΑ '1643': ΚΑΛΛΙΕΡΓΕΙΑ ΚΑΠΝΟΥ '1644': ΔΙΟΙΚΗΣΗ ΜΟΝΑΣΤΗΡΙΩΝ '1645': ΚΤΗΝΙΑΤΡΙΚΑ ΙΔΙΟΣΚΕΥΑΣΜΑΤΑ '1646': ΜΟΝΙΜΟΙ ΚΑΙ ΕΘΕΛΟΝΤΕΣ '1647': ΦΟΡΟΛΟΓΙΑ ΚΕΡΔΩΝ ΕΙΣΑΓΩΓΕΩΝ '1648': ΑΓΩΓΕΣ ΕΞΩΣΕΩΣ ΜΙΣΘΩΤΩΝ '1649': ΟΡΓΑΝΩΣΗ ΕΞΩΤΕΡΙΚΟΥ ΕΜΠΟΡΙΟΥ '1650': ΑΓΩΓΕΣ ΜΗΧΑΝΙΚΩΝ '1651': ΝΑΥΤΙΚΗ ΣΧΟΛΗ ΠΟΛΕΜΟΥ '1652': ΜΕΤΑΦΟΡΑ ΘΕΣΕΩΝ '1653': ΕΙΣΑΓΩΓΗ ΕΠΑΓΓΕΛΜΑΤΙΚΟΥ ΥΛΙΚΟΥ '1654': ΣΥΓΚΡΟΤΗΣΗ ΚΑΙ ΛΕΙΤΟΥΡΓΙΑ '1655': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΑΕΡΟΠΟΡΙΚΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ (T.Ε.Α.Π.Α.Ε.) '1656': ΣΥΛΛΟΓΗ ΚΑΙ ΔΙΑΚΙΝΗΣΗ ΠΕΤΡΕΛΑΙΟΕΙΔΩΝ ΕΡΜΑΤΩΝ '1657': ΚΕΝΤΡΑ ΑΔΥΝΑΤΙΣΜΑΤΟΣ – ΔΙΑΙΤΟΛΟΓΙΑΣ '1658': ΟΜΑΔΙΚΗ ΚΑΤΑΓΓΕΛΙΑ ΣΥΜΒΑΣΕΩΣ ΕΡΓΑΣΙΑΣ '1659': ΔΙΑΦΟΡΑ ΜΟΥΣΕΙΑ '1660': ΒΕΒΑΙΩΣΗ ΚΑΙ ΕΙΣΠΡΑΞΗ ΕΣΟΔΩΝ '1661': ΓΡΑΦΕΙΑ ΤΥΠΟΥ '1662': ΔΙΟΙΚΗΤΙΚΟ ΠΡΟΣΩΠΙΚΟ '1663': ΣΥΝΕΡΓΕΙΑ ΕΠΙΣΚΕΥΩΝ '1664': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΗΣ ΠΡΟΝΟΙΑΣ ΚΑΙ ΑΣΘΕΝΕΙΑΣ ΕΡΓΑΖΟΜΕΝΩΝ ΣΤΑ ΛΙΜΑΝΙΑ (Τ.Ε.Α.Π.Α.Ε.Λ.) '1665': ΑΣΦΑΛΙΣΗ ΚΑΠΝΕΡΓΑΤΩΝ '1666': ΑΝΤΙΣΗΚΩΜΑΤΑ (ΕΞΑΓΟΡΑ ΘΗΤΕΙΑΣ) '1667': ΡΥΜΟΥΛΚΟΥΜΕΝΑ ΟΧΗΜΑΤΑ '1668': ΝΟΜΟΙ ΑΝΑΦΕΡΟΜΕΝΟΙ ΣΕ ΠΟΛΛΕΣ ΦΟΡΟΛΟΓΙΕΣ '1669': ΟΙΚΟΣΥΣΤΗΜΑΤΑ–ΒΙΟΤΟΠΟΙ '1670': ΠΡΟΣΤΑΣΙΑ ΠΡΟΣΩΠΩΝ '1671': ΕΘΝΙΚΟ ΤΥΠΟΓΡΑΦΕΙΟ '1672': ΔΙΚΑΣΤΙΚΑ ΚΑΤΑΣΤΗΜΑΤΑ '1673': ΠΡΟΣΤΑΣΙΑ ΒΙΒΛΙΟΥ-ΕΘΝΙΚΟ ΚΕΝΤΡΟ ΒΙΒΛΙΟΥ-ΛΟΓΟΤΕΧΝΙΑ '1674': ΔΑΣΜΟΙ ΑΝΤΙΝΤΑΜΠΙΓΚ '1675': ΔΑΣΗ ΠΑΡΑΜΕΘΟΡΙΩΝ ΠΕΡΙΟΧΩΝ '1676': ΘΕΟΛΟΓΙΚΗ ΣΧΟΛΗ '1677': ΟΡΟΙ - ΠΡΟΔΙΑΓΡΑΦΕΣ ΤΥΠΟΠΟΙΗΣΗΣ '1678': ΦΟΡΟΛΟΓΙΑ ΒΥΝΗΣ ΚΑΙ ΖΥΘΟΥ '1679': ΑΠΟΘΗΚΗ ΚΤΗΝΙΑΤΡΙΚΩΝ ΕΦΟΔΙΩΝ '1680': ΠΑΡΟΧΗ ΤΗΛΕΦΩΝΙΚΩΝ ΣΥΝΔΕΣΕΩΝ '1681': ΠΑΡΑΧΩΡΗΣΗ ΙΑΜΑΤΙΚΩΝ ΠΗΓΩΝ '1682': ΜΑΘΗΤΙΚΑ ΣΥΣΣΙΤΙΑ '1683': ΠΡΟΣΛΗΨΗ ΕΦΕΔΡΩΝ, ΑΝΑΠΗΡΩΝ ΠΟΛΕΜΟΥ, ΠΟΛΥΤΕΚΝΩΝ ΚΑΙ ΑΛΛΩΝ ΑΤΟΜΩΝ ΜΕ ΕΙΔΙΚΕΣ ΑΝΑΓΚΕΣ '1684': ΕΡΤ – 3 '1685': ΣΧΟΛΗ ΠΟΛΕΜΟΥ ΑΕΡΟΠΟΡΙΑΣ '1686': ΤΟΠΟΘΕΤΗΣΕΙΣ - ΜΕΤΑΤΑΞΕΙΣ '1687': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ ΠΡΟΣΤΑΣΙΑΣ '1688': ΦΥΣΙΚΟ ΑΕΡΙΟ '1689': ΤΕΧΝΙΚΑ ΕΡΓΑ '1690': ΔΙΠΛΩΜΑΤΟΥΧΟΙ ΑΝΩΤΑΤΩΝ '1691': ΕΘΝΙΚΟ ΝΟΜΙΣΜΑΤΙΚΟ ΜΟΥΣΕΙΟ '1692': ΟΙΚΟΝΟΜΙΚΗ ΑΣΤΥΝΟΜΙΑ ΣΤΗ ΘΑΛΑΣΣΑ '1693': ΑΣΦΑΛΕΙΑ, ΛΕΙΤΟΥΡΓΙΑ ΚΑΙ ΕΚΜΕΤΑΛΛΕΥΣΗ '1694': ΕΙΔΙΚΑ ΠΡΟΝΟΜΙΑ ΑΝΩΝΥΜΩΝ ΕΤΑΙΡΕΙΩΝ '1695': ΓΡΑΜΜΑΤΕΙΑ ΤΩΝ ΔΙΚΑΣΤΗΡΙΩΝ ΚΑΙ ΕΙΣΑΓΓΕΛΙΩΝ '1696': ΑΛΙΠΑΣΤΑ '1697': ΕΠΙΔΟΣΗ ΔΙΚΟΓΡΑΦΩΝ '1698': ΚΕΝΤΡΙΚΟ ΤΑΜΕΙΟ ΓΕΩΡΓΙΑΣ '1699': ΣΤΡΑΤΙΩΤΙΚΑ ΣΥΜΒΟΥΛΙΑ '1700': ΤΑΜΕΙΑΚΗ ΥΠΗΡΕΣΙΑ ΤΕΛΩΝΕΙΩΝ '1701': ΝΟΣΗΛΕΥΤΙΚΟ ΙΔΡΥΜΑ Μ.Τ.Σ '1702': ΔΙΚΑΙΟ ΘΑΛΑΣΣΑΣ-ΥΦΑΛΟΚΡΗΠΙΔΑ '1703': ΕΙΔΙΚΟΣ ΦΟΡΟΣ ΚΑΤΑΝΑΛΩΣΗΣ '1704': ΜΕΙΟΝΟΤΙΚΑ ΣΧΟΛΕΙΑ '1705': ΓΡΑΦΕΙΑ ΕΜΠΟΡΙΚΩΝ ΠΛΗΡΟΦΟΡΙΩΝ '1706': ΣΥΝΤΟΝΙΣΤΙΚΟΝ ΣΥΜΒΟΥΛΙΟΝ ΝΕΩΝ ΠΡΟΣΦΥΓΩΝ '1707': ΠΕΡΙΘΑΛΨΗ ΑΠΟΡΩΝ ΚΑΙ ΑΝΑΣΦΑΛΙΣΤΩΝ '1708': ΦΟΡΟΛΟΓΙΑ ΚΕΝΤΡΩΝ ΔΙΑΣΚΕΔΑΣΕΩΣ ΚΑΙ ΠΟΛΥΤΕΛΕΙΑΣ '1709': ΣΠΟΓΓΑΛΙΕΥΤΙΚΑ – ΔΥΤΕΣ '1710': ΔΙΕΘΝΕΣ ΝΟΜΙΣΜΑΤΙΚΟ ΤΑΜΕΙΟ '1711': ΒΙΒΛΙΟ ΔΙΕΚΔΙΚΗΣΕΩΝ '1712': ΕΓΚΑΤΑΣΤΑΣΗ - ΛΕΙΤΟΥΡΓΙΑ ΚΑΤΑΣΚΕΥΩΝ ΚΕΡΑΙΩΝ '1713': ΕΝΩΣΗ ΔΗΜΩΝ ΚΑΙ ΚΟΙΝΟΤΗΤΩΝ '1714': ΛΟΓΙΣΤΙΚΟΣ ΚΑΙ ΟΙΚΟΝΟΜΙΚΟΣ ΚΑΝΟΝΙΣΜΟΣ '1715': ΚΑΤΩΤΕΡΑ ΟΡΓΑΝΑ ΣΩΜΑΤΩΝ ΑΣΦΑΛΕΙΑΣ '1716': ΥΠΟΥΡΓΕΙΟ ΕΜΠΟΡΙΚΗΣ ΝΑΥΤΙΛΙΑΣ '1717': ΟΡΓΑΝΙΣΜΟΣ ΕΛΕΓΚΤΙΚΟΥ ΣΥΝΕΔΡΙΟΥ '1718': ΑΓΟΡΕΣ ΑΓΡΟΤΙΚΩΝ ΠΡΟΙΟΝΤΩΝ '1719': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΜΙΣΘΩΤΩΝ ΚΛΩΣΤΟΥΦΑΝΤΟΥΡΓΙΑΣ '1720': ΞΕΝΑΓΟΙ ΚΑΙ ΔΙΕΡΜΗΝΕΙΣ '1721': ΠΟΛΕΜΙΚΕΣ ΣΥΝΤΑΞΕΙΣ '1722': ΑΣΤΙΚΕΣ ΣΥΓΚΟΙΝΩΝΙΕΣ ΑΘΗΝΩΝ-ΠΕΙΡΑΙΩΣ ΚΑΙ ΠΕΡΙΧΩΡΩΝ-Ο.Α.Σ.Α '1723': ΚΑΤΑΣΤΑΤΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΤΑΜΕΙΟΥ ΑΣΦΑΛΙΣΕΩΣ ΑΡΤΕΡΓΑΤΩΝ Κ.Λ.Π '1724': ΑΤΥΧΗΜΑΤΑ ΣΕ ΜΕΤΑΛΛΕΙΑ ΚΛΠ '1725': ΦΟΡΟΛΟΓΙΑ ΠΟΛΕΜΙΚΩΝ ΚΕΡΔΩΝ '1726': ΣΧΕΔΙΟ ΠΟΛΕΩΣ ΘΕΣΣΑΛΟΝΙΚΗΣ '1727': ΟΙΚΟΝΟΜΙΚΗ ΔΙΟΙΚΗΣΗ ΑΓΡΟΤ. ΑΣΦΑΛΕΙΑΣ '1728': ΚΡΑΤΙΚΟ ΩΔΕΙΟ ΘΕΣΣΑΛΟΝΙΚΗΣ '1729': ΚΕΝΤΡΑ ΑΝΩΤΕΡΗΣ ΤΕΧΝΙΚΗΣ ΕΚΠΑΙΔΕΥΣΗΣ (Κ.A.Τ.Ε.) '1730': ΤΗΛΕΦΩΝΙΚΗ ΑΝΤΑΠΟΚΡΙΣΗ '1731': ΟΙΚΟΝΟΜΙΚΑ ΓΥΜΝΑΣΙΑ '1732': ΒΙΒΛΙΑ ΚΑΙ ΕΥΡΕΤΗΡΙΑ ΣΥΝΕΤΑΙΡΙΣΜΩΝ '1733': ΕΠΙΔΟΜΑ ΑΝΕΡΓΙΑΣ '1734': ΕΓΓΡΑΦΕΣ, ΕΞΕΤΑΣΕΙΣ, ΠΡΟΓΡΑΜΜΑΤΑ ΚΛΠ '1735': ΣΧΟΛΗ ΜΟΝΙΜΩΝ ΥΠΑΞΙΩΜΑΤΙΚΩΝ '1736': ΕΚΚΛΗΣΙΑ ΑΜΕΡΙΚΗΣ '1737': ΜΕΤΟΧΙΚΟ ΤΑΜΕΙΟ ΣΤΡΑΤΟΥ '1738': ΝΟΣΗΛΕΙΑ '1739': ΣΧΟΛΗ ΕΥΕΛΠΙΔΩΝ '1740': ΥΠΟΥΡΓΕΙΟ ΕΡΓΑΣΙΑΣ ΚΑΙ ΚΟΙΝΩΝΙΚΩΝ ΑΣΦΑΛΙΣΕΩΝ '1741': ΚΑΝΟΝΙΣΜΟΣ ΧΡΗΜΑΤΙΣΤΗΡΙΟΥ ΑΞΙΩΝ ΑΘΗΝΩΝ '1742': ΑΝΤΙΣΕΙΣΜΙΚΟΣ ΚΑΝΟΝΙΣΜΟΣ '1743': ΦΑΡΜΑΚΕΥΤΙΚΗ ΔΕΟΝΤΟΛΟΓΙΑ '1744': ΦΟΡΟΛΟΓΙΑ ΕΛΑΙΩΔΩΝ ΠΡΟΙΟΝΤΩΝ '1745': ΕΙΔΙΚΑ ΡΑΔΙΟΤΗΛΕΦΩΝΙΚΑ ΔΙΚΤΥΑ '1746': ΤΕΧΝΙΚΕΣ ΥΠΗΡΕΣΙΕΣ '1747': ΑΡΧΕΙΑ ΥΓΙΕΙΝΗΣ '1748': ΟΔΟΙΠΟΡΙΚΑ ΚΑΙ ΑΠΟΖΗΜΙΩΣΕΙΣ ΑΠΟΣΤΟΛΩΝ ΕΞΩΤΕΡΙΚΟΥ '1749': ΔΙΑΦΟΡΟΙ ΛΟΓΙΣΤΙΚΟΙ ΝΟΜΟΙ '1750': ΕΚΚΛΗΣΙΑΣΤΙΚΟΙ ΥΠΑΛΛΗΛΟΙ '1751': ΝΑΥΤΙΚΑ ΕΠΑΓΓΕΛΜΑΤΙΚΑ ΣΩΜΑΤΕΙΑ ΚΑΙ ΟΜΟΣΠΟΝΔΙΕΣ '1752': ΤΕΛΗ ΧΡΗΣΗΣ ΑΕΡΟΛΙΜΕΝΩΝ '1753': ΠΡΟΑΙΡΕΤΙΚΗ ΑΣΦΑΛΙΣΗ '1754': ΜΕ ΤΗ ΛΙΒΥΗ '1755': ΠΟΤΑΜΟΠΛΟΙΑ ΦΟΡΤΙΟΥ ΥΓΡΩΝ ΚΑΥΣΙΜΩΝ '1756': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΠΡΟΣΩΠΙΚΟΥ ΕΛΛΗΝΙΚΩΝ ΗΛΕΚΤΡΙΚΩΝ ΣΙΔΗΡΟΔΡΟΜΩΝ ΑΘΗΝΩΝ-ΠΕΙΡΑΙΩΣ (Τ.Σ.Π.-Η.Σ.Α.Π) '1757': ΜΕΣΑΖΟΝΤΕΣ '1758': ΣΤΡΑΤΙΩΤΙΚΟΣ ΠΟΙΝΙΚΟΣ '1759': ΔΙΚΑΙΩΜΑΤΑ ΚΑΙ ΚΑΘΗΚΟΝΤΑ ΦΟΙΤΗΤΩΝ '1760': ΠΡΟΕΔΡΙΑ ΔΗΜΟΚΡΑΤΙΑΣ '1761': ΚΩΔΙΚΑΣ ΕΜΠΟΡΙΚΟΥ ΝΟΜΟΥ '1762': ΣΥΝΤΑΞΙΟΔΟΤΗΣΗ Ο.Γ.Α '1763': ΣΑΝΑΤΟΡΙΑ '1764': ΕΛΕΓΧΟΣ ΕΜΠΟΡΙΟΥ ΕΙΔΩΝ ΠΡΩΤΗΣ ΑΝΑΓΚΗΣ '1765': ΒΑΛΑΝΙΔΙΑ '1766': ΠΟΛΥΤΕΧΝΙΚΗ ΣΧΟΛΗ ΠΑΝΕΠΙΣΤΗΜΙΟΥ ΠΑΤΡΩΝ '1767': ΣΕΙΣΜΟΠΛΗΚΤΟΙ ΠΕΛΟΠΟΝΝΗΣΟΥ '1768': ΔΙΕΘΝΗΣ ΟΡΓΑΝΙΣΜΟΣ ΧΡΗΜΑΤΟΔΟΤΗΣΕΩΣ '1769': ΜΕΤΑΦΟΡΑ ΣΤΟ ΕΣΩΤΕΡΙΚΟ '1770': ΙΣΤΟΡΙΚΟ ΑΡΧΕΙΟ ΥΔΡΑΣ '1771': ΕΓΚΑΤΑΣΤΑΣΗ ΚΑΙ ΚΙΝΗΣΗ ΑΛΛΟΔΑΠΩΝ '1772': ΣΧΟΛΗ ΤΕΧΝΙΚΗΣ ΕΚΠΑΙΔΕΥΣΗΣ ΑΞΙΩΜΑΤΙΚΩΝ '1773': ΓΑΜΟΣ ΣΤΡΑΤΙΩΤΙΚΩΝ '1774': ΑΠΑΓΟΡΕΥΣΗ ΕΞΟΔΟΥ ΟΦΕΙΛΕΤΩΝ '1775': ΠΡΩΤΕΣ ΥΛΕΣ ΨΕΚΑΣΤΗΡΩΝ '1776': ΦΙΛΕΚΠΑΙΔΕΥΤΙΚΗ ΕΤΑΙΡΕΙΑ '1777': ΑΔΕΙΕΣ ΟΔΗΓΩΝ ΑΥΤΟΚΙΝΗΤΩΝ '1778': ΕΘΝΙΚΗ ΠΙΝΑΚΟΘΗΚΗ ΚΑΙ ΜΟΥΣΕΙΟ ΑΛ. ΣΟΥΤΣΟΥ '1779': ΤΑΧΥΔΡΟΜΙΚΑ ΔΕΜΑΤΑ '1780': ΕΙΣΠΡΑΞΗ ΠΟΡΩΝ '1781': ΟΡΓΑΝΩΣΗ ΚΑΙ ΛΕΙΤΟΥΡΓΙΑ ΤΕΧΝΙΚΩΝ ΣΧΟΛΩΝ '1782': ΔΙΑΘΕΣΗ ΓΑΙΩΝ ΣΤΗ ΘΕΣΣΑΛΙΑ '1783': ΔΙΑΚΡΙΣΗ ΑΣΦΑΛΙΣΜΕΝΩΝ '1784': ΑΓΑΘΟΕΡΓΑ ΙΔΡΥΜΑΤΑ ΚΕΡΚΥΡΑΣ '1785': ΥΠΑΙΘΡΙΟ-ΠΛΑΝΟΔΙΟ ΕΜΠΟΡΙΟ ΚΑΙ ΕΜΠΟΡΟΠΑΝΗΓΥΡΕΙΣ '1786': ΕΞΑΓΩΓΙΚΑ ΤΕΛΗ '1787': ΥΠΟΥΡΓΙΚΟ ΣΥΜΒΟΥΛΙΟ - ΟΡΓΑΝΩΣΗ ΥΠΟΥΡΓΕΙΩΝ - ΚΥΒΕΡΝΗΤΙΚΕΣ ΕΠΙΤΡΟΠΕΣ '1788': ΑΥΤΟΚΙΝΗΤΑ ΚΑΙ ΑΜΑΞΙΔΙΑ ΑΝΑΠΗΡΩΝ ΠΟΛΕΜΟΥ '1789': ΥΠΗΡΕΣΙΕΣ ΠΕΡΙΦΕΡΕΙΑΚΗΣ ΑΝΑΠΤΥΞΗΣ '1790': ΔΙΑΤΙΜΗΣΗ ΦΑΡΜΑΚΩΝ '1791': ΦΟΡΟΛΟΓΙΑ ΕΙΔΩΝ ΠΟΛΥΤΕΛΕΙΑΣ '1792': ΝΑΥΤΙΚΗ ΠΟΙΝΙΚΗ ΝΟΜΟΘΕΣΙΑ '1793': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΕΤΑΙΡΕΙΩΝ ΠΕΤΡΕΛΑΙΟΕΙΔΩΝ '1794': ΔΩΡΟ ΕΟΡΤΩΝ ΕΦΗΜΕΡΙΔΟΠΩΛΩΝ '1795': ΔΙΕΥΚΟΛΥΝΣΕΙΣ ΓΙΑ ΤΗΝ ΑΝΟΙΚΟΔΟΜΗΣΗ '1796': ΕΠΙΣΚΕΥΑΣΤΕΣ - ΣΥΝΕΡΓΕΙΑ ΕΠΙΣΚΕΥΗΣ ΑΥΤΟΚΙΝΗΤΩΝΟΔΙΚΗ ΒΟΗΘΕΙΑ ΟΧΗΜΑΤΩΝ '1797': ΠΑΡΑΧΩΡΗΣΗ ΔΑΣΩΝ '1798': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΑΣΘΕΝΕΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΤΡΑΠΕΖΩΝ ΠΙΣΤΕΩΣ, ΓΕΝΙΚΗΣ ΚΑΙ ΑΜΕΡΙΚΑΝ ΕΞΠΡΕΣ '1799': ΠΛΗΤΤΟΜΕΝΑ ΑΠΟ ΤΗΝ ΑΝΕΡΓΙΑ ΕΠΑΓΓΕΛΜΑΤΑ '1800': ΤΑΜΕΙΑ Κ.Α.Τ.Ε '1801': ΕΙΔΙΚΟΙ ΣΤΡΑΤΙΩΤΙΚΟΙ ΟΡΓΑΝΙΣΜΟΙ '1802': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΙΟΝΙΚΗΣ ΚΑΙ ΛΑΙΚΗΣ ΤΡΑΠΕΖΑΣ (Τ.Α.Π.- Ι.Λ.Τ.) '1803': ΠΡΟΣΤΑΣΙΑ ΑΠΟ ΑΚΤΙΝΟΒΟΛΙΕΣ '1804': ΚΡΑΤΙΚΟ ΘΕΑΤΡΟ Β. ΕΛΛΑΔΟΣ '1805': ΥΓΕΙΟΝΟΜΙΚΟΣ ΕΛΕΓΧΟΣ ΦΟΙΤΗΤΩΝ '1806': ΔΙΑΦΟΡΑ '1807': ΤΕΛΩΝΕΙΑΚΗ ΥΠΗΡΕΣΙΑ ΣΙΔΗΡΟΔΡΟΜΩΝ '1808': ΕΦΕΥΡΕΣΕΙΣ ΑΦΟΡΩΣΑΙ ΕΘΝ. ΑΜΥΝΑ '1809': ΥΠΟΒΡΥΧΙΟΣ ΤΗΛΕΓΡΑΦΟΣ '1810': ΑΔΕΙΕΣ ΟΙΚΟΔΟΜΗΣ ΞΕΝΟΔΟΧΕΙΩΝ '1811': ΙΝΣΤΙΤΟΥΤΟ ΒΥΖΑΝΤΙΝΩΝ ΣΠΟΥΔΩΝ '1812': ΣΧΟΛΗ ΓΕΩΤΕΧΝΙΚΩΝ ΕΠΙΣΤΗΜΩΝ ΠΑΝΜΙΟΥ ΘΕΣΝΙΚΗΣ '1813': ΒΙΒΛΙΟΘΗΚΕΣ '1814': ΤΑΜΕΙΑ ΑΝΕΓΕΡΣΕΩΣ ΔΙΔΑΚΤΗΡΙΩΝ '1815': ΕΠΙΔΟΜΑ ΒΙΒΛΙΟΘΗΚΗΣ '1816': ΚΑΤΑΣΤΗΜΑΤΑ ΑΦΟΡΟΛΟΓΗΤΩΝ ΕΙΔΩΝ '1817': ΕΠΙΧΕΙΡΗΣΕΙΣ ΠΕΡΙΘΑΛΨΕΩΣ ΗΛΙΚΙΩΜΕΝΩΝ Η ΑΝΑΠΗΡΩΝ '1818': ΛΙΜΕΝΙΚΟΙ ΣΤΑΘΜΟΙ '1819': ΝΟΜΟΘΕΤΙΚΕΣ ΕΞΟΥΣΙΟΔΟΤΗΣΕΙΣ '1820': ΘΑΛΑΜΟΙ ΡΑΔΙΟΙΣΟΤΟΠΩΝ '1821': ΔΙΟΙΚΗΣΗ ΕΚΚΛΗΣΙΑΣΤΙΚΗΣ ΕΚΠΑΙΔΕΥΣΗΣ '1822': ΑΠΑΓΟΡΕΥΜΕΝΕΣ ΚΑΙ '1823': ΗΘΟΠΟΙΟΙ '1824': ΣΥΜΒΑΣΕΙΣ ΠΕΡΙ ΔΙΕΘΝΩΝ ΕΚΘΕΣΕΩΝ '1825': ΣΦΡΑΓΙΣΤΟΣ ΧΑΡΤΗΣ '1826': ΕΤΑΙΡΕΙΕΣ ΔΙΑΧΕΙΡΙΖΟΜΕΝΕΣ ΔΗΜΟΣΙΑ ΣΥΜΦΕΡΟΝΤΑ '1827': ΤΕΛΩΝΕΙΑΚΕΣ ΔΙΕΥΚΟΛΥΝΣΕΙΣ '1828': ΔΕΞΑΜΕΝΟΠΛΟΙΑ '1829': ΚΕΝΤΡΟ ΔΙΕΘΝΟΥΣ ΚΑΙ ΕΥΡΩΠΑΙΚΟΥ '1830': ΕΠΙΒΑΤΗΓΑ ΜΕΣΟΓΕΙΑΚΑ ΚΑΙ ΤΟΥΡΙΣΤΙΚΑ ΠΛΟΙΑ '1831': ΕΠΙΘΕΩΡΗΣΗ ΔΙΚΑΣΤΙΚΩΝ ΥΠΑΛΛΗΛΩΝ '1832': ΚΑΝΟΝΙΣΜΟΣ ΘΕΑΤΡΩΝ ΚΙΝΗΜΑΤΟΓΡΑΦΩΝ ΚΛΠ '1833': ΜΕΤΑΛΛΕΥΤΙΚΟΣ ΚΩΔΙΚΑΣ '1834': ΚΑΤΑΣΤΑΤΙΚΟ Τ.Ε.Α.Α.Π.Α.Ε '1835': ΠΑΝΕΠΙΣΤΗΜΙΑΚΗ ΛΕΣΧΗ '1836': ΕΜΠΟΡΙΚΑ ΚΑΙ ΒΙΟΜΗΧΑΝΙΚΑ ΣΗΜΑΤΑ - (ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ) '1837': ΕΠΙΔΟΜΑΤΑ ΑΠΟΛΥΟΜΕΝΩΝ ΟΠΛΙΤΩΝ ΩΣ ΑΝΙΚΑΝΩΝ '1838': ΣΥΜΒΟΥΛΙΟ ΕΝΕΡΓΕΙΑΣ '1839': ΣΧΟΛΗ ΝΟΜΙΚΩΝ,ΟΙΚΟΝΟΜΙΚΩΝ ΚΑΙ ΠΟΛΙΤΙΚΩΝ ΕΠΙΣΤΗΜΩΝ '1840': ΠΡΟΠΛΗΡΩΜΕΣ ΚΑΙ ΠΡΟΚΑΤΑΒΟΛΕΣ '1841': ΚΛΑΔΟΣ ΑΣΘΕΝΕΙΑΣ Τ.Ε.Β.Ε '1842': ΔΙΑΝΟΜΗ ΓΑΙΩΝ ΚΩΠΑΙΔΑΣ '1843': ΠΡΟΣΩΠΙΚΟ ΑΣΦΑΛΕΙΑΣ Ν.Π.Δ.Δ. - ΟΡΓΑΝΙΣΜΩΝ & ΕΠΙΧΕΙΡΗΣΕΩΝ '1844': ΥΠΟΥΡΓΕΙΟ ΥΠΟΔΟΜΩΝ, ΜΕΤΑΦΟΡΩΝ ΚΑΙ ΔΙΚΤΥΩΝ '1845': ΑΕΡΟΝΑΥΑΓΟΣΩΣΤΙΚΗ ΜΟΝΑΔΑ '1846': ΚΟΥΡΕΙΑ, ΚΟΜΜΩΤΗΡΙΑ Κ.Λ.Π '1847': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΔΙΚΑΣΤΙΚΩΝ ΕΠΙΜΕΛΗΤΩΝ '1848': ΕΙΔΙΚΑ ΣΥΝΕΡΓΕΙΑ '1849': ΚΑΤΕΨΥΓΜΕΝΑ ΚΡΕΑΤΑ '1850': ΜΕΣΟΓΕΙΑΚΑ ΔΡΟΜΟΛΟΓΙΑ ΕΠΙΒΑΤΗΓΩΝ ΠΛΟΙΩΝ '1851': ΣΥΓΚΡΟΤΗΣΗ ΠΡΟΣΩΠΙΚΟΥ ΑΕΡΟΠΟΡΙΑΣ '1852': ΥΠΑΛΛΗΛΙΚΟΣ ΚΩΔΙΚΑΣ '1853': ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΠΕΡΙ ΦΑΡΜΑΚΕΙΩΝ '1854': ΔΙΑΦΟΡΟΙ ΣΤΕΓΑΣΤΙΚΟΙ ΝΟΜΟΙ '1855': ΥΠΟΥΡΓΕΙΟ ΣΥΝΤΟΝΙΣΜΟΥ '1856': ΠΡΟΣΛΗΨΕΙΣ ΣΤΟ ΔΗΜΟΣΙΟ '1857': ΤΑΜΕΙΟ ΕΠΙΚ. ΑΣΦΑΛ. ΠΡΟΣΩΠ. Ο.Ε.Α.Σ. ΚΑΙ ΥΠΑΛΛ. ΓΡΑΦΕΙΩΝ ΚΟΙΝΩΝ ΤΑΜΕΙΩΝ ΙΔΙΩΤΙΚΩΝ ΛΕΩΦΟΡΕΙΩΝ '1858': ΣΤΡΑΤΙΩΤΙΚΗ ΑΣΤΥΝΟΜΙΑ '1859': ΝΟΜΙΣΜΑΤΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '1860': ΑΡΧΗ ΔΙΑΣΦΑΛΙΣΗΣ ΑΠΟΡΡΗΤΟΥ ΕΠΙΚΟΙΝΩΝΙΩΝ (Α.Δ.Α.Ε.) '1861': ΣΤΡΑΤΙΩΤΙΚΑ ΣΥΝΕΡΓΕΙΑ '1862': ΠΡΟΣΩΠΙΚΗ ΚΡΑΤΗΣΗ '1863': ΕΦΗΜΕΡΙΔΑ ΤΗΣ ΚΥΒΕΡΝΗΣΕΩΣ '1864': ΑΝΩΤΑΤΟ ΥΓΕΙΟΝΟΜΙΚΟ ΣΥΜΒΟΥΛΙΟ '1865': ΓΡΑΜΜΑΤΕΙΣ ΣΤΡΑΤΟΔΙΚΕΙΩΝ '1866': ΚΑΤΑΣΤΑΣΗ ΔΙΟΠΩΝ, ΝΑΥΤΩΝ ΚΑΙ ΝΑΥΤΟΠΑΙΔΩΝ '1867': ΠΕΡΙΠΤΩΣΕΙΣ ΑΜΟΙΒΑΙΑΣ ΣΥΝΔΡΟΜΗΣ '1868': ΥΠΟΝΟΜΟΙ ΠΡΩΤΕΥΟΥΣΑΣ '1869': ΤΕΛΗ ΔΙΑΔΡΟΜΗΣ ΕΝΑΕΡΙΟΥ ΧΩΡΟΥ '1870': ΥΓΕΙΟΝΟΜΙΚΑΙ ΕΠΙΤΡΟΠΑΙ '1871': ΙΑΤΡΙΚΕΣ ΕΙΔΙΚΟΤΗΤΕΣ '1872': ΕΡΤ – 2 '1873': ΕΚΤΕΛΕΣΗ ΕΡΓΩΝ Ο.Σ.Ε.ΚΑΙ ΣΥΝΔΕΔΕΜΕΝΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ '1874': ΓΕΩΡΓΙΚΕΣ ΣΧΟΛΕΣ '1875': ΣΥΜΜΕΤΟΧΗ ΣΥΝΕΤΑΙΡΙΣΜΩΝ ΣΕ ΠΡΟΜΗΘΕΙΕΣ ΔΗΜΟΣΙΟΥ '1876': ΔΙΚΑΙΩΜΑ ΧΟΡΤΟΝΟΜΗΣ '1877': ΟΙΚΟΚΥΡΙΚΕΣ ΣΧΟΛΕΣ '1878': ΚΕΝΤΡΑ ΥΓΕΙΑΣ-ΠΟΛΥΙΑΤΡΕΙΑ '1879': ΔΙΚΑΣΤΗΡΙΟ ΣΥΝΔΙΑΛΛΑΓΗΣ ΚΑΙ ΔΙΑΙΤΗΣΙΑΣ '1880': ΕΠΙΘΕΩΡΗΣΗ ΙΧΘΥΩΝ '1881': ΣΥΝΕΤΑΙΡΙΣΜΟΙ ΕΞΕΥΓΕΝΙΣΜΟΥ ΔΕΝΔΡΩΝ '1882': ΦΟΙΤΗΤΕΣ '1883': ΔΟΜΗΣΗ ΕΠΙ ΡΥΜΟΤΟΜΟΥΜΕΝΩΝ ΑΚΙΝΗΤΩΝ '1884': ΑΠΑΣΧΟΛΗΣΗ - ΕΞΕΙΔΙΚΕΥΣΗ - ΚΑΤΑΡΤΙΣΗ ΑΝΕΡΓΩΝ '1885': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΥΠΑΛΛΗΛΩΝ ΦΑΡΜΑΚΕΥΤΙΚΩΝ ΕΡΓΑΣΙΩΝ (Τ.Ε.Α.Υ.Φ.Ε.) '1886': ΝΟΜΙΣΜΑΤΙΚΟ ΣΥΣΤΗΜΑ '1887': ΑΠΟΓΡΑΦΗ ΝΑΥΤΙΚΩΝ '1888': ΕΘΝΙΚΟ ΘΕΑΤΡΟ '1889': ΥΠΗΡΕΣΙΑ ΕΠΙΣΤΗΜΟΝΙΚΗΣ ΄ΕΡΕΥΝΑΣ ΚΑΙ ΑΝΑΠΤΥΞΕΩΣ '1890': ΠΑΡΟΧΕΣ ΑΣΤΥΝΟΜΙΚΟΥ ΠΡΟΣΩΠΙΚΟΥ ΕΛΛΗΝΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ '1891': ΣΙΒΙΤΑΝΙΔΕΙΟΣ ΣΧΟΛΗ '1892': ΣΤΡΑΤΙΩΤΙΚΗ ΙΑΤΡΙΚΗ ΣΧΟΛΗ '1893': ΥΠΟΥΡΓΕΙΟ ΚΟΙΝΩΝΙΚΩΝ ΥΠΗΡΕΣΙΩΝ '1894': ΑΠΑΓΟΡΕΥΣΗ ΑΠΑΛΛΟΤΡΙΩΣΗΣ ΠΛΟΙΩΝ '1895': ΠΑΝΕΠΙΣΤΗΜΙΑΚΑ ΣΥΓΓΡΑΜΜΑΤΑ '1896': ΜΟΥΣΟΥΛΜΑΝΟΙ '1897': ΔΙΚΑΣΤΙΚΟΙ ΣΥΜΒΟΥΛΟΙ ΠΟΛΕΜΙΚΟΥ ΝΑΥΤΙΚΟΥ '1898': ΑΕΡΟΠΟΡΙΚΑ ΕΡΓΑ ΚΑΙ ΠΡΟΜΗΘΕΙΕΣ '1899': ΤΟΠΙΚΑ ΕΓΓΕΙΟΒΕΛΤΙΩΤΙΚΑ ΕΡΓΑ '1900': ΦΟΡΟΛΟΓΙΑ ΖΩΩΝ '1901': ΣΥΝΤΑΓΜΑ '1902': ΝΟΜΟΙ ΠΕΡΙ ΧΡΗΜΑΤΙΣΤΗΡΙΟΥ - ΕΠΙΤΡΟΠΗ ΚΕΦΑΛΑΙΑΓΟΡΑΣ - ΧΡΗΜΑΤΙΣΤΗΡΙΑΚΗ ΑΓΟΡΑ ΠΑΡΑΓΩΓΩΝ '1903': ΓΕΩΤΡΗΣΕΙΣ '1904': ΤΑΜΕΙΑ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΚΑΙ ΠΡΟΝΟΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΕΜΠΟΡΙΚΗΣ ΤΡΑΠΕΖΑΣ ΕΛΛΑΔΑΣ (Τ.Ε.Α.Π.Ε.Τ.Ε ΚΑΙ Τ.Α.Π.Ε.Τ.Ε.) '1905': ΕΦΕΔΡΟΙ ΑΕΡΟΠΟΡΙΑΣ '1906': ΚΑΤ’ ΙΔΙΑΝ ΙΔΙΩΤΙΚΑ ΕΚΠΑΙΔΕΥΤΗΡΙΑ '1907': ΣΧΟΛΗ ΝΟΜΙΚΩΝ ΚΑΙ ΟΙΚΟΝΟΜΙΚΩΝ ΕΠΙΣΤΗΜΩΝ '1908': ΚΑΤΑΒΟΛΗ ΕΙΣΦΟΡΩΝ ΜΕ ΔΟΣΕΙΣ '1909': ΠΑΛΑΙΟΤΕΡΕΣ ΑΕΡΟΠΟΡΙΚΕΣ ΕΤΑΙΡΕΙΕΣ '1910': ΤΡΟΜΟΚΡΑΤΙΑ - ΟΡΓΑΝΩΜΕΝΗ '1911': ΤΑΜΕΙΑ ΕΛΙΑΣ-ΔΑΚΟΚΤΟΝΙΑ '1912': ΓΡΑΦΕΙΑ ΕΥΡΕΣΕΩΣ ΝΑΥΤΙΚΗΣ ΕΡΓΑΣΙΑΣ '1913': ΑΡΤΟΠΟΙΕΙΑ '1914': ΦΟΡΟΛΟΓΙΑ ΚΥΚΛΟΥ ΕΡΓΑΣΙΩΝ '1915': ΣΥΝΑΛΛΑΓΜΑΤΙΚΗ ΚΑΙ ΓΡΑΜΜΑΤΙΟ ΣΕ ΔΙΑΤΑΓΗ '1916': ΠΕΡΙΦΕΡΕΙΑΚΕΣ ΥΠΗΡΕΣΙΕΣ ΥΠΟΥΡΓΕΙΟΥ ΜΕΤΑΦΟΡΩΝ ΚΑΙ ΕΠΙΚΟΙΝΩΝΙΩΝ '1917': ΕΛΛΗΝΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ ΤΟΥΡΙΣΜΟΥ '1918': ΠΡΟΣΤΑΣΙΑ ΤΡΑΥΜΑΤΙΩΝ, ΑΙΧΜΑΛΩΤΩΝ ΚΑΙ ΑΜΑΧΟΥ ΠΛΗΘΥΣΜΟΥ '1919': ΚΑΝΟΝΙΣΜΟΣ ΛΕΙΤΟΥΡΓΙΑΣ Τ.Ε.Β.Ε '1920': ΣΤΕΓΑΣΗ ΑΝΑΠΗΡΩΝ ΠΟΛΕΜΟΥ '1921': ΑΘΛΗΤΙΣΜΟΣ ΚΑΙ ΨΥΧΑΓΩΓΙΑ Π. ΝΑΥΤΙΚΟΥ '1922': ΑΝΕΛΚΥΣΤΗΡΕΣ - ΑΝΥΨΩΤΙΚΑ ΜΕΣΑ ΚΑΙ ΜΗΧΑΝΗΜΑΤΑ '1923': ΣΥΝΤΑΞΕΙΣ ΠΛΗΡΩΜΑΤΩΝ ΕΠΙΤΑΚΤΩΝ ΠΛΟΙΩΝ '1924': ΔΙΚΑΙΩΜΑΤΑ ΥΠΕΡΗΜΕΡΙΑΣ '1925': ΚΩΔΙΚΑΣ ΠΟΛΕΜΙΚΩΝ ΣΥΝΤΑΞΕΩΝ '1926': ΚΑΠΝΟΣ '1927': ΠΡΟΣΤΑΣΙΑ ΣΕΙΣΜΟΠΛΗΚΤΩΝ '1928': ΑΠΟΣΤΡΑΤΕΙΕΣ ΚΑΙ ΑΠΟΚΑΤΑΣΤΑΣΕΙΣ '1929': ΠΡΟΣΩΠΙΚΟ ΕΠΑΓΓΕΛΜΑΤΙΚΩΝ ΣΧΟΛΩΝ '1930': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ ΓΙΑ ΤΗΝ ΠΡΟΣΤΑΣΙΑ ΤΩΝ ΕΡΓΑΖΟΜΕΝΩΝ ΑΝΗΛΙΚΩΝ '1931': ΚΕΝΤΡΙΚΗ ΑΓΟΡΑ ΑΘΗΝΩΝ '1932': ΕΝΙΣΧΥΣΗ ΕΛΑΙΟΠΑΡΑΓΩΓΗΣ '1933': ΑΝΟΙΚΤΑ ΣΩΦΡΟΝΙΣΤΙΚΑ ΚΑΤΑΣΤΗΜΑΤΑ '1934': ΦΙΛΑΝΘΡΩΠΙΚΑ ΙΔΡΥΜΑΤΑ ΖΑΚΥΝΘΟΥ '1935': ΔΙΑΦΟΡΑ ΕΙΔΗ ΤΡΟΦΙΜΩΝ, ΠΟΤΩΝ & ΑΝΤΙΚΕΙΜΕΝΩΝ '1936': ΦΟΡΟΛΟΓΙΑ ΕΠΙΧΕΙΡΗΣΕΩΝ ΤΥΠΟΥ '1937': ΠΕΡΙΟΡΙΣΜΟΙ ΕΙΣΑΓΩΓΗΣ '1938': ΠΡΟΣΩΡΙΝΗ ΕΙΣΔΟΧΗ ΕΜΠΟΡΕΥΜΑΤΩΝ '1939': ΑΡΧΕΙΟ '1940': ΔΙΥΛΙΣΤΗΡΙΑ ΠΕΤΡΕΛΑΙΟΥ '1941': ΕΙΣΑΓΩΓΗ ΠΑΙΔΑΓΩΓΙΚΟΥ ΥΛΙΚΟΥ '1942': ΕΠΙΘΕΩΡΗΣΗ ΚΛΗΡΟΔΟΤΗΜΑΤΩΝ '1943': ΣΙΔΗΡΟΔΡΟΜΟΙ ΒΟΡΕΙΟΔΥΤΙΚΗΣ ΕΛΛΑΔΟΣ '1944': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΕΡΓΑΤΟΤΕΧΝΙΤΩΝ ΔΟΜΙΚΩΝ ΚΑΙ ΞΥΛΟΥΡΓΙΚΩΝ ΕΡΓΑΣΙΩΝ (Τ.Ε.Α.Ε.Δ.Ξ.Ε.) '1945': ΤΑΜΕΙΑ ΠΡΟΝΟΙΑΣ ΣΤΙΣ ΠΡΕΣΒΕΙΕΣ '1946': ΟΙΚΟΓΕΝΕΙΑΚΟΣ ΠΡΟΓΡΑΜΜΑΤΙΣΜΟΣ - ΥΓΕΙΑ ΠΑΙΔΙΟΥ '1947': ΑΡΧΙΕΡΕΙΣ '1948': ΣΥΜΒΟΥΛΙΑ ΥΠΟΥΡΓΕΙΟΥ ΔΙΚΑΙΟΣΥΝΗΣ '1949': ΝΟΣΟΚΟΜΕΙΑΚΗ ΠΕΡΙΘΑΛΨΗ '1950': ΚΑΤΑΣΤΗΜΑΤΑ ΠΩΛΗΣΕΩΣ ΟΙΝΟΠΝΕΥΜΑΤΩΔΩΝ ΠΟΤΩΝ ΚΑΙ ΚΕΝΤΡΑ ΔΙΑΣΚΕΔΑΣΕΩΣ '1951': ΠΡΩΤΕΥΟΥΣΑ '1952': ΠΟΛΥΤΕΧΝΕΙΟ ΚΡΗΤΗΣ '1953': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΕΤΑΙΡΕΙΩΝ ΤΣΙΜΕΝΤΩΝ (Τ.Ε.Α.Π.Ε.Τ.) '1954': ΕΛΛΗΝΙΚΟΣ ΤΑΠΗΤΟΥΡΓΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ '1955': ΕΦΑΡΜΟΓΗ ΔΗΜΟΣΙΟΥΠΑΛΛΗΛΙΚΟΥ ΚΩΔΙΚΑ '1956': ΗΛΕΚΤΡΟΛΟΓΙΚΟ ΕΡΓΑΣΤΗΡΙΟ '1957': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΜΗΧΑΝΙΚΩΝ ΚΑΙ ΕΡΓΟΛΗΠΤΩΝ '1958': ΜΕΣΙΤΕΣ ΑΣΤΙΚΩΝ ΣΥΜΒΑΣΕΩΝ '1959': ΠΛΩΤΕΣ ΔΕΞΑΜΕΝΕΣ '1960': ΚΑΝΟΝΙΣΜΟΙ ΦΟΡΤΩΣΕΩΝ '1961': ΕΙΔΙΚΑ ΕΠΙΔΟΜΑΤΑ '1962': ΠΟΙΝΙΚΟΣ ΚΩΔΙΚΑΣ '1963': ΕΙΔΙΚΟΣ ΛΟΓΑΡΙΑΣΜΟΣ ΠΡΟΝΟΙΑΣ (Τ.Σ.Ε.Υ.Π.) '1964': ΕΘΝΙΚΗ ΑΝΤΙΣΤΑΣΗ '1965': ΟΡΓΑΝΙΣΜΟΣ ΒΙΟΜΗΧΑΝΙΚΗΣ ΑΝΑΠΤΥΞΗΣ '1966': ΕΡΓΑ ΚΟΙΝΗΣ ΥΠΟΔΟΜΗΣ '1967': ΔΙΕΥΘΥΝΣΗ TΕΛΩΝΕΙΩΝ ΠΕΙΡΑΙΑ '1968': ΙΑΤΡΙΚΗ ΣΧΟΛΗ ΙΩΑΝΝΙΝΩΝ '1969': ΖΩΟΚΛΟΠΗ ΚΑΙ ΖΩΟΚΤΟΝΙΑ '1970': ΡΥΘΜΙΣΙΣ ΚΙΝΗΣΕΩΣ ΕΝ ΟΔΟΙΣ '1971': ΕΤΑΙΡΕΙΕΣ ΠΡΟΣΤΑΣΙΑΣ ΚΡΑΤΟΥΜΕΝΩΝ - ΑΠΟΦΥΛΑΚΙΖΟΜΕΝΩΝ '1972': ΔΑΣΙΚΗ ΔΙΕΥΘΕΤΗΣΗ ΧΕΙΜΑΡΡΩΝ '1973': ΣΥΝΟΡΙΑΚΟΙ ΦΥΛΑΚΕΣ '1974': ΣΧΟΛΗ ΘΕΤΙΚΩΝ ΕΠΙΣΤΗΜΩΝ ΠΑΝΜΙΟΥ ΙΩΑΝΝΙΝΩΝ '1975': ΕΚΠΑΙΔΕΥΣΗ Π.ΝΑΥΤΙΚΟΥ '1976': ΔΙΚΑΙΟΣΤΑΣΙΟ ΕΠΙΣΤΡΑΤΕΥΣΕΩΣ 1974 '1977': ΡΑΔΙΟΤΗΛΕΓΡΑΦΙΚΗ ΚΑΙ ΡΑΔΙΟΤΗΛΕΦΩΝΙΚΗ ΥΠΗΡΕΣΙΑ '1978': ΦΑΡΜΑΚΑ-ΙΔΙΟΣΚΕΥΑΣΜΑΤΑ '1979': ΣΥΝΤΕΛΕΣΤΕΣ ΚΕΡΔΟΥΣ ΕΠΑΓΓΕΛΜΑΤΙΩΝ '1980': ΕΘΝΙΚΟ ΚΕΝΤΡΟ ΚΟΙΝΩΝΙΚΩΝ ΕΡΕΥΝΩΝ '1981': ΚΕΦΑΛΑΙΟ ΝΑΥΤΙΚΗΣ ΕΚΠΑΙΔΕΥΣΕΩΣ '1982': ΕΙΣΠΡΑΞΗ ΕΣΟΔΩΝ ΠΑΡΕΛΘΟΥΣΩΝ ΧΡΗΣΕΩΝ '1983': ΟΡΓΑΝΙΣΜΟΣ ΗΝΩΜΕΝΩΝ ΕΘΝΩΝ '1984': ΣΕΙΣΜΟΠΛΗΚΤΟΙ ΝΗΣΟΥ ΘΗΡΑΣ '1985': ΚΕΝΤΡΙΚΗ ΑΓΟΡΑ ΘΕΣΣΑΛΟΝΙΚΗΣ '1986': ΔΙΑΦΘΟΡΑ ΑΛΛΟΔΑΠΩΝ ΔΗΜΟΣΙΩΝ ΛΕΙΤΟΥΡΓΩΝ '1987': ΓΕΩΠΟΝΙΚΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΑΘΗΝΩΝ '1988': ΚΑΝΟΝΙΣΜΟΣ ΣΤΡΑΤΟΔΙΚΕΙΩΝ '1989': ΔΙΑΦΟΡΕΣ ΥΓΕΙΟΝΟΜΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '1990': ΤΟΥΡΙΣΤΙΚΑ ΛΕΩΦΟΡΕΙΑ '1991': ΔΑΝΕΙΑ ΑΠΟ ΕΚΔΟΤΙΚΕΣ ΤΡΑΠΕΖΕΣ '1992': ΕΠΙΘΑΛΑΣΣΙΑ ΑΡΩΓΗ - ΡΥΜΟΥΛΚΗΣΗ ΠΛΟΙΩΝ '1993': ΠΡΟΣΤΑΣΙΑ ΤΟΥ ΚΑΘΕΣΤΩΤΟΣ '1994': ΣΥΜΒΑΣΕΙΣ ΠΕΡΙ ΥΛΙΚΟΥ ΕΥΗΜΕΡΙΑΣ ΝΑΥΤΙΛΛΟΜΕΝΩΝ '1995': ΜΕΣΙΤΕΣ ΕΓΧΩΡΙΩΝ ΠΡΟΙΟΝΤΩΝ '1996': ΚΡΑΤΙΚΗ ΟΡΧΗΣΤΡΑ ΑΘΗΝΩΝ '1997': ΤΜΗΜΑΤΑ ΜΟΥΣΙΚΩΝ - ΘΕΑΤΡΙΚΩΝ ΣΠΟΥΔΩΝ ΚΑΙ ΕΠΙΚΟΙΝΩΝΙΑΣ - ΜΕΣΩΝ ΜΑΖΙΚΗΣ ΕΝΗΜΕΡΩΣΗΣ '1998': ΠΕΙΘΑΡΧΙΚΗ ΕΞΟΥΣΙΑ ΛΙΜΕΝΙΚΩΝ ΑΡΧΩΝ '1999': ΙΝΣΤΙΤΟΥΤΟ ΑΜΥΝΤΙΚΩΝ ΑΝΑΛΥΣΕΩΝ (Ι.Α.Α.) '2000': ΙΔΙΩΤΙΚΟΙ ΣΤΑΘΜΟΙ ΑΣΥΡΜΑΤΟΥ - ΧΡΗΣΗ ΡΑΔΙΟΣΥΧΝΟΤΗΤΩΝ '2001': ΑΝΑΓΝΩΡΙΣΗ ΞΕΝΩΝ ΚΑΤΑΜΕΤΡΗΣΕΩΝ '2002': ΓΕΝΟΚΤΟΝΙΑ '2003': ΕΠΕΞΕΡΓΑΣΙΑ ΚΑΠΝΟΥ '2004': ΣΥΜΒΟΥΛΙΟ ΕΠΙΚΡΑΤΕΙΑΣ '2005': ΙΑΤΡΟΙ Ι.Κ.Α '2006': ΥΠΟΘΗΚΗ '2007': ΑΡΜΟΔΙΟΤΗΤΑ ΛΙΜΕΝΙΚΟΥ ΣΩΜΑΤΟΣ '2008': ΕΙΣΑΓΩΓΕΣ ΓΙΑ ΕΚΘΕΣΕΙΣ, ΣΥΝΕΔΡΙΑ ΚΛΠ '2009': ΕΥΡΩΠΑΙΚΗ ΤΡΑΠΕΖΑ ΑΝΑΣΥΓΚΡΟΤΗΣΗ-ΑΝΑΠΤΥΞΗ '2010': ΑΕΡΟΔΡΟΜΙΟ ΣΠΑΤΩΝ '2011': ΤΜΗΜΑ ΔΗΜΟΣΙΟΓΡΑΦΙΑΣ - ΜΕΣΩΝ ΜΑΖΙΚΗΣ ΕΠΙΚΟΙΝΩΝΙΑΣ '2012': ΤΟΚΟΣ '2013': ΕΝΙΣΧΥΣΗ ΠΟΛΕΜΟΠΑΘΩΝ ΚΛΠ. ΑΓΡΟΤΩΝ '2014': ΕΞΟΔΑ ΚΗΔΕΙΑΣ ΣΤΡΑΤΙΩΤΙΚΩΝ '2015': ΠΑΡΟΧΕΣ ΥΠΑΛΛΗΛΩΝ '2016': ΠΡΟΣΤΑΣΙΑ ΣΙΤΟΠΑΡΑΓΩΓΗΣ '2017': ΑΣΦΑΛΙΣΗ Ο.Γ.Α ΑΠΟ ΑΝΕΜΟΘΥΕΛΛΑ ΚΑΙ ΠΛΗΜΜΥΡΑ '2018': ΔΙΕΥΘΥΝΣΗ ΚΑΤΑΣΚΕΥΩΝ ΚΑΙ ΕΞΟΠΛΙΣΜΟΥ '2019': ΤΕΛΩΝΕΙΑΚΟΙ ΥΠΟΛΟΓΟΙ '2020': ΓΕΝΙΚΗ ΓΡΑΜΜΑΤΕΙΑ ΑΘΛΗΤΙΣΜΟΥ '2021': ΣΥΝΤΑΞΕΙΣ '2022': ΑΔΕΙΕΣ ΠΡΟΣΩΠΙΚΟΥ Λ.Σ '2023': ΣΥΝΤΑΞΕΙΣ ΣΤΡΑΤΙΩΤΙΚΩΝ ΠΑΘΟΝΤΩΝ ΣΤΗΝ '2024': ΑΣΦΑΛΙΣΗ ΕΠΙΒΑΤΩΝ '2025': ΑΠΑΛΛΟΤΡΙΩΣΗ ΑΚΙΝΗΤΩΝ '2026': ΣΧΟΛΗ ΕΠΙΣΤΗΜΩΝ ΥΓΕΙΑΣ '2027': ΕΝΟΙΚΙΟΣΤΑΣΙΟ ΒΟΣΚΩΝ '2028': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΗΘΟΠΟΙΩΝ - ΣΥΓΓΡΑΦΕΩΝ ΤΕΧΝΙΚΩΝ ΘΕΑΤΡΟΥ '2029': ΕΥΡΩΠΑΙΚΟ ΕΝΤΑΛΜΑ ΣΥΛΛΗΨΗΣ '2030': ΑΝΤΙΚΕΙΜΕΝΑ ΔΕΔΗΛΩΜΕΝΗΣ ΑΞΙΑΣ ΑΝΤΙΚΑΤΑΒΟΛΕΣ '2031': ΓΕΝΙΚΗ ΔΙΕΥΘΥΝΣΗ ΜΕΤΑΦΟΡΩΝ '2032': ΟΡΓΑΝΙΣΜΟΣ ΥΠΟΥΡΓΕΙΟΥ ΔΙΚΑΙΟΣΥΝΗΣ '2033': ΕΥΘΥΝΗ ΥΠΟΥΡΓΩΝ '2034': ΤΜΗΜΑ ΚΤΗΝΙΑΤΡΙΚΗΣ '2035': ΔΙΚΑΣΤΙΚΟ ΣΩΜΑ ΕΝΟΠΛΩΝ ΔΥΝΑΜΕΩΝ '2036': ΕΝΟΡΙΑΚΟΙ ΝΑΟΙ ΚΑΙ ΕΦΗΜΕΡΙΟΙ '2037': ΥΓΕΙΟΝΟΜΙΚΕΣ ΕΠΙΤΡΟΠΕΣ ΝΑΥΤΙΚΟΥ '2038': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΚΑΙ ΠΡΟΝΟΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΕΛΛΗΝΙΚΗΣ ΡΑΡΙΟΦΩΝΙΑΣ-ΤΗΛΕΟΡΑΣΕΩΣ-ΤΟΥΡΙΣΜΟΥ (Τ.Ε.Α.Π.Π. Ε.Ρ.Τ. Τ.) '2039': ΣΤΡΑΤΙΩΤΙΚΗ ΒΟΗΘΕΙΑ Η.Π.Α '2040': ΣΥΝΤΑΞΕΙΣ ΠΡΟΣΩΠΙΚΟΥ '2041': ΧΡΗΜΑΤΙΚΗ ΔΙΑΧΕΙΡΙΣΗ Π. ΝΑΥΤΙΚΟΥ '2042': ΠΟΛΙΤΙΚΟ ΓΡΑΦΕΙΟ ΠΡΩΘΥΠΟΥΡΓΟΥ '2043': ΛΟΥΤΡΟΘΕΡΑΠΕΙΑ ΚΑΙ ΑΕΡΟΘΕΡΑΠΕΙΑ '2044': ΣΥΜΒΟΥΛΙΟ ΚΟΙΝΩΝΙΚΩΝ ΑΣΦΑΛΙΣΕΩΝ '2045': ΕΝΤΟΚΑ ΓΡΑΜΜΑΤΙΑ '2046': ΣΩΦΡΟΝΙΣΤΙΚΟΣ ΚΩΔΙΚΑΣ '2047': ΔΗΜΟΤΙΚΕΣ ΕΠΙΧΕΙΡΗΣΕΙΣ '2048': ΚΩΔΙΚΑΣ ΠΟΛΙΤΙΚΗΣ ΔΙΚΟΝΟΜΙΑΣ - ΝΕΟΣ '2049': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΥΠΑΛΛΗΛΩΝ ΚΟΥΡΕΙΩΝ ΚΑΙ ΚΟΜΜΩΤΗΡΙΩΝ '2050': ΠΡΟΣΩΠΙΚΟ ΣΙΔΗΡΟΔΡΟΜΩΝ- Ο.Σ.Ε.- ΣΙΔΗΡΟΔΡΟΜΙΚΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ '2051': ΔΙΑΦΟΡΟΙ ΝΟΜΟΙ ΓΙΑ ΤΟΝ ΤΥΠΟ '2052': ΤΑΧΥΔΡΟΜΙΚΑ ΔΕΛΤΑΡΙΑ '2053': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΗΛΕΚΤΡ. ΕΤ. ΑΘΗΝΩΝ - ΠΕΙΡΑΙΩΣ ΚΑΙ ΕΛΛΗΝ. ΗΛΕΚΤΡ. ΕΤΑΙΡΙΑΣ (Τ.Α.Π Η.Ε.Α.Π.- Ε.Η.Ε.) '2054': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΗΣ ΑΡΤΟΠΟΙΩΝ '2055': ΔΗΜΟΤΙΚΟΙ ΚΑΙ ΚΟΙΝΟΤΙΚΟΙ ΑΡΧΟΝΤΕΣ '2056': ΜΕΤΑΦΟΡΑ ΤΑΧΥΔΡΟΜΕΙΟΥ '2057': ΚΑΝΟΝΙΣΜΟΣ ΠΑΡΟΧΩΝ ΤΑΜΕΙΟΥ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΑΣΦΑΛΙΣΤΩΝ ΚΑΙ ΠΡΟΣΩΠΙΚΟΥ ΑΣΦΑΛΙΣΤΙΚΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ (Τ.Ε.Α.Α.Π.Α.Ε.) '2058': ΠΡΟΣΩΠΙΚΟ '2059': ΔΗΜΟΣΙΑ ΕΠΙΧΕΙΡΗΣΗ ΗΛΕΚΤΡΙΣΜΟΥ '2060': ΚΑΝΟΝΙΣΜΟΙ ΕΡΓΩΝ ΩΠΛΙΣΜΕΝΟΥ ΣΚΥΡΟΔΕΜΑΤΟΣ '2061': ΑΛΕΥΡΑ-ΑΡΤΟΣ '2062': ΤΕΛΗ ΠΡΟΣΟΡΜΙΣΕΩΣ, ΠΑΡΑΒΟΛΗΣ ΚΑΙ ΠΑΡΟΠΛΙΣΜΟΥ '2063': ΙΔΙΩΤΙΚΑ ΕΚΠΑΙΔΕΥΤΗΡΙΑ ΦΡΟΝΤΙΣΤΗΡΙΑ '2064': ΑΡΧΑΙΟΛΟΓΙΚΗ ΥΠΗΡΕΣΙΑ '2065': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΤΥΠΟΓΡΑΦΩΝ ΚΑΙ ΜΙΣΘΩΤΩΝ ΓΡΑΦΙΚΩΝ ΤΕΧΝΩΝ (Τ.Α.Τ. & Μ.Γ.Τ) '2066': ΕΙΔΙΚΕΣ ΕΦΑΡΜΟΓΕΣ ΚΥΡΙΑΚΗΣ ΑΡΓΙΑΣ '2067': ΔΙΑΦΟΡΟΙ ΝΟΜΟΙ ΓΙΑ ΤΑ ΠΛΗΡΩΜΑΤΑ '2068': ΑΣΤΙΚΑ ΣΧΟΛΕΙΑ '2069': ΤΑΜΕΙΑ ΣΥΝΤΑΞΕΩΝ ΕΦΗΜΕΡΙΔΟΠΩΛΩΝ ΚΑΙ ΥΠΑΛΛΗΛΩΝ ΠΡΑΚΤΟΡΕΙΩΝ ΑΘΗΝΩΝ-ΘΕΣΝΙΚΗΣ (Τ.Σ.Ε.Υ.Π.) '2070': ΔΟΜΙΚΑ ΕΡΓΑ '2071': ΝΑΥΣΤΑΘΜΟΣ '2072': ΑΝΤΙΓΡΑΦΙΚΑ ΔΙΚΑΙΩΜΑΤΑ '2073': ΕΠΙΔΟΜΑ ΟΙΚΟΓΕΝΕΙΑΚΩΝ ΒΑΡΩΝ '2074': ΕΛΛΗΝΙΚΗ-ΕΥΡΩΠΑΙΚΗ ΦΑΡΜΑΚΟΠΟΙΙΑ '2075': ΔΕΛΤΙΑ ΤΑΥΤΟΤΗΤΟΣ '2076': ΣΧΟΛΙΑΤΡΙΚΗ ΥΠΗΡΕΣΙΑ '2077': ΥΔΡΟΓΟΝΑΝΘΡΑΚΕΣ '2078': ΓΕΝΙΚΑ ΠΕΡΙ ΕΚΘΕΣΕΩΝ '2079': ΦΟΡΟΛΟΓΙΚΕΣ ΔΙΕΥΚΟΛΥΝΣΕΙΣ '2080': ΛΣΜΟΣ ΠΡΟΝΟΙΑΣ ΠΡΟΣΩΠΙΚΟΥ Ι.Κ.Α '2081': ΕΛΕΓΧΟΣ ΚΤΙΡΙΑΚΩΝ ΕΡΓΩΝ '2082': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΗΣ '2083': ΕΛΑΙΟΠΥΡΗΝΕΣ '2084': ΕΜΦΥΤΕΥΤΙΚΑ ΚΤΗΜΑΤΑ '2085': ΤΟΥΡΙΣΤΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '2086': ΚΛΑΔΟΣ ΑΣΦΑΛΙΣΕΩΣ ΤΕΧΝΙΚΩΝ ΤΥΠΟΥ ΘΕΣΣΑΛΟΝΙΚΗΣ (Κ.Α.Τ.Τ.Θ.) '2087': ΜΕΤΕΩΡΟΛΟΓΙΚΗ ΥΠΗΡΕΣΙΑ '2088': ΑΓΡΟΤΙΚΟΣ ΚΩΔΙΚΑΣ '2089': ΤΕΧΝΙΚΟ ΕΠΙΜΕΛΗΤΗΡΙΟ '2090': ΕΛΕΓΧΟΣ ΝΟΜΙΜΟΦΡΟΣΥΝΗΣ '2091': ΑΡΧΑΙΟΛΟΓΙΚΗ ΕΤΑΙΡΙΑ '2092': ΣΧΟΛΑΖΟΥΣΕΣ ΚΛΗΡΟΝΟΜΙΕΣ '2093': ΓΕΦΥΡΑ ΡΙΟΥ - ΑΝΤΙΡΡΙΟΥ '2094': ΦΟΙΤΗΣΗ, ΕΞΕΤΑΣΕΙΣ ΚΛΠ '2095': ΤΥΧΕΡΑ, ΜΙΚΤΑ ΚΑΙ ΤΕΧΝΙΚΑ ΠΑΙΓΝΙΑ '2096': ΟΡΓΑΝΙΚΟΙ ΑΡΙΘΜΟΙ ΥΠΑΞΙΩΜΑΤΙΚΩΝ '2097': ΦΟΡΟΛΟΓΙΑ ΚΙΝΗΤΗΣ ΚΑΙ ΑΚΙΝΗΤΗΣ ΠΕΡΙΟΥΣΙΑΣ '2098': ΑΤΕΛΕΙΕΣ ΑΓΙΟΥ ΟΡΟΥΣ '2099': ΜΟΝΟΠΩΛΙΟ ΑΛΑΤΙΟΥ '2100': ΑΣΦΑΛΙΣΗ ΕΛΛΗΝΩΝ ΕΞΩΤΕΡΙΚΟΥ '2101': ΔΙΕΘΝΕΣ ΚΕΝΤΡΟ ΑΝΩΤΑΤΩΝ '2102': ΑΝΑΠΡΟΣΑΡΜΟΓΕΣ ΣΥΝΤΑΞΕΩΝ '2103': ΓΕΝΙΚΕΣ ΕΠΙΘΕΩΡΗΣΕΙΣ-ΔΙΕΥΘΥΝΣΕΙΣ '2104': ΣΩΜΑ ΟΡΚΩΤΩΝ ΛΟΓΙΣΤΩΝ '2105': ΣΕΙΣΜΟΠΛΗΚΤΟΙ ΒΟΡΕΙΟΥ ΕΛΛΑΔΟΣ '2106': ΠΑΝΕΠΙΣΤΗΜΙΑ ΠΕΙΡΑΙΩΣ-ΜΑΚΕΔΟΝΙΑΣ '2107': ΧΩΡΟΤΑΞΙΑ ΚΑΙ ΠΕΡΙΒΑΛΛΟΝ '2108': ΕΣΩΤΕΡΙΚΟΙ ΚΑΝΟΝΙΣΜΟΙ ΕΡΓΑΣΙΑΣ '2109': ΕΛΕΓΧΟΣ ΝΑΥΤΙΚΩΝ ΑΤΥΧΗΜΑΤΩΝ '2110': ΠΝΕΥΜΑΤΙΚΑ ΚΕΝΤΡΑ '2111': ΠΛΟΗΓΙΚΑ ΔΙΚΑΙΩΜΑΤΑ '2112': ΣΤΡΑΤΕΥΟΜΕΝΟΙ ΔΙΚΗΓΟΡΟΙ '2113': ΣΥΣΤΑΤΙΚΑ ΑΥΤΟΚΙΝΗΤΩΝ '2114': ΣΙΔΗΡΟΔΡΟΜΟΙ ΠΕΛΟΠΟΝΝΗΣΟΥ '2115': ΤΜΗΜΑ ΜΕΘΟΔΟΛΟΓΙΑΣ, ΙΣΤΟΡΙΑΣ ΚΑΙ ΘΕΩΡΙΑΣ ΤΗΣ ΕΠΙΣΤΗΜΗΣ '2116': ΕΥΡΩΠΑΙΚΟ ΠΟΛΙΤΙΣΤΙΚΟ ΚΕΝΤΡΟ ΔΕΛΦΩΝ '2117': ΣΥΝΕΤΑΙΡΙΣΜΟΙ ΕΓΓΕΙΩΝ ΒΕΛΤΙΩΣΕΩΝ '2118': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΗΣ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ (Τ.Ε.Α.Δ.Υ.) '2119': ΙΕΡΟΚΗΡΥΚΕΣ '2120': ΕΙΡΗΝΟΔΙΚΕΙΑ - ΠΤΑΙΣΜΑΤΟΔΙΚΕΙΑ '2121': ΑΓΟΡΑΝΟΜΙΚΗ ΝΟΜΟΘΕΣΙΑ '2122': ΤΡΑΠΕΖΙΤΙΚΗ ΕΠΙΤΑΓΗ '2123': ΝΑΥΑΓΟΣΩΣΤΙΚΑ ΚΑΙ ΡΥΜΟΥΛΚΑ '2124': ΦΟΡΟΛΟΓΙΚΕΣ ΔΙΑΦΟΡΕΣΙ '2125': ΜΕΤΡΑ ΚΑΙ ΣΤΑΘΜΑ '2126': ΓΕΝΙΚΟ ΧΗΜΕΙΟ ΤΟΥ ΚΡΑΤΟΥΣ '2127': ΣΥΜΦΩΝΙΑ ΓΙΑ ΙΣΑ ΟΙΚΟΝΟΜΙΚΑ ΚΟΙΝΩΝΙΚΑ '2128': ΣΥΝΟΡΙΑΚΟΙ ΣΤΑΘΜΟΙ '2129': ΑΞΙΩΜΑΤΙΚΟΙ ΣΩΜΑΤΩΝ ΑΣΦΑΛΕΙΑΣ '2130': ΥΠΗΡΕΣΙΑΚΑ ΣΥΜΒΟΥΛΙΑ '2131': ΕΙΣΑΓΩΓΙΚΟΣ ΝΟΜΟΣ '2132': ΚΤΗΜΑΤΟΛΟΓΙΟ '2133': ΕΤΑΙΡΕΙΑ ΔΙΑΧΕΙΡΙΣΕΩΣ ΥΠΕΓΓΥΩΝ ΠΡΟΣΟΔΩΝ '2134': ΥΠΟΥΡΓΕΙΟ ΜΑΚΕΔΟΝΙΑΣ – ΘΡΑΚΗΣ '2135': ΤΟΥΡΙΣΤΙΚΑ ΓΡΑΦΕΙΑ ΚΑΙ ΣΩΜΑΤΕΙΑ '2136': ΔΑΝΕΙΑ ΑΝΑΣΥΓΚΡΟΤΗΣΗΣ '2137': ΑΣΤΙΚΕΣ ΣΥΓΚΟΙΝΩΝΙΕΣ ΘΕΣΣΑΛΟΝΙΚΗΣ-Ο.Α.Σ.Θ '2138': ΕΘΕΛΟΝΤΕΣ ΑΕΡΟΠΟΡΙΑΣ '2139': ΣΗΜΕΙΩΤΕΣ '2140': ΤΕΛΗ ΕΓΚΑΤΑΣΤΑΣΗΣ - ΛΕΙΤΟΥΡΓΙΑΣ ΚΕΡΑΙΩΝ '2141': Η.Π.Α '2142': ΠΑΝΕΠΙΣΤΗΜΙΑ ΑΙΓΑΙΟΥ, ΙΟΝΙΟΥ ΚΑΙ ΘΕΣΣΑΛΙΑΣ '2143': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΞΕΝΟΔΟΧΩΝ '2144': ΣΥΜΒΟΥΛΙΑ ΣΤΕΓΑΣΕΩΣ '2145': ΤΕΧΝΙΚΗ ΕΚΜΕΤΑΛΛΕΥΣΗ ΙΔΙΩΤΙΚΩΝ ΑΕΡΟΠΛΑΝΩΝ '2146': ΦΟΡΟΛΟΓΙΑ ΔΗΜΟΣΙΩΝ ΘΕΑΜΑΤΩΝ '2147': ΣΤΡΑΤΟΛΟΓΙΑ ΟΠΛΙΤΩΝ ΧΩΡΟΦΥΛΑΚΗΣ '2148': ΓΥΜΝΑΣΙΑ ΑΡΙΣΤΟΥΧΩΝ '2149': ΣΧΟΛΙΚΗ ΑΝΤΙΛΗΨΗ '2150': ΕΥΘΥΝΗ ΣΤΡΑΤΙΩΤΙΚΩΝ '2151': ΣΤΑΘΜΟΙ ΕΠΙΒΗΤΟΡΩΝ '2152': ΒΕΒΑΙΩΣΗ ΠΤΑΙΣΜΑΤΩΝ ΑΠΟ '2153': ΔΙΑΖΥΓΙΟ '2154': ΔΙΕΘΝΗΣ ΣΥΜΒΑΣΗ ΠΕΡΙ ΑΝΑΓΚΑΣΤΙΚΗΣ ΕΡΓΑΣΙΑΣ '2155': ΔΙΕΥΚΟΛΥΝΣΗ ΔΙΕΘΝΟΥΣ ΝΑΥΤΙΛΙΑΚΗΣ ΚΙΝΗΣΕΩΣ '2156': ΕΝΟΙΚΙΟΣΤΑΣΙΟ '2157': ΕΚΘΕΣΕΙΣ ΖΑΠΠΕΙΟΥ ΜΕΓΑΡΟΥ '2158': ΔΙΑΧΕΙΡΙΣΗ ΥΛΙΚΟΥ Π. ΝΑΥΤΙΚΟΥ '2159': ΕΦΕΔΡΙΚΑ ΤΑΜΕΙΑ ΚΡΗΤΗΣ '2160': ΣΙΤΑΡΙ '2161': ΦΟΡΤΗΓΑ 501-4500 ΤΟΝΝΩΝ '2162': ΤΡΑΠΕΖΑ ΕΡΓΑΣΙΑΣ '2163': ΑΤΕΛΕΙΕΣ ΥΠΕΡ ΤΗΣ ΓΕΩΡΓΙΑΣ '2164': ΑΙΓΙΑΛΟΣ ΚΑΙ ΠΑΡΑΛΙΑ '2165': ΔΑΣΗ ΙΔΡΥΜΑΤΩΝ '2166': ΙΧΘΥΟΤΡΟΦΕΙΑ '2167': ΑΠΟΓΡΑΦΕΣ Π. ΝΑΥΤΙΚΟΥ '2168': ΣΗΜΑΤΑ ΚΑΙ ΔΕΛΤΙΑ ΑΝΑΠΗΡΩΝ ΠΟΛΕΜΟΥ '2169': ΠΕΙΘΑΡΧΙΚΟ ΔΙΚΑΙΟ ΑΣΤΥΝΟΜΙΚΟΥ ΠΡΟΣΩΠΙΚΟΥ ΕΛΛΗΝΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ '2170': ΑΤΜΟΛΕΒΗΤΕΣ '2171': ΤΑΧΥΔΡΟΜΙΚΗ ΥΠΗΡΕΣΙΑ ΣΤΡΑΤΟΥ '2172': ΠΡΟΣΤΑΣΙΑ ΠΙΝΑΚΙΔΩΝ '2173': ΑΓΡΟΤΙΚΑ ΚΤΗΝΙΑΤΡΕΙΑ '2174': ΧΡΗΜΑΤΙΣΤΗΡΙΑΚΑ ΔΙΚΑΣΤΗΡΙΑ '2175': ΕΓΓΡΑΦΗ ΠΡΟΕΡΧΟΜΕΝΩΝ ΑΠΟ ΤΗΝ ΑΛΛΟΔΑΠΗ '2176': ΟΡΓΑΝΙΣΜΟΣ ΔΙΑΧΕΙΡΙΣΗΣ ΔΗΜΟΣΙΟΥ ΥΛΙΚΟΥ '2177': ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΥΠΡΟΥ '2178': ΚΑΤΕΡΓΑΣΙΑ ΞΗΡΑΣ ΣΤΑΦΙΔΑΣ '2179': ΤΕΛΩΝΕΙΑΚΗ ΔΙΑΙΡΕΣΗ '2180': ΑΖΗΤΗΤΑ '2181': ΜΕΛΙΣΣΟΤΡΟΦΙΑ '2182': ΔΙΕΥΘΥΝΣΗ ΘΑΛΑΣΣΙΩΝ ΚΡΑΤΙΚΩΝ ΜΕΤΑΦΟΡΩΝ '2183': ΕΚΜΕΤΑΛΛΕΥΣΗ ΜΕΤΑΛΛΕΙΩΝ ΜΕ ΕΓΓΥΗΣΗ '2184': ΙΔΙΩΤΙΚΕΣ ΕΠΑΓΓΕΛΜΑΤΙΚΕΣ ΣΧΟΛΕΣ '2185': ΔΙΑΘΕΣΗ ΑΧΡΗΣΤΟΥ ΥΛΙΚΟΥ '2186': ΤΑΧΥΔΡΟΜΙΚΕΣ ΜΕΤΑΦΟΡΕΣ '2187': ΕΡΥΘΡΟ ΠΙΠΕΡΙ '2188': ΠΙΚΠΑ-ΕΟΠ-ΚΕΝΤΡΟ ΒΡΕΦΩΝ Η ΜΗΤΕΡΑ-ΕΛΕΠΑΠ '2189': ΣΥΜΜΕΤΟΧΗ ΣΕ ΣΥΜΒΟΥΛΙΑ '2190': ΓΥΜΝΑΣΤΗΡΙΟ '2191': ΙΑΤΡΙΚΟΙ- ΟΔΟΝΤΙΑΤΡΙΚΟΙ ΣΥΛΛΟΓΟΙ '2192': ΕΙΣΑΓΩΓΗ ΦΟΙΤΗΤΩΝ '2193': ΕΛΛΗΝΙΚΟ ΄ΙΔΡΥΜΑ ΠΟΛΙΤΙΣΜΟΥ '2194': ΛΟΙΜΟΚΑΘΑΡΤΗΡΙΑ ΖΩΩΝ '2195': ΔΙΕΘΝΗΣ ΟΡΓΑΝΙΣΜΟΣ ΑΤΟΜΙΚΗΣ ΕΝΕΡΓΕΙΑΣ '2196': ΤΑΜΕΙΟ ΕΞΟΔΟΥ ΚΑΙ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΜΙΣΘΩΤΩΝ ΒΙΟΜΗΧΑΝΙΑΣ ΚΑΠΝΟΥ '2197': ΚΑΘΗΓΗΤΕΣ Ε.Μ.Π '2198': ΟΙΚΟΝΟΜΙΚΗ ΔΙΟΙΚΗΣΗ '2199': ΒΕΒΑΙΩΣΗ ΦΟΡΟΛΟΓΙΑΣ ΚΑΘΑΡΑΣ ΠΡΟΣΟΔΟΥ '2200': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΠΡΟΣΩΠΙΚΟΥ ΤΡΑΠΕΖΩΝ ΕΛΛΑΔΟΣ ΚΑΙ ΚΤΗΜΑΤΙΚΗΣ '2201': ΔΗΜΟΨΗΦΙΣΜΑΤΑ '2202': ΕΛΛΗΝΙΚΟ ΑΝΟΙΚΤΟ ΠΑΝΕΠΙΣΤΗΜΙΟ '2203': ΚΑΛΛΙΤΕΧΝΙΚΟ ΕΠΑΓΓΕΛΜΑΤΙΚΟ ΕΠΙΜΕΛΗΤΗΡΙΟ '2204': ΑΝΟΙΚΟΔΟΜΗΣΙΣ '2205': ΔΑΣΙΚΟΣ ΚΩΔΙΚΑΣ '2206': ΚΑΝΟΝΙΣΜΟΣ ΠΥΡΟΣΒΕΣΤΙΚΩΝ ΜΕΣΩΝ ΤΩΝ ΠΛΟΙΩΝ '2207': ΔΙΦΘΕΡΙΤΙΔΑ '2208': ΒΙΒΛΙΑ ΚΑΙ ΦΟΡΟΛΟΓΙΚΑ ΣΤΟΙΧΕΙΑ '2209': ΕΛΕΓΧΟΣ ΕΞΑΓΟΜΕΝΩΝ ΕΛΑΙΩΝ '2210': ΕΠΙΔΟΜΑΤΑ ΟΙΚΟΓΕΝΕΙΩΝ ΣΤΡΑΤΙΩΤΙΚΩΝ '2211': ΕΥΡΩΠΑΙΚΕΣ ΣΥΜΦΩΝΙΕΣ ΠΟΥ ΑΦΟΡΟΥΝ ΤΗΝ ΤΗΛΕΟΡΑΣΗ '2212': ΕΚΤΑΚΤΑ ΣΤΡΑΤΟΔΙΚΕΙΑ '2213': ΠΟΛΕΜΙΚΗ ΒΙΟΜΗΧΑΝΙΑ '2214': ΑΣΕΜΝΟΙ ΓΥΝΑΙΚΕΣ '2215': ΑΠΕΛΕΥΘΕΡΩΣΗ ΑΓΟΡΑΣ ΗΛΕΚΤΡΙΚΗΣ ΕΝΕΡΓΕΙΑΣ ΕΝΕΡΓΕΙΑΚΗ ΠΟΛΙΤΙΚΗ Ρ.Α.Ε '2216': ΠΡΟΕΙΣΠΡΑΞΗ ΔΙΚΗΓΟΡΙΚΗΣ ΑΜΟΙΒΗΣ '2217': ΕΘΝΙΚΗ ΣΧΟΛΗ ΔΗΜΟΣΙΑΣ ΥΓΕΙΑΣ (Ε.Σ.Δ.Υ.) '2218': ΠΡΟΜΗΘΕΙΑ ΘΕΙΟΥ ΚΑΙ ΘΕΙΙΚΟΥ ΧΑΛΚΟΥ '2219': ΧΗΜΙΚΟΙ - ΧΗΜΙΚΕΣ ΒΙΟΜΗΧΑΝΙΕΣ '2220': ΑΣΦΑΛΙΣΗ ΚΑΤΑ ΤΗΣ ΑΣΘΕΝΕΙΑΣ '2221': ΤΑΜΕΙΟ ΑΛΛΗΛΟΒΟΗΘΕΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΕΘΝΙΚΟΥ ΤΥΠΟΓΡΑΦΕΙΟΥ (Τ.Α.Π.Ε.Τ.) '2222': ΟΡΓΑΝΙΣΜΟΣ ΥΠΟΥΡΓΕΙΟΥ ΟΙΚΟΝΟΜΙΚΩΝ '2223': ΠΕΡΙΕΧΟΜΕΝΟ ΔΗΛΩΣΗΣ ΦΟΡΟΥ ΕΙΣΟΔΗΜΑΤΟΣ '2224': ΠΡΩΤΕΣ ΥΛΕΣ ΣΙΔΕΡΕΝΙΩΝ ΒΑΡΕΛΙΩΝ '2225': ΕΥΡΩΠΑΙΚΟΣ ΚΩΔΙΚΑΣ ΚΟΙΝΩΝΙΚΗΣ ΑΣΦΑΛΕΙΑΣ '2226': ΔΙΑΦΟΡΟΙ ΓΕΩΡΓΙΚΟΙ ΣΥΝΕΤΑΙΡΙΣΜΟΙ '2227': ΣΧΕΔΙΑ ΠΟΛΕΩΝ ΙΟΝΙΩΝ ΝΗΣΩΝ '2228': ΕΥΡΩΠΑΙΚΗ ΟΙΚΟΝΟΜΙΚΗ ΚΟΙΝΟΤΗΤΑ ΕΥΡΩΠΑΙΚΗ ΕΝΩΣΗ '2229': ΣΧΟΛΗ ΔΙΟΙΚΗΣΕΩΣ ΝΟΣΗΛΕΥΤ. ΙΔΡΥΜΑΤΩΝ '2230': ΔΙΑΦΟΡΟΙ ΝΟΜΟΙ ΕΜΠΡΑΓΜΑΤΟΥ ΔΙΚΑΙΟΥ '2231': ΕΠΙΜΕΛΗΤΕΙΑ ΚΑΙ ΟΙΚΟΝΟΜΙΚΕΣ ΥΠΗΡΕΣΙΕΣ '2232': ΔΙΑΔΙΚΑΣΙΑ ΑΤΕΛΕΙΑΣ '2233': ΠΑΙΔΙΚΕΣ ΕΞΟΧΕΣ '2234': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΠΡΟΣΩΠΙΚΟΥ ΕΘΝΙΚΗΣ ΤΡΑΠΕΖΑΣ ΤΗΣ ΕΛΛΑΔΟΣ '2235': ΚΡΑΤΙΚΗ ΕΚΜΕΤΑΛΛΕΥΣΗ ΔΑΣΩΝ '2236': ΑΝΕΞΑΡΤΗΣΙΑ ΤΗΣ ΕΚΚΛΗΣΙΑΣ ΤΗΣ ΕΛΛΑΔΟΣ '2237': ΤΕΧΝΙΚΑ ΠΤΥΧΙΑ '2238': ΕΠΙΒΑΤΙΚΑ ΑΥΤΟΚΙΝΗΤΑ (ΔΗΜΟΣΙΑΣ ΚΑΙ ΙΔΙΩΤΙΚΗΣ ΧΡΗΣΗΣ) '2239': ΣΥΜΒΑΣΕΙΣ ΒΟΥΛΕΥΤΩΝ '2240': ΟΡΓΑΝΙΣΜΟΣ ΤΩΝ ΔΙΚΑΣΤΗΡΙΩΝ '2241': ΕΚΠΑΙΔΕΥΤΙΚΟΙ ΛΕΙΤΟΥΡΓΟΙ ΕΝ ΓΕΝΕΙ '2242': ΑΡΜΟΔΙΟΤΗΤΑ ΤΕΛΩΝΕΙΑΚΩΝ ΑΡΧΩΝ '2243': ΕΙΔΙΚΑ ΕΦΕΤΕΙΑ '2244': ΑΞΙΩΜΑΤΙΚΟΙ ΑΕΡΟΠΟΡΙΑΣ '2245': ΠΑΝΕΠΙΣΤΗΜΙΑΚΗ ΒΙΒΛΙΟΘΗΚΗ '2246': ΕΠΙΤΡΟΠΗ ΣΥΝΤΑΞΗΣ ΣΧΕΔΙΟΥ ΚΩΔΙΚΑ ΕΡΓΑΣΙΑΣ '2247': ΕΛΟΝΟΣΙΑ '2248': ΝΑΥΛΟΣΥΜΦΩΝΑ '2249': ΣΙΔΗΡΟΔΡΟΜΟΙ ΘΕΣΣΑΛΙΚΟΙ '2250': ΡΑΔΙΟΦΩΝΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '2251': ΠΡΟΩΘΗΣΗ ΓΕΩΡΓΙΚΗΣ ΠΑΡΑΓΩΓΗΣ-ΕΘ.Ι.ΑΓ.Ε '2252': ΕΠΟΧΙΑΚΩΣ ΕΡΓΑΖΟΜΕΝΟΙ ΜΙΣΘΩΤΟΙ '2253': ΔΙΔΑΚΤΙΚΟ ΠΡΟΣΩΠΙΚΟ '2254': ΚΩΔΙΚΑΣ ΚΕΝΤΡΙΚΗΣ, ΠΡΕΣΒΕΥΤΙΚΗΣ ΚΑΙ '2255': ΠΟΛΙΤΙΚΟ ΠΡΟΣΩΠΙΚΟ ΥΠΟΥΡΓΕΙΟΥ ΕΘΝΙΚΗΣ ΑΜΥΝΑΣ '2256': ΔΙΠΛΩΜΑΤΑ ΕΥΡΕΣΙΤΕΧΝΙΑΣ '2257': ΣΩΜΑΤΕΙΑ ΓΕΩΡΓΙΚΩΝ ΕΡΓΑΤΩΝ '2258': ΚΩΔΙΚΑΣ ΠΕΡΙ ΕΙΣΠΡΑΞΕΩΣ ΔΗΜΟΣΙΩΝ ΕΣΟΔΩΝ '2259': ΤΡΑΠΕΖΟΓΡΑΜΜΑΤΙΑ '2260': ΠΡΟΜΗΘΕΥΤΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ Ε.Β.Α '2261': ΕΛΕΓΧΟΣ ΑΣΦΑΛΕΙΑΣ ΑΥΤΟΚΙΝΗΤΩΝΚΕΝΤΡΑ ΤΕΧΝΙΚΟΥ ΕΛΕΓΧΟΥ ΟΧΗΜΑΤΩΝ (Κ.Τ.Ε.Ο.) '2262': ΕΞΑΓΩΓΗ ΤΥΡΟΥ '2263': ΝΑΥΤΙΛΙΑΚΟ ΣΥΝΑΛΛΑΓΜΑ '2264': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΗΛΕΤΡΟΤΕΧΝΙΤΩΝ ΕΛΛΑΔΟΣ (T.E.A.H.E.) '2265': ΜΙΣΘΟΙ ΣΤΡΑΤΙΩΤΙΚΩΝ ΚΑΙ ΠΡΟΣΑΥΞΗΣΕΙΣ '2266': ΑΣΤΙΚΟΣ ΚΩΔΙΚΑΣ '2267': ΜΕ ΤΙΣ ΗΝΩΜΕΝΕΣ ΠΟΛΙΤΕΙΕΣ ΑΜΕΡΙΚΗΣ '2268': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ Ο.Τ.Ε. (Τ.Α.Π.-Ο.Τ.Ε.) '2269': ΜΑΙΕΣ '2270': ΦΥΓΟΔΙΚΙΑ '2271': ΟΡΓΑΝΙΣΜΟΣ ΞΕΝΟΔΟΧΕΙΑΚΗΣ ΠΙΣΤΗΣ '2272': ΔΗΜΟΤΙΚΟΙ ΣΤΡΑΤΟΛΟΓΟΙ '2273': ΑΝΩΤΑΤΟ ΔΙΚΑΣΤΙΚΟ ΣΥΜΒΟΥΛΙΟ '2274': ΙΣΤΟΡΙΚΟ ΑΡΧΕΙΟ ΚΡΗΤΗΣ '2275': ΕΛΛΗΝΙΚΗ ΘΑΛΑΣΣΙΑ ΄ΕΝΩΣΗ '2276': ΕΚΠΟΙΗΣΕΙΣ ΚΑΙ ΕΚΜΙΣΘΩΣΕΙΣ '2277': ΤΑΧΥΔΡΟΜΙΚΕΣ ΕΠΙΤΑΓΕΣ '2278': ΥΠΗΡΕΣΙΑ ΜΗΤΡΩΟΥ '2279': ΔΙΑΦΟΡΑ ΟΙΚΟΝΟΜΙΚΑ ΘΕΜΑΤΑ '2280': ΕΝΔΙΚΑ ΜΕΣΑ '2281': ΤΕΛΗ ΑΕΡΟΠΟΡΙΚΩΝ ΤΑΞΙΔΙΩΝ '2282': ΜΕ ΤΗΝ ΑΙΓΥΠΤΟ '2283': ΔΙΑΦΟΡΕΣ ΒΙΒΛΙΟΘΗΚΕΣ '2284': ΚΕΝΤΡΙΚΗ ΥΠΗΡΕΣΙΑ splits: - name: train num_bytes: 216757887 num_examples: 28536 - name: test num_bytes: 71533786 num_examples: 9516 - name: validation num_bytes: 68824457 num_examples: 9511 download_size: 45606292 dataset_size: 357116130 --- # Dataset Card for Greek Legal Code ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://doi.org/10.5281/zenodo.5528002 - Repository: https://github.com/christospi/glc-nllp-21 - Paper: TBA - Leaderboard: N/A - Point of Contact: [Christos Papaloukas](mailto:christospap@di.uoa.gr) ### Dataset Summary Greek_Legal_Code (GLC) is a dataset consisting of approx. 47k legal resources from Greek legislation. The origin of GLC is “Permanent Greek Legislation Code - Raptarchis”, a collection of Greek legislative documents classified into multi-level (from broader to more specialized) categories. Topics GLC consists of 47 legislative volumes and each volume corresponds to a main thematic topic. Each volume is divided into thematic sub categories which are called chapters and subsequently, each chapter breaks down to subjects which contain the legal resources. The total number of chapters is 389 while the total number of subjects is 2285, creating an interlinked thematic hierarchy. So, for the upper thematic level (volume) GLC has 47 classes. For the next thematic level (chapter) GLC offers 389 classes and for the inner and last thematic level (subject), GLC has 2285 classes. GLC classes are divided into three categories for each thematic level: frequent classes, which occur in more than 10 training documents and can be found in all three subsets (training, development and test); few-shot classes which appear in 1 to 10 training documents and also appear in the documents of the development and test sets, and zero-shot classes which appear in the development and/or test, but not in the training documents. ### Supported Tasks and Leaderboards The dataset supports: Multi-class Text Classification: Given the text of a document, a model predicts the corresponding class. Few-shot and Zero-shot learning: As already noted, the classes can be divided into three groups: frequent, few-shot, and zero- shot, depending on whether they were assigned to more than 10, fewer than 10 but at least one, or no training documents, respectively. \| Level \| Total \| Frequent \| Few-Shot (<10) \| Zero-Shot \| \|---\|---\|---\|---\|---\| \|Volume\|47\|47\|0\|0\| \|Chapter\|389\|333\|53\|3\| \|Subject\|2285\|712\|1431\|142\| ### Languages All documents are written in Greek. ## Dataset Structure ### Data Instances ```json { "text": "179. ΑΠΟΦΑΣΗ ΥΠΟΥΡΓΟΥ ΜΕΤΑΦΟΡΩΝ ΚΑΙ ΕΠΙΚΟΙΝΩΝΙΩΝ Αριθ. Β-οικ. 68425/4765 της 2/17 Νοεμ. 2000 (ΦΕΚ Β΄ 1404) Τροποποίηση της 42000/2030/81 κοιν. απόφασης του Υπουργού Συγκοινωνιών «Κωδικοποίηση και συμπλήρωση καν. Αποφάσεων» που εκδόθηκαν κατ’ εξουσιοδότηση του Ν.Δ. 102/73 «περί οργανώσεως των δια λεωφορείων αυτοκινήτων εκτελουμένων επιβατικών συγκοινωνιών». ", "volume": 24, # "ΣΥΓΚΟΙΝΩΝΙΕΣ" } ``` ### Data Fields The following data fields are provided for documents (`train`, `dev`, `test`): `text`: (str) The full content of each document, which is represented by its `header` and `articles` (i.e., the `main_body`).\ `label`: (class label): Depending on the configurarion, the volume/chapter/subject of the document. For volume-level class it belongs to specifically: ["ΚΟΙΝΩΝΙΚΗ ΠΡΟΝΟΙΑ", "ΓΕΩΡΓΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΡΑΔΙΟΦΩΝΙΑ ΚΑΙ ΤΥΠΟΣ", "ΒΙΟΜΗΧΑΝΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΥΓΕΙΟΝΟΜΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΠΟΛΕΜΙΚΟ ΝΑΥΤΙΚΟ", "ΤΑΧΥΔΡΟΜΕΙΑ - ΤΗΛΕΠΙΚΟΙΝΩΝΙΕΣ", "ΔΑΣΗ ΚΑΙ ΚΤΗΝΟΤΡΟΦΙΑ", "ΕΛΕΓΚΤΙΚΟ ΣΥΝΕΔΡΙΟ ΚΑΙ ΣΥΝΤΑΞΕΙΣ", "ΠΟΛΕΜΙΚΗ ΑΕΡΟΠΟΡΙΑ", "ΝΟΜΙΚΑ ΠΡΟΣΩΠΑ ΔΗΜΟΣΙΟΥ ΔΙΚΑΙΟΥ", "ΝΟΜΟΘΕΣΙΑ ΑΝΩΝΥΜΩΝ ΕΤΑΙΡΕΙΩΝ ΤΡΑΠΕΖΩΝ ΚΑΙ ΧΡΗΜΑΤΙΣΤΗΡΙΩΝ", "ΠΟΛΙΤΙΚΗ ΑΕΡΟΠΟΡΙΑ", "ΕΜΜΕΣΗ ΦΟΡΟΛΟΓΙΑ", "ΚΟΙΝΩΝΙΚΕΣ ΑΣΦΑΛΙΣΕΙΣ", "ΝΟΜΟΘΕΣΙΑ ΔΗΜΩΝ ΚΑΙ ΚΟΙΝΟΤΗΤΩΝ", "ΝΟΜΟΘΕΣΙΑ ΕΠΙΜΕΛΗΤΗΡΙΩΝ ΣΥΝΕΤΑΙΡΙΣΜΩΝ ΚΑΙ ΣΩΜΑΤΕΙΩΝ", "ΔΗΜΟΣΙΑ ΕΡΓΑ", "ΔΙΟΙΚΗΣΗ ΔΙΚΑΙΟΣΥΝΗΣ", "ΑΣΦΑΛΙΣΤΙΚΑ ΤΑΜΕΙΑ", "ΕΚΚΛΗΣΙΑΣΤΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΕΚΠΑΙΔΕΥΤΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΔΗΜΟΣΙΟ ΛΟΓΙΣΤΙΚΟ", "ΤΕΛΩΝΕΙΑΚΗ ΝΟΜΟΘΕΣΙΑ", "ΣΥΓΚΟΙΝΩΝΙΕΣ", "ΕΘΝΙΚΗ ΑΜΥΝΑ", "ΣΤΡΑΤΟΣ ΞΗΡΑΣ", "ΑΓΟΡΑΝΟΜΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΔΗΜΟΣΙΟΙ ΥΠΑΛΛΗΛΟΙ", "ΠΕΡΙΟΥΣΙΑ ΔΗΜΟΣΙΟΥ ΚΑΙ ΝΟΜΙΣΜΑ", "ΟΙΚΟΝΟΜΙΚΗ ΔΙΟΙΚΗΣΗ", "ΛΙΜΕΝΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΑΣΤΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΠΟΛΙΤΙΚΗ ΔΙΚΟΝΟΜΙΑ", "ΔΙΠΛΩΜΑΤΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΔΙΟΙΚΗΤΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΑΜΕΣΗ ΦΟΡΟΛΟΓΙΑ", "ΤΥΠΟΣ ΚΑΙ ΤΟΥΡΙΣΜΟΣ", "ΕΘΝΙΚΗ ΟΙΚΟΝΟΜΙΑ", "ΑΣΤΥΝΟΜΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΑΓΡΟΤΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΕΡΓΑΤΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΠΟΙΝΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΕΜΠΟΡΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΕΠΙΣΤΗΜΕΣ ΚΑΙ ΤΕΧΝΕΣ", "ΕΜΠΟΡΙΚΗ ΝΑΥΤΙΛΙΑ", "ΣΥΝΤΑΓΜΑΤΙΚΗ ΝΟΜΟΘΕΣΙΑ" ] \ The labels can also be a the chapter-level or subject-level class it belongs to. Some chapter labels are omitted due to size (389 classes). Some subject labels are also omitted due to size (2285 classes). ### Data Splits \| Split \| No of Documents \| Avg. words \| \| ------------------- \| ------------------------------------ \| --- \| \| Train \| 28,536 \| 600 \| \|Development \| 9,511 \| 574 \| \|Test \| 9,516 \| 595 \| ## Dataset Creation ### Curation Rationale The dataset was curated by Papaloukas et al. (2021) with the hope to support and encourage further research in NLP for the Greek language. ### Source Data #### Initial Data Collection and Normalization The ``Permanent Greek Legislation Code - Raptarchis`` is a thorough catalogue of Greek legislation since the creation of the Greek state in 1834 until 2015. It includes Laws, Royal and Presidential Decrees, Regulations and Decisions, retrieved from the Official Government Gazette, where Greek legislation is published. This collection is one of the official, publicly available sources of classified Greek legislation suitable for classification tasks. Currently, the original catalogue is publicly offered in MS Word (.doc) format through the portal e-Themis, the legal database and management service of it, under the administration of the Ministry of the Interior (Affairs). E-Themis is primarily focused on providing legislation on a multitude of predefined thematic categories, as described in the catalogue. The main goal is to help users find legislation of interest using the thematic index. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information The dataset does not include personal or sensitive information. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Papaloukas et al. (2021) ### Licensing Information [More Information Needed] ### Citation Information Christos Papaloukas, Ilias Chalkidis, Konstantinos Athinaios, Despina-Athanasia Pantazi and Manolis Koubarakis. Multi-granular Legal Topic Classification on Greek Legislation. Proceedings of the 3rd Natural Legal Language Processing (NLLP) Workshop, Punta Cana, Dominican Republic, 2021 ``` @inproceedings{papaloukas-etal-2021-glc, title = "Multi-granular Legal Topic Classification on Greek Legislation", author = "Papaloukas, Christos and Chalkidis, Ilias and Athinaios, Konstantinos and Pantazi, Despina-Athanasia and Koubarakis, Manolis", booktitle = "Proceedings of the 3rd Natural Legal Language Processing (NLLP) Workshop", year = "2021", address = "Punta Cana, Dominican Republic", publisher = "", url = "https://arxiv.org/abs/2109.15298", doi = "", pages = "" } ``` ### Contributions Thanks to [@christospi](https://github.com/christospi) for adding this dataset.
guardian_authorship	--- annotations_creators: - found language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification - topic-classification pretty_name: GuardianAuthorship dataset_info: - config_name: cross_topic_1 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 677054 num_examples: 112 - name: test num_bytes: 1283126 num_examples: 207 - name: validation num_bytes: 374390 num_examples: 62 download_size: 3100749 dataset_size: 2334570 - config_name: cross_genre_1 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 406144 num_examples: 63 - name: test num_bytes: 1657512 num_examples: 269 - name: validation num_bytes: 677054 num_examples: 112 download_size: 3100749 dataset_size: 2740710 - config_name: cross_topic_2 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 677054 num_examples: 112 - name: test num_bytes: 1104764 num_examples: 179 - name: validation num_bytes: 552752 num_examples: 90 download_size: 3100749 dataset_size: 2334570 - config_name: cross_topic_3 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 677054 num_examples: 112 - name: test num_bytes: 927138 num_examples: 152 - name: validation num_bytes: 730378 num_examples: 117 download_size: 3100749 dataset_size: 2334570 - config_name: cross_topic_4 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 374390 num_examples: 62 - name: test num_bytes: 1283126 num_examples: 207 - name: validation num_bytes: 677054 num_examples: 112 download_size: 3100749 dataset_size: 2334570 - config_name: cross_topic_5 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 374390 num_examples: 62 - name: test num_bytes: 1407428 num_examples: 229 - name: validation num_bytes: 552752 num_examples: 90 download_size: 3100749 dataset_size: 2334570 - config_name: cross_topic_6 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 374390 num_examples: 62 - name: test num_bytes: 1229802 num_examples: 202 - name: validation num_bytes: 730378 num_examples: 117 download_size: 3100749 dataset_size: 2334570 - config_name: cross_topic_7 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 552752 num_examples: 90 - name: test num_bytes: 1104764 num_examples: 179 - name: validation num_bytes: 677054 num_examples: 112 download_size: 3100749 dataset_size: 2334570 - config_name: cross_topic_8 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 552752 num_examples: 90 - name: test num_bytes: 1407428 num_examples: 229 - name: validation num_bytes: 374390 num_examples: 62 download_size: 3100749 dataset_size: 2334570 - config_name: cross_topic_9 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 552752 num_examples: 90 - name: test num_bytes: 1051440 num_examples: 174 - name: validation num_bytes: 730378 num_examples: 117 download_size: 3100749 dataset_size: 2334570 - config_name: cross_topic_10 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 730378 num_examples: 117 - name: test num_bytes: 927138 num_examples: 152 - name: validation num_bytes: 677054 num_examples: 112 download_size: 3100749 dataset_size: 2334570 - config_name: cross_topic_11 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 730378 num_examples: 117 - name: test num_bytes: 1229802 num_examples: 202 - name: validation num_bytes: 374390 num_examples: 62 download_size: 3100749 dataset_size: 2334570 - config_name: cross_topic_12 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 730378 num_examples: 117 - name: test num_bytes: 1051440 num_examples: 174 - name: validation num_bytes: 552752 num_examples: 90 download_size: 3100749 dataset_size: 2334570 - config_name: cross_genre_2 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 406144 num_examples: 63 - name: test num_bytes: 1960176 num_examples: 319 - name: validation num_bytes: 374390 num_examples: 62 download_size: 3100749 dataset_size: 2740710 - config_name: cross_genre_3 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 406144 num_examples: 63 - name: test num_bytes: 1781814 num_examples: 291 - name: validation num_bytes: 552752 num_examples: 90 download_size: 3100749 dataset_size: 2740710 - config_name: cross_genre_4 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 406144 num_examples: 63 - name: test num_bytes: 1604188 num_examples: 264 - name: validation num_bytes: 730378 num_examples: 117 download_size: 3100749 dataset_size: 2740710 --- # Dataset Card for "guardian_authorship" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [http://www.icsd.aegean.gr/lecturers/stamatatos/papers/JLP2013.pdf](http://www.icsd.aegean.gr/lecturers/stamatatos/papers/JLP2013.pdf) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 49.61 MB - Size of the generated dataset: 38.98 MB - Total amount of disk used: 88.59 MB ### Dataset Summary A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. 1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ). 2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W). 3- The same-topic/genre scenario is created by grouping all the datasts as follows. For ex., to use same_topic and split the data 60-40 use: train_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", split='train[:60%]+validation[:60%]+test[:60%]') tests_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", split='train[-40%:]+validation[-40%:]+test[-40%:]') IMPORTANT: train+validation+test[:60%] will generate the wrong splits because the data is imbalanced * See https://huggingface.co/docs/datasets/splits.html for detailed/more examples ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### cross_genre_1 - Size of downloaded dataset files: 3.10 MB - Size of the generated dataset: 2.74 MB - Total amount of disk used: 5.84 MB An example of 'train' looks as follows. ``` { "article": "File 1a\n", "author": 0, "topic": 4 } ``` #### cross_genre_2 - Size of downloaded dataset files: 3.10 MB - Size of the generated dataset: 2.74 MB - Total amount of disk used: 5.84 MB An example of 'validation' looks as follows. ``` { "article": "File 1a\n", "author": 0, "topic": 1 } ``` #### cross_genre_3 - Size of downloaded dataset files: 3.10 MB - Size of the generated dataset: 2.74 MB - Total amount of disk used: 5.84 MB An example of 'validation' looks as follows. ``` { "article": "File 1a\n", "author": 0, "topic": 2 } ``` #### cross_genre_4 - Size of downloaded dataset files: 3.10 MB - Size of the generated dataset: 2.74 MB - Total amount of disk used: 5.84 MB An example of 'validation' looks as follows. ``` { "article": "File 1a\n", "author": 0, "topic": 3 } ``` #### cross_topic_1 - Size of downloaded dataset files: 3.10 MB - Size of the generated dataset: 2.34 MB - Total amount of disk used: 5.43 MB An example of 'validation' looks as follows. ``` { "article": "File 1a\n", "author": 0, "topic": 1 } ``` ### Data Fields The data fields are the same among all splits. #### cross_genre_1 - `author`: a classification label, with possible values including `catherinebennett` (0), `georgemonbiot` (1), `hugoyoung` (2), `jonathanfreedland` (3), `martinkettle` (4). - `topic`: a classification label, with possible values including `Politics` (0), `Society` (1), `UK` (2), `World` (3), `Books` (4). - `article`: a `string` feature. #### cross_genre_2 - `author`: a classification label, with possible values including `catherinebennett` (0), `georgemonbiot` (1), `hugoyoung` (2), `jonathanfreedland` (3), `martinkettle` (4). - `topic`: a classification label, with possible values including `Politics` (0), `Society` (1), `UK` (2), `World` (3), `Books` (4). - `article`: a `string` feature. #### cross_genre_3 - `author`: a classification label, with possible values including `catherinebennett` (0), `georgemonbiot` (1), `hugoyoung` (2), `jonathanfreedland` (3), `martinkettle` (4). - `topic`: a classification label, with possible values including `Politics` (0), `Society` (1), `UK` (2), `World` (3), `Books` (4). - `article`: a `string` feature. #### cross_genre_4 - `author`: a classification label, with possible values including `catherinebennett` (0), `georgemonbiot` (1), `hugoyoung` (2), `jonathanfreedland` (3), `martinkettle` (4). - `topic`: a classification label, with possible values including `Politics` (0), `Society` (1), `UK` (2), `World` (3), `Books` (4). - `article`: a `string` feature. #### cross_topic_1 - `author`: a classification label, with possible values including `catherinebennett` (0), `georgemonbiot` (1), `hugoyoung` (2), `jonathanfreedland` (3), `martinkettle` (4). - `topic`: a classification label, with possible values including `Politics` (0), `Society` (1), `UK` (2), `World` (3), `Books` (4). - `article`: a `string` feature. ### Data Splits \| name \|train\|validation\|test\| \|-------------\|----:\|---------:\|---:\| \|cross_genre_1\| 63\| 112\| 269\| \|cross_genre_2\| 63\| 62\| 319\| \|cross_genre_3\| 63\| 90\| 291\| \|cross_genre_4\| 63\| 117\| 264\| \|cross_topic_1\| 112\| 62\| 207\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{article, author = {Stamatatos, Efstathios}, year = {2013}, month = {01}, pages = {421-439}, title = {On the robustness of authorship attribution based on character n-gram features}, volume = {21}, journal = {Journal of Law and Policy} } @inproceedings{stamatatos2017authorship, title={Authorship attribution using text distortion}, author={Stamatatos, Efstathios}, booktitle={Proc. of the 15th Conf. of the European Chapter of the Association for Computational Linguistics}, volume={1} pages={1138--1149}, year={2017} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@eltoto1219](https://github.com/eltoto1219), [@malikaltakrori](https://github.com/malikaltakrori) for adding this dataset.
gutenberg_time	--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification paperswithcode_id: gutenberg-time-dataset pretty_name: the Gutenberg Time dataset dataset_info: features: - name: guten_id dtype: string - name: hour_reference dtype: string - name: time_phrase dtype: string - name: is_ambiguous dtype: bool_ - name: time_pos_start dtype: int64 - name: time_pos_end dtype: int64 - name: tok_context dtype: string config_name: gutenberg splits: - name: train num_bytes: 108550391 num_examples: 120694 download_size: 35853781 dataset_size: 108550391 --- # Dataset Card for the Gutenberg Time dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - [Repository](https://github.com/allenkim/what-time-is-it) - [Paper](https://arxiv.org/abs/2011.04124) ### Dataset Summary A clean data resource containing all explicit time references in a dataset of 52,183 novels whose full text is available via Project Gutenberg. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Time-of-the-day classification from excerpts. ## Dataset Structure ### Data Instances ``` { "guten_id": 28999, "hour_reference": 12, "time_phrase": "midday", "is_ambiguous": False, "time_pos_start": 133, "time_pos_end": 134, "tok_context": "Sorrows and trials she had had in plenty in her life , but these the sweetness of her nature had transformed , so that from being things difficult to bear , she had built up with them her own character . Sorrow had increased her own power of sympathy ; out of trials she had learnt patience ; and failure and the gradual sinking of one she had loved into the bottomless slough of evil habit had but left her with an added dower of pity and tolerance . So the past had no sting left , and if iron had ever entered into her soul it now but served to make it strong . She was still young , too ; it was not near sunset with her yet , nor even midday , and the future that , humanly speaking , she counted to be hers was almost dazzling in its brightness . For love had dawned for her again , and no uncertain love , wrapped in the mists of memory , but one that had ripened through liking and friendship and intimacy into the authentic glory . He was in England , too ; she was going back to him . And before very long she would never go away from him again ." } ``` ### Data Fields ``` guten_id - Gutenberg ID number hour_reference - hour from 0 to 23 time_phrase - the phrase corresponding to the referenced hour is_ambiguous - boolean whether it is clear whether time is AM or PM time_pos_start - token position where time_phrase begins time_pos_end - token position where time_phrase ends (exclusive) tok_context - context in which time_phrase appears as space-separated tokens ``` ### Data Splits No data splits. ## Dataset Creation ### Curation Rationale The flow of time is an indispensable guide for our actions, and provides a framework in which to see a logical progression of events. Just as in real life,the clock provides the background against which literary works play out: when characters wake, eat,and act. In most works of fiction, the events of the story take place during recognizable time periods over the course of the day. Recognizing a story’s flow through time is essential to understanding the text.In this paper, we try to capture the flow of time through novels by attempting to recognize what time of day each event in the story takes place at. ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? Novel authors. ### Annotations #### Annotation process Manually annotated. #### Who are the annotators? Two of the authors. ### Personal and Sensitive Information No Personal or sensitive information. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Allen Kim, Charuta Pethe and Steven Skiena, Stony Brook University ### Licensing Information [More Information Needed] ### Citation Information ``` @misc{kim2020time, title={What time is it? Temporal Analysis of Novels}, author={Allen Kim and Charuta Pethe and Steven Skiena}, year={2020}, eprint={2011.04124}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@TevenLeScao](https://github.com/TevenLeScao) for adding this dataset.
hans	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - natural-language-inference paperswithcode_id: hans pretty_name: Heuristic Analysis for NLI Systems dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': non-entailment - name: parse_premise dtype: string - name: parse_hypothesis dtype: string - name: binary_parse_premise dtype: string - name: binary_parse_hypothesis dtype: string - name: heuristic dtype: string - name: subcase dtype: string - name: template dtype: string config_name: plain_text splits: - name: train num_bytes: 15916371 num_examples: 30000 - name: validation num_bytes: 15893137 num_examples: 30000 download_size: 30947358 dataset_size: 31809508 --- # Dataset Card for "hans" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://github.com/tommccoy1/hans](https://github.com/tommccoy1/hans) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 30.94 MB - Size of the generated dataset: 31.81 MB - Total amount of disk used: 62.76 MB ### Dataset Summary The HANS dataset is an NLI evaluation set that tests specific hypotheses about invalid heuristics that NLI models are likely to learn. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### plain_text - Size of downloaded dataset files: 30.94 MB - Size of the generated dataset: 31.81 MB - Total amount of disk used: 62.76 MB An example of 'train' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `non-entailment` (1). - `parse_premise`: a `string` feature. - `parse_hypothesis`: a `string` feature. - `binary_parse_premise`: a `string` feature. - `binary_parse_hypothesis`: a `string` feature. - `heuristic`: a `string` feature. - `subcase`: a `string` feature. - `template`: a `string` feature. ### Data Splits \| name \|train\|validation\| \|----------\|----:\|---------:\| \|plain_text\|30000\| 30000\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{DBLP:journals/corr/abs-1902-01007, author = {R. Thomas McCoy and Ellie Pavlick and Tal Linzen}, title = {Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference}, journal = {CoRR}, volume = {abs/1902.01007}, year = {2019}, url = {http://arxiv.org/abs/1902.01007}, archivePrefix = {arXiv}, eprint = {1902.01007}, timestamp = {Tue, 21 May 2019 18:03:36 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1902-01007.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@TevenLeScao](https://github.com/TevenLeScao), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
hansards	--- paperswithcode_id: null pretty_name: hansards dataset_info: - config_name: senate features: - name: fr dtype: string - name: en dtype: string splits: - name: test num_bytes: 5711686 num_examples: 25553 - name: train num_bytes: 40324278 num_examples: 182135 download_size: 15247360 dataset_size: 46035964 - config_name: house features: - name: fr dtype: string - name: en dtype: string splits: - name: test num_bytes: 22906629 num_examples: 122290 - name: train num_bytes: 191459584 num_examples: 947969 download_size: 67584000 dataset_size: 214366213 --- # Dataset Card for "hansards" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://www.isi.edu/natural-language/download/hansard/](https://www.isi.edu/natural-language/download/hansard/) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 82.83 MB - Size of the generated dataset: 260.40 MB - Total amount of disk used: 343.23 MB ### Dataset Summary This release contains 1.3 million pairs of aligned text chunks (sentences or smaller fragments) from the official records (Hansards) of the 36th Canadian Parliament. The complete Hansards of the debates in the House and Senate of the 36th Canadian Parliament, as far as available, were aligned. The corpus was then split into 5 sets of sentence pairs: training (80% of the sentence pairs), two sets of sentence pairs for testing (5% each), and two sets of sentence pairs for final evaluation (5% each). The current release consists of the training and testing sets. The evaluation sets are reserved for future MT evaluation purposes and currently not available. Caveats 1. This release contains only sentence pairs. Even though the order of the sentences is the same as in the original, there may be gaps resulting from many-to-one, many-to-many, or one-to-many alignments that were filtered out. Therefore, this release may not be suitable for discourse-related research. 2. Neither the sentence splitting nor the alignments are perfect. In particular, watch out for pairs that differ considerably in length. You may want to filter these out before you do any statistical training. The alignment of the Hansards was performed as part of the ReWrite project under funding from the DARPA TIDES program. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### house - Size of downloaded dataset files: 67.58 MB - Size of the generated dataset: 214.37 MB - Total amount of disk used: 281.95 MB An example of 'train' looks as follows. ``` { "en": "Mr. Walt Lastewka (Parliamentary Secretary to Minister of Industry, Lib.):", "fr": "M. Walt Lastewka (secrétaire parlementaire du ministre de l'Industrie, Lib.):" } ``` #### senate - Size of downloaded dataset files: 15.25 MB - Size of the generated dataset: 46.03 MB - Total amount of disk used: 61.28 MB An example of 'train' looks as follows. ``` { "en": "Mr. Walt Lastewka (Parliamentary Secretary to Minister of Industry, Lib.):", "fr": "M. Walt Lastewka (secrétaire parlementaire du ministre de l'Industrie, Lib.):" } ``` ### Data Fields The data fields are the same among all splits. #### house - `fr`: a `string` feature. - `en`: a `string` feature. #### senate - `fr`: a `string` feature. - `en`: a `string` feature. ### Data Splits \| name \|train \| test \| \|------\|-----:\|-----:\| \|house \|947969\|122290\| \|senate\|182135\| 25553\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf), [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
hard	--- annotations_creators: - found language_creators: - found language: - ar license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification paperswithcode_id: hard pretty_name: Hotel Arabic-Reviews Dataset dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': '1' '1': '2' '2': '3' '3': '4' '4': '5' config_name: plain_text splits: - name: train num_bytes: 27507085 num_examples: 105698 download_size: 8508677 dataset_size: 27507085 --- # Dataset Card for Hard ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Hard](https://github.com/elnagara/HARD-Arabic-Dataset) - Repository: [Hard](https://github.com/elnagara/HARD-Arabic-Dataset) - Paper: [Hotel Arabic-Reviews Dataset Construction for Sentiment Analysis Applications](https://link.springer.com/chapter/10.1007/978-3-319-67056-0_3) - Point of Contact: [Ashraf Elnagar](ashraf@sharjah.ac.ae) ### Dataset Summary This dataset contains 93,700 hotel reviews in Arabic language.The hotel reviews were collected from Booking.com website during June/July 2016.The reviews are expressed in Modern Standard Arabic as well as dialectal Arabic.The following table summarize some tatistics on the HARD Dataset. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset is based on Arabic. ## Dataset Structure ### Data Instances A typical data point comprises a rating from 1 to 5 for hotels. ### Data Fields [More Information Needed] ### Data Splits The dataset is not split. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ### Contributions Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) for adding this dataset.
harem	--- annotations_creators: - expert-generated language_creators: - found language: - pt license: - unknown multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: HAREM dataset_info: - config_name: default features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PESSOA '2': I-PESSOA '3': B-ORGANIZACAO '4': I-ORGANIZACAO '5': B-LOCAL '6': I-LOCAL '7': B-TEMPO '8': I-TEMPO '9': B-VALOR '10': I-VALOR '11': B-ABSTRACCAO '12': I-ABSTRACCAO '13': B-ACONTECIMENTO '14': I-ACONTECIMENTO '15': B-COISA '16': I-COISA '17': B-OBRA '18': I-OBRA '19': B-OUTRO '20': I-OUTRO splits: - name: train num_bytes: 1506373 num_examples: 121 - name: test num_bytes: 1062714 num_examples: 128 - name: validation num_bytes: 51318 num_examples: 8 download_size: 1887281 dataset_size: 2620405 - config_name: selective features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PESSOA '2': I-PESSOA '3': B-ORGANIZACAO '4': I-ORGANIZACAO '5': B-LOCAL '6': I-LOCAL '7': B-TEMPO '8': I-TEMPO '9': B-VALOR '10': I-VALOR splits: - name: train num_bytes: 1506373 num_examples: 121 - name: test num_bytes: 1062714 num_examples: 128 - name: validation num_bytes: 51318 num_examples: 8 download_size: 1715873 dataset_size: 2620405 --- # Dataset Card for HAREM ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [HAREM homepage](https://www.linguateca.pt/primeiroHAREM/harem_coleccaodourada_en.html) - Repository: [HAREM repository](https://www.linguateca.pt/primeiroHAREM/harem_coleccaodourada_en.html) - Paper: [HAREM: An Advanced NER Evaluation Contest for Portuguese](http://comum.rcaap.pt/bitstream/10400.26/76/1/SantosSecoCardosoVilelaLREC2006.pdf) - Point of Contact: [Diana Santos](mailto:diana.santos@sintef.no) ### Dataset Summary The HAREM is a Portuguese language corpus commonly used for Named Entity Recognition tasks. It includes about 93k words, from 129 different texts, from several genres, and language varieties. The split of this dataset version follows the division made by [1], where 7% HAREM documents are the validation set and the miniHAREM corpus (with about 65k words) is the test set. There are two versions of the dataset set, a version that has a total of 10 different named entity classes (Person, Organization, Location, Value, Date, Title, Thing, Event, Abstraction, and Other) and a "selective" version with only 5 classes (Person, Organization, Location, Value, and Date). It's important to note that the original version of the HAREM dataset has 2 levels of NER details, namely "Category" and "Sub-type". The dataset version processed here ONLY USE the "Category" level of the original dataset. [1] Souza, Fábio, Rodrigo Nogueira, and Roberto Lotufo. "BERTimbau: Pretrained BERT Models for Brazilian Portuguese." Brazilian Conference on Intelligent Systems. Springer, Cham, 2020. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Portuguese ## Dataset Structure ### Data Instances ``` { "id": "HAREM-871-07800", "ner_tags": [3, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, ], "tokens": [ "Abraço", "Página", "Principal", "ASSOCIAÇÃO", "DE", "APOIO", "A", "PESSOAS", "COM", "VIH", "/", "SIDA" ] } ``` ### Data Fields - `id`: id of the sample - `tokens`: the tokens of the example text - `ner_tags`: the NER tags of each token The NER tags correspond to this list: ``` "O", "B-PESSOA", "I-PESSOA", "B-ORGANIZACAO", "I-ORGANIZACAO", "B-LOCAL", "I-LOCAL", "B-TEMPO", "I-TEMPO", "B-VALOR", "I-VALOR", "B-ABSTRACCAO", "I-ABSTRACCAO", "B-ACONTECIMENTO", "I-ACONTECIMENTO", "B-COISA", "I-COISA", "B-OBRA", "I-OBRA", "B-OUTRO", "I-OUTRO" ``` The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. ### Data Splits The data is split into train, validation and test set for each of the two versions (default and selective). The split sizes are as follow: \| Train \| Val \| Test \| \| ------ \| ----- \| ---- \| \| 121 \| 8 \| 128 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{santos2006harem, title={Harem: An advanced ner evaluation contest for portuguese}, author={Santos, Diana and Seco, Nuno and Cardoso, Nuno and Vilela, Rui}, booktitle={quot; In Nicoletta Calzolari; Khalid Choukri; Aldo Gangemi; Bente Maegaard; Joseph Mariani; Jan Odjik; Daniel Tapias (ed) Proceedings of the 5 th International Conference on Language Resources and Evaluation (LREC'2006)(Genoa Italy 22-28 May 2006)}, year={2006} } ``` ### Contributions Thanks to [@jonatasgrosman](https://github.com/jonatasgrosman) for adding this dataset.
has_part	--- annotations_creators: - machine-generated language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended\|other-Generics-KB task_categories: - text-classification task_ids: - text-scoring paperswithcode_id: haspart-kb pretty_name: hasPart KB tags: - Meronym-Prediction dataset_info: features: - name: arg1 dtype: string - name: arg2 dtype: string - name: score dtype: float64 - name: wikipedia_primary_page sequence: string - name: synset sequence: string splits: - name: train num_bytes: 4363417 num_examples: 49848 download_size: 7437382 dataset_size: 4363417 --- # Dataset Card for [HasPart] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://allenai.org/data/haspartkb - Repository: - Paper: https://arxiv.org/abs/2006.07510 - Leaderboard: - Point of Contact: Peter Clark <peterc@allenai.org> ### Dataset Summary This dataset is a new knowledge-base (KB) of hasPart relationships, extracted from a large corpus of generic statements. Complementary to other resources available, it is the first which is all three of: accurate (90% precision), salient (covers relationships a person may mention), and has high coverage of common terms (approximated as within a 10 year old’s vocabulary), as well as having several times more hasPart entries than in the popular ontologies ConceptNet and WordNet. In addition, it contains information about quantifiers, argument modifiers, and links the entities to appropriate concepts in Wikipedia and WordNet. ### Supported Tasks and Leaderboards Text Classification / Scoring - meronyms (e.g., `plant` has part `stem`) ### Languages English ## Dataset Structure ### Data Instances [More Information Needed] ``` {'arg1': 'plant', 'arg2': 'stem', 'score': 0.9991798414303377, 'synset': ['wn.plant.n.02', 'wn.stalk.n.02'], 'wikipedia_primary_page': ['Plant']} ``` ### Data Fields - `arg1`, `arg2`: These are the entities of the meronym, i.e., `arg1` _has\_part_ `arg2` - `score`: Meronymic score per the procedure described below - `synset`: Ontological classification from WordNet for the two entities - `wikipedia_primary_page`: Wikipedia page of the entities Note: some examples contain synset / wikipedia info for only one of the entities. ### Data Splits Single training file ## Dataset Creation Our approach to hasPart extraction has five steps: 1. Collect generic sentences from a large corpus 2. Train and apply a RoBERTa model to identify hasPart relations in those sentences 3. Normalize the entity names 4. Aggregate and filter the entries 5. Link the hasPart arguments to Wikipedia pages and WordNet senses Rather than extract knowledge from arbitrary text, we extract hasPart relations from generic sentences, e.g., “Dogs have tails.”, in order to bias the process towards extractions that are general (apply to most members of a category) and salient (notable enough to write down). As a source of generic sentences, we use GenericsKB, a large repository of 3.4M standalone generics previously harvested from a Webcrawl of 1.7B sentences. ### Annotations #### Annotation process For each sentence _S_ in GenericsKB, we identify all noun chunks in the sentence using a noun chunker (spaCy's Doc.noun chunks). Each chunk is a candidate whole or part. Then, for each possible pair, we use a RoBERTa model to classify whether a hasPart relationship exists between them. The input sentence is presented to RoBERTa as a sequence of wordpiece tokens, with the start and end of the candidate hasPart arguments identified using special tokens, e.g.: > `[CLS] [ARG1-B]Some pond snails[ARG1-E] have [ARG2-B]gills[ARG2-E] to breathe in water.` where `[ARG1/2-B/E]` are special tokens denoting the argument boundaries. The `[CLS]` token is projected to two class labels (hasPart/notHasPart), and a softmax layer is then applied, resulting in output probabilities for the class labels. We train with cross-entropy loss. We use RoBERTa-large (24 layers), each with a hidden size of 1024, and 16 attention heads, and a total of 355M parameters. We use the pre-trained weights available with the model and further fine-tune the model parameters by training on our labeled data for 15 epochs. To train the model, we use a hand-annotated set of ∼2k examples. #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information @misc{bhakthavatsalam2020dogs, title={Do Dogs have Whiskers? A New Knowledge Base of hasPart Relations}, author={Sumithra Bhakthavatsalam and Kyle Richardson and Niket Tandon and Peter Clark}, year={2020}, eprint={2006.07510}, archivePrefix={arXiv}, primaryClass={cs.CL} } ### Contributions Thanks to [@jeromeku](https://github.com/jeromeku) for adding this dataset.
hate_offensive	--- annotations_creators: - crowdsourced language_creators: - machine-generated language: - en license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification paperswithcode_id: hate-speech-and-offensive-language pretty_name: HateOffensive tags: - hate-speech-detection dataset_info: features: - name: total_annotation_count dtype: int32 - name: hate_speech_annotations dtype: int32 - name: offensive_language_annotations dtype: int32 - name: neither_annotations dtype: int32 - name: label dtype: class_label: names: '0': hate-speech '1': offensive-language '2': neither - name: tweet dtype: string splits: - name: train num_bytes: 2811298 num_examples: 24783 download_size: 2546446 dataset_size: 2811298 --- # Dataset Card for HateOffensive ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage : https://arxiv.org/abs/1905.12516 - Repository : https://github.com/t-davidson/hate-speech-and-offensive-language - Paper : https://arxiv.org/abs/1905.12516 - Leaderboard : - Point of Contact : trd54 at cornell dot edu ### Dataset Summary ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English (`en`) ## Dataset Structure ### Data Instances ``` { "count": 3, "hate_speech_annotation": 0, "offensive_language_annotation": 0, "neither_annotation": 3, "label": 2, # "neither" "tweet": "!!! RT @mayasolovely: As a woman you shouldn't complain about cleaning up your house. & as a man you should always take the trash out...") } ``` ### Data Fields count: (Integer) number of users who coded each tweet (min is 3, sometimes more users coded a tweet when judgments were determined to be unreliable, hate_speech_annotation: (Integer) number of users who judged the tweet to be hate speech, offensive_language_annotation: (Integer) number of users who judged the tweet to be offensive, neither_annotation: (Integer) number of users who judged the tweet to be neither offensive nor non-offensive, label: (Class Label) integer class label for majority of CF users (0: 'hate-speech', 1: 'offensive-language' or 2: 'neither'), tweet: (string) ### Data Splits This dataset is not splitted, only the train split is available. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information Usernames are not anonymized in the dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information MIT License ### Citation Information @inproceedings{hateoffensive, title = {Automated Hate Speech Detection and the Problem of Offensive Language}, author = {Davidson, Thomas and Warmsley, Dana and Macy, Michael and Weber, Ingmar}, booktitle = {Proceedings of the 11th International AAAI Conference on Web and Social Media}, series = {ICWSM '17}, year = {2017}, location = {Montreal, Canada}, pages = {512-515} } ### Contributions Thanks to [@MisbahKhan789](https://github.com/MisbahKhan789) for adding this dataset.
hate_speech18	--- annotations_creators: - found language_creators: - found language: - en license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - intent-classification paperswithcode_id: hate-speech pretty_name: Hate Speech dataset_info: features: - name: text dtype: string - name: user_id dtype: int64 - name: subforum_id dtype: int64 - name: num_contexts dtype: int64 - name: label dtype: class_label: names: '0': noHate '1': hate '2': idk/skip '3': relation splits: - name: train num_bytes: 1375340 num_examples: 10944 download_size: 3664530 dataset_size: 1375340 train-eval-index: - config: default task: text-classification task_id: multi_class_classification splits: train_split: train col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/Vicomtech/hate-speech-dataset - Repository: https://github.com/Vicomtech/hate-speech-dataset - Paper: https://www.aclweb.org/anthology/W18-51.pdf - Leaderboard: - Point of Contact: ### Dataset Summary These files contain text extracted from Stormfront, a white supremacist forum. A random set of forums posts have been sampled from several subforums and split into sentences. Those sentences have been manually labelled as containing hate speech or not, according to certain annotation guidelines. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - text: the provided sentence - user_id: information to make it possible to re-build the conversations these sentences belong to - subforum_id: information to make it possible to re-build the conversations these sentences belong to - num_contexts: number of previous posts the annotator had to read before making a decision over the category of the sentence - label: hate, noHate, relation (sentence in the post doesn't contain hate speech on their own, but combination of serveral sentences does) or idk/skip (sentences that are not written in English or that don't contain information as to be classified into hate or noHate) ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{gibert2018hate, title = "{Hate Speech Dataset from a White Supremacy Forum}", author = "de Gibert, Ona and Perez, Naiara and Garc{\'\i}a-Pablos, Aitor and Cuadros, Montse", booktitle = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)", month = oct, year = "2018", address = "Brussels, Belgium", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/W18-5102", doi = "10.18653/v1/W18-5102", pages = "11--20", } ``` ### Contributions Thanks to [@czabo](https://github.com/czabo) for adding this dataset.
hate_speech_filipino	--- annotations_creators: - machine-generated language_creators: - crowdsourced language: - tl license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended\|other-twitter-data-philippine-election task_categories: - text-classification task_ids: - sentiment-analysis pretty_name: Hate Speech in Filipino dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 995919 num_examples: 10000 - name: test num_bytes: 995919 num_examples: 10000 - name: validation num_bytes: 424365 num_examples: 4232 download_size: 822927 dataset_size: 2416203 --- # Dataset Card for Hate Speech in Filipino ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Hate Speech Dataset in Filipino homepage](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks) - Repository: [Hate Speech Dataset in Filipino homepage](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks) - Paper: [PCJ paper](https://pcj.csp.org.ph/index.php/pcj/issue/download/29/PCJ%20V14%20N1%20pp1-14%202019) - Leaderboard: - Point of Contact: [Jan Christian Cruz](mailto:jan_christian_cruz@dlsu.edu.ph) ### Dataset Summary Contains 10k tweets (training set) that are labeled as hate speech or non-hate speech. Released with 4,232 validation and 4,232 testing samples. Collected during the 2016 Philippine Presidential Elections. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset is primarily in Filipino, with the addition of some English words commonly used in Filipino vernacular ## Dataset Structure ### Data Instances Sample data: ``` { "text": "Taas ni Mar Roxas ah. KULTONG DILAW NGA NAMAN", "label": 1 } ``` ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale This study seeks to contribute to the filling of this gap through the development of a model that can automate hate speech detection and classification in Philippine election-related tweets. The role of the microblogging site Twitter as a platform for the expression of support and hate during the 2016 Philippine presidential election has been supported in news reports and systematic studies. Thus, the particular question addressed in this paper is: Can existing techniques in language processing and machine learning be applied to detect hate speech in the Philippine election context? ### Source Data #### Initial Data Collection and Normalization The dataset used in this study was a subset of the corpus 1,696,613 tweets crawled by Andrade et al. and posted from November 2015 to May 2016 during the campaign period for the Philippine presidential election. They were culled based on the presence of candidate names (e.g., Binay, Duterte, Poe, Roxas, and Santiago) and election-related hashtags (e.g., #Halalan2016, #Eleksyon2016, and #PiliPinas2016). Data preprocessing was performed to prepare the tweets for feature extraction and classification. It consisted of the following steps: data de-identification, uniform resource locator (URL) removal, special character processing, normalization, hashtag processing, and tokenization. [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [Jan Christian Cruz](mailto:jan_christian_cruz@dlsu.edu.ph) ### Licensing Information [More Information Needed] ### Citation Information @article{Cabasag-2019-hate-speech, title={Hate speech in Philippine election-related tweets: Automatic detection and classification using natural language processing.}, author={Neil Vicente Cabasag, Vicente Raphael Chan, Sean Christian Lim, Mark Edward Gonzales, and Charibeth Cheng}, journal={Philippine Computing Journal}, volume={XIV}, number={1}, month={August}, year={2019} } ### Contributions Thanks to [@anaerobeth](https://github.com/anaerobeth) for adding this dataset.
hate_speech_offensive	--- annotations_creators: - expert-generated - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: [] paperswithcode_id: hate-speech-and-offensive-language pretty_name: Hate Speech and Offensive Language tags: - hate-speech-detection dataset_info: features: - name: count dtype: int64 - name: hate_speech_count dtype: int64 - name: offensive_language_count dtype: int64 - name: neither_count dtype: int64 - name: class dtype: class_label: names: '0': hate speech '1': offensive language '2': neither - name: tweet dtype: string splits: - name: train num_bytes: 3207826 num_examples: 24783 download_size: 2546446 dataset_size: 3207826 train-eval-index: - config: default task: text-classification task_id: multi_class_classification splits: train_split: train col_mapping: tweet: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/t-davidson/hate-speech-and-offensive-language - Repository: https://github.com/t-davidson/hate-speech-and-offensive-language - Paper: https://arxiv.org/abs/1703.04009 - Leaderboard: - Point of Contact: https://docs.google.com/forms/d/e/1FAIpQLSdrPNlfVBlqxun2tivzAtsZaOoPC5YYMocn-xscCgeRakLXHg/viewform?usp=pp_url&entry.1506871634&entry.147453066&entry.1390333885&entry.516829772 ### Dataset Summary An annotated dataset for hate speech and offensive language detection on tweets. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English (`en`) ## Dataset Structure ### Data Instances ``` { "count": 3, "hate_speech_annotation": 0, "offensive_language_annotation": 0, "neither_annotation": 3, "label": 2, # "neither" "tweet": "!!! RT @mayasolovely: As a woman you shouldn't complain about cleaning up your house. & as a man you should always take the trash out...") } ``` ### Data Fields ``` count: (Integer) number of users who coded each tweet (min is 3, sometimes more users coded a tweet when judgments were determined to be unreliable, hate_speech_annotation: (Integer) number of users who judged the tweet to be hate speech, offensive_language_annotation: (Integer) number of users who judged the tweet to be offensive, neither_annotation: (Integer) number of users who judged the tweet to be neither offensive nor non-offensive, label: (Class Label) class label for majority of CF users (0: 'hate-speech', 1: 'offensive-language' or 2: 'neither'), tweet: (string) ``` ### Data Splits This dataset is not splitted, only the train split is available. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information Usernames are not anonymized in the dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information MIT License ### Citation Information @inproceedings{hateoffensive, title = {Automated Hate Speech Detection and the Problem of Offensive Language}, author = {Davidson, Thomas and Warmsley, Dana and Macy, Michael and Weber, Ingmar}, booktitle = {Proceedings of the 11th International AAAI Conference on Web and Social Media}, series = {ICWSM '17}, year = {2017}, location = {Montreal, Canada}, pages = {512-515} } ### Contributions Thanks to [@hugoabonizio](https://github.com/hugoabonizio) for adding this dataset.
hate_speech_pl	--- annotations_creators: - expert-generated language_creators: - found language: - pl license: - cc-by-nc-sa-3.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - text-scoring - multi-class-classification - multi-label-classification - sentiment-classification - sentiment-scoring - topic-classification paperswithcode_id: null pretty_name: HateSpeechPl dataset_info: features: - name: id dtype: uint16 - name: text_id dtype: uint32 - name: annotator_id dtype: uint8 - name: minority_id dtype: uint8 - name: negative_emotions dtype: bool - name: call_to_action dtype: bool - name: source_of_knowledge dtype: uint8 - name: irony_sarcasm dtype: bool - name: topic dtype: uint8 - name: text dtype: string - name: rating dtype: uint8 splits: - name: train num_bytes: 3436190 num_examples: 13887 download_size: 3877954 dataset_size: 3436190 --- # Dataset Card for HateSpeechPl ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: http://zil.ipipan.waw.pl/HateSpeech - Repository: [N/A] - Paper: http://www.qualitativesociologyreview.org/PL/Volume38/PSJ_13_2_Troszynski_Wawer.pdf - Leaderboard: [N/A] - Point of Contact: [Marek Troszyński](mtroszynski@civitas.edu.pl), [Aleksander Wawer](axw@ipipan.waw.pl) ### Dataset Summary The dataset was created to analyze the possibility of automating the recognition of hate speech in Polish. It was collected from the Polish forums and represents various types and degrees of offensive language, expressed towards minorities. The original dataset is provided as an export of MySQL tables, what makes it hard to load. Due to that, it was converted to CSV and put to a Github repository. ### Supported Tasks and Leaderboards - `text-classification`: The dataset might be used to perform the text classification on different target fields, like the presence of irony/sarcasm, minority it describes or a topic. - `text-scoring`: The sentiment analysis is another task which might be solved on a dataset. ### Languages Polish, collected from public forums, including the HTML formatting of the text. ## Dataset Structure ### Data Instances The dataset consists of three collections, originally provided as separate MySQL tables. Here represented as three CSV files. ``` { 'id': 1, 'text_id': 121713, 'annotator_id': 1, 'minority_id': 72, 'negative_emotions': false, 'call_to_action': false, 'source_of_knowledge': 2, 'irony_sarcasm': false, 'topic': 18, 'text': ' <font color=\"blue\"> Niemiec</font> mówi co innego', 'rating': 0 } ``` ### Data Fields List and describe the fields present in the dataset. Mention their data type, and whether they are used as input or output in any of the tasks the dataset currently supports. If the data has span indices, describe their attributes, such as whether they are at the character level or word level, whether they are contiguous or not, etc. If the datasets contains example IDs, state whether they have an inherent meaning, such as a mapping to other datasets or pointing to relationships between data points. - `id`: unique identifier of the entry - `text_id`: text identifier, useful when a single text is rated several times by different annotators - `annotator_id`: identifier of the person who annotated the text - `minority_id`: the internal identifier of the minority described in the text - `negative_emotions`: boolean indicator of the presence of negative emotions in the text - `call_to_action`: boolean indicator set to true, if the text calls the audience to perform any action, typically with a negative emotions - `source_of_knowledge`: categorical variable, describing the source of knowledge for the post rating - 0, 1 or 2 (direct, lexical or contextual, but the description of the meaning for different values couldn't be found) - `irony_sarcasm`: boolean indicator of the present of irony or sarcasm - `topic`: internal identifier of the topic the text is about - `text`: post text content - `rating`: integer value, from 0 to 4 - the higher the value, the more negative the text content is ### Data Splits The dataset was not originally split at all. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data The dataset was collected from the public forums. [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information The dataset doesn't contain any personal or sensitive information. ## Considerations for Using the Data ### Social Impact of Dataset The automated hate speech recognition is the main beneficial outcome of using the dataset. ### Discussion of Biases The dataset contains negative posts only and due to that might underrepresent the whole language. ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators The dataset was created by Marek Troszyński and Aleksander Wawer, during work done at [IPI PAN](https://www.ipipan.waw.pl/). ### Licensing Information According to [Metashare](http://metashare.nlp.ipipan.waw.pl/metashare/repository/browse/polish-hatespeech-corpus/21b7e2366b0011e284b6000423bfd61cbc7616f601724f09bafc8a62c42d56de/), the dataset is licensed under CC-BY-NC-SA, but the version is not mentioned. ### Citation Information ``` @article{troszynski2017czy, title={Czy komputer rozpozna hejtera? Wykorzystanie uczenia maszynowego (ML) w jako{\'s}ciowej analizie danych}, author={Troszy{\'n}ski, Marek and Wawer, Aleksandra}, journal={Przegl{\k{a}}d Socjologii Jako{\'s}ciowej}, volume={13}, number={2}, pages={62--80}, year={2017}, publisher={Uniwersytet {\L}{\'o}dzki, Wydzia{\l} Ekonomiczno-Socjologiczny, Katedra Socjologii~…} } ``` ### Contributions Thanks to [@kacperlukawski](https://github.com/kacperlukawski) for adding this dataset.
hate_speech_portuguese	--- annotations_creators: - expert-generated language_creators: - found language: - pt license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: [] pretty_name: HateSpeechPortuguese tags: - hate-speech-detection dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': no-hate '1': hate - name: hatespeech_G1 dtype: string - name: annotator_G1 dtype: string - name: hatespeech_G2 dtype: string - name: annotator_G2 dtype: string - name: hatespeech_G3 dtype: string - name: annotator_G3 dtype: string splits: - name: train num_bytes: 826130 num_examples: 5670 download_size: 763846 dataset_size: 826130 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/paulafortuna/Portuguese-Hate-Speech-Dataset - Repository: https://github.com/paulafortuna/Portuguese-Hate-Speech-Dataset - Paper: https://www.aclweb.org/anthology/W19-3510/ - Leaderboard: - Point of Contact: ### Dataset Summary Portuguese dataset for hate speech detection composed of 5,668 tweets with binary annotations (i.e. 'hate' vs. 'no-hate'). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@hugoabonizio](https://github.com/hugoabonizio) for adding this dataset.
hatexplain	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: [] paperswithcode_id: hatexplain pretty_name: hatexplain tags: - hate-speech-detection dataset_info: features: - name: id dtype: string - name: annotators sequence: - name: label dtype: class_label: names: '0': hatespeech '1': normal '2': offensive - name: annotator_id dtype: int32 - name: target sequence: string - name: rationales sequence: sequence: int32 - name: post_tokens sequence: string config_name: plain_text splits: - name: train num_bytes: 7114730 num_examples: 15383 - name: validation num_bytes: 884940 num_examples: 1922 - name: test num_bytes: 884784 num_examples: 1924 download_size: 12848091 dataset_size: 8884454 --- # Dataset Card for hatexplain ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Needs More Information] - Repository: https://github.com/punyajoy/HateXplain/ - Paper: https://arxiv.org/abs/2012.10289 - Leaderboard: [Needs More Information] - Point of Contact: Punyajoy Saha (punyajoys@iitkgp.ac.in) ### Dataset Summary Hatexplain is the first benchmark hate speech dataset covering multiple aspects of the issue. Each post in the dataset is annotated from three different perspectives: the basic, commonly used 3-class classification (i.e., hate, offensive or normal), the target community (i.e., the community that has been the victim of hate speech/offensive speech in the post), and the rationales, i.e., the portions of the post on which their labeling decision (as hate, offensive or normal) is based. WARNING: This dataset contains content that are offensive and/or hateful in nature. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages The language supported is English. ## Dataset Structure ### Data Instances Sample Entry: ``` { "id": "24198545_gab", "annotators": [ { "label": 0, # hatespeech "annotator_id": 4, "target": ["African"] }, { "label": 0, # hatespeech "annotator_id": 3, "target": ["African"] }, { "label": 2, # offensive "annotator_id": 5, "target": ["African"] } ], "rationales":[ [0,0,0,0,0,0,0,0,1,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0], [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], [0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] ], "post_tokens": ["and","this","is","why","i","end","up","with","nigger","trainee","doctors","who","can","not","speak","properly","lack","basic","knowledge","of","biology","it","truly","scary","if","the","public","only","knew"] } } ``` ### Data Fields :small_blue_diamond:post_id : Unique id for each post<br/> :small_blue_diamond:annotators : The list of annotations from each annotator<br/> :small_blue_diamond:annotators[label] : The label assigned by the annotator to this post. Possible values: `hatespeech` (0), `normal` (1) or `offensive` (2)<br/> :small_blue_diamond:annotators[annotator_id] : The unique Id assigned to each annotator<br/> :small_blue_diamond:annotators[target] : A list of target community present in the post<br/> :small_blue_diamond:rationales : A list of rationales selected by annotators. Each rationales represents a list with values 0 or 1. A value of 1 means that the token is part of the rationale selected by the annotator. To get the particular token, we can use the same index position in "post_tokens"<br/> :small_blue_diamond:post_tokens : The list of tokens representing the post which was annotated<br/> ### Data Splits [Post_id_divisions](https://github.com/hate-alert/HateXplain/blob/master/Data/post_id_divisions.json) has a dictionary having train, valid and test post ids that are used to divide the dataset into train, val and test set in the ratio of 8:1:1. ## Dataset Creation ### Curation Rationale The existing hate speech datasets do not provide human rationale which could justify the human reasoning behind their annotation process. This dataset allows researchers to move a step in this direction. The dataset provides token-level annotatoins for the annotation decision. ### Source Data We collected the data from Twitter and Gab. #### Initial Data Collection and Normalization We combined the lexicon set provided by [Davidson 2017](https://arxiv.org/abs/1703.04009), [Ousidhoum 2019](https://arxiv.org/abs/1908.11049), and [Mathew 2019](https://arxiv.org/abs/1812.01693) to generate a single lexicon. We do not consider reposts and remove duplicates. We also ensure that the posts do not contain links, pictures, or videos as they indicate additional information that mightnot be available to the annotators. However, we do not exclude the emojis from the text as they might carry importantinformation for the hate and offensive speech labeling task. #### Who are the source language producers? The dataset is human generated using Amazon Mechanical Turk (AMT). ### Annotations #### Annotation process Each post in our dataset contains three types of annotations. First, whether the text is a hate speech, offensive speech, or normal. Second, the target communities in the text. Third, if the text is considered as hate speech, or offensive by majority of the annotators, we further ask the annotators to annotate parts of the text, which are words orphrases that could be a potential reason for the given annotation. Before starting the annotation task, workers are explicitly warned that the annotation task displays some hateful or offensive content. We prepare instructions for workers that clearly explain the goal of the annotation task, how to annotate spans and also include a definition for each category. We provide multiple examples with classification, target community and span annotations to help the annotators understand the task. #### Who are the annotators? To ensure high quality dataset, we use built-in MTurk qualification requirements, namely the HITApproval Rate(95%) for all Requesters’ HITs and the Number of HITs Approved(5,000) requirements. Pilot annotation: In the pilot task, each annotator was provided with 20 posts and they were required to do the hate/offensive speech classification as well as identify the target community (if any). In order to have a clear understanding of the task, they were provided with multiple examples along with explanations for the labelling process. The main purpose of the pilot task was to shortlist those annotators who were able to do the classification accurately. We also collected feedback from annotators to improve the main annotation task. A total of 621 annotators took part in the pilot task. Out of these, 253 were selected for the main task. Main annotation: After the pilot annotation, once we had ascertained the quality of the annotators, we started with the main annotation task. In each round, we would select a batch of around 200 posts. Each post was annotated by three annotators, then majority voting was applied to decide the final label. The final dataset is composed of 9,055 posts from Twitter and 11,093 posts from Gab. The Krippendorff's alpha for the inter-annotator agreement is 0.46 which is higher than other hate speech datasets. ### Personal and Sensitive Information The posts were anonymized by replacing the usernames with <user> token. ## Considerations for Using the Data ### Social Impact of Dataset The dataset could prove beneficial to develop models which are more explainable and less biased. ### Discussion of Biases [Needs More Information] ### Other Known Limitations The dataset has some limitations. First is the lack of external context. The dataset lacks any external context such as profile bio, user gender, history of posts etc., which might be helpful in the classification task. Another issue is the focus on English language and lack of multilingual hate speech. ## Additional Information ### Dataset Curators Binny Mathew - IIT Kharagpur, India Punyajoy Saha - IIT Kharagpur, India Seid Muhie Yimam - Universit ̈at Hamburg, Germany Chris Biemann - Universit ̈at Hamburg, Germany Pawan Goyal - IIT Kharagpur, India Animesh Mukherjee - IIT Kharagpur, India ### Licensing Information MIT License ### Citation Information ```bibtex @article{mathew2020hatexplain, title={HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection}, author={Binny Mathew and Punyajoy Saha and Seid Muhie Yimam and Chris Biemann and Pawan Goyal and Animesh Mukherjee}, year={2021}, conference={AAAI conference on artificial intelligence} } ### Contributions Thanks to [@kushal2000](https://github.com/kushal2000) for adding this dataset.
hausa_voa_ner	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - ha license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: Hausa VOA NER Corpus dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-DATE '8': I-DATE config_name: hausa_voa_ner splits: - name: train num_bytes: 483634 num_examples: 1015 - name: validation num_bytes: 69673 num_examples: 146 - name: test num_bytes: 139227 num_examples: 292 download_size: 324962 dataset_size: 692534 --- # Dataset Card for Hausa VOA NER Corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://www.aclweb.org/anthology/2020.emnlp-main.204/ - Repository: [Hausa VOA NER](https://github.com/uds-lsv/transfer-distant-transformer-african/tree/master/data/hausa_ner) - Paper: https://www.aclweb.org/anthology/2020.emnlp-main.204/ - Leaderboard: - Point of Contact: [David Adelani](mailto:didelani@lsv.uni-saarland.de) ### Dataset Summary The Hausa VOA NER is a named entity recognition (NER) dataset for Hausa language based on the [VOA Hausa news](https://www.voahausa.com/) corpus. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The language supported is Hausa. ## Dataset Structure ### Data Instances A data point consists of sentences seperated by empty line and tab-seperated tokens and tags. {'id': '0', 'ner_tags': [B-PER, 0, 0, B-LOC, 0], 'tokens': ['Trump', 'ya', 'ce', 'Rasha', 'ma'] } ### Data Fields - `id`: id of the sample - `tokens`: the tokens of the example text - `ner_tags`: the NER tags of each token The NER tags correspond to this list: ``` "O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-DATE", "I-DATE", ``` The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and dates & times (DATE). (O) is used for tokens not considered part of any named entity. ### Data Splits Training (1,014 sentences), validation (145 sentences) and test split (291 sentences) ## Dataset Creation ### Curation Rationale The data was created to help introduce resources to new language - Hausa. [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The dataset is based on the news domain and was crawled from [VOA Hausa news](https://www.voahausa.com/). [More Information Needed] #### Who are the source language producers? The dataset was collected from VOA Hausa news. Most of the texts used in creating the Hausa VOA NER are news stories from Nigeria, Niger Republic, United States, and other parts of the world. [More Information Needed] ### Annotations Named entity recognition annotation #### Annotation process [More Information Needed] #### Who are the annotators? The data was annotated by Jesujoba Alabi and David Adelani for the paper: [Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages](https://www.aclweb.org/anthology/2020.emnlp-main.204/). [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The annotated data sets were developed by students of Saarland University, Saarbrücken, Germany . ### Licensing Information The data is under the [Creative Commons Attribution 4.0 ](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @inproceedings{hedderich-etal-2020-transfer, title = "Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on {A}frican Languages", author = "Hedderich, Michael A. and Adelani, David and Zhu, Dawei and Alabi, Jesujoba and Markus, Udia and Klakow, Dietrich", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-main.204", doi = "10.18653/v1/2020.emnlp-main.204", pages = "2580--2591", } ``` ### Contributions Thanks to [@dadelani](https://github.com/dadelani) for adding this dataset.
hausa_voa_topics	--- annotations_creators: - expert-generated language_creators: - found language: - ha license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - topic-classification pretty_name: Hausa Voa News Topic Classification Dataset (HausaVoaTopics) dataset_info: features: - name: news_title dtype: string - name: label dtype: class_label: names: '0': Africa '1': Health '2': Nigeria '3': Politics '4': World splits: - name: train num_bytes: 144932 num_examples: 2045 - name: validation num_bytes: 20565 num_examples: 290 - name: test num_bytes: 41195 num_examples: 582 download_size: 195824 dataset_size: 206692 --- # Dataset Card for Hausa VOA News Topic Classification dataset (hausa_voa_topics) ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: - - Repository: https://github.com/uds-lsv/transfer-distant-transformer-african - Paper: https://www.aclweb.org/anthology/2020.emnlp-main.204/ - Leaderboard: - - Point of Contact: Michael A. Hedderich and David Adelani {mhedderich, didelani} (at) lsv.uni-saarland.de ### Dataset Summary A news headline topic classification dataset, similar to AG-news, for Hausa. The news headlines were collected from [VOA Hausa](https://www.voahausa.com/). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Hausa (ISO 639-1: ha) ## Dataset Structure ### Data Instances An instance consists of a news title sentence and the corresponding topic label. ### Data Fields - `news_title`: A news title - `label`: The label describing the topic of the news title. Can be one of the following classes: Nigeria, Africa, World, Health or Politics. ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@michael-aloys](https://github.com/michael-aloys) for adding this dataset.
hda_nli_hindi	--- annotations_creators: - machine-generated language_creators: - found language: - hi license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended\|hindi_discourse task_categories: - text-classification task_ids: - natural-language-inference pretty_name: Hindi Discourse Analysis Dataset dataset_info: - config_name: HDA hindi nli features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': not-entailment '1': entailment - name: topic dtype: class_label: names: '0': Argumentative '1': Descriptive '2': Dialogic '3': Informative '4': Narrative splits: - name: train num_bytes: 8721972 num_examples: 31892 - name: validation num_bytes: 2556118 num_examples: 9460 - name: test num_bytes: 2646453 num_examples: 9970 download_size: 13519261 dataset_size: 13924543 - config_name: hda nli hindi features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': not-entailment '1': entailment - name: topic dtype: class_label: names: '0': Argumentative '1': Descriptive '2': Dialogic '3': Informative '4': Narrative splits: - name: train num_bytes: 8721972 num_examples: 31892 - name: validation num_bytes: 2556118 num_examples: 9460 - name: test num_bytes: 2646453 num_examples: 9970 download_size: 13519261 dataset_size: 13924543 --- # Dataset Card for Hindi Discourse Analysis Dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - HomePage: [GitHub](https://github.com/midas-research/hindi-nli-data) - Paper: [Aclweb](https://www.aclweb.org/anthology/2020.aacl-main.71) - Point of Contact: [GitHub](https://github.com/midas-research/hindi-nli-data) ### Dataset Summary - Dataset for Natural Language Inference in Hindi Language. Hindi Discourse Analysis (HDA) Dataset consists of textual-entailment pairs. - Each row of the Datasets if made up of 4 columns - Premise, Hypothesis, Label and Topic. - Premise and Hypothesis is written in Hindi while Entailment_Label is in English. - Entailment_label is of 2 types - entailed and not-entailed. - Entailed means that hypotheis can be inferred from premise and not-entailed means vice versa - Dataset can be used to train models for Natural Language Inference tasks in Hindi Language. ### Supported Tasks and Leaderboards - Natural Language Inference for Hindi ### Languages - Dataset is in Hindi ## Dataset Structure - Data is structured in TSV format. - train, test and dev files are in seperate files ### Dataset Instances An example of 'train' looks as follows. ``` {'hypothesis': 'यह एक वर्णनात्मक कथन है।', 'label': 1, 'premise': 'जैसे उस का सारा चेहरा अपना हो और आँखें किसी दूसरे की जो चेहरे पर पपोटों के पीछे महसूर कर दी गईं।', 'topic': 1} ``` ### Data Fields Each row contatins 4 columns: - premise: string - hypothesis: string - label: class label with values that correspond to "not-entailment" (0) or "entailment" (1) - topic: class label with values that correspond to "Argumentative" (0), "Descriptive" (1), "Dialogic" (2), "Informative" (3) or "Narrative" (4). ### Data Splits - Train : 31892 - Valid : 9460 - Test : 9970 ## Dataset Creation - We employ a recasting technique from Poliak et al. (2018a,b) to convert publicly available Hindi Discourse Analysis classification datasets in Hindi and pose them as TE problems - In this recasting process, we build template hypotheses for each class in the label taxonomy - Then, we pair the original annotated sentence with each of the template hypotheses to create TE samples. - For more information on the recasting process, refer to paper https://www.aclweb.org/anthology/2020.aacl-main.71 ### Source Data Source Dataset for the recasting process is the BBC Hindi Headlines Dataset(https://github.com/NirantK/hindi2vec/releases/tag/bbc-hindi-v0.1) #### Initial Data Collection and Normalization - Initial Data was collected by members of MIDAS Lab from Hindi Websites. They crowd sourced the data annotation process and selected two random stories from our corpus and had the three annotators work on them independently and classify each sentence based on the discourse mode. - Please refer to this paper for detailed information: https://www.aclweb.org/anthology/2020.lrec-1.149/ - The Discourse is further classified into "Argumentative" , "Descriptive" , "Dialogic" , "Informative" and "Narrative" - 5 Clases. #### Who are the source language producers? Please refer to this paper for detailed information: https://www.aclweb.org/anthology/2020.lrec-1.149/ ### Annotations #### Annotation process Annotation process has been described in Dataset Creation Section. #### Who are the annotators? Annotation is done automatically by machine and corresponding recasting process. ### Personal and Sensitive Information No Personal and Sensitive Information is mentioned in the Datasets. ## Considerations for Using the Data Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71 ### Discussion of Biases No known bias exist in the dataset. Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71 ### Other Known Limitations No other known limitations . Size of data may not be enough to train large models ## Additional Information Pls refer to this link: https://github.com/midas-research/hindi-nli-data ### Dataset Curators It is written in the repo : https://github.com/midas-research/hindi-nli-data that - This corpus can be used freely for research purposes. - The paper listed below provide details of the creation and use of the corpus. If you use the corpus, then please cite the paper. - If interested in commercial use of the corpus, send email to midas@iiitd.ac.in. - If you use the corpus in a product or application, then please credit the authors and Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi appropriately. Also, if you send us an email, we will be thrilled to know about how you have used the corpus. - Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India disclaims any responsibility for the use of the corpus and does not provide technical support. However, the contact listed above will be happy to respond to queries and clarifications. - Rather than redistributing the corpus, please direct interested parties to this page - Please feel free to send us an email: - with feedback regarding the corpus. - with information on how you have used the corpus. - if interested in having us analyze your data for natural language inference. - if interested in a collaborative research project. ### Licensing Information Copyright (C) 2019 Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi (MIDAS, IIIT-Delhi). Pls contact authors for any information on the dataset. ### Citation Information ``` @inproceedings{uppal-etal-2020-two, title = "Two-Step Classification using Recasted Data for Low Resource Settings", author = "Uppal, Shagun and Gupta, Vivek and Swaminathan, Avinash and Zhang, Haimin and Mahata, Debanjan and Gosangi, Rakesh and Shah, Rajiv Ratn and Stent, Amanda", booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing", month = dec, year = "2020", address = "Suzhou, China", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.aacl-main.71", pages = "706--719", abstract = "An NLP model{'}s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.", } ``` ### Contributions Thanks to [@avinsit123](https://github.com/avinsit123) for adding this dataset.
head_qa	--- annotations_creators: - no-annotation language_creators: - expert-generated language: - en - es license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: headqa pretty_name: HEAD-QA configs: - en - es dataset_info: - config_name: es features: - name: name dtype: string - name: year dtype: string - name: category dtype: string - name: qid dtype: int32 - name: qtext dtype: string - name: ra dtype: int32 - name: image dtype: image - name: answers list: - name: aid dtype: int32 - name: atext dtype: string splits: - name: train num_bytes: 1229678 num_examples: 2657 - name: test num_bytes: 1204006 num_examples: 2742 - name: validation num_bytes: 573354 num_examples: 1366 download_size: 79365502 dataset_size: 3007038 - config_name: en features: - name: name dtype: string - name: year dtype: string - name: category dtype: string - name: qid dtype: int32 - name: qtext dtype: string - name: ra dtype: int32 - name: image dtype: image - name: answers list: - name: aid dtype: int32 - name: atext dtype: string splits: - name: train num_bytes: 1156808 num_examples: 2657 - name: test num_bytes: 1131536 num_examples: 2742 - name: validation num_bytes: 539892 num_examples: 1366 download_size: 79365502 dataset_size: 2828236 --- # Dataset Card for HEAD-QA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [HEAD-QA homepage](https://aghie.github.io/head-qa/) - Repository: [HEAD-QA repository](https://github.com/aghie/head-qa) - Paper: [HEAD-QA: A Healthcare Dataset for Complex Reasoning](https://www.aclweb.org/anthology/P19-1092/) - Leaderboard: [HEAD-QA leaderboard](https://aghie.github.io/head-qa/#leaderboard-general) - Point of Contact: [María Grandury](mailto:mariagrandury@gmail.com) (Dataset Submitter) ### Dataset Summary HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans. They are designed by the [Ministerio de Sanidad, Consumo y Bienestar Social](https://www.mscbs.gob.es/), who also provides direct [access](https://fse.mscbs.gob.es/fseweb/view/public/datosanteriores/cuadernosExamen/busquedaConvocatoria.xhtml) to the exams of the last 5 years (in Spanish). ``` Date of the last update of the documents object of the reuse: January, 14th, 2019. ``` HEAD-QA tries to make these questions accesible for the Natural Language Processing community. We hope it is an useful resource towards achieving better QA systems. The dataset contains questions about the following topics: - Medicine - Nursing - Psychology - Chemistry - Pharmacology - Biology ### Supported Tasks and Leaderboards - `multiple-choice-qa`: HEAD-QA is a multi-choice question answering testbed to encourage research on complex reasoning. ### Languages The questions and answers are available in both Spanish (BCP-47 code: 'es-ES') and English (BCP-47 code: 'en'). The language by default is Spanish: ``` from datasets import load_dataset data_es = load_dataset('head_qa') data_en = load_dataset('head_qa', 'en') ``` ## Dataset Structure ### Data Instances A typical data point comprises a question `qtext`, multiple possible answers `atext` and the right answer `ra`. An example from the HEAD-QA dataset looks as follows: ``` { 'qid': '1', 'category': 'biology', 'qtext': 'Los potenciales postsinápticos excitadores:', 'answers': [ { 'aid': 1, 'atext': 'Son de tipo todo o nada.' }, { 'aid': 2, 'atext': 'Son hiperpolarizantes.' }, { 'aid': 3, 'atext': 'Se pueden sumar.' }, { 'aid': 4, 'atext': 'Se propagan a largas distancias.' }, { 'aid': 5, 'atext': 'Presentan un periodo refractario.' }], 'ra': '3', 'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=675x538 at 0x1B42B6A1668>, 'name': 'Cuaderno_2013_1_B', 'year': '2013' } ``` ### Data Fields - `qid`: question identifier (int) - `category`: category of the question: "medicine", "nursing", "psychology", "chemistry", "pharmacology", "biology" - `qtext`: question text - `answers`: list of possible answers. Each element of the list is a dictionary with 2 keys: - `aid`: answer identifier (int) - `atext`: answer text - `ra`: `aid` of the right answer (int) - `image`: (optional) a `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]` - `name`: name of the exam from which the question was extracted - `year`: year in which the exam took place ### Data Splits The data is split into train, validation and test set for each of the two languages. The split sizes are as follow: \| \| Train \| Val \| Test \| \| ----- \| ------ \| ----- \| ---- \| \| Spanish \| 2657 \| 1366 \| 2742 \| \| English \| 2657 \| 1366 \| 2742 \| ## Dataset Creation ### Curation Rationale As motivation for the creation of this dataset, here is the abstract of the paper: "We present HEAD-QA, a multi-choice question answering testbed to encourage research on complex reasoning. The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans. We then consider monolingual (Spanish) and cross-lingual (to English) experiments with information retrieval and neural techniques. We show that: (i) HEAD-QA challenges current methods, and (ii) the results lag well behind human performance, demonstrating its usefulness as a benchmark for future work." ### Source Data #### Initial Data Collection and Normalization The questions come from exams to access a specialized position in the Spanish healthcare system, and are designed by the [Ministerio de Sanidad, Consumo y Bienestar Social](https://www.mscbs.gob.es/), who also provides direct [access](https://fse.mscbs.gob.es/fseweb/view/public/datosanteriores/cuadernosExamen/busquedaConvocatoria.xhtml) to the exams of the last 5 years (in Spanish). #### Who are the source language producers? The dataset was created by David Vilares and Carlos Gómez-Rodríguez. ### Annotations The dataset does not contain any additional annotations. #### Annotation process [N/A] #### Who are the annotators? [N/A] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The dataset was created by David Vilares and Carlos Gómez-Rodríguez. ### Licensing Information According to the [HEAD-QA homepage](https://aghie.github.io/head-qa/#legal-requirements): The Ministerio de Sanidad, Consumo y Biniestar Social allows the redistribution of the exams and their content under [certain conditions:](https://www.mscbs.gob.es/avisoLegal/home.htm) - The denaturalization of the content of the information is prohibited in any circumstance. - The user is obliged to cite the source of the documents subject to reuse. - The user is obliged to indicate the date of the last update of the documents object of the reuse. According to the [HEAD-QA repository](https://github.com/aghie/head-qa/blob/master/LICENSE): The dataset is licensed under the [MIT License](https://mit-license.org/). ### Citation Information ``` @inproceedings{vilares-gomez-rodriguez-2019-head, title = "{HEAD}-{QA}: A Healthcare Dataset for Complex Reasoning", author = "Vilares, David and G{\'o}mez-Rodr{\'i}guez, Carlos", booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P19-1092", doi = "10.18653/v1/P19-1092", pages = "960--966", abstract = "We present HEAD-QA, a multi-choice question answering testbed to encourage research on complex reasoning. The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans. We then consider monolingual (Spanish) and cross-lingual (to English) experiments with information retrieval and neural techniques. We show that: (i) HEAD-QA challenges current methods, and (ii) the results lag well behind human performance, demonstrating its usefulness as a benchmark for future work.", } ``` ### Contributions Thanks to [@mariagrandury](https://github.com/mariagrandury) for adding this dataset.
health_fact	--- annotations_creators: - expert-generated language_creators: - found language: - en license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - fact-checking - multi-class-classification paperswithcode_id: pubhealth pretty_name: PUBHEALTH dataset_info: features: - name: claim_id dtype: string - name: claim dtype: string - name: date_published dtype: string - name: explanation dtype: string - name: fact_checkers dtype: string - name: main_text dtype: string - name: sources dtype: string - name: label dtype: class_label: names: '0': 'false' '1': mixture '2': 'true' '3': unproven - name: subjects dtype: string splits: - name: train num_bytes: 53985377 num_examples: 9832 - name: test num_bytes: 6825221 num_examples: 1235 - name: validation num_bytes: 6653044 num_examples: 1225 download_size: 24892660 dataset_size: 67463642 train-eval-index: - config: default task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: claim: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for PUBHEALTH ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [PUBHEALTH homepage](https://github.com/neemakot/Health-Fact-Checking) - Repository: [PUBHEALTH repository](https://github.com/neemakot/Health-Fact-Checking/blob/master/data/DATASHEET.md) - Paper: [Explainable Automated Fact-Checking for Public Health Claims"](https://arxiv.org/abs/2010.09926) - Point of Contact:[Neema Kotonya](mailto:nk2418@ic.ac.uk) ### Dataset Summary PUBHEALTH is a comprehensive dataset for explainable automated fact-checking of public health claims. Each instance in the PUBHEALTH dataset has an associated veracity label (true, false, unproven, mixture). Furthermore each instance in the dataset has an explanation text field. The explanation is a justification for which the claim has been assigned a particular veracity label. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The text in the dataset is in English. ## Dataset Structure ### Data Instances The following is an example instance of the PUBHEALTH dataset: \| Field \| Example \| \| ----------------- \| -------------------------------------------------------------\| \| __claim__ \| Expired boxes of cake and pancake mix are dangerously toxic. \| \| __explanation__ \| What's True: Pancake and cake mixes that contain mold can cause life-threatening allergic reactions. What's False: Pancake and cake mixes that have passed their expiration dates are not inherently dangerous to ordinarily healthy people, and the yeast in packaged baking products does not "over time develops spores." \| \| __label__ \| mixture \| \| __author(s)__ \| David Mikkelson \| \| __date published__ \| April 19, 2006 \| \| __tags__ \| food, allergies, baking, cake \| \| __main_text__ \| In April 2006, the experience of a 14-year-old who had eaten pancakes made from a mix that had gone moldy was described in the popular newspaper column Dear Abby. The account has since been circulated widely on the Internet as scores of concerned homemakers ponder the safety of the pancake and other baking mixes lurking in their larders [...] \| \| __evidence sources__ \| [1] Bennett, Allan and Kim Collins. “An Unusual Case of Anaphylaxis: Mold in Pancake Mix.” American Journal of Forensic Medicine & Pathology. September 2001 (pp. 292-295). [2] Phillips, Jeanne. “Dear Abby.” 14 April 2006 [syndicated column]. \| ### Data Fields Mentioned above in data instances. ### Data Splits \| \| # Instances \| \|-----------\|-------------\| \| train.tsv \| 9832 \| \| dev.tsv \| 1221 \| \| test.tsv \| 1235 \| \| total \| 12288 \| ## Dataset Creation ### Curation Rationale The dataset was created to explore fact-checking of difficult to verify claims i.e., those which require expertise from outside of the journalistics domain, in this case biomedical and public health expertise. It was also created in response to the lack of fact-checking datasets which provide gold standard natural language explanations for verdicts/labels. ### Source Data #### Initial Data Collection and Normalization The dataset was retrieved from the following fact-checking, news reviews and news websites: \| URL \| Type \| \|-----------------------------------\|--------------------\| \| http://snopes.com/ \| fact-checking \| \| http://politifact.com/ \| fact-checking \| \| http://truthorfiction.com/ \| fact-checking \| \| https://www.factcheck.org/ \| fact-checking \| \| https://fullfact.org/ \| fact-checking \| \| https://apnews.com/ \| news \| \| https://uk.reuters.com/ \| news \| \| https://www.healthnewsreview.org/ \| health news review \| #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information Not to our knowledge, but if it is brought to our attention that we are mistaken we will make the appropriate corrections to the dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The dataset was created by Neema Kotonya, and Francesca Toni, for their research paper "Explainable Automated Fact-Checking for Public Health Claims" presented at EMNLP 2020. ### Licensing Information MIT License ### Citation Information ``` @inproceedings{kotonya-toni-2020-explainable, title = "Explainable Automated Fact-Checking for Public Health Claims", author = "Kotonya, Neema and Toni, Francesca", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-main.623", pages = "7740--7754", } ``` ### Contributions Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset.
hebrew_projectbenyehuda	--- annotations_creators: - expert-generated language_creators: - found language: - he license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: null pretty_name: Hebrew Projectbenyehuda dataset_info: features: - name: id dtype: int32 - name: url dtype: string - name: title dtype: string - name: authors dtype: string - name: translators dtype: string - name: original_language dtype: string - name: genre dtype: string - name: source_edition dtype: string - name: text dtype: string splits: - name: train num_bytes: 318732537 num_examples: 10078 download_size: 317749152 dataset_size: 318732537 --- # Dataset Card for Hebrew Projectbenyehuda ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/projectbenyehuda/public_domain_dump - Repository: https://github.com/projectbenyehuda/public_domain_dump - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This repository contains a dump of thousands of public domain works in Hebrew, from Project Ben-Yehuda, in plaintext UTF-8 files, with and without diacritics (nikkud), and in HTML files. The pseudocatalogue.csv file is a list of titles, authors, genres, and file paths, to help you process the dump. The Releases tab contains a downloadable ZIP archive of the full release. The git repo can be used to track individual file changes, or for incremenetal updates. In the ZIPs, each format (plaintext, plaintext stripped of diacritics, and HTML) has a ZIP file containing one directory per author, with all the author's works under that directory. To request changes or improvements to this dump, file an issue against this repository. All these works are in the public domain, so you are free to make any use of them, and do not need to ask for permission. If you would like to give credit, please credit "Project Ben-Yehuda volunteers", and include a link to the site. We'd also love to hear about the uses you've made of this dump, as it encourages us to keep producing the dump. E-mail us with a brief description (and links, if/as appropriate) of your re-use, at editor@benyehuda.org. There are 10078 files, 3181136 lines Data Annotation: ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Hebrew ## Dataset Structure ### Data Instances Sample: ``` { 'id': 10, 'url': 'https://raw.githubusercontent.com/projectbenyehuda/public_domain_dump/master/txt/p23/m10.txt', 'title': 'חצי-נחמה', 'authors': 'אחד העם', 'translators': '', 'original_language': '', 'genre': 'מאמרים ומסות', 'source_edition': '', 'text': '\n\n\n\t\n\tחצי-נחמה\n\t\n\n\n\n1\n\nבין כל הצרות שנתחדשו עלינו בעת האחרונה תעשׂה ביחוד רושם מעציב בלב כל איש ישׂראל התחדשות ‘עלילת־הדם’. העלילה הנתעבה הזאת, בכל יָשנה, היתה ותהיה תמיד בעינינו כחדשה, ומימי הבינים ועד עתה תצטין בפעולתה החזקה על רוח עמנו, לא רק במקום המעשׂה, כי אם גם בארצות רחוקות שהגיעה אליהן השמועה.\n\nאמרתי: ‘על רוח עמנו’, כי אמנם רואה אני מקור החזיון הזה לא בסבּות חיצוניות, כי אם עמוק ברוח העם. בימי הבינים, שהיה כלל ישׂראל במקרים כאלה רגיל לחשוב עצמו כעומד במשפט ביחד עם אותם האומללים שעלה עליהם הגורל להיות כפּרותו, – יש מקום אמנם לראות בזה רק תוצאת הסכנה הגשמית הגדולה להכלל כולו, שהיתה כרוכה אז באמת בעקב כל עלילה כזו. גם לפני חמשים שנה, בימי מנוחה ושלוה, שעוררה עלילת דמשׂק רעש גדול כל־כך בארצות המערב, עדיין יש מקום לאמר, כי היתה בזה, להפך, יד הקנאה הגדולה לכבודם וזכויותיהם ששׂררה אז בלבות אחינו המערביים, אשר זה מעט יצאו מעבדות לחרות. אך בימינו אלה הרי מצד אחד אין הסכנה הגשמית גדולה עוד הרבה, ביחוד לקהלות רחוקות, ומצד אחר כבר הורגלנו לשמוע חרפתנו בקור רוח וקנאת כבודנו לא תאכלנו עוד, ואם בכל זאת גם עתה עודנו מתעוררים ומתנודדים בחזקה לשמע ‘עלילת־דם’, ורגש הכלל יתפרץ החוצה מכל עברים להשליך מעליו את החלאה הזאת, – אות הוא, כי לא הפחד ולא הכבוד החיצוני הם המניעים לזה, כי אם רוח העם הוא המרגיש פה את קלונו והוא זה המתעורר והמעורר; כי אעפ"י שבכל יתר הדברים כבר הביאונו צרותינו לאותו המצב שעליו אמר הנשׂיא החכם בימי קדם: ‘אין בשׂר המת מרגיש באיזמל’, – הנה פה אין ‘האיזמל’ חותך את ‘הבשׂר’ בלבד, כי אם עד הנפש יגע…\n\nאבל – ‘אין רע בלא טוב’, כלומר, בלא לקח טוב. גם הרע הגדול הזה שאנו עסוקים בו אינו ריק מלקח טוב, ואנחנו, אשר לא אדונים אנחנו לגורלנו וגם את הטוב גם את הרע נקבל מן החוץ שלא בטובתנו, ראוי לנו לבקש ברעותינו תמיד את התועלת הלמודית הצפונה בהן, והיתה לנו זאת, לפחות, חצי נחמה.\n\n\n\nאחד הכוחות היותר גדולים בחיי החברה הוא – ‘ההסכמה הכללית’. היו ימים שגם הפלוסופים ראו בהסכמה זו מופת נאמן על הדבר המוסכם ונתנו לה מקום בתוך שאר מופתיהם על מציאות האלהות. עתה אמנם יודעים הפלוסופים , שאין שקר ואין אולת אשר לא תוכל לבוא עליו ‘ההסכמה הכללית’, אם אך תנאי החיים נאותים לזה. אבל רק הפלוסופים יודעים זאת, ובעיני ההמון עוד גם עתה אין אַבטוֹריטט גדול מן ‘ההסכמה’: אם ‘כל העולם’ מאמינים שהדבר כן, בודאי כן הוא; ואם אני איני מבינו, אחרים מבינים; ואם אני רואה כעין סתירה לו, הרי ‘הכל’ רואים גם כן ואעפ"כ מאמינים, וכי חכם אני מכל העולם? – זה הוא בקירוב מהלך הרעיונות של האיש הפשוט, בדעת או בלי דעת ברורה, ומתוך כך הוא מסכים גם מצדו ונעשׂה בעצמו חלק מן ‘ההסכמה’.\n\nוכל־כך גדול כוח ‘ההסכמה’, עד שעל הרוב לא יוכל האדם למַלט נפשו מפעולתה גם כשהוא עצמו הוא ‘הדבר המוסכם’. אם ‘כל העולם’ אומרים על פלוני שגדול הוא בחכמה או ביראה, שיש בו מדה פלונית, טובה או רעה, – סופו להסכים לזה גם בעצמו, אע"פ שמתחלה לא מצא בנפשו אותו היתרון או החסרון שאחרים מיחסים לו. ולא זו בלבד אלא שההסכמה הזאת מצד ‘המוסכם’ עצמו פועלת מעט מעט על תכונת רוחו עד שמקרבתו באמת (או, לפחות, מולידה בו נטיה להתקרב) אל המצב ההוא שרואה בו ‘כל העולם’. על כן יזהירו הפדגוגים בצדק, לבלתי עורר את הילדים על מגרעותיהם המוסריות בראשית התפתחותן, וכל שכּן לבלתי יחס להם מגרעות שאין בהם, כי על ידי זה אפשר שנחזק בלבם את הראשונות ונוליד בם נטיה להאחרונות.\n\nואולם, הדבר מובן, כי ‘כל העולם’ אינו אחד לכל אחד. האדם רואה ‘עולמו’ רק באותה החברה שהוא חושב עצמו לחלק ממנה ורואה באישיה אנשים הקרובים לו מאיזה צד; אבל אין אדם חושב למאומה הסכמת אנשים שרוחם זרה לו לגמרי, שאינו מרגיש בנפשו שום יחס פנימי בינו ובינם. ככה אין האוֹרתוֹדוֹכּסים והמשׂכילים שלנו שׂמים לב כלל אלו להסכמתם של אלו, אף בדברים שאינם נוגעים לאמונה ודת, ושׂחקם ולעגם של אלו על אלו אינו עושׂה שום רושם בלבם של שניהם, לפי שכּל אחת משתי הכּתּות רואה את חברתה כאלו אינה. ואולם כשתנאי החיים מכריחים את בני הכתות השונות להמצא במשׂא ומתן תמידי זה עם זה והם מתרגלים לראות זה בזה קודם כל את האדם, – אז יתרחב ‘עולמם’ והשקפותיהם סובלות שנויים רבים על פי הסכמת ‘העולם’ במובנו החדש.\n\n\n\nלפיכך, בדורות שעברו, כשהיו אבותינו מאמינים בפשטו של ‘אתה בחרתנו’, לא היתה החרפּה שחרפום האומות פועלת כלל על טוהר נפשם פנימה. הם ידעו את ערכם ולא התפעלו עד מה מן ‘ההסכמה הכללית’ אשר מחוץ להם, בהיות כל חברת ‘המסכימים’ נחשבת בעיניהם למין מיוחד של בריות זרות להם ושונות מהם שנוי עצמי, בלי כל יחס וכל דמיון בינם ובינן. אז היה היהודי יכול לשמוע במנוחת לב כל המגרעות המוסריות והחטאים המעשׂיים שטפלה עליו הסכמת העמים, מבלי להרגיש בנפשו שום בושה או שפלוּת פנימית. כי מה לו ולמחשבות ‘הנכרים’ עליו ועל ערכּוֹ? לוּ רק יתנו לו לישב בשלוה! – אבל בדור הזה אין הדבר כן, עתה ‘עולמנו’ נתרחב הרבה, וההסכמה האירופּית פועלת עלינו בחזקה בכל ענפי החיים. ולפי שאין אנו מוציאים עוד את ‘הכל’ מן הכלל, לכן נתפעל בעל כרחנו ממה ש’הכל\' מוציאים אותנו מן הכלל, סופר אחד רוסי שאל באלו הימים בתמימוּת: אחר שכל העולם שׂונאים את היהודים, וכי אפשר לאמור, שכל העולם חייבים והיהודים זכאים? – ושאלה כזו מתגנבת עתה גם אל לב רבים מאחינו: וכי אפשר לאמור, שכל אותן התכונות הנשחתות והמעשׂים הרעים שכל העולם מיחס ליהודים אינם אלא ‘בדותא’?\n\nוהספק הזה, מכיון שנתעורר, מוצא לו מחיה בנקל באותם ההיקשים המוטעים ‘מן הפרט אל הכלל’ הרגילים מאד אצל המון בני האדם. הספור הידוע על דבר נוסע אחד, שבא לאחת הערים ונזדמן לאכסניא שהיה בה משרת כבד־פה, וכתב בפנקסו: בעיר פלונית משרתי האכסניות הם כבדי־פה, – הספור הזה מצייר בצורה של התוּל דרכי־ההגיון של ההמון ברוב משפטיו הכלליים. כל החזיונות הנראים באיזה דבר פרטי רגיל ההמון ליחס אל הכלל שהדבר ההוא מתחשב עליו לפי שמו התמידי, מבלי להתבונן, כי ‘פרט’ אחד יוכל להתחשב על ‘כללים’ רבים ביחד, כלומר, להיות שוּתף בתכוּנה אחת עם פרטיו של כלל אחד ובתכונה אחרת עם פרטיו של כלל אחר, בעוד שהשם הנקרא עליו מציין רק את התיחסותו לאחד הכללים באחד מצדדיו, לא בכולם. – על משפטים ממין זה תוכל להשען, וגם תשען באמת, ההסכמה הכללית ביחוסה אלינו: פלוני ופלוני הם יהודים לפי שמם ורמאים לפי תכוּנתם; שמע מינה, שהיהודים הם לפי תכונתם רמאים. ההגיון האמתי ישיב אמנם על זה, כי אף אם היו באמת כל היהודים בדורנו רמאים, אין מזה עוד ראיה, שהיהודים הם רמאים, כלומר, שתכוּנת הרמאוּת הנמצאת בכל יהודי נמצאת בו מצד התיחסותו אל הכלל ‘יהודים’ ולא מצד איזה כלל אחר (למשל, כלל ‘סוחרים’), שגם אליו מתיחס היהודי בתור פרט, ביחד עם אחרים אשר דבר אין להם עם הכלל ‘יהודים’. וכדי לברר הדבר, צריך לבדוֹק תחלה אותם ‘האחרים’ המשתתפים יחד עם היהודים בכללים אחרים. ורק אחר שנמצא על ידי בדיקה זו, שאין תכוּנת הרמאוּת מצויה בשום ‘כלל’ אחר המשותף ליהודים ולאחרים, – רק אז תהיה לנו צדקה לחרוץ משפט, כי היהדות היא אֵם הרמאוּת. – אבל, כאמור, אין דרכם של בני אדם להעמיק בהגיון, ואין אנו יכולים לדרוש כזאת גם מהמון בני עמנו. הם שומעים את המשפט החרוץ של ההסכמה הכללית ורואים עם זה, שרבים בקרבּנוּ כך הם באמת כמו שאומרת ההסכמה, ובזה די להם, והרי הם מתחילים להסכים גם בעצמם. וככה עוברות ‘תכוּנות היהודים’ כמטבע כשרה מיד ליד, מן ההסכמה החיצונית של העמים אל ההסכמה הפנימית בקרב עמנו, רק עם ההבדל הזה, שהעמים מונים את תכוּנותינו הרעות אחת לאחת בקול ענוֹת גבוּרה ולעג השאננים, ואנחנו עונים אחריהם מלה במלה בקול דממה דקה והצטדקות חלושה; הם ממשילים אותנו לכלי חרס, שאין לו תקנה אלא שבירה, ואנחנו ממשילים עצמנו לכלי מתכת, שאפשר לו בהגעלה ולבּוּן…\n\nהמצב הזה, אם יאריך ימים, יוכל לגרום לנו נזק מוסרי גדול. אין דבר מסוכּן לגוי ולאדם כהודאה על חטאים שאין בו. מי שחטא באמת, הרי שערי תשובה לא ננעלו, וברצונו הטוב יכול להסיר חלאתו מעליו. אבל מי שאחרים הביאוהו לחשוֹד עצמו במה שאין בו, איך יוכל להטהר בעיני עצמו? מצד אחד מאמין הוא לדברי האומרים לו: טול קורה מבין עיניך, ומצד אחר מרגיש הוא, שאינו יכול לטול את הקורה מבין עיניו, אחר שאינה באמת אלא בדמיון, והרי הוא במצב אותם המונומַנים הידועים, שמאיזו סבּה באו לידי אמונה, כי משׂא כבד תלוי להם בחוטמם מבלי שיוכלו להסירו. ולא עוד אלא שלפעמים תביא אמונה זו את האיש הפרטי להשתתף באותה המדה המגוּנה שלפי אמונתו היא קנין הכלל כולו, אעפ“י שהוא עצמו מצד פרטיותו אינו נוטה כלל לזה. אין ספק, למשל, כי בקרב העם שיצאו מתוכו אנשים כהרמב”ם נמצאים גם עתה בעלי דעה מיושבת ואוהבי סדר ושיטה בכל דבר, והם, בקחתם חלק בעבודת הצבּוּר, היו יכולים לתת בה את רוחם ולפעול גם על יתר העובדים. אבל מה נעשׂה, וכל גזרה ‘ההסכמה’, ששׂנאת הסדרים היא תכוּנה יהודית, וכבר הסכמנו גם אנחנו להסכמה זו (אעפ"י שעוד לא נתברר, אם התכוּנה הזאת, המצויה באמת בחלק גדול מעמנו, מתיחסת אל הכלל ‘יהודים’, או אולי – מה שיותר מתקבל על הלב – אל הכלל ‘חניכי־החדר’). ועל כן תרפינה ידי אוהבי הסדר, בהאמינם, כי אין עצה ואין תבונה נגד תכוּנת העם. ואם פטריוטים הם, יעקרו גם מלבם את האהבה לסדרים, המתנגדת לרוח עמם, ויעשׂו גם הם את מעשׂיהם כראוי ליהודים אמתיים…\n\n\n\nצריך איפוא לבקש איזה אמצעי, איך להוציא את עצמנו מתחת השפעת ‘ההסכמה הכללית’ בנוגע לתכוּנות ישׂראל וערכו המוסרי, כדי שלא נהיה בזויים בעיני עצמנו ולא נחשוב, שבאמת גרועים אנחנו מכל בני האדם תחת השמש, וכדי שלא נבוא עי"ז להיות ברבות הימים בפועל מה שאין אנו עתה אלא בדמיון.\n\nואת האמצעי הזה נותנת לנו ‘ההסכמה הכללית’ עצמה על ידי עלילת־הדם. העלילה הזאת היא היחידה בין כל רעותיה אשר בה לא תוכל ההסכמה להביא גם אותנו לידי ספק, אם באמת ‘כל העולם חייבים ואנחנו זכאים’, בהיותה מיוסדת כולה על שקר מוחלט ואין לה משען באיזה היקש מוטעה ‘מן הפרט על הכלל’. כל איש ישׂראל שנתחנך בתוך עמו יודע בבירור גמור, שאין בתוך כלל ישׂראל אף פרט אחד האוכל דם אדם לשם שמים. ואת הידיעה הברורה הזאת משגיאת ‘ההסכמה הכללית’, המתחדשת בלבנו מזמן לזמן על ידי התחדשות עלילת־הדם, צריכים אנו לשמור תמיד בזכרוננו, והיא תעזור לנו לעקור מלבנו את הנטיה להכּנע מפני האַבטוֹריטט של ‘כל העולם’ גם ביתר הדברים. יאמר כל העולם מה שיאמר על דבר פחיתוּת ערכּנוּ המוסרי, – אנחנו יודעים, כי ‘ההסכמה’ הזאת נשענת רק על הגיון המוני, בלי כל יסוד מדעי אמתּי. כי מי בא בסוד עמקי רוחנו וראה את ‘היהודי’ כמו שהוא מצד עצמו? מי שקל זה לעומת זה יהודים ושאינם יהודים הדומים אלו לאלו בכל יתר ‘הכללים’: סוחרים לעומת סוחרים, נרדפים לעומת נרדפים, רעבים לעומת רעבים וכו\'. – מי שקל כל אלה במאזני החכמה האמתּית ומצא את הכף מַכרעת לאחד הצדדים?\n\n‘וכי אפשר שכּל העולם חייבים והיהודים זכאים?’\n\nאפשר ואפשר, ועלילת־הדם תוכיח. פה הרי היהודים זכאים וטהורים כמלאכי השרת: יהודי ודם! היש שני הפכים גדולים מאלו? – ואף על פי כן…\n\n\n\nה\' תשרי תרנ"ג\n\n\n\n\n\n\nנדפס ב‘המליץ’ י“ד תשרי תרנ”ג. \xa0↩\n\n\n\n\n\n\n\n\n\n\nאת הטקסט לעיל הפיקו מתנדבי פרויקט בן־יהודה באינטרנט. הוא זמין תמיד בכתובת הבאה:https://benyehuda.org/read/10' } ``` ### Data Fields - `authors` - `genre` - `id` - `original_language` - `source_edition` - `text` - `title` - `translators` - `url` ### Data Splits \| \| train \| \|--------\|------:\| \| corpus \| 10078 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? Researchers. ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information MIT License Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ### Citation Information ``` @article{, author = {}, title = {Public domain texts from Project Ben-Yehuda}, journal = {}, url = {https://github.com/projectbenyehuda/public_domain_dump}, year = {2020}, } ``` ### Contributions Thanks to [@imvladikon](https://github.com/imvladikon) for adding this dataset.
hebrew_sentiment	--- annotations_creators: - expert-generated language_creators: - found language: - he license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification paperswithcode_id: modern-hebrew-sentiment-dataset pretty_name: HebrewSentiment dataset_info: - config_name: token features: - name: text dtype: string - name: label dtype: class_label: names: '0': pos '1': neg '2': off-topic splits: - name: train num_bytes: 2159738 num_examples: 10244 - name: test num_bytes: 540883 num_examples: 2560 download_size: 2593643 dataset_size: 2700621 - config_name: morph features: - name: text dtype: string - name: label dtype: class_label: names: '0': pos '1': neg '2': off-topic splits: - name: train num_bytes: 2258128 num_examples: 10221 - name: test num_bytes: 571401 num_examples: 2555 download_size: 2722672 dataset_size: 2829529 --- # Dataset Card for HebrewSentiment ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/omilab/Neural-Sentiment-Analyzer-for-Modern-Hebrew - Repository: https://github.com/omilab/Neural-Sentiment-Analyzer-for-Modern-Hebrew - Paper: http://aclweb.org/anthology/C18-1190 - Leaderboard: - Point of Contact: ### Dataset Summary HebrewSentiment is a data set consists of 12,804 user comments to posts on the official Facebook page of Israel’s president, Mr. Reuven Rivlin. In October 2015, we used the open software application Netvizz (Rieder, 2013) to scrape all the comments to all of the president’s posts in the period of June – August 2014, the first three months of Rivlin’s presidency.2 While the president’s posts aimed at reconciling tensions and called for tolerance and empathy, the sentiment expressed in the comments to the president’s posts was polarized between citizens who warmly thanked the president, and citizens that fiercely critiqued his policy. Of the 12,804 comments, 370 are neutral; 8,512 are positive, 3,922 negative. Data Annotation: ### Supported Tasks and Leaderboards Sentiment Analysis ### Languages Hebrew ## Dataset Structure tsv format: {hebrew_sentence}\t{sentiment_label} ### Data Instances רובי הייתי רוצה לראות ערביה נישאת ליהודי 1 תמונה יפיפיה-שפו 0 חייבים לעשות סוג של חרם כשכתבים שונאי ישראל עולים לשידור צריכים להעביר לערוץ אחר ואז תראו מה יעשה כוחו של הרייטינג ( בהקשר לדבריה של רינה מצליח ) 2 ### Data Fields - `text`: The modern hebrew inpput text. - `label`: The sentiment label. 0=positive , 1=negative, 2=off-topic. ### Data Splits \| \| train \| test \| \|--------------------------\|--------\|---------\| \| HebrewSentiment (token) \| 10243 \| 2559 \| \| HebrewSentiment (morph) \| 10243 \| 2559 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization User comments to posts on the official Facebook page of Israel’s president, Mr. Reuven Rivlin. In October 2015, we used the open software application Netvizz (Rieder, 2013) to scrape all the comments to all of the president’s posts in the period of June – August 2014, the first three months of Rivlin’s presidency. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process A trained researcher examined each comment and determined its sentiment value, where comments with an overall positive sentiment were assigned the value 0, comments with an overall negative sentiment were assigned the value 1, and comments that are off-topic to the post’s content were assigned the value 2. We validated the coding scheme by asking a second trained researcher to code the same data. There was substantial agreement between raters (N of agreements: 10623, N of disagreements: 2105, Coehn’s Kappa = 0.697, p = 0). #### Who are the annotators? Researchers ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators OMIlab, The Open University of Israel ### Licensing Information MIT License Copyright (c) 2018 OMIlab, The Open University of Israel Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ### Citation Information @inproceedings{amram-etal-2018-representations, title = "Representations and Architectures in Neural Sentiment Analysis for Morphologically Rich Languages: A Case Study from {M}odern {H}ebrew", author = "Amram, Adam and Ben David, Anat and Tsarfaty, Reut", booktitle = "Proceedings of the 27th International Conference on Computational Linguistics", month = aug, year = "2018", address = "Santa Fe, New Mexico, USA", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/C18-1190", pages = "2242--2252", abstract = "This paper empirically studies the effects of representation choices on neural sentiment analysis for Modern Hebrew, a morphologically rich language (MRL) for which no sentiment analyzer currently exists. We study two dimensions of representational choices: (i) the granularity of the input signal (token-based vs. morpheme-based), and (ii) the level of encoding of vocabulary items (string-based vs. character-based). We hypothesise that for MRLs, languages where multiple meaning-bearing elements may be carried by a single space-delimited token, these choices will have measurable effects on task perfromance, and that these effects may vary for different architectural designs {---} fully-connected, convolutional or recurrent. Specifically, we hypothesize that morpheme-based representations will have advantages in terms of their generalization capacity and task accuracy, due to their better OOV coverage. To empirically study these effects, we develop a new sentiment analysis benchmark for Hebrew, based on 12K social media comments, and provide two instances of these data: in token-based and morpheme-based settings. Our experiments show that representation choices empirical effects vary with architecture type. While fully-connected and convolutional networks slightly prefer token-based settings, RNNs benefit from a morpheme-based representation, in accord with the hypothesis that explicit morphological information may help generalize. Our endeavour also delivers the first state-of-the-art broad-coverage sentiment analyzer for Hebrew, with over 89{\%} accuracy, alongside an established benchmark to further study the effects of linguistic representation choices on neural networks{'} task performance.", } ### Contributions Thanks to [@elronbandel](https://github.com/elronbandel) for adding this dataset.
hebrew_this_world	--- annotations_creators: - expert-generated language_creators: - found language: - he license: - agpl-3.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: null pretty_name: HebrewSentiment dataset_info: features: - name: issue_num dtype: int64 - name: page_count dtype: int64 - name: date dtype: string - name: date_he dtype: string - name: year dtype: string - name: href dtype: string - name: pdf dtype: string - name: coverpage dtype: string - name: backpage dtype: string - name: content dtype: string - name: url dtype: string splits: - name: train num_bytes: 678389435 num_examples: 2028 download_size: 678322912 dataset_size: 678389435 --- # Dataset Card for HebrewSentiment ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://thisworld.online/ - Repository: https://github.com/thisworld1/thisworld.online - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary HebrewThisWorld is a data set consists of 2028 issues of the newspaper 'This World' edited by Uri Avnery and were published between 1950 and 1989. Released under the AGPLv3 license. Data Annotation: ### Supported Tasks and Leaderboards Language modeling ### Languages Hebrew ## Dataset Structure csv file with "," delimeter ### Data Instances Sample: ```json { "issue_num": 637, "page_count": 16, "date": "1950-01-01", "date_he": "1 בינואר 1950", "year": "1950", "href": "https://thisworld.online/1950/637", "pdf": "https://olam.eu-central-1.linodeobjects.com/pdfs/B-I0637-D010150.pdf", "coverpage": "https://olam.eu-central-1.linodeobjects.com/pages/637/t-1.png", "backpage": "https://olam.eu-central-1.linodeobjects.com/pages/637/t-16.png", "content": "\nלפיד\nהנוער ־ בירושלים צילומים :\n\nב. רותנברג\n\nוזהו הלפיד\n...", "url": "https://thisworld.online/api/1950/637" } ``` ### Data Fields - `issue_num`: ID/Number of the issue - `page_count`: Page count of the current issue - `date`: Published date - `date_he`: Published date in Hebrew - `year`: Year of the issue - `href`: URL to the issue to scan/print etc. - `pdf`: URL to the issue to scan in pdf - `coverpage`: URL to coverpage - `backpage`: URL to backpage - `content`: text content of the issue - `url`: URL ### Data Splits \| \| train \| \|--------\|------:\| \| corpus \| 2028 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [thisworld.online](https://thisworld.online/) #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? Researchers ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information GNU AGPLv3+ This is free software, and you are welcome to redistribute it under certain conditions. This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details. You should have received a copy of the GNU Affero General Public License along with this program. If not, see <http://www.gnu.org/licenses/>. ### Citation Information https://thisworld.online/ ### Contributions Thanks to [@lhoestq](https://github.com/lhoestq), [@imvladikon](https://github.com/imvladikon) for adding this dataset.
hellaswag	--- language: - en paperswithcode_id: hellaswag pretty_name: HellaSwag dataset_info: features: - name: ind dtype: int32 - name: activity_label dtype: string - name: ctx_a dtype: string - name: ctx_b dtype: string - name: ctx dtype: string - name: endings sequence: string - name: source_id dtype: string - name: split dtype: string - name: split_type dtype: string - name: label dtype: string splits: - name: train num_bytes: 43232624 num_examples: 39905 - name: test num_bytes: 10791853 num_examples: 10003 - name: validation num_bytes: 11175717 num_examples: 10042 download_size: 71494896 dataset_size: 65200194 --- # Dataset Card for "hellaswag" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://rowanzellers.com/hellaswag/](https://rowanzellers.com/hellaswag/) - Repository: [https://github.com/rowanz/hellaswag/](https://github.com/rowanz/hellaswag/) - Paper: [HellaSwag: Can a Machine Really Finish Your Sentence?](https://aclanthology.org/P19-1472.pdf) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 71.49 MB - Size of the generated dataset: 65.32 MB - Total amount of disk used: 136.81 MB ### Dataset Summary HellaSwag: Can a Machine Really Finish Your Sentence? is a new dataset for commonsense NLI. A paper was published at ACL2019. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 71.49 MB - Size of the generated dataset: 65.32 MB - Total amount of disk used: 136.81 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "activity_label": "Removing ice from car", "ctx": "Then, the man writes over the snow covering the window of a car, and a woman wearing winter clothes smiles. then", "ctx_a": "Then, the man writes over the snow covering the window of a car, and a woman wearing winter clothes smiles.", "ctx_b": "then", "endings": "[\", the man adds wax to the windshield and cuts it.\", \", a person board a ski lift, while two men supporting the head of the per...", "ind": 4, "label": "3", "source_id": "activitynet~v_-1IBHYS3L-Y", "split": "train", "split_type": "indomain" } ``` ### Data Fields The data fields are the same among all splits. #### default - `ind`: a `int32` feature. - `activity_label`: a `string` feature. - `ctx_a`: a `string` feature. - `ctx_b`: a `string` feature. - `ctx`: a `string` feature. - `endings`: a `list` of `string` features. - `source_id`: a `string` feature. - `split`: a `string` feature. - `split_type`: a `string` feature. - `label`: a `string` feature. ### Data Splits \| name \|train\|validation\|test \| \|-------\|----:\|---------:\|----:\| \|default\|39905\| 10042\|10003\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information MIT https://github.com/rowanz/hellaswag/blob/master/LICENSE ### Citation Information ``` @inproceedings{zellers2019hellaswag, title={HellaSwag: Can a Machine Really Finish Your Sentence?}, author={Zellers, Rowan and Holtzman, Ari and Bisk, Yonatan and Farhadi, Ali and Choi, Yejin}, booktitle ={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics}, year={2019} } ``` ### Contributions Thanks to [@albertvillanova](https://github.com/albertvillanova), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.
hendrycks_test	--- annotations_creators: - no-annotation language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: mmlu pretty_name: Measuring Massive Multitask Language Understanding language_bcp47: - en-US dataset_info: - config_name: abstract_algebra features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 19328 num_examples: 100 - name: validation num_bytes: 2024 num_examples: 11 - name: dev num_bytes: 830 num_examples: 5 download_size: 166184960 dataset_size: 160623559 - config_name: anatomy features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 33121 num_examples: 135 - name: validation num_bytes: 3140 num_examples: 14 - name: dev num_bytes: 967 num_examples: 5 download_size: 166184960 dataset_size: 160638605 - config_name: astronomy features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 46771 num_examples: 152 - name: validation num_bytes: 5027 num_examples: 16 - name: dev num_bytes: 2076 num_examples: 5 download_size: 166184960 dataset_size: 160655251 - config_name: business_ethics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 33252 num_examples: 100 - name: validation num_bytes: 3038 num_examples: 11 - name: dev num_bytes: 2190 num_examples: 5 download_size: 166184960 dataset_size: 160639857 - config_name: clinical_knowledge features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 62754 num_examples: 265 - name: validation num_bytes: 6664 num_examples: 29 - name: dev num_bytes: 1210 num_examples: 5 download_size: 166184960 dataset_size: 160672005 - config_name: college_biology features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 48797 num_examples: 144 - name: validation num_bytes: 4819 num_examples: 16 - name: dev num_bytes: 1532 num_examples: 5 download_size: 166184960 dataset_size: 160656525 - config_name: college_chemistry features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 24708 num_examples: 100 - name: validation num_bytes: 2328 num_examples: 8 - name: dev num_bytes: 1331 num_examples: 5 download_size: 166184960 dataset_size: 160629744 - config_name: college_computer_science features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 42641 num_examples: 100 - name: validation num_bytes: 4663 num_examples: 11 - name: dev num_bytes: 2765 num_examples: 5 download_size: 166184960 dataset_size: 160651446 - config_name: college_mathematics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 24711 num_examples: 100 - name: validation num_bytes: 2668 num_examples: 11 - name: dev num_bytes: 1493 num_examples: 5 download_size: 166184960 dataset_size: 160630249 - config_name: college_medicine features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 82397 num_examples: 173 - name: validation num_bytes: 7909 num_examples: 22 - name: dev num_bytes: 1670 num_examples: 5 download_size: 166184960 dataset_size: 160693353 - config_name: college_physics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 30181 num_examples: 102 - name: validation num_bytes: 3490 num_examples: 11 - name: dev num_bytes: 1412 num_examples: 5 download_size: 166184960 dataset_size: 160636460 - config_name: computer_security features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 27124 num_examples: 100 - name: validation num_bytes: 4549 num_examples: 11 - name: dev num_bytes: 1101 num_examples: 5 download_size: 166184960 dataset_size: 160634151 - config_name: conceptual_physics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 40709 num_examples: 235 - name: validation num_bytes: 4474 num_examples: 26 - name: dev num_bytes: 934 num_examples: 5 download_size: 166184960 dataset_size: 160647494 - config_name: econometrics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 46547 num_examples: 114 - name: validation num_bytes: 4967 num_examples: 12 - name: dev num_bytes: 1644 num_examples: 5 download_size: 166184960 dataset_size: 160654535 - config_name: electrical_engineering features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 25142 num_examples: 145 - name: validation num_bytes: 2903 num_examples: 16 - name: dev num_bytes: 972 num_examples: 5 download_size: 166184960 dataset_size: 160630394 - config_name: elementary_mathematics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 70108 num_examples: 378 - name: validation num_bytes: 8988 num_examples: 41 - name: dev num_bytes: 1440 num_examples: 5 download_size: 166184960 dataset_size: 160681913 - config_name: formal_logic features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 49785 num_examples: 126 - name: validation num_bytes: 6252 num_examples: 14 - name: dev num_bytes: 1757 num_examples: 5 download_size: 166184960 dataset_size: 160659171 - config_name: global_facts features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 18403 num_examples: 100 - name: validation num_bytes: 1865 num_examples: 10 - name: dev num_bytes: 1229 num_examples: 5 download_size: 166184960 dataset_size: 160622874 - config_name: high_school_biology features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 109732 num_examples: 310 - name: validation num_bytes: 11022 num_examples: 32 - name: dev num_bytes: 1673 num_examples: 5 download_size: 166184960 dataset_size: 160723804 - config_name: high_school_chemistry features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 58464 num_examples: 203 - name: validation num_bytes: 7092 num_examples: 22 - name: dev num_bytes: 1220 num_examples: 5 download_size: 166184960 dataset_size: 160668153 - config_name: high_school_computer_science features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 44476 num_examples: 100 - name: validation num_bytes: 3343 num_examples: 9 - name: dev num_bytes: 2918 num_examples: 5 download_size: 166184960 dataset_size: 160652114 - config_name: high_school_european_history features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 270300 num_examples: 165 - name: validation num_bytes: 29632 num_examples: 18 - name: dev num_bytes: 11564 num_examples: 5 download_size: 166184960 dataset_size: 160912873 - config_name: high_school_geography features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 42034 num_examples: 198 - name: validation num_bytes: 4332 num_examples: 22 - name: dev num_bytes: 1403 num_examples: 5 download_size: 166184960 dataset_size: 160649146 - config_name: high_school_government_and_politics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 66074 num_examples: 193 - name: validation num_bytes: 7063 num_examples: 21 - name: dev num_bytes: 1779 num_examples: 5 download_size: 166184960 dataset_size: 160676293 - config_name: high_school_macroeconomics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 117687 num_examples: 390 - name: validation num_bytes: 13020 num_examples: 43 - name: dev num_bytes: 1328 num_examples: 5 download_size: 166184960 dataset_size: 160733412 - config_name: high_school_mathematics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 54854 num_examples: 270 - name: validation num_bytes: 5765 num_examples: 29 - name: dev num_bytes: 1297 num_examples: 5 download_size: 166184960 dataset_size: 160663293 - config_name: high_school_microeconomics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 75703 num_examples: 238 - name: validation num_bytes: 7553 num_examples: 26 - name: dev num_bytes: 1298 num_examples: 5 download_size: 166184960 dataset_size: 160685931 - config_name: high_school_physics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 59538 num_examples: 151 - name: validation num_bytes: 6771 num_examples: 17 - name: dev num_bytes: 1489 num_examples: 5 download_size: 166184960 dataset_size: 160669175 - config_name: high_school_psychology features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 159407 num_examples: 545 - name: validation num_bytes: 17269 num_examples: 60 - name: dev num_bytes: 1905 num_examples: 5 download_size: 166184960 dataset_size: 160779958 - config_name: high_school_statistics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 110702 num_examples: 216 - name: validation num_bytes: 9997 num_examples: 23 - name: dev num_bytes: 2528 num_examples: 5 download_size: 166184960 dataset_size: 160724604 - config_name: high_school_us_history features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 296734 num_examples: 204 - name: validation num_bytes: 31706 num_examples: 22 - name: dev num_bytes: 8864 num_examples: 5 download_size: 166184960 dataset_size: 160938681 - config_name: high_school_world_history features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 378617 num_examples: 237 - name: validation num_bytes: 45501 num_examples: 26 - name: dev num_bytes: 4882 num_examples: 5 download_size: 166184960 dataset_size: 161030377 - config_name: human_aging features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 46098 num_examples: 223 - name: validation num_bytes: 4707 num_examples: 23 - name: dev num_bytes: 1008 num_examples: 5 download_size: 166184960 dataset_size: 160653190 - config_name: human_sexuality features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 32110 num_examples: 131 - name: validation num_bytes: 2421 num_examples: 12 - name: dev num_bytes: 1077 num_examples: 5 download_size: 166184960 dataset_size: 160636985 - config_name: international_law features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 53531 num_examples: 121 - name: validation num_bytes: 6473 num_examples: 13 - name: dev num_bytes: 2418 num_examples: 5 download_size: 166184960 dataset_size: 160663799 - config_name: jurisprudence features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 33986 num_examples: 108 - name: validation num_bytes: 3729 num_examples: 11 - name: dev num_bytes: 1303 num_examples: 5 download_size: 166184960 dataset_size: 160640395 - config_name: logical_fallacies features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 50117 num_examples: 163 - name: validation num_bytes: 5103 num_examples: 18 - name: dev num_bytes: 1573 num_examples: 5 download_size: 166184960 dataset_size: 160658170 - config_name: machine_learning features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 33880 num_examples: 112 - name: validation num_bytes: 3232 num_examples: 11 - name: dev num_bytes: 2323 num_examples: 5 download_size: 166184960 dataset_size: 160640812 - config_name: management features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 20002 num_examples: 103 - name: validation num_bytes: 1820 num_examples: 11 - name: dev num_bytes: 898 num_examples: 5 download_size: 166184960 dataset_size: 160624097 - config_name: marketing features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 63025 num_examples: 234 - name: validation num_bytes: 7394 num_examples: 25 - name: dev num_bytes: 1481 num_examples: 5 download_size: 166184960 dataset_size: 160673277 - config_name: medical_genetics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 20864 num_examples: 100 - name: validation num_bytes: 3005 num_examples: 11 - name: dev num_bytes: 1089 num_examples: 5 download_size: 166184960 dataset_size: 160626335 - config_name: miscellaneous features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 147704 num_examples: 783 - name: validation num_bytes: 14330 num_examples: 86 - name: dev num_bytes: 699 num_examples: 5 download_size: 166184960 dataset_size: 160764110 - config_name: moral_disputes features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 107818 num_examples: 346 - name: validation num_bytes: 12420 num_examples: 38 - name: dev num_bytes: 1755 num_examples: 5 download_size: 166184960 dataset_size: 160723370 - config_name: moral_scenarios features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 374026 num_examples: 895 - name: validation num_bytes: 42338 num_examples: 100 - name: dev num_bytes: 2058 num_examples: 5 download_size: 166184960 dataset_size: 161019799 - config_name: nutrition features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 92410 num_examples: 306 - name: validation num_bytes: 8436 num_examples: 33 - name: dev num_bytes: 2085 num_examples: 5 download_size: 166184960 dataset_size: 160704308 - config_name: philosophy features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 80073 num_examples: 311 - name: validation num_bytes: 9184 num_examples: 34 - name: dev num_bytes: 988 num_examples: 5 download_size: 166184960 dataset_size: 160691622 - config_name: prehistory features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 89594 num_examples: 324 - name: validation num_bytes: 10285 num_examples: 35 - name: dev num_bytes: 1878 num_examples: 5 download_size: 166184960 dataset_size: 160703134 - config_name: professional_accounting features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 124550 num_examples: 282 - name: validation num_bytes: 14372 num_examples: 31 - name: dev num_bytes: 2148 num_examples: 5 download_size: 166184960 dataset_size: 160742447 - config_name: professional_law features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 1891762 num_examples: 1534 - name: validation num_bytes: 203519 num_examples: 170 - name: dev num_bytes: 6610 num_examples: 5 download_size: 166184960 dataset_size: 162703268 - config_name: professional_medicine features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 217561 num_examples: 272 - name: validation num_bytes: 23847 num_examples: 31 - name: dev num_bytes: 3807 num_examples: 5 download_size: 166184960 dataset_size: 160846592 - config_name: professional_psychology features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 225899 num_examples: 612 - name: validation num_bytes: 29101 num_examples: 69 - name: dev num_bytes: 2267 num_examples: 5 download_size: 166184960 dataset_size: 160858644 - config_name: public_relations features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 28760 num_examples: 110 - name: validation num_bytes: 4566 num_examples: 12 - name: dev num_bytes: 1496 num_examples: 5 download_size: 166184960 dataset_size: 160636199 - config_name: security_studies features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 204844 num_examples: 245 - name: validation num_bytes: 22637 num_examples: 27 - name: dev num_bytes: 5335 num_examples: 5 download_size: 166184960 dataset_size: 160834193 - config_name: sociology features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 66243 num_examples: 201 - name: validation num_bytes: 7184 num_examples: 22 - name: dev num_bytes: 1613 num_examples: 5 download_size: 166184960 dataset_size: 160676417 - config_name: us_foreign_policy features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 28443 num_examples: 100 - name: validation num_bytes: 3264 num_examples: 11 - name: dev num_bytes: 1611 num_examples: 5 download_size: 166184960 dataset_size: 160634695 - config_name: virology features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 38759 num_examples: 166 - name: validation num_bytes: 5463 num_examples: 18 - name: dev num_bytes: 1096 num_examples: 5 download_size: 166184960 dataset_size: 160646695 - config_name: world_religions features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 25274 num_examples: 171 - name: validation num_bytes: 2765 num_examples: 19 - name: dev num_bytes: 670 num_examples: 5 download_size: 166184960 dataset_size: 160630086 --- # Dataset Card for HendrycksTest ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Repository: https://github.com/hendrycks/test - Paper: https://arxiv.org/abs/2009.03300 ### Dataset Summary [Measuring Massive Multitask Language Understanding](https://arxiv.org/pdf/2009.03300) by [Dan Hendrycks](https://people.eecs.berkeley.edu/~hendrycks/), [Collin Burns](http://collinpburns.com), [Steven Basart](https://stevenbas.art), Andy Zou, Mantas Mazeika, [Dawn Song](https://people.eecs.berkeley.edu/~dawnsong/), and [Jacob Steinhardt](https://www.stat.berkeley.edu/~jsteinhardt/) (ICLR 2021). This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge. The test spans subjects in the humanities, social sciences, hard sciences, and other areas that are important for some people to learn. This covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability. A complete list of tasks: ['abstract_algebra', 'anatomy', 'astronomy', 'business_ethics', 'clinical_knowledge', 'college_biology', 'college_chemistry', 'college_computer_science', 'college_mathematics', 'college_medicine', 'college_physics', 'computer_security', 'conceptual_physics', 'econometrics', 'electrical_engineering', 'elementary_mathematics', 'formal_logic', 'global_facts', 'high_school_biology', 'high_school_chemistry', 'high_school_computer_science', 'high_school_european_history', 'high_school_geography', 'high_school_government_and_politics', 'high_school_macroeconomics', 'high_school_mathematics', 'high_school_microeconomics', 'high_school_physics', 'high_school_psychology', 'high_school_statistics', 'high_school_us_history', 'high_school_world_history', 'human_aging', 'human_sexuality', 'international_law', 'jurisprudence', 'logical_fallacies', 'machine_learning', 'management', 'marketing', 'medical_genetics', 'miscellaneous', 'moral_disputes', 'moral_scenarios', 'nutrition', 'philosophy', 'prehistory', 'professional_accounting', 'professional_law', 'professional_medicine', 'professional_psychology', 'public_relations', 'security_studies', 'sociology', 'us_foreign_policy', 'virology', 'world_religions'] ### Supported Tasks and Leaderboards \| Model \| Authors \| Humanities \| Social Science \| STEM \| Other \| Average \| \|------------------------------------\|----------\|:-------:\|:-------:\|:-------:\|:-------:\|:-------:\| \| [UnifiedQA](https://arxiv.org/abs/2005.00700) \| Khashabi et al., 2020 \| 45.6 \| 56.6 \| 40.2 \| 54.6 \| 48.9 \| [GPT-3](https://arxiv.org/abs/2005.14165) (few-shot) \| Brown et al., 2020 \| 40.8 \| 50.4 \| 36.7 \| 48.8 \| 43.9 \| [GPT-2](https://arxiv.org/abs/2005.14165) \| Radford et al., 2019 \| 32.8 \| 33.3 \| 30.2 \| 33.1 \| 32.4 \| Random Baseline \| N/A \| 25.0 \| 25.0 \| 25.0 \| 25.0 \| 25.0 \| 25.0 ### Languages English ## Dataset Structure ### Data Instances An example from anatomy subtask looks as follows: ``` { "question": "What is the embryological origin of the hyoid bone?", "choices": ["The first pharyngeal arch", "The first and second pharyngeal arches", "The second pharyngeal arch", "The second and third pharyngeal arches"], "answer": "D" } ``` ### Data Fields - `question`: a string feature - `choices`: a list of 4 string features - `answer`: a ClassLabel feature ### Data Splits - `auxiliary_train`: auxiliary multiple-choice training questions from ARC, MC_TEST, OBQA, RACE, etc. - `dev`: 5 examples per subtask, meant for few-shot setting - `test`: there are at least 100 examples per subtask \| \| auxiliary_train \| dev \| val \| test \| \| ----- \| :------: \| :-----: \| :-----: \| :-----: \| \| TOTAL \| 99842 \| 285 \| 1531 \| 14042 ## Dataset Creation ### Curation Rationale Transformer models have driven this recent progress by pretraining on massive text corpora, including all of Wikipedia, thousands of books, and numerous websites. These models consequently see extensive information about specialized topics, most of which is not assessed by existing NLP benchmarks. To bridge the gap between the wide-ranging knowledge that models see during pretraining and the existing measures of success, we introduce a new benchmark for assessing models across a diverse set of subjects that humans learn. ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [MIT License](https://github.com/hendrycks/test/blob/master/LICENSE) ### Citation Information If you find this useful in your research, please consider citing the test and also the [ETHICS](https://arxiv.org/abs/2008.02275) dataset it draws from: ``` @article{hendryckstest2021, title={Measuring Massive Multitask Language Understanding}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} } @article{hendrycks2021ethics, title={Aligning AI With Shared Human Values}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} } ``` ### Contributions Thanks to [@andyzoujm](https://github.com/andyzoujm) for adding this dataset.
hind_encorp	--- annotations_creators: - expert-generated language_creators: - crowdsourced - machine-generated language: - en - hi license: - cc-by-nc-sa-3.0 multilinguality: - translation size_categories: - 100K<n<1M source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: hindencorp pretty_name: HindEnCorp dataset_info: features: - name: id dtype: string - name: source dtype: string - name: alignment_type dtype: string - name: alignment_quality dtype: string - name: translation dtype: translation: languages: - en - hi splits: - name: train num_bytes: 78945714 num_examples: 273885 download_size: 23899723 dataset_size: 78945714 --- # Dataset Card for HindEnCorp ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0023-625F-0 - Repository: https://lindat.mff.cuni.cz/repository/xmlui/ - Paper: http://www.lrec-conf.org/proceedings/lrec2014/pdf/835_Paper.pdf - Leaderboard: - Point of Contact: ### Dataset Summary HindEnCorp parallel texts (sentence-aligned) come from the following sources: Tides, which contains 50K sentence pairs taken mainly from news articles. This dataset was originally col- lected for the DARPA-TIDES surprise-language con- test in 2002, later refined at IIIT Hyderabad and provided for the NLP Tools Contest at ICON 2008 (Venkatapathy, 2008). Commentaries by Daniel Pipes contain 322 articles in English written by a journalist Daniel Pipes and translated into Hindi. EMILLE. This corpus (Baker et al., 2002) consists of three components: monolingual, parallel and annotated corpora. There are fourteen monolingual sub- corpora, including both written and (for some lan- guages) spoken data for fourteen South Asian lan- guages. The EMILLE monolingual corpora contain in total 92,799,000 words (including 2,627,000 words of transcribed spoken data for Bengali, Gujarati, Hindi, Punjabi and Urdu). The parallel corpus consists of 200,000 words of text in English and its accompanying translations into Hindi and other languages. Smaller datasets as collected by Bojar et al. (2010) include the corpus used at ACL 2005 (a subcorpus of EMILLE), a corpus of named entities from Wikipedia (crawled in 2009), and Agriculture domain parallel corpus. For the current release, we are extending the parallel corpus using these sources: Intercorp (Čermák and Rosen,2012) is a large multilingual parallel corpus of 32 languages including Hindi. The central language used for alignment is Czech. Intercorp’s core texts amount to 202 million words. These core texts are most suitable for us because their sentence alignment is manually checked and therefore very reliable. They cover predominately short sto- ries and novels. There are seven Hindi texts in Inter- corp. Unfortunately, only for three of them the English translation is available; the other four are aligned only with Czech texts. The Hindi subcorpus of Intercorp contains 118,000 words in Hindi. TED talks 3 held in various languages, primarily English, are equipped with transcripts and these are translated into 102 languages. There are 179 talks for which Hindi translation is available. The Indic multi-parallel corpus (Birch et al., 2011; Post et al., 2012) is a corpus of texts from Wikipedia translated from the respective Indian language into English by non-expert translators hired over Mechanical Turk. The quality is thus somewhat mixed in many respects starting from typesetting and punctuation over capi- talization, spelling, word choice to sentence structure. A little bit of control could be in principle obtained from the fact that every input sentence was translated 4 times. We used the 2012 release of the corpus. Launchpad.net is a software collaboration platform that hosts many open-source projects and facilitates also collaborative localization of the tools. We downloaded all revisions of all the hosted projects and extracted the localization (.po) files. Other smaller datasets. This time, we added Wikipedia entities as crawled in 2013 (including any morphological variants of the named entitity that appears on the Hindi variant of the Wikipedia page) and words, word examples and quotes from the Shabdkosh online dictionary. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Hindi, English ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields HindEncorp Columns: - source identifier (where do the segments come from) - alignment type (number of English segments - number of Hindi segments) - alignment quality, which is one of the following: "manual" ... for sources that were sentence-aligned manually "implied" ... for sources where one side was constructed by translating segment by segment float ... a value somehow reflecting the goodness of the automatic alignment; not really reliable - English segment or segments - Hindi segment or segments Each of the segments field is in the plaintext or export format as described above. If there are more than one segments on a line (e.g. for lines with alignment type 2-1 where there are two English segments), then the segments are delimited with `<s>` in the text field. ### Data Splits [More Information Needed] ## Dataset Creation ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? Daniel Pipes,Baker,Bojar,"Čermák and Rosen,2012","Birch et al., 2011; Post et al., 2012" ### Annotations #### Annotation process the 1st part of data TIDES was originally col- lected for the DARPA-TIDES surprise-language con- test in 2002, later refined at IIIT Hyderabad and provided for the NLP Tools Contest at ICON 2008 (Venkatapathy, 2008). #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators Bojar, Ondřej ; Diatka, Vojtěch ; Straňák, Pavel ; Tamchyna, Aleš ; Zeman, Daniel ### Licensing Information CC BY-NC-SA 3.0 ### Citation Information @InProceedings{hindencorp05:lrec:2014, author = {Ond{\v{r}}ej Bojar and Vojt{\v{e}}ch Diatka and Pavel Rychl{\'{y}} and Pavel Stra{\v{n}}{\'{a}}k and V{\'{\i}}t Suchomel and Ale{\v{s}} Tamchyna and Daniel Zeman}, title = "{HindEnCorp - Hindi-English and Hindi-only Corpus for Machine Translation}", booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, month = {may}, date = {26-31}, address = {Reykjavik, Iceland}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-8-4}, language = {english} } ### Contributions Thanks to [@rahul-art](https://github.com/rahul-art) for adding this dataset.
hindi_discourse	--- annotations_creators: - other language_creators: - found language: - hi license: - other multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - multi-label-classification pretty_name: Discourse Analysis dataset tags: - discourse-analysis dataset_info: features: - name: Story_no dtype: int32 - name: Sentence dtype: string - name: Discourse Mode dtype: class_label: names: '0': Argumentative '1': Descriptive '2': Dialogue '3': Informative '4': Narrative '5': Other splits: - name: train num_bytes: 1998930 num_examples: 9968 download_size: 4176677 dataset_size: 1998930 --- # Dataset Card for Discourse Analysis dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: - Repository: https://github.com/midas-research/hindi-discourse - Paper: [An Annotated Dataset of Discourse Modes in Hindi Stories](https://aclanthology.org/2020.lrec-1.149/) - Point of Contact: https://github.com/midas-research/MeTooMA ### Dataset Summary - The Hindi Discourse Analysis dataset is a corpus for analyzing discourse modes present in its sentences. - It contains sentences from stories written by 11 famous authors from the 20th Century. - 4-5 stories by each author have been selected which were available in the public domain resulting in a collection of 53 stories. - Most of these short stories were originally written in Hindi but some of them were written in other Indian languages and later translated to Hindi. The corpus contains a total of 10472 sentences belonging to the following categories: - Argumentative - Descriptive - Dialogic - Informative - Narrative ### Supported Tasks and Leaderboards - Discourse Analysis of Hindi. ### Languages Hindi ## Dataset Structure - The dataset is structured into JSON format. ### Data Instances {'Story_no': 15, 'Sentence': ' गाँठ से साढ़े तीन रुपये लग गये, जो अब पेट में जाकर खनकते भी नहीं! जो तेरी करनी मालिक! ” “इसमें मालिक की क्या करनी है? ”', 'Discourse Mode': 'Dialogue'} ### Data Fields Sentence number, story number, sentence and discourse mode ### Data Splits - Train: 9983 ## Dataset Creation ### Curation Rationale - Present a new publicly available corpus consisting of sentences from short stories written in a low-resource language of Hindi having high quality annotation for five different discourse modes - argumentative, narrative, descriptive, dialogic and informative. - Perform a detailed analysis of the proposed annotated corpus and characterize the performance of different classification algorithms. ### Source Data - Source of all the data points in this dataset is Hindi stories written by famous authors of Hindi literature. #### Initial Data Collection and Normalization - All the data was collected from various Hindi websites. - We chose against crowd-sourcing the annotation pro- cess because we wanted to directly work with the an- notators for qualitative feedback and to also ensure high quality annotations. - We employed three native Hindi speakers with college level education for the an- notation task. - We first selected two random stories from our corpus and had the three annotators work on them independently and classify each sentence based on the discourse mode. - Please refer to this paper for detailed information: https://www.aclweb.org/anthology/2020.lrec-1.149/ #### Who are the source language producers? Please refer to this paper for detailed information: https://www.aclweb.org/anthology/2020.lrec-1.149/ ### Annotations #### Annotation process - The authors chose against crowd sourcing for labeling this dataset due to its highly sensitive nature. - The annotators are domain experts having degress in advanced clinical psychology and gender studies. - They were provided a guidelines document with instructions about each task and its definitions, labels and examples. - They studied the document, worked a few examples to get used to this annotation task. - They also provided feedback for improving the class definitions. - The annotation process is not mutually exclusive, implying that presence of one label does not mean the absence of the other one. #### Who are the annotators? - The annotators were three native Hindi speakers with college level education. - Please refer to the accompnaying paper for a detailed annotation process. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset - As a future work we would also like to use the presented corpus to see how it could be further used in certain downstream tasks such as emotion analysis, machine translation, textual entailment, and speech sythesis for improving storytelling experience in Hindi language. ### Discussion of Biases [More Information Needed] ### Other Known Limitations - We could not get the best performance using the deep learning model trained on the data, due to insufficient data for DL models. ## Additional Information Please refer to this link: https://github.com/midas-research/hindi-discourse ### Dataset Curators - If you use the corpus in a product or application, then please credit the authors and [Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi] (http://midas.iiitd.edu.in) appropriately. Also, if you send us an email, we will be thrilled to know about how you have used the corpus. - If interested in commercial use of the corpus, send email to midas@iiitd.ac.in. - Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India disclaims any responsibility for the use of the corpus and does not provide technical support. However, the contact listed above will be happy to respond to queries and clarifications - Please feel free to send us an email: - with feedback regarding the corpus. - with information on how you have used the corpus. - if interested in having us analyze your social media data. - if interested in a collaborative research project. ### Licensing Information - If you use the corpus in a product or application, then please credit the authors and [Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi] (http://midas.iiitd.edu.in) appropriately. ### Citation Information Please cite the following publication if you make use of the dataset: https://aclanthology.org/2020.lrec-1.149/ ``` @inproceedings{dhanwal-etal-2020-annotated, title = "An Annotated Dataset of Discourse Modes in {H}indi Stories", author = "Dhanwal, Swapnil and Dutta, Hritwik and Nankani, Hitesh and Shrivastava, Nilay and Kumar, Yaman and Li, Junyi Jessy and Mahata, Debanjan and Gosangi, Rakesh and Zhang, Haimin and Shah, Rajiv Ratn and Stent, Amanda", booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://www.aclweb.org/anthology/2020.lrec-1.149", pages = "1191--1196", abstract = "In this paper, we present a new corpus consisting of sentences from Hindi short stories annotated for five different discourse modes argumentative, narrative, descriptive, dialogic and informative. We present a detailed account of the entire data collection and annotation processes. The annotations have a very high inter-annotator agreement (0.87 k-alpha). We analyze the data in terms of label distributions, part of speech tags, and sentence lengths. We characterize the performance of various classification algorithms on this dataset and perform ablation studies to understand the nature of the linguistic models suitable for capturing the nuances of the embedded discourse structures in the presented corpus.", language = "English", ISBN = "979-10-95546-34-4", } ``` ### Contributions Thanks to [@duttahritwik](https://github.com/duttahritwik) for adding this dataset.
hippocorpus	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - other multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - text-scoring paperswithcode_id: null pretty_name: hippocorpus tags: - narrative-flow dataset_info: features: - name: AssignmentId dtype: string - name: WorkTimeInSeconds dtype: string - name: WorkerId dtype: string - name: annotatorAge dtype: float32 - name: annotatorGender dtype: string - name: annotatorRace dtype: string - name: distracted dtype: float32 - name: draining dtype: float32 - name: frequency dtype: float32 - name: importance dtype: float32 - name: logTimeSinceEvent dtype: string - name: mainEvent dtype: string - name: memType dtype: string - name: mostSurprising dtype: string - name: openness dtype: string - name: recAgnPairId dtype: string - name: recImgPairId dtype: string - name: similarity dtype: string - name: similarityReason dtype: string - name: story dtype: string - name: stressful dtype: string - name: summary dtype: string - name: timeSinceEvent dtype: string splits: - name: train num_bytes: 7229795 num_examples: 6854 download_size: 0 dataset_size: 7229795 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Hippocorpus](https://msropendata.com/datasets/0a83fb6f-a759-4a17-aaa2-fbac84577318) - Repository: [Hippocorpus](https://msropendata.com/datasets/0a83fb6f-a759-4a17-aaa2-fbac84577318) - Paper: [Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models](http://erichorvitz.com/cognitive_studies_narrative.pdf) - Point of Contact: [Eric Horvitz](mailto:horvitz@microsoft.com) ### Dataset Summary To examine the cognitive processes of remembering and imagining and their traces in language, we introduce Hippocorpus, a dataset of 6,854 English diary-like short stories about recalled and imagined events. Using a crowdsourcing framework, we first collect recalled stories and summaries from workers, then provide these summaries to other workers who write imagined stories. Finally, months later, we collect a retold version of the recalled stories from a subset of recalled authors. Our dataset comes paired with author demographics (age, gender, race), their openness to experience, as well as some variables regarding the author's relationship to the event (e.g., how personal the event is, how often they tell its story, etc.). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset can be found in English ## Dataset Structure [More Information Needed] ### Data Instances [More Information Needed] ### Data Fields This CSV file contains all the stories in Hippcorpus v2 (6854 stories) These are the columns in the file: - `AssignmentId`: Unique ID of this story - `WorkTimeInSeconds`: Time in seconds that it took the worker to do the entire HIT (reading instructions, storywriting, questions) - `WorkerId`: Unique ID of the worker (random string, not MTurk worker ID) - `annotatorAge`: Lower limit of the age bucket of the worker. Buckets are: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55+ - `annotatorGender`: Gender of the worker - `annotatorRace`: Race/ethnicity of the worker - `distracted`: How distracted were you while writing your story? (5-point Likert) - `draining`: How taxing/draining was writing for you emotionally? (5-point Likert) - `frequency`: How often do you think about or talk about this event? (5-point Likert) - `importance`: How impactful, important, or personal is this story/this event to you? (5-point Likert) - `logTimeSinceEvent`: Log of time (days) since the recalled event happened - `mainEvent`: Short phrase describing the main event described - `memType`: Type of story (recalled, imagined, retold) - `mostSurprising`: Short phrase describing what the most surpring aspect of the story was - `openness`: Continuous variable representing the openness to experience of the worker - `recAgnPairId`: ID of the recalled story that corresponds to this retold story (null for imagined stories). Group on this variable to get the recalled-retold pairs. - `recImgPairId`: ID of the recalled story that corresponds to this imagined story (null for retold stories). Group on this variable to get the recalled-imagined pairs. - `similarity`: How similar to your life does this event/story feel to you? (5-point Likert) - `similarityReason`: Free text annotation of similarity - `story`: Story about the imagined or recalled event (15-25 sentences) - `stressful`: How stressful was this writing task? (5-point Likert) - `summary`: Summary of the events in the story (1-3 sentences) - `timeSinceEvent`: Time (num. days) since the recalled event happened ### Data Splits [More Information Needed] ## Dataset Creation [More Information Needed] ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data [More Information Needed] ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information [More Information Needed] ### Dataset Curators The dataset was initially created by Maarten Sap, Eric Horvitz, Yejin Choi, Noah A. Smith, James W. Pennebaker, during work done at Microsoft Research. ### Licensing Information Hippocorpus is distributed under the [Open Use of Data Agreement v1.0](https://msropendata-web-api.azurewebsites.net/licenses/f1f352a6-243f-4905-8e00-389edbca9e83/view). ### Citation Information ``` @inproceedings{sap-etal-2020-recollection, title = "Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models", author = "Sap, Maarten and Horvitz, Eric and Choi, Yejin and Smith, Noah A. and Pennebaker, James", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.178", doi = "10.18653/v1/2020.acl-main.178", pages = "1970--1978", abstract = "We investigate the use of NLP as a measure of the cognitive processes involved in storytelling, contrasting imagination and recollection of events. To facilitate this, we collect and release Hippocorpus, a dataset of 7,000 stories about imagined and recalled events. We introduce a measure of narrative flow and use this to examine the narratives for imagined and recalled events. Additionally, we measure the differential recruitment of knowledge attributed to semantic memory versus episodic memory (Tulving, 1972) for imagined and recalled storytelling by comparing the frequency of descriptions of general commonsense events with more specific realis events. Our analyses show that imagined stories have a substantially more linear narrative flow, compared to recalled stories in which adjacent sentences are more disconnected. In addition, while recalled stories rely more on autobiographical events based on episodic memory, imagined stories express more commonsense knowledge based on semantic memory. Finally, our measures reveal the effect of narrativization of memories in stories (e.g., stories about frequently recalled memories flow more linearly; Bartlett, 1932). Our findings highlight the potential of using NLP tools to study the traces of human cognition in language.", } ``` ### Contributions Thanks to [@manandey](https://github.com/manandey) for adding this dataset.
hkcancor	--- annotations_creators: - expert-generated language_creators: - found language: - yue license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation - text-generation - fill-mask task_ids: - dialogue-modeling paperswithcode_id: hong-kong-cantonese-corpus pretty_name: The Hong Kong Cantonese Corpus (HKCanCor) dataset_info: features: - name: conversation_id dtype: string - name: speaker dtype: string - name: turn_number dtype: int16 - name: tokens sequence: string - name: transcriptions sequence: string - name: pos_tags_prf sequence: class_label: names: '0': '!' '1': '"' '2': '#' '3': '''' '4': ',' '5': '-' '6': . '7': '...' '8': '?' '9': A '10': AD '11': AG '12': AIRWAYS0 '13': AN '14': AND '15': B '16': BG '17': BEAN0 '18': C '19': CENTRE0 '20': CG '21': D '22': D1 '23': DG '24': E '25': ECHO0 '26': F '27': G '28': G1 '29': G2 '30': H '31': HILL0 '32': I '33': IG '34': J '35': JB '36': JM '37': JN '38': JNS '39': JNT '40': JNZ '41': K '42': KONG '43': L '44': L1 '45': LG '46': M '47': MG '48': MONTY0 '49': MOUNTAIN0 '50': N '51': N1 '52': NG '53': NR '54': NS '55': NSG '56': NT '57': NX '58': NZ '59': O '60': P '61': PEPPER0 '62': Q '63': QG '64': R '65': RG '66': S '67': SOUND0 '68': T '69': TELECOM0 '70': TG '71': TOUCH0 '72': U '73': UG '74': U0 '75': V '76': V1 '77': VD '78': VG '79': VK '80': VN '81': VU '82': VUG '83': W '84': X '85': XA '86': XB '87': XC '88': XD '89': XE '90': XJ '91': XJB '92': XJN '93': XJNT '94': XJNZ '95': XJV '96': XJA '97': XL1 '98': XM '99': XN '100': XNG '101': XNR '102': XNS '103': XNT '104': XNX '105': XNZ '106': XO '107': XP '108': XQ '109': XR '110': XS '111': XT '112': XV '113': XVG '114': XVN '115': XX '116': Y '117': YG '118': Y1 '119': Z - name: pos_tags_ud sequence: class_label: names: '0': DET '1': PRON '2': VERB '3': NOUN '4': ADJ '5': PUNCT '6': INTJ '7': ADV '8': V '9': PART '10': X '11': NUM '12': PROPN '13': AUX '14': CCONJ '15': ADP splits: - name: train num_bytes: 5746381 num_examples: 10801 download_size: 961514 dataset_size: 5746381 --- # Dataset Card for The Hong Kong Cantonese Corpus (HKCanCor) ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: http://compling.hss.ntu.edu.sg/hkcancor/ - Repository: https://github.com/fcbond/hkcancor - Paper: [Luke and Wang, 2015](https://github.com/fcbond/hkcancor/blob/master/data/LukeWong_Hong-Kong-Cantonese-Corpus.pdf) - Leaderboard: N/A - Point of Contact: Luke Kang Kwong ### Dataset Summary The Hong Kong Cantonese Corpus (HKCanCor) comprise transcribed conversations recorded between March 1997 and August 1998. It contains recordings of spontaneous speech (51 texts) and radio programmes (42 texts), which involve 2 to 4 speakers, with 1 text of monologue. In total, the corpus contains around 230,000 Chinese words. The text is word-segmented (i.e., tokenization is at word-level, and each token can span multiple Chinese characters). Tokens are annotated with part-of-speech (POS) tags and romanised Cantonese pronunciation. * Romanisation * Follows conventions set by the Linguistic Society of Hong Kong (LSHK). * POS * The tagset used by this corpus extends the one in the Peita-Fujitsu-Renmin Ribao (PRF) corpus (Duan et al., 2000). Extensions were made to further capture Cantonese-specific phenomena. * To facilitate everyday usage and for better comparability across languages and/or corpora, this dataset also includes the tags mapped to the [Universal Dependencies 2.0](https://universaldependencies.org/u/pos/index.html) format. This mapping references the [PyCantonese](https://github.com/jacksonllee/pycantonese) library. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Yue Chinese / Cantonese (Hong Kong). ## Dataset Structure This corpus has 10801 utterances and approximately 230000 Chinese words. There is no predefined split. ### Data Instances Each instance contains a conversation id, speaker id within that conversation, turn number, part-of-speech tag for each Chinese word in the PRF format and UD2.0 format, and the utterance written in Chinese characters as well as its LSHK format romanisation. For example: ```python { 'conversation_id': 'TNR016-DR070398-HAI6V' 'pos_tags_prf': ['v', 'w'], 'pos_tags_ud': ['VERB', 'PUNCT'], 'speaker': 'B', 'transcriptions': ['hai6', 'VQ1'], 'turn_number': 112, 'tokens': ['係', '。'] } ``` ### Data Fields - conversation_id: unique dialogue-level id - pos_tags_prf: POS tag using the PRF format at token-level - pos_tag_ud: POS tag using the UD2.0 format at token-level - speaker: unique speaker id within dialogue - transcriptions: token-level romanisation in the LSHK format - turn_number: turn number in dialogue - tokens: Chinese word or punctuation at token-level ### Data Splits There are no specified splits in this dataset. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information This work is licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/deed.ast). ### Citation Information This corpus was developed by [Luke and Wong, 2015](http://compling.hss.ntu.edu.sg/hkcancor/data/LukeWong_Hong-Kong-Cantonese-Corpus.pdf). ``` @article{luke2015hong, author={Luke, Kang-Kwong and Wong, May LY}, title={The Hong Kong Cantonese corpus: design and uses}, journal={Journal of Chinese Linguistics}, year={2015}, pages={309-330}, month={12} } ``` The POS tagset to Universal Dependency tagset mapping is provided by Jackson Lee, as a part of the [PyCantonese](https://github.com/jacksonllee/pycantonese) library. ``` @misc{lee2020, author = {Lee, Jackson}, title = {PyCantonese: Cantonese Linguistics and NLP in Python}, year = {2020}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/jacksonllee/pycantonese}}, commit = {1d58f44e1cb097faa69de6b617e1d28903b84b98} } ``` ### Contributions Thanks to [@j-chim](https://github.com/j-chim) for adding this dataset.
hlgd	--- annotations_creators: - crowdsourced language_creators: - expert-generated language: - en license: - apache-2.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: [] pretty_name: Headline Grouping (HLGD) tags: - headline-grouping dataset_info: features: - name: timeline_id dtype: class_label: names: '0': 0 '1': 1 '2': 2 '3': 3 '4': 4 '5': 5 '6': 6 '7': 7 '8': 8 '9': 9 - name: headline_a dtype: string - name: headline_b dtype: string - name: date_a dtype: string - name: date_b dtype: string - name: url_a dtype: string - name: url_b dtype: string - name: label dtype: class_label: names: '0': same_event '1': different_event splits: - name: train num_bytes: 6447212 num_examples: 15492 - name: test num_bytes: 941145 num_examples: 2495 - name: validation num_bytes: 798302 num_examples: 2069 download_size: 1858948 dataset_size: 8186659 --- # Dataset Card for Headline Grouping (HLGD) ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://github.com/tingofurro/headline_grouping](https://github.com/tingofurro/headline_grouping) - Repository: [https://github.com/tingofurro/headline_grouping](https://github.com/tingofurro/headline_grouping) - Paper: [https://people.eecs.berkeley.edu/~phillab/pdfs/NAACL2021_HLG.pdf](https://people.eecs.berkeley.edu/~phillab/pdfs/NAACL2021_HLG.pdf) - Leaderboard: N/A - Point of Contact: phillab (at) berkeley (dot) edu ### Dataset Summary HLGD is a binary classification dataset consisting of 20,056 labeled news headlines pairs indicating whether the two headlines describe the same underlying world event or not. The dataset comes with an existing split between `train`, `validation` and `test` (60-20-20). ### Supported Tasks and Leaderboards The paper (NAACL2021) introducing HLGD proposes three challenges making use of various amounts of data: - Challenge 1: Headline-only. Models must make predictions using only the text of both headlines. - Challenge 2: Headline + Time. Models must make predictions using the headline and publication date of the two headlines. - Challenge 3: Headline + Time + Other. Models can make predictions using the headline, publication date as well as any other relevant meta-data that can be obtained through the URL attached to the headline (full article content, authors, news source, etc.) ### Languages Dataset is in english. ## Dataset Structure ### Data Instances A typical dataset consists of a timeline_id, two headlines (A/B), each associated with a URL, and a date. Finally, a label indicates whether the two headlines describe the same underlying event (1) or not (0). Below is an example from the training set: ``` {'timeline_id': 4, 'headline_a': 'France fines Google nearly $57 million for first major violation of new European privacy regime', 'headline_b': "France hits Google with record EUR50mn fine over 'forced consent' data collection", 'date_a': '2019-01-21', 'date_b': '2019-01-21', 'url_a': 'https://www.chicagotribune.com/business/ct-biz-france-fines-google-privacy-20190121-story.html', 'url_b': 'https://www.rt.com/news/449369-france-hits-google-with-record-fine/', 'label': 1} ``` ### Data Fields - `timeline_id`: Represents the id of the timeline that the headline pair belongs to (values 0 to 9). The dev set is composed of timelines 0 and 5, and the test set timelines 7 and 8 - `headline_a`, `headline_b`: Raw text for the headline pair being compared - `date_a`, `date_b`: Publication date of the respective headlines, in the `YYYY-MM-DD` format - `url_a`, `url_b`: Original URL of the respective headlines. Can be used to retrieve additional meta-data on the headline. - `label`: 1 if the two headlines are part of the the same headline group and describe the same underlying event, 0 otherwise. ### Data Splits \| \| Train \| Dev \| Test \| \| --------------------------- \| ------- \| ------ \| ----- \| \| Number of examples \| 15,492 \| 2,069 \| 2,495 \| ## Dataset Creation ### Curation Rationale The task of grouping headlines from diverse news sources discussing a same underlying event is important to enable interfaces that can present the diversity of coverage of unfolding news events. Many news aggregators (such as Google or Yahoo news) present several sources for a given event, with an objective to highlight coverage diversity. Automatic grouping of news headlines and articles remains challenging as headlines are short, heavily-stylized texts. The HeadLine Grouping Dataset introduces the first benchmark to evaluate NLU model's ability to group headlines according to the underlying event they describe. ### Source Data #### Initial Data Collection and Normalization The data was obtained by collecting 10 news timelines from the NewsLens project by selecting timelines diversified in topic each contained between 80 and 300 news articles. #### Who are the source language producers? The source language producers are journalists or members of the newsroom of 34 news organizations listed in the paper. ### Annotations #### Annotation process Each timeline was annotated for group IDs by 5 independent annotators. The 5 annotations were merged into a single annotation named the global groups. The global group IDs are then used to generate all pairs of headlines within timelines with binary labels: 1 if two headlines are part of the same global group, and 0 otherwise. A heuristic is used to remove negative examples to obtain a final dataset that has class imbalance of 1 positive example to 5 negative examples. #### Who are the annotators? Annotators were authors of the papers and 8 crowd-workers on the Upwork platform. The crowd-workers were native English speakers with experience either in proof-reading or data-entry. ### Personal and Sensitive Information Annotators identity has been anonymized. Due to the public nature of news headline, it is not expected that the headlines will contain personal sensitive information. ## Considerations for Using the Data ### Social Impact of Dataset The purpose of this dataset is to facilitate applications that present diverse news coverage. By simplifying the process of developing models that can group headlines that describe a common event, we hope the community can build applications that show news readers diverse sources covering similar events. We note however that the annotations were performed in majority by crowd-workers and that even though inter-annotator agreement was high, it was not perfect. Bias of the annotators therefore remains in the dataset. ### Discussion of Biases There are several sources of bias in the dataset: - Annotator bias: 10 annotators participated in the creation of the dataset. Their opinions and perspectives influenced the creation of the dataset. - Subject matter bias: HLGD consists of headlines from 10 news timelines from diverse topics (space, tech, politics, etc.). This choice has an impact on the types of positive and negative examples that appear in the dataset. - Source selection bias: 33 English-language news sources are represented in the dataset. This selection of news sources has an effect on the content in the timeline, and the overall dataset. - Time-range of the timelines: the timelines selected range from 2010 to 2020, which has an influence on the language and style of news headlines. ### Other Known Limitations For the task of Headline Grouping, inter-annotator agreement is high (0.814) but not perfect. Some decisions for headline grouping are subjective and depend on interpretation of the reader. ## Additional Information ### Dataset Curators The dataset was initially created by Philippe Laban, Lucas Bandarkar and Marti Hearst at UC Berkeley. ### Licensing Information The licensing status of the dataset depends on the legal status of news headlines. It is commonly held that News Headlines fall under "fair-use" ([American Bar blog post](https://www.americanbar.org/groups/gpsolo/publications/gp_solo/2011/september/fair_use_news_reviews/)) The dataset only distributes headlines, a URL and a publication date. Users of the dataset can then retrieve additional information (such as the body content, author, etc.) directly by querying the URL. ### Citation Information ``` @inproceedings{Laban2021NewsHG, title={News Headline Grouping as a Challenging NLU Task}, author={Laban, Philippe and Bandarkar, Lucas and Hearst, Marti A}, booktitle={NAACL 2021}, publisher = {Association for Computational Linguistics}, year={2021} } ``` ### Contributions Thanks to [@tingofurro](https://github.com/<tingofurro>) for adding this dataset.
hope_edi	--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - en - ml - ta license: - cc-by-4.0 multilinguality: - monolingual - multilingual size_categories: - 10K<n<100K - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: [] paperswithcode_id: hopeedi pretty_name: 'HopeEDI: A Multilingual Hope Speech Detection Dataset for Equality, Diversity, and Inclusion' configs: - english - malayalam - tamil tags: - hope-speech-classification dataset_info: - config_name: english features: - name: text dtype: string - name: label dtype: class_label: names: '0': Hope_speech '1': Non_hope_speech '2': not-English splits: - name: train num_bytes: 2306656 num_examples: 22762 - name: validation num_bytes: 288663 num_examples: 2843 download_size: 2739901 dataset_size: 2595319 - config_name: tamil features: - name: text dtype: string - name: label dtype: class_label: names: '0': Hope_speech '1': Non_hope_speech '2': not-Tamil splits: - name: train num_bytes: 1531013 num_examples: 16160 - name: validation num_bytes: 197378 num_examples: 2018 download_size: 1795767 dataset_size: 1728391 - config_name: malayalam features: - name: text dtype: string - name: label dtype: class_label: names: '0': Hope_speech '1': Non_hope_speech '2': not-malayalam splits: - name: train num_bytes: 1492031 num_examples: 8564 - name: validation num_bytes: 180713 num_examples: 1070 download_size: 1721534 dataset_size: 1672744 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Hope Speech Detection for Equality, Diversity, and Inclusion-EACL 2021](https://competitions.codalab.org/competitions/27653#learn_the_details) - Repository: [HopeEDI data repository](https://competitions.codalab.org/competitions/27653#participate-get_data) - Paper: [HopeEDI: A Multilingual Hope Speech Detection Dataset for Equality, Diversity, and Inclusion](https://www.aclweb.org/anthology/2020.peoples-1.5/) - Leaderboard: [Rank list](https://competitions.codalab.org/competitions/27653#results) - Point of Contact: [Bharathi Raja Chakravarthi](mailto:bharathiraja.akr@gmail.com) ### Dataset Summary A Hope Speech dataset for Equality, Diversity and Inclusion (HopeEDI) containing user-generated comments from the social media platform YouTube with 28,451, 20,198 and 10,705 comments in English, Tamil and Malayalam, respectively, manually labelled as containing hope speech or not. To our knowledge, this is the first research of its kind to annotate hope speech for equality, diversity and inclusion in a multilingual setting. ### Supported Tasks and Leaderboards To identify hope speech in the comments/posts in social media. ### Languages English, Tamil and Malayalam ## Dataset Structure ### Data Instances An example from the English dataset looks as follows: \| text \| label \| \| :------ \| :----- \| \| all lives matter .without that we never have peace so to me forever all lives matter. \| Hope_speech \| \| I think it's cool that you give people a voice to speak out with here on this channel. \| Hope_speech \| An example from the Tamil dataset looks as follows: \| text \| label \| \| :------ \| :----- \| \| Idha solla ivalo naala \| Non_hope_speech \| \| இன்று தேசிய பெண் குழந்தைகள் தினம்.. பெண் குழந்தைகளை போற்றுவோம்..அவர்களை பாதுகாப்போம்... \| Hope_speech \| An example from the Malayalam dataset looks as follows: \| text \| label \| \| :------ \| :----- \| \| ഇത്രെയും കഷ്ടപ്പെട്ട് വളർത്തിയ ആ അമ്മയുടെ മുഖം കണ്ടപ്പോൾ കണ്ണ് നിറഞ്ഞു പോയി \| Hope_speech \| \| snehikunavar aanayalum pennayalum onnichu jeevikatte..aareyum compel cheythitallalooo..parasparamulla ishtathodeyalle...avarum jeevikatte..🥰🥰 \| Hope_speech \| ### Data Fields English - `text`: English comment. - `label`: list of the possible values: "Hope_speech", "Non_hope_speech", "not-English" Tamil - `text`: Tamil-English code mixed comment. - `label`: list of the possible values: "Hope_speech", "Non_hope_speech", "not-Tamil" Malayalam - `text`: Malayalam-English code mixed comment. - `label`: list of the possible values: "Hope_speech", "Non_hope_speech", "not-malayalam" ### Data Splits \| \| train \| validation \| \| ----- \|------:\|-----------:\| \| English \| 22762 \| 2843 \| \| Tamil \| 16160 \| 2018 \| \| Malayalam \| 8564 \| 1070 \| ## Dataset Creation ### Curation Rationale Hope is considered significant for the well-being, recuperation and restoration of human life by health professionals. Hate speech or offensive language detection dataset is not available for code-mixed Tamil and code-mixed Malayalam, and it does not take into account LGBTIQ, women in STEM and other minorities. Thus, we cannot use existing hate speech or offensive language detection datasets to detect hope or non-hope for EDI of minorities. ### Source Data #### Initial Data Collection and Normalization For English, we collected data on recent topics of EDI, including women in STEM, LGBTIQ issues, COVID-19, Black Lives Matters, United Kingdom (UK) versus China, United States of America (USA) versus China and Australia versus China from YouTube video comments. The data was collected from videos of people from English-speaking countries, such as Australia, Canada, the Republic of Ireland, United Kingdom, the United States of America and New Zealand. For Tamil and Malayalam, we collected data from India on the recent topics regarding LGBTIQ issues, COVID-19, women in STEM, the Indo-China war and Dravidian affairs. #### Who are the source language producers? Youtube users ### Annotations #### Annotation process We created Google forms to collect annotations from annotators. Each form contained a maximum of 100 comments, and each page contained a maximum of 10 comments to maintain the quality of annotation. We collected information on the gender, educational background and the medium of schooling of the annotator to know the diversity of the annotator and avoid bias. We educated annotators by providing them with YouTube videos on EDI. A minimum of three annotators annotated each form. #### Who are the annotators? For English language comments, annotators were from Australia, the Republic of Ireland, the United Kingdom and the United States of America. For Tamil, we were able to get annotations from both people from the state of Tamil Nadu of India and from Sri Lanka. Most of the annotators were graduate or post-graduate students. ### Personal and Sensitive Information Social media data is highly sensitive, and even more so when it is related to the minority population, such as the LGBTIQ community or women. We have taken full consideration to minimise the risk associated with individual identity in the data by removing personal information from dataset, such as names but not celebrity names. However, to study EDI, we needed to keep information relating to the following characteristics; racial, gender, sexual orientation, ethnic origin and philosophical beliefs. Annotators were only shown anonymised posts and agreed to make no attempts to contact the comment creator. The dataset will only be made available for research purpose to the researcher who agree to follow ethical guidelines ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information This work is licensed under a [Creative Commons Attribution 4.0 International Licence](http://creativecommons.org/licenses/by/4.0/.) ### Citation Information ``` @inproceedings{chakravarthi-2020-hopeedi, title = "{H}ope{EDI}: A Multilingual Hope Speech Detection Dataset for Equality, Diversity, and Inclusion", author = "Chakravarthi, Bharathi Raja", booktitle = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media", month = dec, year = "2020", address = "Barcelona, Spain (Online)", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.peoples-1.5", pages = "41--53", abstract = "Over the past few years, systems have been developed to control online content and eliminate abusive, offensive or hate speech content. However, people in power sometimes misuse this form of censorship to obstruct the democratic right of freedom of speech. Therefore, it is imperative that research should take a positive reinforcement approach towards online content that is encouraging, positive and supportive contents. Until now, most studies have focused on solving this problem of negativity in the English language, though the problem is much more than just harmful content. Furthermore, it is multilingual as well. Thus, we have constructed a Hope Speech dataset for Equality, Diversity and Inclusion (HopeEDI) containing user-generated comments from the social media platform YouTube with 28,451, 20,198 and 10,705 comments in English, Tamil and Malayalam, respectively, manually labelled as containing hope speech or not. To our knowledge, this is the first research of its kind to annotate hope speech for equality, diversity and inclusion in a multilingual setting. We determined that the inter-annotator agreement of our dataset using Krippendorff{'}s alpha. Further, we created several baselines to benchmark the resulting dataset and the results have been expressed using precision, recall and F1-score. The dataset is publicly available for the research community. We hope that this resource will spur further research on encouraging inclusive and responsive speech that reinforces positiveness.", } ``` ### Contributions Thanks to [@jamespaultg](https://github.com/jamespaultg) for adding this dataset.
hotpot_qa	--- annotations_creators: - crowdsourced language: - en language_creators: - found license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: HotpotQA size_categories: - 100K<n<1M source_datasets: - original task_categories: - question-answering task_ids: [] paperswithcode_id: hotpotqa tags: - multi-hop dataset_info: - config_name: distractor features: - name: id dtype: string - name: question dtype: string - name: answer dtype: string - name: type dtype: string - name: level dtype: string - name: supporting_facts sequence: - name: title dtype: string - name: sent_id dtype: int32 - name: context sequence: - name: title dtype: string - name: sentences sequence: string splits: - name: train num_bytes: 552949315 num_examples: 90447 - name: validation num_bytes: 45716111 num_examples: 7405 download_size: 612746344 dataset_size: 598665426 - config_name: fullwiki features: - name: id dtype: string - name: question dtype: string - name: answer dtype: string - name: type dtype: string - name: level dtype: string - name: supporting_facts sequence: - name: title dtype: string - name: sent_id dtype: int32 - name: context sequence: - name: title dtype: string - name: sentences sequence: string splits: - name: train num_bytes: 552949315 num_examples: 90447 - name: validation num_bytes: 46848601 num_examples: 7405 - name: test num_bytes: 46000102 num_examples: 7405 download_size: 660094672 dataset_size: 645798018 --- # Dataset Card for "hotpot_qa" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://hotpotqa.github.io/](https://hotpotqa.github.io/) - Repository: https://github.com/hotpotqa/hotpot - Paper: [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://arxiv.org/abs/1809.09600) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 1.27 GB - Size of the generated dataset: 1.24 GB - Total amount of disk used: 2.52 GB ### Dataset Summary HotpotQA is a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowingQA systems to reason with strong supervision and explain the predictions; (4) we offer a new type of factoid comparison questions to test QA systems’ ability to extract relevant facts and perform necessary comparison. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### distractor - Size of downloaded dataset files: 612.75 MB - Size of the generated dataset: 598.66 MB - Total amount of disk used: 1.21 GB An example of 'validation' looks as follows. ``` { "answer": "This is the answer", "context": { "sentences": [["Sent 1"], ["Sent 21", "Sent 22"]], "title": ["Title1", "Title 2"] }, "id": "000001", "level": "medium", "question": "What is the answer?", "supporting_facts": { "sent_id": [0, 1, 3], "title": ["Title of para 1", "Title of para 2", "Title of para 3"] }, "type": "comparison" } ``` #### fullwiki - Size of downloaded dataset files: 660.10 MB - Size of the generated dataset: 645.80 MB - Total amount of disk used: 1.31 GB An example of 'train' looks as follows. ``` { "answer": "This is the answer", "context": { "sentences": [["Sent 1"], ["Sent 2"]], "title": ["Title1", "Title 2"] }, "id": "000001", "level": "hard", "question": "What is the answer?", "supporting_facts": { "sent_id": [0, 1, 3], "title": ["Title of para 1", "Title of para 2", "Title of para 3"] }, "type": "bridge" } ``` ### Data Fields The data fields are the same among all splits. #### distractor - `id`: a `string` feature. - `question`: a `string` feature. - `answer`: a `string` feature. - `type`: a `string` feature. - `level`: a `string` feature. - `supporting_facts`: a dictionary feature containing: - `title`: a `string` feature. - `sent_id`: a `int32` feature. - `context`: a dictionary feature containing: - `title`: a `string` feature. - `sentences`: a `list` of `string` features. #### fullwiki - `id`: a `string` feature. - `question`: a `string` feature. - `answer`: a `string` feature. - `type`: a `string` feature. - `level`: a `string` feature. - `supporting_facts`: a dictionary feature containing: - `title`: a `string` feature. - `sent_id`: a `int32` feature. - `context`: a dictionary feature containing: - `title`: a `string` feature. - `sentences`: a `list` of `string` features. ### Data Splits #### distractor \| \|train\|validation\| \|----------\|----:\|---------:\| \|distractor\|90447\| 7405\| #### fullwiki \| \|train\|validation\|test\| \|--------\|----:\|---------:\|---:\| \|fullwiki\|90447\| 7405\|7405\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information HotpotQA is distributed under a [CC BY-SA 4.0 License](http://creativecommons.org/licenses/by-sa/4.0/). ### Citation Information ``` @inproceedings{yang2018hotpotqa, title={{HotpotQA}: A Dataset for Diverse, Explainable Multi-hop Question Answering}, author={Yang, Zhilin and Qi, Peng and Zhang, Saizheng and Bengio, Yoshua and Cohen, William W. and Salakhutdinov, Ruslan and Manning, Christopher D.}, booktitle={Conference on Empirical Methods in Natural Language Processing ({EMNLP})}, year={2018} } ``` ### Contributions Thanks to [@albertvillanova](https://github.com/albertvillanova), [@ghomasHudson](https://github.com/ghomasHudson) for adding this dataset.
hover	--- annotations_creators: - expert-generated language_creators: - expert-generated - found language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-retrieval task_ids: - fact-checking-retrieval paperswithcode_id: hover pretty_name: HoVer dataset_info: features: - name: id dtype: int32 - name: uid dtype: string - name: claim dtype: string - name: supporting_facts list: - name: key dtype: string - name: value dtype: int32 - name: label dtype: class_label: names: '0': NOT_SUPPORTED '1': SUPPORTED - name: num_hops dtype: int32 - name: hpqa_id dtype: string splits: - name: train num_bytes: 5532178 num_examples: 18171 - name: validation num_bytes: 1299252 num_examples: 4000 - name: test num_bytes: 927513 num_examples: 4000 download_size: 12257835 dataset_size: 7758943 --- # Dataset Card for HoVer ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://hover-nlp.github.io/ - Repository: https://github.com/hover-nlp/hover - Paper: https://arxiv.org/abs/2011.03088 - Leaderboard: https://hover-nlp.github.io/ - Point of Contact: [More Information Needed] ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances A sample training set is provided below ``` {'id': 14856, 'uid': 'a0cf45ea-b5cd-4c4e-9ffa-73b39ebd78ce', 'claim': 'The park at which Tivolis Koncertsal is located opened on 15 August 1843.', 'supporting_facts': [{'key': 'Tivolis Koncertsal', 'value': 0}, {'key': 'Tivoli Gardens', 'value': 1}], 'label': 'SUPPORTED', 'num_hops': 2, 'hpqa_id': '5abca1a55542993a06baf937'} ``` Please note that in test set sentence only id, uid and claim are available. Labels are not available in test set and are represented by -1. ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
hrenwac_para	--- annotations_creators: - no-annotation language_creators: - found language: - en - hr license: - cc-by-sa-3.0 multilinguality: - translation size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: HrenwacPara dataset_info: features: - name: translation dtype: translation: languages: - en - hr config_name: hrenWaC splits: - name: train num_bytes: 29602110 num_examples: 99001 download_size: 11640281 dataset_size: 29602110 --- # Dataset Card for hrenwac_para ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: http://nlp.ffzg.hr/resources/corpora/hrenwac/ - Repository: http://nlp.ffzg.hr/data/corpora/hrenwac/hrenwac.en-hr.txt.gz - Paper: http://workshop2013.iwslt.org/downloads/IWSLT-2013-Cettolo.pdf - Leaderboard: - Point of Contact: [Nikola Ljubešič](mailto:nikola.ljubesic@ffzg.hr) ### Dataset Summary The hrenWaC corpus version 2.0 consists of parallel Croatian-English texts crawled from the .hr top-level domain for Croatia. The corpus was built with Spidextor (https://github.com/abumatran/spidextor), a tool that glues together the output of SpiderLing used for crawling and Bitextor used for bitext extraction. The accuracy of the extracted bitext on the segment level is around 80% and on the word level around 84%. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Dataset is bilingual with Croatian and English languages. ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Dataset is under the [CC-BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/) license. ### Citation Information ``` @misc{11356/1058, title = {Croatian-English parallel corpus {hrenWaC} 2.0}, author = {Ljube{\v s}i{\'c}, Nikola and Espl{\`a}-Gomis, Miquel and Ortiz Rojas, Sergio and Klubi{\v c}ka, Filip and Toral, Antonio}, url = {http://hdl.handle.net/11356/1058}, note = {Slovenian language resource repository {CLARIN}.{SI}}, copyright = {{CLARIN}.{SI} User Licence for Internet Corpora}, year = {2016} } ``` ### Contributions Thanks to [@IvanZidov](https://github.com/IvanZidov) for adding this dataset.
hrwac	--- annotations_creators: - no-annotation language_creators: - found language: - hr license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 1B<n<10B source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: null pretty_name: HrWac dataset_info: features: - name: sentence dtype: string config_name: hrwac splits: - name: train num_bytes: 43994569015 num_examples: 1736944727 download_size: 9217221471 dataset_size: 43994569015 --- # Dataset Card for HrWac ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: http://nlp.ffzg.hr/resources/corpora/hrwac/ - Repository: https://www.clarin.si/repository/xmlui/handle/11356/1064 - Paper: http://nlp.ffzg.hr/data/publications/nljubesi/ljubesic11-hrwac.pdf - Leaderboard: - Point of Contact: [Nikola Ljubešič](mailto:nikola.ljubesic@ffzg.hr) ### Dataset Summary The Croatian web corpus hrWaC was built by crawling the .hr top-level domain in 2011 and again in 2014. The corpus was near-deduplicated on paragraph level, normalised via diacritic restoration, morphosyntactically annotated and lemmatised. The corpus is shuffled by paragraphs. Each paragraph contains metadata on the URL, domain and language identification (Croatian vs. Serbian). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Dataset is monolingual in Croatian language. ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - sentence: sentences as strings ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Dataset is under the [CC-BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/) license. ### Citation Information ``` @misc{11356/1064, title = {Croatian web corpus {hrWaC} 2.1}, author = {Ljube{\v s}i{\'c}, Nikola and Klubi{\v c}ka, Filip}, url = {http://hdl.handle.net/11356/1064}, note = {Slovenian language resource repository {CLARIN}.{SI}}, copyright = {Creative Commons - Attribution-{ShareAlike} 4.0 International ({CC} {BY}-{SA} 4.0)}, year = {2016} } ``` ### Contributions Thanks to [@IvanZidov](https://github.com/IvanZidov) for adding this dataset.
humicroedit	--- annotations_creators: - crowdsourced - expert-generated language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - text-scoring paperswithcode_id: humicroedit pretty_name: Humicroedit configs: - subtask-1 - subtask-2 tags: - funnier-headline-identification - funniness-score-prediction dataset_info: - config_name: subtask-1 features: - name: id dtype: string - name: original dtype: string - name: edit dtype: string - name: grades dtype: string - name: meanGrade dtype: float32 splits: - name: train num_bytes: 1058589 num_examples: 9652 - name: test num_bytes: 332113 num_examples: 3024 - name: validation num_bytes: 269083 num_examples: 2419 - name: funlines num_bytes: 942376 num_examples: 8248 download_size: 1621456 dataset_size: 2602161 - config_name: subtask-2 features: - name: id dtype: string - name: original1 dtype: string - name: edit1 dtype: string - name: grades1 dtype: string - name: meanGrade1 dtype: float32 - name: original2 dtype: string - name: edit2 dtype: string - name: grades2 dtype: string - name: meanGrade2 dtype: float32 - name: label dtype: class_label: names: '0': equal '1': sentence1 '2': sentence2 splits: - name: train num_bytes: 2102667 num_examples: 9381 - name: test num_bytes: 665087 num_examples: 2960 - name: validation num_bytes: 535044 num_examples: 2355 - name: funlines num_bytes: 451416 num_examples: 1958 download_size: 1621456 dataset_size: 3754214 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage:[Humicroedit](https://www.cs.rochester.edu/u/nhossain/humicroedit.html) - Repository: - Paper:["President Vows to Cut Taxes Hair": Dataset and Analysis of Creative Text Editing for Humorous Headlines.](http://cs.rochester.edu/~nhossain/humicroedit-naacl-19.pdf) - Leaderboard: - Point of Contact:[nhossain@cs.rochester.edu] ### Dataset Summary This is the task dataset for SemEval-2020 Task 7: Assessing Humor in Edited News Headlines. ### Supported Tasks and Leaderboards [Task Description Page](https://competitions.codalab.org/competitions/20970) - Regression Task: In this task, given the original and the edited headline, the participant is required to predict the mean funniness of the edited headline. Success on this task is typically measured by achieving a low Mean Square Error. - Predict the funnier of the two edited headlines: Given the original headline and two edited versions, the participant has to predict which edited version is the funnier of the two. Success on this task is typically measured by achieving a high accuracy. ### Languages English ## Dataset Structure ### Data Instances For subtask-1, i.e Given the original and the edited headline, predict the mean funniness of the edited headline. ``` { 'id': 1183, 'original': 'Kushner to visit <Mexico/> following latest trump tirades.', 'edit': 'therapist', 'grades': '33332', 'meanGrade': 2.8 } ``` For subtask-2, i.e Given the original headline and two edited versions, predict which edited version is the funnier of the two. ``` { 'id': 1183, 'original1': 'Gene Cernan , Last <Astronaut/> on the Moon , Dies at 82', 'edit1': 'Dancer', 'grades1': '1113', 'meanGrade1': 1.2, 'original2': 'Gene Cernan , Last Astronaut on the Moon , <Dies/> at 82', 'edit2': 'impregnated', 'grades2': '30001', 'meanGrade2': 0.8, 'label': 1 } ``` ### Data Fields For subtask-1 - `id`: Unique identifier of an edited headline. - `original`: The headline with replaced word(s) identified with the </> tag. - `edit`: The new word which replaces the word marked in </> tag in the original field. - `grades`: 'grades' are the concatenation of all the grades by different annotators. - `mean` is the mean of all the judges scores. For subtask-2 - `id`: Unique identifier of an edited headline. - `original1`: The original headline with replaced word(s) identified with </> tag. - `edit1`: The new word which replaces the word marked in </> tag in the `original1` field. - `grades1`: The concatenation of all the grades annotated by different annotators for sentence1. - `meanGrade1` is the mean of all the judges scores for sentence1. - `original2`: The original headline with replaced word(s) identified with </> tag. - `edit2`: The new word which replaces the word marked in </> tag in the `original1` field. - `grades2`: The concatenation of all the grades annotated by different annotators for the sentence2. - `meanGrade2` is the mean of all the judges scores for sentence2. - `label` is 1 if sentence1 is more humourous than sentence2, 2 if sentence 2 is more humorous than sentence1, 0 if both the sentences are equally humorous ### Data Splits \| Sub Task \| Train \| Dev \| Test \| Funlines\| \| ----- \| ------ \| ---- \| ---- \|-----\| \| Subtask-1:Regression \| 9652 \| 2419 \| 3024\| 8248 \| \| Subtask-2: Funnier headline prediction\| 9381 \| 2355 \| 2960\| 1958 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Crowd-sourced the data by gamifying it as on the website funlines.co. Players rate the headlines on a scale of 0-4. Players are scored based on their editing and rating, and they are ranked on the game’s leaderboard page. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @article{hossain2019president, title={" President Vows to Cut< Taxes> Hair": Dataset and Analysis of Creative Text Editing for Humorous Headlines}, author={Hossain, Nabil and Krumm, John and Gamon, Michael}, journal={arXiv preprint arXiv:1906.00274}, year={2019} }``` ### Contributions Thanks to [@saradhix](https://github.com/saradhix) for adding this dataset.
hybrid_qa	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: [] paperswithcode_id: hybridqa pretty_name: HybridQA tags: - multihop-tabular-text-qa dataset_info: features: - name: question_id dtype: string - name: question dtype: string - name: table_id dtype: string - name: answer_text dtype: string - name: question_postag dtype: string - name: table struct: - name: url dtype: string - name: title dtype: string - name: header sequence: string - name: data list: - name: value dtype: string - name: urls list: - name: url dtype: string - name: summary dtype: string - name: section_title dtype: string - name: section_text dtype: string - name: uid dtype: string - name: intro dtype: string config_name: hybrid_qa splits: - name: train num_bytes: 2745712769 num_examples: 62682 - name: validation num_bytes: 153512016 num_examples: 3466 - name: test num_bytes: 148795919 num_examples: 3463 download_size: 217436855 dataset_size: 3048020704 --- # Dataset Card for HybridQA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://hybridqa.github.io/index.html - Repository: [GitHub](https://github.com/wenhuchen/HybridQA) - Paper: [HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data](https://arxiv.org/abs/1909.05358) - Leaderboard: [HybridQA Competition](https://competitions.codalab.org/competitions/24420) - Point of Contact: [Wenhu Chen](wenhuchen@cs.ucsb.edu) ### Dataset Summary Existing question answering datasets focus on dealing with homogeneous information, based either only on text or KB/Table information alone. However, as human knowledge is distributed over heterogeneous forms, using homogeneous information alone might lead to severe coverage problems. To fill in the gap, we present HybridQA, a new large-scale question-answering dataset that requires reasoning on heterogeneous information. Each question is aligned with a Wikipedia table and multiple free-form corpora linked with the entities in the table. The questions are designed to aggregate both tabular information and text information, i.e., lack of either form would render the question unanswerable. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset is in English language. ## Dataset Structure ### Data Instances A typical example looks like this ``` { "question_id": "00009b9649d0dd0a", "question": "Who were the builders of the mosque in Herat with fire temples ?", "table_id": "List_of_mosques_in_Afghanistan_0", "answer_text": "Ghurids", "question_postag": "WP VBD DT NNS IN DT NN IN NNP IN NN NNS .", "table": { "url": "https://en.wikipedia.org/wiki/List_of_mosques_in_Afghanistan", "title": "List of mosques in Afghanistan", "header": [ "Name", "Province", "City", "Year", "Remarks" ], "data": [ { "value": "Kabul", "urls": [ { "summary": "Kabul ( Persian : کابل , romanized : Kābol , Pashto : کابل , romanized : Kābəl ) is the capital and largest city of Afghanistan...", "url": "/wiki/Kabul" } ] } ] }, "section_title": "", "section_text": "", "uid": "List_of_mosques_in_Afghanistan_0", "intro": "The following is an incomplete list of large mosques in Afghanistan:" } ``` ### Data Fields - `question_id` (str) - `question` (str) - `table_id` (str) - `answer_text` (str) - `question_postag` (str) - `table` (dict): - `url` (str) - `title` (str) - `header` (list of str) - `data` (list of dict): - `value` (str) - `urls` (list of dict): - `url` (str) - `summary` (str) - `section_title` (str) - `section_text` (str) - `uid` (str) - `intro` (str) ### Data Splits The dataset is split into `train`, `dev` and `test` splits. \| \| train \| validation \| test \| \| --------------- \|------:\|-----------:\|-----:\| \| N. Instances \| 62682 \| 3466 \| 3463 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The dataset is under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). ### Citation Information [More Information Needed] ``` @article{chen2020hybridqa, title={HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data}, author={Chen, Wenhu and Zha, Hanwen and Chen, Zhiyu and Xiong, Wenhan and Wang, Hong and Wang, William}, journal={Findings of EMNLP 2020}, year={2020} } ``` ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.
hyperpartisan_news_detection	--- annotations_creators: - crowdsourced - expert-generated language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - text-classification task_ids: [] pretty_name: HyperpartisanNewsDetection tags: - bias-classification dataset_info: - config_name: byarticle features: - name: text dtype: string - name: title dtype: string - name: hyperpartisan dtype: bool - name: url dtype: string - name: published_at dtype: string splits: - name: train num_bytes: 2803943 num_examples: 645 download_size: 1000352 dataset_size: 2803943 - config_name: bypublisher features: - name: text dtype: string - name: title dtype: string - name: hyperpartisan dtype: bool - name: url dtype: string - name: published_at dtype: string - name: bias dtype: class_label: names: '0': right '1': right-center '2': least '3': left-center '4': left splits: - name: train num_bytes: 2805711609 num_examples: 600000 - name: validation num_bytes: 2805711609 num_examples: 600000 download_size: 1003195420 dataset_size: 5611423218 --- # Dataset Card for "hyperpartisan_news_detection" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://pan.webis.de/semeval19/semeval19-web/](https://pan.webis.de/semeval19/semeval19-web/) - Repository: https://github.com/pan-webis-de/pan-code/tree/master/semeval19 - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 1.00 GB - Size of the generated dataset: 5.61 GB - Total amount of disk used: 6.62 GB ### Dataset Summary Hyperpartisan News Detection was a dataset created for PAN @ SemEval 2019 Task 4. Given a news article text, decide whether it follows a hyperpartisan argumentation, i.e., whether it exhibits blind, prejudiced, or unreasoning allegiance to one party, faction, cause, or person. There are 2 parts: - byarticle: Labeled through crowdsourcing on an article basis. The data contains only articles for which a consensus among the crowdsourcing workers existed. - bypublisher: Labeled by the overall bias of the publisher as provided by BuzzFeed journalists or MediaBiasFactCheck.com. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### byarticle - Size of downloaded dataset files: 1.00 MB - Size of the generated dataset: 2.80 MB - Total amount of disk used: 3.81 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "hyperpartisan": true, "published_at": "2020-01-01", "text": "\"<p>This is a sample article which will contain lots of text</p>\\n \\n<p>Lorem ipsum dolor sit amet, consectetur adipiscing el...", "title": "Example article 1", "url": "http://www.example.com/example1" } ``` #### bypublisher - Size of downloaded dataset files: 1.00 GB - Size of the generated dataset: 5.61 GB - Total amount of disk used: 6.61 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "bias": 3, "hyperpartisan": false, "published_at": "2020-01-01", "text": "\"<p>This is a sample article which will contain lots of text</p>\\n \\n<p>Phasellus bibendum porta nunc, id venenatis tortor fi...", "title": "Example article 4", "url": "https://example.com/example4" } ``` ### Data Fields The data fields are the same among all splits. #### byarticle - `text`: a `string` feature. - `title`: a `string` feature. - `hyperpartisan`: a `bool` feature. - `url`: a `string` feature. - `published_at`: a `string` feature. #### bypublisher - `text`: a `string` feature. - `title`: a `string` feature. - `hyperpartisan`: a `bool` feature. - `url`: a `string` feature. - `published_at`: a `string` feature. - `bias`: a classification label, with possible values including `right` (0), `right-center` (1), `least` (2), `left-center` (3), `left` (4). ### Data Splits #### byarticle \| \|train\| \|---------\|----:\| \|byarticle\| 645\| #### bypublisher \| \|train \|validation\| \|-----------\|-----:\|---------:\| \|bypublisher\|600000\| 600000\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The collection (including labels) are licensed under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/). ### Citation Information ``` @article{kiesel2019data, title={Data for pan at semeval 2019 task 4: Hyperpartisan news detection}, author={Kiesel, Johannes and Mestre, Maria and Shukla, Rishabh and Vincent, Emmanuel and Corney, David and Adineh, Payam and Stein, Benno and Potthast, Martin}, year={2019} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@ghomasHudson](https://github.com/ghomasHudson) for adding this dataset.
iapp_wiki_qa_squad	--- annotations_creators: - expert-generated language_creators: - found language: - th license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - extended\|other-iapp-wiki-qa-dataset task_categories: - question-answering task_ids: - extractive-qa - open-domain-qa paperswithcode_id: null pretty_name: IappWikiQaSquad dataset_info: features: - name: question_id dtype: string - name: article_id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: answer_end dtype: int32 config_name: iapp_wiki_qa_squad splits: - name: train num_bytes: 16107541 num_examples: 5761 - name: validation num_bytes: 2120768 num_examples: 742 - name: test num_bytes: 2032016 num_examples: 739 download_size: 2876630 dataset_size: 20260325 --- # Dataset Card for `iapp_wiki_qa_squad` ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/iapp-technology/iapp-wiki-qa-dataset - Repository: https://github.com/iapp-technology/iapp-wiki-qa-dataset - Paper: - Leaderboard: - Point of Contact: https://github.com/iapp-technology/iapp-wiki-qa-dataset ### Dataset Summary `iapp_wiki_qa_squad` is an extractive question answering dataset from Thai Wikipedia articles. It is adapted from [the original iapp-wiki-qa-dataset](https://github.com/iapp-technology/iapp-wiki-qa-dataset) to [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format, resulting in 5761/742/739 questions from 1529/191/192 articles. ### Supported Tasks and Leaderboards extractive question answering ### Languages Thai ## Dataset Structure ### Data Instances An example from the dataset: ``` {'article_id': '0U2lA8nJQESIxbZrjZQc', 'question_id': '0U2lA8nJQESIxbZrjZQc_000', 'context': 'นายสุวัฒน์ วรรณศิริกุล (1 พฤศจิกายน พ.ศ. 2476 - 31 กรกฎาคม พ.ศ. 2555) อดีตรองหัวหน้าพรรคพลังประชาชน อดีตประธานสมาชิกสภาผู้แทนราษฎร และประธานภาคกรุงเทพมหานคร พรรคพลังประชาชน อดีตสมาชิกสภาผู้แทนราษฎรกรุงเทพมหานครหลายสมัย ได้รับการเลือกตั้งเป็นสมาชิกสภาผู้แทนราษฎรครั้งแรกในปี พ.ศ. 2529 ในสังกัดพรรคประชากรไทย และสังกัดพรรคพลังประชาชน เป็นพรรคสุดท้าย', 'question': 'สุวัฒน์ วรรณศิริกุล เกิดวันที่เท่าไร', 'answers': {'text': ['1 พฤศจิกายน พ.ศ. 2476'], 'answer_start': [24], 'answer_end': [45]}, 'title': 'สุวัฒน์ วรรณศิริกุล', 'created_by': 'gmnjGRF0y0g7QRZDd9Qgz3AgiHJ3', 'created_on': '2019-08-18 05:05:51.358000+00:00', 'is_pay': {'date': None, 'status': False}} {'article_id': '01KZTrxgvC5mOovXFMPJ', 'question_id': '01KZTrxgvC5mOovXFMPJ_000', 'context': 'พัทธ์ธีรา ศรุติพงศ์โภคิน (เกิด 3 ธันวาคม พ.ศ. 2533) หรือชื่อเล่นว่า อร เป็นนักแสดงหญิงชาวไทย สำเร็จมัธยมศึกษาจากCatholic Cathedral College ประเทศนิวซีแลนด์ และปริญญาตรีจากRaffles International College สาขา Business Marketing\n\nเข้าสู่วงการตั้งแต่อายุ 6 ขวบ จากการแสดงละครเวทีกับ ครูชลประคัลภ์ จันทร์เรือง จากนั้นก็เล่นโฆษณาในวัยเด็ก 2- 3 ชิ้น และยังเคยแสดงช่วงละครสั้น ในรายการซุปเปอร์จิ๋ว ประมาณปี 2542\n\nปัจจุบันเป็นทั้ง นักแสดง , พิธีกร และ วีเจ อยู่ที่คลื่น เก็ท 102.5 Bangkok International Hits Music Station และยังเป็นพิธีกรให้กับช่อง ทรู มิวสิก', 'question': 'พัทธ์ธีรา ศรุติพงศ์โภคิน เกิดวันที่เท่าไร', 'answers': {'text': ['3 ธันวาคม พ.ศ. 2533'], 'answer_start': [31], 'answer_end': [50]}, 'title': 'พัทธ์ธีรา ศรุติพงศ์โภคิน', 'created_by': 'gmnjGRF0y0g7QRZDd9Qgz3AgiHJ3', 'created_on': '2019-08-07 14:00:38.778000+00:00', 'is_pay': {'status': True, 'total': 2.5, 'date': '2019-08-13 10:47:28.095000+00:00'}} ``` ### Data Fields ``` { "question_id": question id "article_id": article id "title": article title "context": article texts "question": question "answers": { "text": answer text "answer_start": answer beginning position "answer_end": answer exclusive upper bound position } ), } ``` ### Data Splits \| \| train \| valid \| test \| \|-------------\|-------\|-------\|------\| \| # questions \| 5761 \| 742 \| 739 \| \| # articles \| 1529 \| 191 \| 192 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization From the original `iapp-wiki-qa-dataset`, [@cstorm125](https://github.com/cstorm125/) applied the following processing: - Select questions with one, non-empty answer - Select questions whose answers match `textDetection` fields - Select questions whose answers are 100-character long or shorter - 80/10/10 train-validation-split at article level #### Who are the source language producers? Wikipedia authors for contexts and annotators hired by [iApp](https://iapp.co.th/) for questions and answer annotations ### Annotations #### Annotation process Annotators hired by [iApp](https://iapp.co.th/) are asked create questions and answers for each article. #### Who are the annotators? Annotators hired by [iApp](https://iapp.co.th/) ### Personal and Sensitive Information All contents are from Wikipedia. No personal and sensitive information is expected to be included. ## Considerations for Using the Data ### Social Impact of Dataset - open-domain, extractive question answering in Thai ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Original dataset by [iApp](https://iapp.co.th/). SQuAD formattting by [PyThaiNLP](https://github.com/PyThaiNLP/). ### Licensing Information MIT ### Citation Information ``` @dataset{kobkrit_viriyayudhakorn_2021_4539916, author = {Kobkrit Viriyayudhakorn and Charin Polpanumas}, title = {iapp\_wiki\_qa\_squad}, month = feb, year = 2021, publisher = {Zenodo}, version = 1, doi = {10.5281/zenodo.4539916}, url = {https://doi.org/10.5281/zenodo.4539916} } ``` ### Contributions Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset.
id_clickbait	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - id license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - fact-checking pretty_name: Indonesian Clickbait Headlines dataset_info: - config_name: annotated features: - name: id dtype: string - name: title dtype: string - name: label dtype: class_label: names: '0': non-clickbait '1': clickbait splits: - name: train num_bytes: 1268698 num_examples: 15000 download_size: 150769127 dataset_size: 1268698 - config_name: raw features: - name: id dtype: string - name: title dtype: string - name: source dtype: string - name: date dtype: string - name: category dtype: string - name: sub-category dtype: string - name: content dtype: string - name: url dtype: string splits: - name: train num_bytes: 81669386 num_examples: 38655 download_size: 150769127 dataset_size: 81669386 --- # Dataset Card for Indonesian Clickbait Headlines ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://data.mendeley.com/datasets/k42j7x2kpn/1 - Repository: - Paper: [CLICK-ID: A Novel Dataset for Indonesian Clickbait Headlines](https://www.sciencedirect.com/science/article/pii/S2352340920311252#!) - Leaderboard: - Point of Contact: [Andika William](mailto:andika.william@mail.ugm.ac.id), [Yunita Sari](mailto:yunita.sari@ugm.ac.id) ### Dataset Summary The CLICK-ID dataset is a collection of Indonesian news headlines that was collected from 12 local online news publishers; detikNews, Fimela, Kapanlagi, Kompas, Liputan6, Okezone, Posmetro-Medan, Republika, Sindonews, Tempo, Tribunnews, and Wowkeren. This dataset is comprised of mainly two parts; (i) 46,119 raw article data, and (ii) 15,000 clickbait annotated sample headlines. Annotation was conducted with 3 annotator examining each headline. Judgment were based only on the headline. The majority then is considered as the ground truth. In the annotated sample, our annotation shows 6,290 clickbait and 8,710 non-clickbait. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Indonesian ## Dataset Structure ### Data Instances An example of the annotated article: ``` { 'id': '100', 'label': 1, 'title': "SAH! Ini Daftar Nama Menteri Kabinet Jokowi - Ma'ruf Amin" } > ``` ### Data Fields #### Annotated - `id`: id of the sample - `title`: the title of the news article - `label`: the label of the article, either non-clickbait or clickbait #### Raw - `id`: id of the sample - `title`: the title of the news article - `source`: the name of the publisher/newspaper - `date`: date - `category`: the category of the article - `sub-category`: the sub category of the article - `content`: the content of the article - `url`: the url of the article ### Data Splits The dataset contains train set. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Creative Commons Attribution 4.0 International license ### Citation Information ``` @article{WILLIAM2020106231, title = "CLICK-ID: A novel dataset for Indonesian clickbait headlines", journal = "Data in Brief", volume = "32", pages = "106231", year = "2020", issn = "2352-3409", doi = "https://doi.org/10.1016/j.dib.2020.106231", url = "http://www.sciencedirect.com/science/article/pii/S2352340920311252", author = "Andika William and Yunita Sari", keywords = "Indonesian, Natural Language Processing, News articles, Clickbait, Text-classification", abstract = "News analysis is a popular task in Natural Language Processing (NLP). In particular, the problem of clickbait in news analysis has gained attention in recent years [1, 2]. However, the majority of the tasks has been focused on English news, in which there is already a rich representative resource. For other languages, such as Indonesian, there is still a lack of resource for clickbait tasks. Therefore, we introduce the CLICK-ID dataset of Indonesian news headlines extracted from 12 Indonesian online news publishers. It is comprised of 15,000 annotated headlines with clickbait and non-clickbait labels. Using the CLICK-ID dataset, we then developed an Indonesian clickbait classification model achieving favourable performance. We believe that this corpus will be useful for replicable experiments in clickbait detection or other experiments in NLP areas." } ``` ### Contributions Thanks to [@cahya-wirawan](https://github.com/cahya-wirawan) for adding this dataset.
id_liputan6	--- annotations_creators: - no-annotation language_creators: - found language: - id license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - summarization task_ids: - news-articles-summarization paperswithcode_id: null pretty_name: Large-scale Indonesian Summarization tags: - extractive-summarization dataset_info: - config_name: canonical features: - name: id dtype: string - name: url dtype: string - name: clean_article dtype: string - name: clean_summary dtype: string - name: extractive_summary dtype: string splits: - name: validation num_bytes: 20944658 num_examples: 10972 - name: test num_bytes: 20526768 num_examples: 10972 - name: train num_bytes: 382245586 num_examples: 193883 download_size: 0 dataset_size: 423717012 - config_name: xtreme features: - name: id dtype: string - name: url dtype: string - name: clean_article dtype: string - name: clean_summary dtype: string - name: extractive_summary dtype: string splits: - name: validation num_bytes: 9652946 num_examples: 4948 - name: test num_bytes: 7574550 num_examples: 3862 download_size: 0 dataset_size: 17227496 --- # Dataset Card for Large-scale Indonesian Summarization ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [IndoLEM (Indonesian Language Evaluation Montage)](https://indolem.github.io/) - Repository: [Liputan6: Summarization Corpus for Indonesian](https://github.com/fajri91/sum_liputan6/) - Paper: https://arxiv.org/abs/2011.00679 - Leaderboard: - Point of Contact: [Fajri Koto](mailto:feryandi.n@gmail.com), [Jey Han Lau](mailto:jeyhan.lau@gmail.com), [Timothy Baldwin](mailto:tbaldwin@unimelb.edu.au), ### Dataset Summary In this paper, we introduce a large-scale Indonesian summarization dataset. We harvest articles from this http URL, an online news portal, and obtain 215,827 document-summary pairs. We leverage pre-trained language models to develop benchmark extractive and abstractive summarization methods over the dataset with multilingual and monolingual BERT-based models. We include a thorough error analysis by examining machine-generated summaries that have low ROUGE scores, and expose both issues with ROUGE it-self, as well as with extractive and abstractive summarization models. The dataset has two variants: "canonical" and "xtreme". The "xtreme" variant discards development and test document–summary pairs where the summary has fewer than 90% novel 4-grams (the training data remains the same as the canonical variant). You need to manually request the liputan6 dataset using the form in https://github.com/fajri91/sum_liputan6/ and uncompress it. The liputan6 dataset can then be loaded using the following command `datasets.load_dataset("id_liputan6", 'canonical', data_dir="<path/to/uncompressed_folder>")` or `datasets.load_dataset("id_liputan6", 'xtreme', data_dir="<path/to/uncompressed_folder>")`. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Indonesian ## Dataset Structure ``` { 'id': 'string', 'url': 'string', 'clean_article': 'string', 'clean_article': 'string', 'extractive_summary': 'string' } ``` ### Data Instances An example of the dataset: ``` { 'clean_article': 'Liputan6.com, Ambon: Partai Bulan Bintang wilayah Maluku bertekad membantu pemerintah menyelesaikan konflik di provinsi tersebut. Syaratnya, penanganan penyelesaian konflik Maluku harus dimulai dari awal kerusuhan, yakni 19 Januari 1999. Demikian hasil Musyawarah Wilayah I PBB Maluku yang dimulai Sabtu pekan silam dan berakhir Senin (31/12) di Ambon. Menurut seorang fungsionaris PBB Ridwan Hasan, persoalan di Maluku bisa selesai asalkan pemerintah dan aparat keamanan serius menangani setiap persoalan di Maluku secara komprehensif dan bijaksana. Itulah sebabnya, PBB wilayah Maluku akan menjadikan penyelesaian konflik sebagai agenda utama partai. PBB Maluku juga akan mendukung penegakan hukum secara terpadu dan tanpa pandang bulu. Siapa saja yang melanggar hukum harus ditindak. Ridwan berharap, Ketua PBB Maluku yang baru, Ali Fauzi, dapat menindak lanjuti agenda politik partai yang telah diamanatkan dan mau mendukung penegakan hukum di Maluku. (ULF/Sahlan Heluth).', 'clean_summary': 'Konflik Ambon telah berlangsung selama tiga tahun. Partai Bulan Bintang wilayah Maluku siap membantu pemerintah menyelesaikan kasus di provinsi tersebut.', 'extractive_summary': 'Liputan6.com, Ambon: Partai Bulan Bintang wilayah Maluku bertekad membantu pemerintah menyelesaikan konflik di provinsi tersebut. Siapa saja yang melanggar hukum harus ditindak.', 'id': '26408', 'url': 'https://www.liputan6.com/news/read/26408/pbb-siap-membantu-penyelesaian-konflik-ambon' } ``` ### Data Fields - `id`: id of the sample - `url`: the url to the original article - `clean_article`: the original article - `clean_article`: the abstractive summarization - `extractive_summary`: the extractive summarization ### Data Splits The dataset is splitted in to train, validation and test sets. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{Koto2020Liputan6AL, title={Liputan6: A Large-scale Indonesian Dataset for Text Summarization}, author={Fajri Koto and Jey Han Lau and Timothy Baldwin}, booktitle={AACL/IJCNLP}, year={2020} } ``` ### Contributions Thanks to [@cahya-wirawan](https://github.com/cahya-wirawan) for adding this dataset.
id_nergrit_corpus	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - id license: - other multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition paperswithcode_id: nergrit-corpus pretty_name: Nergrit Corpus dataset_info: - config_name: ner features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-CRD '1': B-DAT '2': B-EVT '3': B-FAC '4': B-GPE '5': B-LAN '6': B-LAW '7': B-LOC '8': B-MON '9': B-NOR '10': B-ORD '11': B-ORG '12': B-PER '13': B-PRC '14': B-PRD '15': B-QTY '16': B-REG '17': B-TIM '18': B-WOA '19': I-CRD '20': I-DAT '21': I-EVT '22': I-FAC '23': I-GPE '24': I-LAN '25': I-LAW '26': I-LOC '27': I-MON '28': I-NOR '29': I-ORD '30': I-ORG '31': I-PER '32': I-PRC '33': I-PRD '34': I-QTY '35': I-REG '36': I-TIM '37': I-WOA '38': O splits: - name: train num_bytes: 5428411 num_examples: 12532 - name: test num_bytes: 1135577 num_examples: 2399 - name: validation num_bytes: 1086437 num_examples: 2521 download_size: 14988232 dataset_size: 7650425 - config_name: sentiment features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-NEG '1': B-NET '2': B-POS '3': I-NEG '4': I-NET '5': I-POS '6': O splits: - name: train num_bytes: 3167972 num_examples: 7485 - name: test num_bytes: 1097517 num_examples: 2317 - name: validation num_bytes: 337679 num_examples: 782 download_size: 14988232 dataset_size: 4603168 - config_name: statement features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-BREL '1': B-FREL '2': B-STAT '3': B-WHO '4': I-BREL '5': I-FREL '6': I-STAT '7': I-WHO '8': O splits: - name: train num_bytes: 1469081 num_examples: 2405 - name: test num_bytes: 182553 num_examples: 335 - name: validation num_bytes: 105119 num_examples: 176 download_size: 14988232 dataset_size: 1756753 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [PT Gria Inovasi Teknologi](https://grit.id/) - Repository: [Nergrit Corpus](https://github.com/grit-id/nergrit-corpus) - Paper: - Leaderboard: - Point of Contact: [Taufiqur Rohman](mailto:taufiq@grit.id) ### Dataset Summary Nergrit Corpus is a dataset collection of Indonesian Named Entity Recognition, Statement Extraction, and Sentiment Analysis developed by [PT Gria Inovasi Teknologi (GRIT)](https://grit.id/). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Indonesian ## Dataset Structure A data point consists of sentences seperated by empty line and tab-seperated tokens and tags. ``` {'id': '0', 'tokens': ['Gubernur', 'Bank', 'Indonesia', 'menggelar', 'konferensi', 'pers'], 'ner_tags': [9, 28, 28, 38, 38, 38], } ``` ### Data Instances [More Information Needed] ### Data Fields - `id`: id of the sample - `tokens`: the tokens of the example text - `ner_tags`: the NER tags of each token #### Named Entity Recognition The ner_tags correspond to this list: ``` "B-CRD", "B-DAT", "B-EVT", "B-FAC", "B-GPE", "B-LAN", "B-LAW", "B-LOC", "B-MON", "B-NOR", "B-ORD", "B-ORG", "B-PER", "B-PRC", "B-PRD", "B-QTY", "B-REG", "B-TIM", "B-WOA", "I-CRD", "I-DAT", "I-EVT", "I-FAC", "I-GPE", "I-LAN", "I-LAW", "I-LOC", "I-MON", "I-NOR", "I-ORD", "I-ORG", "I-PER", "I-PRC", "I-PRD", "I-QTY", "I-REG", "I-TIM", "I-WOA", "O", ``` The ner_tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. The dataset contains 19 following entities ``` 'CRD': Cardinal 'DAT': Date 'EVT': Event 'FAC': Facility 'GPE': Geopolitical Entity 'LAW': Law Entity (such as Undang-Undang) 'LOC': Location 'MON': Money 'NOR': Political Organization 'ORD': Ordinal 'ORG': Organization 'PER': Person 'PRC': Percent 'PRD': Product 'QTY': Quantity 'REG': Religion 'TIM': Time 'WOA': Work of Art 'LAN': Language ``` #### Sentiment Analysis The ner_tags correspond to this list: ``` "B-NEG", "B-NET", "B-POS", "I-NEG", "I-NET", "I-POS", "O", ``` #### Statement Extraction The ner_tags correspond to this list: ``` "B-BREL", "B-FREL", "B-STAT", "B-WHO", "I-BREL", "I-FREL", "I-STAT", "I-WHO", "O" ``` The ner_tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. ### Data Splits The dataset is splitted in to train, validation and test sets. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The annotators are listed in the [Nergrit Corpus repository](https://github.com/grit-id/nergrit-corpus) ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@cahya-wirawan](https://github.com/cahya-wirawan) for adding this dataset.
id_newspapers_2018	--- annotations_creators: - no-annotation language_creators: - found language: - id license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: null pretty_name: Indonesian Newspapers 2018 dataset_info: features: - name: id dtype: string - name: url dtype: string - name: date dtype: string - name: title dtype: string - name: content dtype: string config_name: id_newspapers_2018 splits: - name: train num_bytes: 1116031922 num_examples: 499164 download_size: 446018349 dataset_size: 1116031922 --- # Dataset Card for Indonesian Newspapers 2018 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Indonesian Newspapers](https://github.com/feryandi/Dataset-Artikel) - Repository: [Indonesian Newspapers](https://github.com/feryandi/Dataset-Artikel) - Paper: - Leaderboard: - Point of Contact: [feryandi.n@gmail.com](mailto:feryandi.n@gmail.com), [cahya.wirawan@gmail.com](mailto:cahya.wirawan@gmail.com) ### Dataset Summary The dataset contains around 500K articles (136M of words) from 7 Indonesian newspapers: Detik, Kompas, Tempo, CNN Indonesia, Sindo, Republika and Poskota. The articles are dated between 1st January 2018 and 20th August 2018 (with few exceptions dated earlier). The size of uncompressed 500K json files (newspapers-json.tgz) is around 2.2GB, and the cleaned uncompressed in a big text file (newspapers.txt.gz) is about 1GB. The original source in Google Drive contains also a dataset in html format which include raw data (pictures, css, javascript, ...) from the online news website. A copy of the original dataset is available at https://cloud.uncool.ai/index.php/s/mfYEAgKQoY3ebbM ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Indonesian ## Dataset Structure ``` { 'id': 'string', 'url': 'string', 'date': 'string', 'title': 'string', 'content': 'string' } ``` ### Data Instances An instance from the dataset is ``` {'id': '0', 'url': 'https://www.cnnindonesia.com/olahraga/20161221234219-156-181385/lorenzo-ingin-samai-rekor-rossi-dan-stoner', 'date': '2016-12-22 07:00:00', 'title': 'Lorenzo Ingin Samai Rekor Rossi dan Stoner', 'content': 'Jakarta, CNN Indonesia -- Setelah bergabung dengan Ducati, Jorge Lorenzo berharap bisa masuk dalam jajaran pebalap yang mampu jadi juara dunia kelas utama dengan dua pabrikan berbeda. Pujian Max Biaggi untuk Valentino Rossi Jorge Lorenzo Hadir dalam Ucapan Selamat Natal Yamaha Iannone: Saya Sering Jatuh Karena Ingin yang Terbaik Sepanjang sejarah, hanya ada lima pebalap yang mampu jadi juara kelas utama (500cc/MotoGP) dengan dua pabrikan berbeda, yaitu Geoff Duke, Giacomo Agostini, Eddie Lawson, Valentino Rossi, dan Casey Stoner. Lorenzo ingin bergabung dalam jajaran legenda tersebut. “Fakta ini sangat penting bagi saya karena hanya ada lima pebalap yang mampu menang dengan dua pabrikan berbeda dalam sejarah balap motor.” “Kedatangan saya ke Ducati juga menghadirkan tantangan yang sangat menarik karena hampir tak ada yang bisa menang dengan Ducati sebelumnya, kecuali Casey Stoner. Hal itu jadi motivasi yang sangat bagus bagi saya,” tutur Lorenzo seperti dikutip dari Crash Lorenzo saat ini diliputi rasa penasaran yang besar untuk menunggang sepeda motor Desmosedici yang dipakai tim Ducati karena ia baru sekali menjajal motor tersebut pada sesi tes di Valencia, usai MotoGP musim 2016 berakhir. “Saya sangat tertarik dengan Ducati arena saya hanya memiliki kesempatan mencoba motor itu di Valencia dua hari setelah musim berakhir. Setelah itu saya tak boleh lagi menjajalnya hingga akhir Januari mendatang. Jadi saya menjalani penantian selama dua bulan yang panjang,” kata pebalap asal Spanyol ini. Dengan kondisi tersebut, maka Lorenzo memanfaatkan waktu yang ada untuk liburan dan melepaskan penat. “Setidaknya apa yang terjadi pada saya saat ini sangat bagus karena saya jadi memiliki waktu bebas dan sedikit liburan.” “Namun tentunya saya tak akan larut dalam liburan karena saya harus lebih bersiap, terutama dalam kondisi fisik dibandingkan sebelumnya, karena saya akan menunggangi motor yang sulit dikendarai,” ucap Lorenzo. Selama sembilan musim bersama Yamaha, Lorenzo sendiri sudah tiga kali jadi juara dunia, yaitu pada 2010, 2012, dan 2015. (kid)'} ``` ### Data Fields - `id`: id of the sample - `url`: the url to the original article - `date`: the publishing date of the article - `title`: the title of the article - `content`: the content of the article ### Data Splits The dataset contains train set of 499164 samples. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. The dataset is shared for the sole purpose of aiding open scientific research in Bahasa Indonesia (computing or linguistics), and can only be used for that purpose. The ownership of each article within the dataset belongs to the respective newspaper from which it was extracted; and the maintainer of the repository does not claim ownership of any of the content within it. If you think, by any means, that this dataset breaches any established copyrights; please contact the repository maintainer. ### Citation Information [N/A] ### Contributions Thanks to [@cahya-wirawan](https://github.com/cahya-wirawan) for adding this dataset.
id_panl_bppt	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en - id license: - unknown multilinguality: - translation size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] pretty_name: IdPanlBppt dataset_info: features: - name: id dtype: string - name: translation dtype: translation: languages: - en - id - name: topic dtype: class_label: names: '0': Economy '1': International '2': Science '3': Sport config_name: id_panl_bppt splits: - name: train num_bytes: 7455924 num_examples: 24021 download_size: 2366973 dataset_size: 7455924 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [PANL BPPT](http://digilib.bppt.go.id/sampul/p92-budiono.pdf) - Repository: [PANL BPPT Repository](https://github.com/cahya-wirawan/indonesian-language-models/raw/master/data/BPPTIndToEngCorpusHalfM.zip) - Paper: [Resource Report: Building Parallel Text Corpora for Multi-Domain Translation System](http://digilib.bppt.go.id/sampul/p92-budiono.pdf) - Leaderboard: - Point of Contact: ### Dataset Summary Parallel Text Corpora for Multi-Domain Translation System created by BPPT (Indonesian Agency for the Assessment and Application of Technology) for PAN Localization Project (A Regional Initiative to Develop Local Language Computing Capacity in Asia). The dataset contains around 24K sentences divided in 4 difference topics (Economic, international, Science and Technology and Sport). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Indonesian ## Dataset Structure [More Information Needed] ### Data Instances An example of the dataset: ``` { 'id': '0', 'topic': 0, 'translation': { 'en': 'Minister of Finance Sri Mulyani Indrawati said that a sharp correction of the composite inde x by up to 4 pct in Wedenesday?s trading was a mere temporary effect of regional factors like decline in plantation commodity prices and the financial crisis in Thailand.', 'id': 'Menteri Keuangan Sri Mulyani mengatakan koreksi tajam pada Indeks Harga Saham Gabungan IHSG hingga sekitar 4 persen dalam perdagangan Rabu 10/1 hanya efek sesaat dari faktor-faktor regional seperti penurunan harga komoditi perkebunan dan krisis finansial di Thailand.' } } ``` ### Data Fields - `id`: id of the sample - `translation`: the parallel sentence english-indonesian - `topic`: the topic of the sentence. It could be one of the following: - Economic - International - Science and Technology - Sport ### Data Splits The dataset is splitted in to train, validation and test sets. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{id_panl_bppt, author = {PAN Localization - BPPT}, title = {Parallel Text Corpora, English Indonesian}, year = {2009}, url = {http://digilib.bppt.go.id/sampul/p92-budiono.pdf}, } ``` ### Contributions Thanks to [@cahya-wirawan](https://github.com/cahya-wirawan) for adding this dataset.
id_puisi	--- annotations_creators: - no-annotation language_creators: - found language: - id license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text2text-generation - text-generation - fill-mask task_ids: [] paperswithcode_id: null pretty_name: Indonesian Puisi tags: - poem-generation dataset_info: features: - name: title dtype: string - name: author dtype: string - name: puisi dtype: string - name: puisi_with_header dtype: string splits: - name: train num_bytes: 10613475 num_examples: 7223 download_size: 10558108 dataset_size: 10613475 --- # Dataset Card for id_puisi ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [puisi-pantun-generator](https://github.com/ilhamfp/puisi-pantun-generator) - Repository: [puisi-pantun-generator](https://github.com/ilhamfp/puisi-pantun-generator) - Paper: [N/A] - Leaderboard: [N/A] - Point of Contact: [Ilham Firdausi Putra](ilhamfputra31@gmail.com) ### Dataset Summary Puisi (poem) is an Indonesian poetic form. The dataset contains 7223 Indonesian puisi with its title and author. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Indonesian ## Dataset Structure ### Data Instances ``` { 'puisi_with_header': 'TEPERANGKAP Oleh Mangku Langit Jingga Mungkin kau membiarkan aku Membiarkan perasaan ini larut Memberi ruang jiwaku hampa Agar tetap terbiasa nikmati Perangkap yang kau buat Perisai yang kau banggakan Takkan jadi tameng bagimu Aku mengerti betapa hebatnya Perangkap mu hei sang dewi Ku akan terus merasa terbiasa Dengan pesona indahmu Ku masih akan nikmati hadirmu Berjalanlah pada hati yang sama Satu hati denganku Walau ku terperangkap Namunku nikmati dan jalani', 'title': 'TEPERANGKAP', 'author': 'Oleh Mangku Langit Jingga', 'puisi': 'Mungkin kau membiarkan aku Membiarkan perasaan ini larut Memberi ruang jiwaku hampa Agar tetap terbiasa nikmati Perangkap yang kau buat Perisai yang kau banggakan Takkan jadi tameng bagimu Aku mengerti betapa hebatnya Perangkap mu hei sang dewi Ku akan terus merasa terbiasa Dengan pesona indahmu Ku masih akan nikmati hadirmu Berjalanlah pada hati yang sama Satu hati denganku Walau ku terperangkap Namunku nikmati dan jalani', } ``` ### Data Fields - `puisi_with_header`: the raw text from scraping - `title`: the title extracted from the raw text using regex - `author`: the author extracted from the raw text using regex - `puisi`: the poem with title and author extracted out using regex ### Data Splits The dataset contains only a train set. ## Dataset Creation ### Curation Rationale The dataset was initially collected as an experiment to generate an Indonesian poem using GPT-2. ### Source Data #### Initial Data Collection and Normalization The dataset was scraped using BeautifulSoup from lokerpuisi.web.id (the data no longer exist on the original blog). The title and author column was produced using regex match from puisi_with_header column. #### Who are the source language producers? The poems were generated by humans. The users of the original blog voluntarily submit their original poems to get published on the blog. ### Annotations #### Annotation process [N/A] #### Who are the annotators? [N/A] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations The regex match used to extract the title & author from the raw text is not perfect. Some title & text is still failed to get extracted. ## Additional Information ### Dataset Curators Ilham Firdausi Putra ### Licensing Information MIT License ### Citation Information [N/A] ### Contributions Thanks to [@ilhamfp](https://github.com/ilhamfp) for adding this dataset.
igbo_english_machine_translation	--- annotations_creators: - found language_creators: - found language: - en - ig license: - unknown multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: igbonlp-datasets pretty_name: IgboNLP Datasets dataset_info: features: - name: id dtype: string - name: translation dtype: translation: languages: - ig - en config_name: ig-en splits: - name: train num_bytes: 2367989 num_examples: 10000 - name: validation num_bytes: 60154 num_examples: 200 - name: test num_bytes: 298670 num_examples: 552 download_size: 2580255 dataset_size: 2726813 --- # Dataset Card for IgboNLP Datasets ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: None - Repository: https://github.com/IgnatiusEzeani/IGBONLP/tree/master/ig_en_mt - Paper: https://arxiv.org/abs/2004.00648 - Leaderboard: [More Information Needed] - Point of Contact: [More Information Needed] ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
igbo_monolingual	--- annotations_creators: - found language_creators: - found language: - ig license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K - n<1K source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: null pretty_name: Igbo Monolingual Dataset configs: - bbc-igbo - eze_goes_to_school - igbo-radio - jw-books - jw-nt-igbo - jw-ot-igbo - jw-teta - jw-ulo_nche - jw-ulo_nche_naamu dataset_info: - config_name: eze_goes_to_school features: - name: format dtype: string - name: title dtype: string - name: chapters sequence: - name: title dtype: string - name: content dtype: string splits: - name: train num_bytes: 128309 num_examples: 1 download_size: 8260947 dataset_size: 128309 - config_name: bbc-igbo features: - name: source dtype: string - name: title dtype: string - name: description dtype: string - name: date dtype: string - name: headline dtype: string - name: content dtype: string - name: tags sequence: string splits: - name: train num_bytes: 3488908 num_examples: 1297 download_size: 8260947 dataset_size: 3488908 - config_name: igbo-radio features: - name: source dtype: string - name: headline dtype: string - name: author dtype: string - name: date dtype: string - name: description dtype: string - name: content dtype: string splits: - name: train num_bytes: 1129644 num_examples: 440 download_size: 8260947 dataset_size: 1129644 - config_name: jw-ot-igbo features: - name: format dtype: string - name: title dtype: string - name: chapters sequence: - name: title dtype: string - name: content dtype: string splits: - name: train num_bytes: 3489314 num_examples: 39 download_size: 8260947 dataset_size: 3489314 - config_name: jw-nt-igbo features: - name: format dtype: string - name: title dtype: string - name: chapters sequence: - name: title dtype: string - name: content dtype: string splits: - name: train num_bytes: 1228779 num_examples: 27 download_size: 8260947 dataset_size: 1228779 - config_name: jw-books features: - name: title dtype: string - name: content dtype: string - name: format dtype: string - name: date dtype: string splits: - name: train num_bytes: 9456342 num_examples: 48 download_size: 8260947 dataset_size: 9456342 - config_name: jw-teta features: - name: title dtype: string - name: content dtype: string - name: format dtype: string - name: date dtype: string splits: - name: train num_bytes: 991111 num_examples: 37 download_size: 8260947 dataset_size: 991111 - config_name: jw-ulo_nche features: - name: title dtype: string - name: content dtype: string - name: format dtype: string - name: date dtype: string splits: - name: train num_bytes: 1952360 num_examples: 55 download_size: 8260947 dataset_size: 1952360 - config_name: jw-ulo_nche_naamu features: - name: title dtype: string - name: content dtype: string - name: format dtype: string - name: date dtype: string splits: - name: train num_bytes: 7248017 num_examples: 88 download_size: 8260947 dataset_size: 7248017 --- # Dataset Card for Igbo Monolingual Dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/IgnatiusEzeani/IGBONLP/tree/master/ig_monoling - Repository: https://github.com/IgnatiusEzeani/IGBONLP/tree/master/ig_monoling - Paper: https://arxiv.org/abs/2004.00648 ### Dataset Summary A dataset is a collection of Monolingual Igbo sentences. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Igbo (ig) ## Dataset Structure ### Data Instances Here is an example from the bb-igbo config: ``` {'content': 'Ike Ekweremmadụ\n\nIke ịda jụụ otụ nkeji banyere oke ogbugbu na-eme n\'ala Naijiria agwụla Ekweremmadụ\n\nOsote onye-isi ndị ome-iwu Naịjirịa bụ Ike Ekweremadu ekwuola na ike agwụla ndị Sịnatị iji otu nkeji darajụụ akwanyere ndị egburu n\'ime oke ọgbaghara dị na Naịjirịa oge ọ bula.\n\nEkweremadu katọrọ mwakpọ na ogbugbu ndị Naịjirịa aka ha dị ọcha nke ndị Fulani na-achị ehi mere, kwuo na ike agwụla ndị ome- iwu ịkwanyere ha ugwu n\'otu nkeji\'\n\nCheta n\'otu ịzụka gara-aga ka emere akwam ozu mmadụ ruru iri asaa egburu na Local Gọọmenti Logo na Guma nke Benue Steeti, e be ihe kariri mmadụ iri ise ka akụkọ kwuru n\'egburu na Taraba Steeti.\n\nEkweremadu gosiri iwe gbasara ogbugbu ndị mmadụ na nzukọ ndị ome-iwu n\'ụbọchị taa, kwuo na Naịjirịa ga-ebu ụzọ nwe udo na nchekwa, tupu e kwuowa okwu iwulite obodo.\n\nỌ sịrị: "Ndị ome-iwu abụghị sọ ọsọ ndị ihe a metụtara, kama ndị Naịjirịa niile.\n\n\'Ike agwụla anyị iji otu nkeji dị jụụ maka nkwanye ugwu. Ihe anyị chọrọ bụ udo na nchekwa tupu echewa echịchị nwuli obodo."', 'date': '2018-01-19T17:07:38Z', 'description': "N'ihi oke ogbugbu ndị mmadụ na Naịjirịa gbagburu gburu, osota onyeisi ndị ome-iwu Naịjirịa bụ Ike Ekweremadu ekwuola na ihe Naịjiria chọrọ bụ nchekwa tara ọchịchị, tupu ekwuwa okwu ihe ọzọ.", 'headline': 'Ekweremadu: Ike agwụla ndị ụlọ ome iwu', 'source': 'https://www.bbc.com/igbo/42712250', 'tags': [], 'title': 'Ekweremadu: Ike agwụla ndị ụlọ ome iwu'} ``` ### Data Fields For config 'eze_goes_to_school': - format, title, chapters For config 'bbc-igbo' : - source, title, description, date (Missing date values replaced with empty strings), headline, content, tags (Missing tags replaced with empty list) For config 'igbo-radio': - source, headline, author, date, description, content For config 'jw-ot-igbo': - format, title, chapters For config 'jw-nt-igbo': - format, title, chapters For config 'jw-books': - title, content, format, date (Missing date values replaced with empty strings) For config 'jw-teta': - title, content, format, date (Missing date values replaced with empty strings) For config 'jw-ulo_nche': - title, content, format, date (Missing date values replaced with empty strings) For config 'jw-ulo_nche_naamu': - title, content, format, date (Missing date values replaced with empty strings) ### Data Splits \| bbc-igbo \| eze_goes_to_school \|igbo-radio\| jw-books\|jw-nt-igbo\| jw-ot-igbo \| jw-teta \|jw-ulo_nche \|jw-ulo_nche_naamu \| ------------- \|:-------------:\|:-------------:\|:-------------:\|:-------------:\|:-------------:\|:-------------:\|:-------------:\|:-------------:\| \| 1297 \| 1 \| 440 \| 48 \| 27 \| 39 \| 37 \| 55 \| 88 ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information @misc{ezeani2020igboenglish, title={Igbo-English Machine Translation: An Evaluation Benchmark}, author={Ignatius Ezeani and Paul Rayson and Ikechukwu Onyenwe and Chinedu Uchechukwu and Mark Hepple}, year={2020}, eprint={2004.00648}, archivePrefix={arXiv}, primaryClass={cs.CL} } ### Contributions Thanks to [@purvimisal](https://github.com/purvimisal) for adding this dataset.
igbo_ner	--- annotations_creators: - found language_creators: - found language: - ig license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition paperswithcode_id: null pretty_name: Igbo NER dataset dataset_info: - config_name: ner_data features: - name: content_n dtype: string - name: named_entity dtype: string - name: sentences sequence: string splits: - name: train num_bytes: 60315228 num_examples: 30715 download_size: 3311204 dataset_size: 60315228 - config_name: free_text features: - name: sentences dtype: string splits: - name: train num_bytes: 1172152 num_examples: 10000 download_size: 1132151 dataset_size: 1172152 --- # Dataset Card for Igbo NER dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/IgnatiusEzeani/IGBONLP/tree/master/ig_ner - Repository: https://github.com/IgnatiusEzeani/IGBONLP/tree/master/ig_ner - Paper: https://arxiv.org/abs/2004.00648 ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances Here is an example from the dataset: ``` {'content_n': 'content_0', 'named_entity': 'Ike Ekweremmadụ', 'sentences': ['Ike Ekweremmadụ', "Ike ịda jụụ otụ nkeji banyere oke ogbugbu na-eme n'ala Naijiria agwụla Ekweremmadụ"]} ``` ### Data Fields - content_n : ID - named_entity : Name of the entity - sentences : List of sentences for the entity ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information @misc{ezeani2020igboenglish, title={Igbo-English Machine Translation: An Evaluation Benchmark}, author={Ignatius Ezeani and Paul Rayson and Ikechukwu Onyenwe and Chinedu Uchechukwu and Mark Hepple}, year={2020}, eprint={2004.00648}, archivePrefix={arXiv}, primaryClass={cs.CL} } ### Contributions Thanks to [@purvimisal](https://github.com/purvimisal) for adding this dataset.
ilist	--- annotations_creators: - no-annotation language_creators: - found language: - awa - bho - bra - hi - mag license: - cc-by-4.0 multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: [] pretty_name: ilist tags: - language-identification dataset_info: features: - name: language_id dtype: class_label: names: '0': AWA '1': BRA '2': MAG '3': BHO '4': HIN - name: text dtype: string splits: - name: train num_bytes: 14362998 num_examples: 70351 - name: test num_bytes: 2146857 num_examples: 9692 - name: validation num_bytes: 2407643 num_examples: 10329 download_size: 18284850 dataset_size: 18917498 --- # Dataset Card for ilist ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: - Repository: https://github.com/kmi-linguistics/vardial2018 - Paper: [Language Identification and Morphosyntactic Tagging: The Second VarDial Evaluation Campaign](https://aclanthology.org/W18-3901/) - Leaderboard: - Point of Contact: linguistics.kmi@gmail.com ### Dataset Summary This dataset is introduced in a task which aimed at identifying 5 closely-related languages of Indo-Aryan language family: Hindi (also known as Khari Boli), Braj Bhasha, Awadhi, Bhojpuri and Magahi. These languages form part of a continuum starting from Western Uttar Pradesh (Hindi and Braj Bhasha) to Eastern Uttar Pradesh (Awadhi and Bhojpuri) and the neighbouring Eastern state of Bihar (Bhojpuri and Magahi). For this task, participants were provided with a dataset of approximately 15,000 sentences in each language, mainly from the domain of literature, published over the web as well as in print. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Hindi, Braj Bhasha, Awadhi, Bhojpuri and Magahi ## Dataset Structure ### Data Instances ``` { "language_id": 4, "text": 'तभी बारिश हुई थी जिसका गीलापन इन मूर्तियों को इन तस्वीरों में एक अलग रूप देता है .' } ``` ### Data Fields - `text`: text which you want to classify - `language_id`: label for the text as an integer from 0 to 4 The language ids correspond to the following languages: "AWA", "BRA", "MAG", "BHO", "HIN". ### Data Splits \| \| train \| valid \| test \| \|----------------------\|-------\|-------\|-------\| \| # of input sentences \| 70351 \| 9692 \| 10329 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data The data for this task was collected from both hard printed and digital sources. Printed materials were obtained from different institutions that promote these languages. We also gathered data from libraries, as well as from local literary and cultural groups. We collected printed stories, novels and essays in books, magazines, and newspapers. #### Initial Data Collection and Normalization We scanned the printed materials, then we performed OCR, and finally we asked native speakers of the respective languages to correct the OCR output. Since there are no specific OCR models available for these languages, we used the Google OCR for Hindi, part of the Drive API. Since all the languages used the Devanagari script, we expected the OCR to work reasonably well, and overall it did. We further managed to get some blogs in Magahi and Bhojpuri. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information This work is licensed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0/ ### Citation Information ``` @inproceedings{zampieri-etal-2018-language, title = "Language Identification and Morphosyntactic Tagging: The Second {V}ar{D}ial Evaluation Campaign", author = {Zampieri, Marcos and Malmasi, Shervin and Nakov, Preslav and Ali, Ahmed and Shon, Suwon and Glass, James and Scherrer, Yves and Samard{\v{z}}i{\'c}, Tanja and Ljube{\v{s}}i{\'c}, Nikola and Tiedemann, J{\"o}rg and van der Lee, Chris and Grondelaers, Stefan and Oostdijk, Nelleke and Speelman, Dirk and van den Bosch, Antal and Kumar, Ritesh and Lahiri, Bornini and Jain, Mayank}, booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)", month = aug, year = "2018", address = "Santa Fe, New Mexico, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/W18-3901", pages = "1--17", } ``` ### Contributions Thanks to [@vasudevgupta7](https://github.com/vasudevgupta7) for adding this dataset.
imdb	--- pretty_name: IMDB annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - other multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification paperswithcode_id: imdb-movie-reviews train-eval-index: - config: plain_text task: text-classification task_id: binary_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy - name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: 0: neg 1: pos config_name: plain_text splits: - name: train num_bytes: 33432835 num_examples: 25000 - name: test num_bytes: 32650697 num_examples: 25000 - name: unsupervised num_bytes: 67106814 num_examples: 50000 download_size: 84125825 dataset_size: 133190346 --- # Dataset Card for "imdb" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [http://ai.stanford.edu/~amaas/data/sentiment/](http://ai.stanford.edu/~amaas/data/sentiment/) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 84.13 MB - Size of the generated dataset: 133.23 MB - Total amount of disk used: 217.35 MB ### Dataset Summary Large Movie Review Dataset. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### plain_text - Size of downloaded dataset files: 84.13 MB - Size of the generated dataset: 133.23 MB - Total amount of disk used: 217.35 MB An example of 'train' looks as follows. ``` { "label": 0, "text": "Goodbye world2\n" } ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `text`: a `string` feature. - `label`: a classification label, with possible values including `neg` (0), `pos` (1). ### Data Splits \| name \|train\|unsupervised\|test \| \|----------\|----:\|-----------:\|----:\| \|plain_text\|25000\| 50000\|25000\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{maas-EtAl:2011:ACL-HLT2011, author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher}, title = {Learning Word Vectors for Sentiment Analysis}, booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies}, month = {June}, year = {2011}, address = {Portland, Oregon, USA}, publisher = {Association for Computational Linguistics}, pages = {142--150}, url = {http://www.aclweb.org/anthology/P11-1015} } ``` ### Contributions Thanks to [@ghazi-f](https://github.com/ghazi-f), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
imdb_urdu_reviews	--- annotations_creators: - found language_creators: - machine-generated language: - ur license: - odbl multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: ImDB Urdu Reviews dataset_info: features: - name: sentence dtype: string - name: sentiment dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 114670811 num_examples: 50000 download_size: 31510992 dataset_size: 114670811 --- # Dataset Card for ImDB Urdu Reviews ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Github](https://github.com/mirfan899/Urdu) - Repository: [Github](https://github.com/mirfan899/Urdu) - Paper: [Aclweb](http://www.aclweb.org/anthology/P11-1015) - Leaderboard: - Point of Contact: [Ikram Ali](https://github.com/akkefa) ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - sentence: The movie review which was translated into Urdu. - sentiment: The sentiment exhibited in the review, either positive or negative. ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@chaitnayabasava](https://github.com/chaitnayabasava) for adding this dataset.
imppres	--- annotations_creators: - machine-generated language_creators: - machine-generated language: - en license: - cc-by-nc-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - natural-language-inference paperswithcode_id: imppres pretty_name: IMPPRES dataset_info: - config_name: presupposition_all_n_presupposition features: - name: premise dtype: string - name: hypothesis dtype: string - name: trigger dtype: string - name: trigger1 dtype: string - name: trigger2 dtype: string - name: presupposition dtype: string - name: gold_label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: UID dtype: string - name: pairID dtype: string - name: paradigmID dtype: int16 splits: - name: all_n_presupposition num_bytes: 458492 num_examples: 1900 download_size: 335088 dataset_size: 458492 - config_name: presupposition_both_presupposition features: - name: premise dtype: string - name: hypothesis dtype: string - name: trigger dtype: string - name: trigger1 dtype: string - name: trigger2 dtype: string - name: presupposition dtype: string - name: gold_label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: UID dtype: string - name: pairID dtype: string - name: paradigmID dtype: int16 splits: - name: both_presupposition num_bytes: 432792 num_examples: 1900 download_size: 335088 dataset_size: 432792 - config_name: presupposition_change_of_state features: - name: premise dtype: string - name: hypothesis dtype: string - name: trigger dtype: string - name: trigger1 dtype: string - name: trigger2 dtype: string - name: presupposition dtype: string - name: gold_label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: UID dtype: string - name: pairID dtype: string - name: paradigmID dtype: int16 splits: - name: change_of_state num_bytes: 308627 num_examples: 1900 download_size: 335088 dataset_size: 308627 - config_name: presupposition_cleft_existence features: - name: premise dtype: string - name: hypothesis dtype: string - name: trigger dtype: string - name: trigger1 dtype: string - name: trigger2 dtype: string - name: presupposition dtype: string - name: gold_label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: UID dtype: string - name: pairID dtype: string - name: paradigmID dtype: int16 splits: - name: cleft_existence num_bytes: 363238 num_examples: 1900 download_size: 335088 dataset_size: 363238 - config_name: presupposition_cleft_uniqueness features: - name: premise dtype: string - name: hypothesis dtype: string - name: trigger dtype: string - name: trigger1 dtype: string - name: trigger2 dtype: string - name: presupposition dtype: string - name: gold_label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: UID dtype: string - name: pairID dtype: string - name: paradigmID dtype: int16 splits: - name: cleft_uniqueness num_bytes: 388779 num_examples: 1900 download_size: 335088 dataset_size: 388779 - config_name: presupposition_only_presupposition features: - name: premise dtype: string - name: hypothesis dtype: string - name: trigger dtype: string - name: trigger1 dtype: string - name: trigger2 dtype: string - name: presupposition dtype: string - name: gold_label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: UID dtype: string - name: pairID dtype: string - name: paradigmID dtype: int16 splits: - name: only_presupposition num_bytes: 349018 num_examples: 1900 download_size: 335088 dataset_size: 349018 - config_name: presupposition_possessed_definites_existence features: - name: premise dtype: string - name: hypothesis dtype: string - name: trigger dtype: string - name: trigger1 dtype: string - name: trigger2 dtype: string - name: presupposition dtype: string - name: gold_label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: UID dtype: string - name: pairID dtype: string - name: paradigmID dtype: int16 splits: - name: possessed_definites_existence num_bytes: 362334 num_examples: 1900 download_size: 335088 dataset_size: 362334 - config_name: presupposition_possessed_definites_uniqueness features: - name: premise dtype: string - name: hypothesis dtype: string - name: trigger dtype: string - name: trigger1 dtype: string - name: trigger2 dtype: string - name: presupposition dtype: string - name: gold_label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: UID dtype: string - name: pairID dtype: string - name: paradigmID dtype: int16 splits: - name: possessed_definites_uniqueness num_bytes: 459403 num_examples: 1900 download_size: 335088 dataset_size: 459403 - config_name: presupposition_question_presupposition features: - name: premise dtype: string - name: hypothesis dtype: string - name: trigger dtype: string - name: trigger1 dtype: string - name: trigger2 dtype: string - name: presupposition dtype: string - name: gold_label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: UID dtype: string - name: pairID dtype: string - name: paradigmID dtype: int16 splits: - name: question_presupposition num_bytes: 397227 num_examples: 1900 download_size: 335088 dataset_size: 397227 - config_name: implicature_connectives features: - name: premise dtype: string - name: hypothesis dtype: string - name: gold_label_log dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: gold_label_prag dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: spec_relation dtype: string - name: item_type dtype: string - name: trigger dtype: string - name: lexemes dtype: string splits: - name: connectives num_bytes: 221868 num_examples: 1200 download_size: 335088 dataset_size: 221868 - config_name: implicature_gradable_adjective features: - name: premise dtype: string - name: hypothesis dtype: string - name: gold_label_log dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: gold_label_prag dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: spec_relation dtype: string - name: item_type dtype: string - name: trigger dtype: string - name: lexemes dtype: string splits: - name: gradable_adjective num_bytes: 153672 num_examples: 1200 download_size: 335088 dataset_size: 153672 - config_name: implicature_gradable_verb features: - name: premise dtype: string - name: hypothesis dtype: string - name: gold_label_log dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: gold_label_prag dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: spec_relation dtype: string - name: item_type dtype: string - name: trigger dtype: string - name: lexemes dtype: string splits: - name: gradable_verb num_bytes: 180702 num_examples: 1200 download_size: 335088 dataset_size: 180702 - config_name: implicature_modals features: - name: premise dtype: string - name: hypothesis dtype: string - name: gold_label_log dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: gold_label_prag dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: spec_relation dtype: string - name: item_type dtype: string - name: trigger dtype: string - name: lexemes dtype: string splits: - name: modals num_bytes: 178560 num_examples: 1200 download_size: 335088 dataset_size: 178560 - config_name: implicature_numerals_10_100 features: - name: premise dtype: string - name: hypothesis dtype: string - name: gold_label_log dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: gold_label_prag dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: spec_relation dtype: string - name: item_type dtype: string - name: trigger dtype: string - name: lexemes dtype: string splits: - name: numerals_10_100 num_bytes: 208620 num_examples: 1200 download_size: 335088 dataset_size: 208620 - config_name: implicature_numerals_2_3 features: - name: premise dtype: string - name: hypothesis dtype: string - name: gold_label_log dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: gold_label_prag dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: spec_relation dtype: string - name: item_type dtype: string - name: trigger dtype: string - name: lexemes dtype: string splits: - name: numerals_2_3 num_bytes: 188784 num_examples: 1200 download_size: 335088 dataset_size: 188784 - config_name: implicature_quantifiers features: - name: premise dtype: string - name: hypothesis dtype: string - name: gold_label_log dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: gold_label_prag dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: spec_relation dtype: string - name: item_type dtype: string - name: trigger dtype: string - name: lexemes dtype: string splits: - name: quantifiers num_bytes: 176814 num_examples: 1200 download_size: 335088 dataset_size: 176814 --- # Dataset Card for IMPPRES ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Github](https://github.com/facebookresearch/Imppres) - Repository: [Github](https://github.com/facebookresearch/Imppres) - Paper: [Aclweb](https://www.aclweb.org/anthology/2020.acl-main.768) - Leaderboard: - Point of Contact: ### Dataset Summary Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures. ### Supported Tasks and Leaderboards Natural Language Inference. ### Languages English. ## Dataset Structure ### Data Instances The data consists of 2 configurations: implicature and presupposition. Each configuration consists of several different sub-datasets: Pressupposition - all_n_presupposition - change_of_state - cleft_uniqueness - possessed_definites_existence - question_presupposition - both_presupposition - cleft_existence - only_presupposition - possessed_definites_uniqueness Implicature - connectives - gradable_adjective - gradable_verb - modals - numerals_10_100 - numerals_2_3 - quantifiers Each sentence type in IMPPRES is generated according to a template that specifies the linear order of the constituents in the sentence. The constituents are sampled from a vocabulary of over 3000 lexical items annotated with grammatical features needed to ensure wellformedness. We semiautomatically generate IMPPRES using a codebase developed by Warstadt et al. (2019a) and significantly expanded for the BLiMP dataset (Warstadt et al., 2019b). Here is an instance of the raw presupposition data from any sub-dataset: ```buildoutcfg { "sentence1": "All ten guys that proved to boast might have been divorcing.", "sentence2": "There are exactly ten guys that proved to boast.", "trigger": "modal", "presupposition": "positive", "gold_label": "entailment", "UID": "all_n_presupposition", "pairID": "9e", "paradigmID": 0 } ``` and the raw implicature data from any sub-dataset: ```buildoutcfg { "sentence1": "That teenager couldn't yell.", "sentence2": "That teenager could yell.", "gold_label_log": "contradiction", "gold_label_prag": "contradiction", "spec_relation": "negation", "item_type": "control", "trigger": "modal", "lexemes": "can - have to" } ``` ### Data Fields Presupposition There is a slight mapping from the raw data fields in the presupposition sub-datasets and the fields appearing in the HuggingFace Datasets. When dealing with the HF Dataset, the following mapping of fields happens: ```buildoutcfg "premise" -> "sentence1" "hypothesis"-> "sentence2" "trigger" -> "trigger" or "Not_In_Example" "trigger1" -> "trigger1" or "Not_In_Example" "trigger2" -> "trigger2" or "Not_In_Example" "presupposition" -> "presupposition" or "Not_In_Example" "gold_label" -> "gold_label" "UID" -> "UID" "pairID" -> "pairID" "paradigmID" -> "paradigmID" ``` For the most part, the majority of the raw fields remain unchanged. However, when it comes to the various `trigger` fields, a new mapping was introduced. There are some examples in the dataset that only have the `trigger` field while other examples have the `trigger1` and `trigger2` field without the `trigger` or `presupposition` field. Nominally, most examples look like the example in the Data Instances section above. Occassionally, however, some examples will look like: ```buildoutcfg { 'sentence1': 'Did that committee know when Lissa walked through the cafe?', 'sentence2': 'That committee knew when Lissa walked through the cafe.', 'trigger1': 'interrogative', 'trigger2': 'unembedded', 'gold_label': 'neutral', 'control_item': True, 'UID': 'question_presupposition', 'pairID': '1821n', 'paradigmID': 95 } ``` In this example, `trigger1` and `trigger2` appear and `presupposition` and `trigger` are removed. This maintains the length of the dictionary. To account for these examples, we have thus introduced the mapping above such that all examples accessed through the HF Datasets interface will have the same size as well as the same fields. In the event that an example does not have a value for one of the fields, the field is maintained in the dictionary but given a value of `Not_In_Example`. To illustrate this point, the example given in the Data Instances section above would look like the following in the HF Datasets: ```buildoutcfg { "premise": "All ten guys that proved to boast might have been divorcing.", "hypothesis": "There are exactly ten guys that proved to boast.", "trigger": "modal", "trigger1": "Not_In_Example", "trigger2": "Not_In_Example" "presupposition": "positive", "gold_label": "entailment", "UID": "all_n_presupposition", "pairID": "9e", "paradigmID": 0 } ``` Below is description of the fields: ```buildoutcfg "premise": The premise. "hypothesis": The hypothesis. "trigger": A detailed discussion of trigger types appears in the paper. "trigger1": A detailed discussion of trigger types appears in the paper. "trigger2": A detailed discussion of trigger types appears in the paper. "presupposition": positive or negative. "gold_label": Corresponds to entailment, contradiction, or neutral. "UID": Unique id. "pairID": Sentence pair ID. "paradigmID": ? ``` It is not immediately clear what the difference is between `trigger`, `trigger1`, and `trigger2` is or what the `paradigmID` refers to. Implicature The `implicature` fields only have the mapping below: ```buildoutcfg "premise" -> "sentence1" "hypothesis"-> "sentence2" ``` Here is a description of the fields: ```buildoutcfg "premise": The premise. "hypothesis": The hypothesis. "gold_label_log": Gold label for a logical reading of the sentence pair. "gold_label_prag": Gold label for a pragmatic reading of the sentence pair. "spec_relation": ? "item_type": ? "trigger": A detailed discussion of trigger types appears in the paper. "lexemes": ? ``` ### Data Splits As the dataset was created to test already trained models, the only split that exists is for testing. ## Dataset Creation ### Curation Rationale IMPPRES was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures. ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The annotations were generated semi-automatically. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information IMPPRES is available under a Creative Commons Attribution-NonCommercial 4.0 International Public License ("The License"). You may not use these files except in compliance with the License. Please see the LICENSE file for more information before you use the dataset. ### Citation Information ```buildoutcfg @inproceedings{jeretic-etal-2020-natural, title = "Are Natural Language Inference Models {IMPPRESsive}? {L}earning {IMPlicature} and {PRESupposition}", author = "Jereti\v{c}, Paloma and Warstadt, Alex and Bhooshan, Suvrat and Williams, Adina", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.768", doi = "10.18653/v1/2020.acl-main.768", pages = "8690--8705", abstract = "Natural language inference (NLI) is an increasingly important task for natural language understanding, which requires one to infer whether a sentence entails another. However, the ability of NLI models to make pragmatic inferences remains understudied. We create an IMPlicature and PRESupposition diagnostic dataset (IMPPRES), consisting of 32K semi-automatically generated sentence pairs illustrating well-studied pragmatic inference types. We use IMPPRES to evaluate whether BERT, InferSent, and BOW NLI models trained on MultiNLI (Williams et al., 2018) learn to make pragmatic inferences. Although MultiNLI appears to contain very few pairs illustrating these inference types, we find that BERT learns to draw pragmatic inferences. It reliably treats scalar implicatures triggered by {``}some{''} as entailments. For some presupposition triggers like {``}only{''}, BERT reliably recognizes the presupposition as an entailment, even when the trigger is embedded under an entailment canceling operator like negation. BOW and InferSent show weaker evidence of pragmatic reasoning. We conclude that NLI training encourages models to learn some, but not all, pragmatic inferences.", } ``` ### Contributions Thanks to [@aclifton314](https://github.com/aclifton314) for adding this dataset.
indic_glue	--- annotations_creators: - other language_creators: - found language: - as - bn - en - gu - hi - kn - ml - mr - or - pa - ta - te license: - other multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - extended\|other task_categories: - text-classification - token-classification - multiple-choice task_ids: - topic-classification - natural-language-inference - sentiment-analysis - semantic-similarity-scoring - named-entity-recognition - multiple-choice-qa pretty_name: IndicGLUE tags: - discourse-mode-classification - paraphrase-identification - cross-lingual-similarity - headline-classification dataset_info: - config_name: wnli.en features: - name: hypothesis dtype: string - name: premise dtype: string - name: label dtype: class_label: names: '0': not_entailment '1': entailment '2': None splits: - name: train num_bytes: 104577 num_examples: 635 - name: validation num_bytes: 11886 num_examples: 71 - name: test num_bytes: 37305 num_examples: 146 download_size: 591249 dataset_size: 153768 - 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name: test num_bytes: 702377 num_examples: 310 download_size: 1742048 dataset_size: 8229508 - config_name: iitp-pr.hi features: - name: text dtype: string - name: label dtype: class_label: names: '0': negative '1': neutral '2': positive splits: - name: train num_bytes: 945593 num_examples: 4182 - name: validation num_bytes: 120104 num_examples: 523 - name: test num_bytes: 121914 num_examples: 523 download_size: 266545 dataset_size: 1187611 - config_name: actsa-sc.te features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 1370911 num_examples: 4328 - name: validation num_bytes: 166093 num_examples: 541 - name: test num_bytes: 168295 num_examples: 541 download_size: 378882 dataset_size: 1705299 - config_name: md.hi features: - name: sentence dtype: string - name: discourse_mode dtype: string - name: story_number dtype: int32 - name: id dtype: int32 splits: - name: train num_bytes: 1672117 num_examples: 7974 - name: validation num_bytes: 211195 num_examples: 997 - name: test num_bytes: 210183 num_examples: 997 download_size: 1048441 dataset_size: 2093495 - config_name: wiki-ner.as features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-LOC '1': B-ORG '2': B-PER '3': I-LOC '4': I-ORG '5': I-PER '6': O - name: additional_info sequence: sequence: string splits: - name: train num_bytes: 375007 num_examples: 1021 - name: validation num_bytes: 49336 num_examples: 157 - name: test num_bytes: 50480 num_examples: 160 download_size: 5980272 dataset_size: 474823 - config_name: wiki-ner.bn features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-LOC '1': B-ORG '2': B-PER '3': I-LOC '4': I-ORG '5': I-PER '6': O - name: additional_info sequence: sequence: string splits: - name: train num_bytes: 7502896 num_examples: 20223 - name: validation num_bytes: 988707 num_examples: 2985 - name: test num_bytes: 985965 num_examples: 2690 download_size: 5980272 dataset_size: 9477568 - config_name: wiki-ner.gu features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-LOC '1': B-ORG '2': B-PER '3': I-LOC '4': I-ORG '5': I-PER '6': O - name: additional_info sequence: sequence: string splits: - name: train num_bytes: 1571612 num_examples: 2343 - name: validation num_bytes: 192828 num_examples: 297 - name: test num_bytes: 197901 num_examples: 255 download_size: 5980272 dataset_size: 1962341 - config_name: wiki-ner.hi features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-LOC '1': B-ORG '2': B-PER '3': I-LOC '4': I-ORG '5': I-PER '6': O - name: additional_info sequence: sequence: string splits: - name: train num_bytes: 3762529 num_examples: 9463 - name: validation num_bytes: 468702 num_examples: 1114 - name: test num_bytes: 475277 num_examples: 1256 download_size: 5980272 dataset_size: 4706508 - config_name: wiki-ner.kn features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-LOC '1': B-ORG '2': B-PER '3': I-LOC '4': I-ORG '5': I-PER '6': O - name: additional_info sequence: sequence: string splits: - name: train num_bytes: 1352051 num_examples: 2679 - name: validation num_bytes: 179562 num_examples: 412 - name: test num_bytes: 180815 num_examples: 476 download_size: 5980272 dataset_size: 1712428 - config_name: wiki-ner.ml features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-LOC '1': B-ORG '2': B-PER '3': I-LOC '4': I-ORG '5': I-PER '6': O - name: additional_info sequence: sequence: string splits: - name: train num_bytes: 7678935 num_examples: 15620 - name: validation num_bytes: 969971 num_examples: 2067 - name: test num_bytes: 991126 num_examples: 2042 download_size: 5980272 dataset_size: 9640032 - config_name: wiki-ner.mr features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-LOC '1': B-ORG '2': B-PER '3': I-LOC '4': I-ORG '5': I-PER '6': O - name: additional_info sequence: sequence: string splits: - name: train num_bytes: 5431537 num_examples: 12151 - name: validation num_bytes: 701661 num_examples: 1498 - name: test num_bytes: 655706 num_examples: 1329 download_size: 5980272 dataset_size: 6788904 - config_name: wiki-ner.or features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-LOC '1': B-ORG '2': B-PER '3': I-LOC '4': I-ORG '5': I-PER '6': O - name: additional_info sequence: sequence: string splits: - name: train num_bytes: 493782 num_examples: 1077 - name: validation num_bytes: 58592 num_examples: 132 - name: test num_bytes: 62235 num_examples: 153 download_size: 5980272 dataset_size: 614609 - config_name: wiki-ner.pa features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-LOC '1': B-ORG '2': B-PER '3': I-LOC '4': I-ORG '5': I-PER '6': O - name: additional_info sequence: sequence: string splits: - name: train num_bytes: 520268 num_examples: 1408 - name: validation num_bytes: 61194 num_examples: 186 - name: test num_bytes: 61812 num_examples: 179 download_size: 5980272 dataset_size: 643274 - config_name: wiki-ner.ta features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-LOC '1': B-ORG '2': B-PER '3': I-LOC '4': I-ORG '5': I-PER '6': O - name: additional_info sequence: sequence: string splits: - name: train num_bytes: 10117152 num_examples: 20466 - name: validation num_bytes: 1267212 num_examples: 2586 - name: test num_bytes: 1321650 num_examples: 2611 download_size: 5980272 dataset_size: 12706014 - config_name: wiki-ner.te features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-LOC '1': B-ORG '2': B-PER '3': I-LOC '4': I-ORG '5': I-PER '6': O - name: additional_info sequence: sequence: string splits: - name: train num_bytes: 3881235 num_examples: 7978 - name: validation num_bytes: 458533 num_examples: 841 - name: test num_bytes: 507830 num_examples: 1110 download_size: 5980272 dataset_size: 4847598 --- # Dataset Card for "indic_glue" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://ai4bharat.iitm.ac.in/indic-glue - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages](https://aclanthology.org/2020.findings-emnlp.445/) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 3.51 GB - Size of the generated dataset: 1.65 GB - Total amount of disk used: 5.16 GB ### Dataset Summary IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te. The Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task in which a system must read a sentence with a pronoun and select the referent of that pronoun from a list of choices. The examples are manually constructed to foil simple statistical methods: Each one is contingent on contextual information provided by a single word or phrase in the sentence. To convert the problem into sentence pair classification, we construct sentence pairs by replacing the ambiguous pronoun with each possible referent. The task is to predict if the sentence with the pronoun substituted is entailed by the original sentence. We use a small evaluation set consisting of new examples derived from fiction books that was shared privately by the authors of the original corpus. While the included training set is balanced between two classes, the test set is imbalanced between them (65% not entailment). Also, due to a data quirk, the development set is adversarial: hypotheses are sometimes shared between training and development examples, so if a model memorizes the training examples, they will predict the wrong label on corresponding development set example. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence between a model's score on this task and its score on the unconverted original task. We call converted dataset WNLI (Winograd NLI). This dataset is translated and publicly released for 3 Indian languages by AI4Bharat. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### actsa-sc.te - Size of downloaded dataset files: 0.38 MB - Size of the generated dataset: 1.71 MB - Total amount of disk used: 2.09 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "label": 0, "text": "\"ప్రయాణాల్లో ఉన్నవారికోసం బస్ స్టేషన్లు, రైల్వే స్టేషన్లలో పల్స్పోలియో బూతులను ఏర్పాటు చేసి చిన్నారులకు పోలియో చుక్కలు వేసేలా ఏర..." } ``` #### bbca.hi - Size of downloaded dataset files: 5.77 MB - Size of the generated dataset: 27.63 MB - Total amount of disk used: 33.40 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "label": "pakistan", "text": "\"नेटिजन यानि इंटरनेट पर सक्रिय नागरिक अब ट्विटर पर सरकार द्वारा लगाए प्रतिबंधों के समर्थन या विरोध में अपने विचार व्यक्त करते है..." } ``` #### copa.en - Size of downloaded dataset files: 0.75 MB - Size of the generated dataset: 0.12 MB - Total amount of disk used: 0.87 MB An example of 'validation' looks as follows. ``` { "choice1": "I swept the floor in the unoccupied room.", "choice2": "I shut off the light in the unoccupied room.", "label": 1, "premise": "I wanted to conserve energy.", "question": "effect" } ``` #### copa.gu - Size of downloaded dataset files: 0.75 MB - Size of the generated dataset: 0.23 MB - Total amount of disk used: 0.99 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "choice1": "\"સ્ત્રી જાણતી હતી કે તેનો મિત્ર મુશ્કેલ સમયમાંથી પસાર થઈ રહ્યો છે.\"...", "choice2": "\"મહિલાને લાગ્યું કે તેના મિત્રએ તેની દયાળુ લાભ લીધો છે.\"...", "label": 0, "premise": "મહિલાએ તેના મિત્રની મુશ્કેલ વર્તન સહન કરી.", "question": "cause" } ``` #### copa.hi - Size of downloaded dataset files: 0.75 MB - Size of the generated dataset: 0.23 MB - Total amount of disk used: 0.99 MB An example of 'validation' looks as follows. ``` { "choice1": "मैंने उसका प्रस्ताव ठुकरा दिया।", "choice2": "उन्होंने मुझे उत्पाद खरीदने के लिए राजी किया।", "label": 0, "premise": "मैंने सेल्समैन की पिच पर शक किया।", "question": "effect" } ``` ### Data Fields The data fields are the same among all splits. #### actsa-sc.te - `text`: a `string` feature. - `label`: a classification label, with possible values including `positive` (0), `negative` (1). #### bbca.hi - `label`: a `string` feature. - `text`: a `string` feature. #### copa.en - `premise`: a `string` feature. - `choice1`: a `string` feature. - `choice2`: a `string` feature. - `question`: a `string` feature. - `label`: a `int32` feature. #### copa.gu - `premise`: a `string` feature. - `choice1`: a `string` feature. - `choice2`: a `string` feature. - `question`: a `string` feature. - `label`: a `int32` feature. #### copa.hi - `premise`: a `string` feature. - `choice1`: a `string` feature. - `choice2`: a `string` feature. - `question`: a `string` feature. - `label`: a `int32` feature. ### Data Splits #### actsa-sc.te \| \|train\|validation\|test\| \|-----------\|----:\|---------:\|---:\| \|actsa-sc.te\| 4328\| 541\| 541\| #### bbca.hi \| \|train\|test\| \|-------\|----:\|---:\| \|bbca.hi\| 3467\| 866\| #### copa.en \| \|train\|validation\|test\| \|-------\|----:\|---------:\|---:\| \|copa.en\| 400\| 100\| 500\| #### copa.gu \| \|train\|validation\|test\| \|-------\|----:\|---------:\|---:\| \|copa.gu\| 362\| 88\| 448\| #### copa.hi \| \|train\|validation\|test\| \|-------\|----:\|---------:\|---:\| \|copa.hi\| 362\| 88\| 449\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{kakwani-etal-2020-indicnlpsuite, title = "{I}ndic{NLPS}uite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for {I}ndian Languages", author = "Kakwani, Divyanshu and Kunchukuttan, Anoop and Golla, Satish and N.C., Gokul and Bhattacharyya, Avik and Khapra, Mitesh M. and Kumar, Pratyush", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.findings-emnlp.445", doi = "10.18653/v1/2020.findings-emnlp.445", pages = "4948--4961", } @inproceedings{Levesque2011TheWS, title={The Winograd Schema Challenge}, author={H. Levesque and E. Davis and L. Morgenstern}, booktitle={KR}, year={2011} } ``` ### Contributions Thanks to [@sumanthd17](https://github.com/sumanthd17) for adding this dataset.
indonli	--- annotations_creators: - expert-generated - crowdsourced language_creators: - expert-generated language: - id license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - natural-language-inference paperswithcode_id: indonli pretty_name: IndoNLI dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction config_name: indonli splits: - name: train num_bytes: 2265687 num_examples: 10330 - name: validation num_bytes: 465299 num_examples: 2197 - name: test_lay num_bytes: 473849 num_examples: 2201 - name: test_expert num_bytes: 911916 num_examples: 2984 download_size: 6977877 dataset_size: 4116751 --- # Dataset Card for IndoNLI ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Repository: [GitHub](https://github.com/ir-nlp-csui/indonli) - Paper: [EMNLP 2021](https://aclanthology.org/2021.emnlp-main.821/) - Point of Contact: [GitHub](https://github.com/ir-nlp-csui/indonli) ### Dataset Summary IndoNLI is the first human-elicited Natural Language Inference (NLI) dataset for Indonesian. IndoNLI is annotated by both crowd workers and experts. The expert-annotated data is used exclusively as a test set. It is designed to provide a challenging test-bed for Indonesian NLI by explicitly incorporating various linguistic phenomena such as numerical reasoning, structural changes, idioms, or temporal and spatial reasoning. ### Supported Tasks and Leaderboards - Natural Language Inference for Indonesian ### Languages Indonesian ## Dataset Structure ### Data Instances An example of `train` looks as follows. ``` { "premise": "Keindahan alam yang terdapat di Gunung Batu Jonggol ini dapat Anda manfaatkan sebagai objek fotografi yang cantik.", "hypothesis": "Keindahan alam tidak dapat difoto.", "label": 2 } ``` ### Data Fields The data fields are: - `premise`: a `string` feature - `hypothesis`: a `string` feature - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). ### Data Splits The data is split across `train`, `valid`, `test_lay`, and `test_expert`. `test_expert` is written by expert annotators, whereas the rest are written by lay annotators. \| split \| # examples \| \|----------\|-------:\| \|train\| 10330\| \|valid\| 2197\| \|test_lay\| 2201\| \|test_expert\| 2984\| A small subset of `test_expert` is used as a diasnostic tool. For more info, please visit https://github.com/ir-nlp-csui/indonli ## Dataset Creation ### Curation Rationale Indonesian NLP is considered under-resourced. Up until now, there is no publicly available human-annotated NLI dataset for Indonesian. ### Source Data #### Initial Data Collection and Normalization The premise were collected from Indonesian Wikipedia and from other public Indonesian dataset: Indonesian PUD and GSD treebanks provided by the [Universal Dependencies 2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) and [IndoSum](https://github.com/kata-ai/indosum) The hypothesis were written by annotators. #### Who are the source language producers? The data was produced by humans. ### Annotations #### Annotation process We start by writing the hypothesis, given the premise and the target label. Then, we ask 2 different independent annotators to predict the label, given the premise and hypothesis. If all 3 (the original hypothesis + 2 independent annotators) agree with the label, then the annotation process ends for that sample. Otherwise, we incrementally ask additional annotator until 3 annotators agree with the label. If there's no majority concensus after 5 annotations, the sample is removed. #### Who are the annotators? Lay annotators were computer science students, and expert annotators were NLP scientists with 7+ years research experience in NLP. All annotators are native speakers. Additionally, expert annotators were explicitly instructed to provide challenging examples by incorporating various linguistic phenomena such as numerical reasoning, structural changes, idioms, or temporal and spatial reasoning. Annotators were compensated based on hourly rate. ### Personal and Sensitive Information There might be some personal information coming from Wikipedia and news, especially the information of famous/important people. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases INDONLI is created using premise sentences taken from Wikipedia and news. These data sources may contain some bias. ### Other Known Limitations No other known limitations ## Additional Information ### Dataset Curators This dataset is the result of the collaborative work of Indonesian researchers from the University of Indonesia, kata.ai, New York University, Fondazione Bruno Kessler, and the University of St Andrews. ### Licensing Information CC-BY-SA 4.0. Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. Please contact authors for any information on the dataset. ### Citation Information ``` @inproceedings{mahendra-etal-2021-indonli, title = "{I}ndo{NLI}: A Natural Language Inference Dataset for {I}ndonesian", author = "Mahendra, Rahmad and Aji, Alham Fikri and Louvan, Samuel and Rahman, Fahrurrozi and Vania, Clara", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.821", pages = "10511--10527", } ``` ### Contributions Thanks to [@afaji](https://github.com/afaji) for adding this dataset.
indonlp/indonlu	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - id license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K - 1K<n<10K - n<1K source_datasets: - original task_categories: - question-answering - text-classification - token-classification task_ids: - closed-domain-qa - multi-class-classification - named-entity-recognition - part-of-speech - semantic-similarity-classification - sentiment-classification paperswithcode_id: indonlu-benchmark pretty_name: IndoNLU configs: - bapos - casa - emot - facqa - hoasa - keps - nergrit - nerp - posp - smsa - terma - wrete tags: - keyphrase-extraction - span-extraction - aspect-based-sentiment-analysis dataset_info: - config_name: emot features: - name: tweet dtype: string - name: label dtype: class_label: names: 0: sadness 1: anger 2: love 3: fear 4: happy splits: - name: train num_bytes: 686418 num_examples: 3521 - name: validation num_bytes: 84082 num_examples: 440 - name: test num_bytes: 84856 num_examples: 440 download_size: 840917 dataset_size: 855356 - config_name: smsa features: - name: text dtype: string - name: label dtype: class_label: names: 0: positive 1: neutral 2: negative splits: - name: train num_bytes: 2209874 num_examples: 11000 - name: validation num_bytes: 249629 num_examples: 1260 - name: test num_bytes: 77041 num_examples: 500 download_size: 2509229 dataset_size: 2536544 - config_name: casa features: - name: sentence dtype: string - name: fuel dtype: class_label: names: 0: negative 1: neutral 2: positive - name: machine dtype: class_label: names: 0: negative 1: neutral 2: positive - name: others dtype: class_label: names: 0: negative 1: neutral 2: positive - name: part dtype: class_label: names: 0: negative 1: neutral 2: positive - name: price dtype: class_label: names: 0: negative 1: neutral 2: positive - name: service dtype: class_label: names: 0: negative 1: neutral 2: positive splits: - name: train num_bytes: 110415 num_examples: 810 - name: validation num_bytes: 11993 num_examples: 90 - name: test num_bytes: 23553 num_examples: 180 download_size: 144903 dataset_size: 145961 - config_name: hoasa features: - name: sentence dtype: string - name: ac dtype: class_label: names: 0: neg 1: neut 2: pos 3: neg_pos - name: air_panas dtype: class_label: names: 0: neg 1: neut 2: pos 3: neg_pos - name: bau dtype: class_label: names: 0: neg 1: neut 2: pos 3: neg_pos - name: general dtype: class_label: names: 0: neg 1: neut 2: pos 3: neg_pos - name: kebersihan dtype: class_label: names: 0: neg 1: neut 2: pos 3: neg_pos - name: linen dtype: class_label: names: 0: neg 1: neut 2: pos 3: neg_pos - name: service dtype: class_label: names: 0: neg 1: neut 2: pos 3: neg_pos - name: sunrise_meal dtype: class_label: names: 0: neg 1: neut 2: pos 3: neg_pos - name: tv dtype: class_label: names: 0: neg 1: neut 2: pos 3: neg_pos - name: wifi dtype: class_label: names: 0: neg 1: neut 2: pos 3: neg_pos splits: - name: train num_bytes: 458177 num_examples: 2283 - name: validation num_bytes: 58248 num_examples: 285 - name: test num_bytes: 56399 num_examples: 286 download_size: 477314 dataset_size: 572824 - config_name: wrete features: - name: premise dtype: string - name: hypothesis dtype: string - name: category dtype: string - name: label dtype: class_label: names: 0: NotEntail 1: Entail_or_Paraphrase splits: - name: train num_bytes: 99999 num_examples: 300 - name: validation num_bytes: 18049 num_examples: 50 - name: test num_bytes: 32617 num_examples: 100 download_size: 151018 dataset_size: 150665 - config_name: posp features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: 0: B-PPO 1: B-KUA 2: B-ADV 3: B-PRN 4: B-VBI 5: B-PAR 6: B-VBP 7: B-NNP 8: B-UNS 9: B-VBT 10: B-VBL 11: B-NNO 12: B-ADJ 13: B-PRR 14: B-PRK 15: B-CCN 16: B-$$$ 17: B-ADK 18: B-ART 19: B-CSN 20: B-NUM 21: B-SYM 22: B-INT 23: B-NEG 24: B-PRI 25: B-VBE splits: - name: train num_bytes: 2751348 num_examples: 6720 - name: validation num_bytes: 343924 num_examples: 840 - name: test num_bytes: 350720 num_examples: 840 download_size: 2407206 dataset_size: 3445992 - config_name: bapos features: - name: tokens sequence: string - name: pos_tags sequence: class_label: names: 0: B-PR 1: B-CD 2: I-PR 3: B-SYM 4: B-JJ 5: B-DT 6: I-UH 7: I-NND 8: B-SC 9: I-WH 10: I-IN 11: I-NNP 12: I-VB 13: B-IN 14: B-NND 15: I-CD 16: I-JJ 17: I-X 18: B-OD 19: B-RP 20: B-RB 21: B-NNP 22: I-RB 23: I-Z 24: B-CC 25: B-NEG 26: B-VB 27: B-NN 28: B-MD 29: B-UH 30: I-NN 31: B-PRP 32: I-SC 33: B-Z 34: I-PRP 35: I-OD 36: I-SYM 37: B-WH 38: B-FW 39: I-CC 40: B-X splits: - name: train num_bytes: 3772459 num_examples: 8000 - name: validation num_bytes: 460058 num_examples: 1000 - name: test num_bytes: 474368 num_examples: 1029 download_size: 3084021 dataset_size: 4706885 - config_name: terma features: - name: tokens sequence: string - name: seq_label sequence: class_label: names: 0: I-SENTIMENT 1: O 2: I-ASPECT 3: B-SENTIMENT 4: B-ASPECT splits: - name: train num_bytes: 817983 num_examples: 3000 - name: validation num_bytes: 276335 num_examples: 1000 - name: test num_bytes: 265922 num_examples: 1000 download_size: 816822 dataset_size: 1360240 - config_name: keps features: - name: tokens sequence: string - name: seq_label sequence: class_label: names: 0: O 1: B 2: I splits: - name: train num_bytes: 173961 num_examples: 800 - name: validation num_bytes: 42961 num_examples: 200 - name: test num_bytes: 66762 num_examples: 247 download_size: 134042 dataset_size: 283684 - config_name: nergrit features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: 0: I-PERSON 1: B-ORGANISATION 2: I-ORGANISATION 3: B-PLACE 4: I-PLACE 5: O 6: B-PERSON splits: - name: train num_bytes: 960710 num_examples: 1672 - name: validation num_bytes: 119567 num_examples: 209 - name: test num_bytes: 117274 num_examples: 209 download_size: 641265 dataset_size: 1197551 - config_name: nerp features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: 0: I-PPL 1: B-EVT 2: B-PLC 3: I-IND 4: B-IND 5: B-FNB 6: I-EVT 7: B-PPL 8: I-PLC 9: O 10: I-FNB splits: - name: train num_bytes: 2751348 num_examples: 6720 - name: validation num_bytes: 343924 num_examples: 840 - name: test num_bytes: 350720 num_examples: 840 download_size: 1725986 dataset_size: 3445992 - config_name: facqa features: - name: question sequence: string - name: passage sequence: string - name: seq_label sequence: class_label: names: 0: O 1: B 2: I splits: - name: train num_bytes: 2454368 num_examples: 2495 - name: validation num_bytes: 306249 num_examples: 311 - name: test num_bytes: 306831 num_examples: 311 download_size: 2591968 dataset_size: 3067448 --- # Dataset Card for IndoNLU ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [IndoNLU Website](https://www.indobenchmark.com/) - Repository: [IndoNLU GitHub](https://github.com/indobenchmark/indonlu) - Paper: [IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding](https://www.aclweb.org/anthology/2020aacl-main.85.pdf) - Leaderboard: [Needs More Information] - Point of Contact: [Needs More Information] ### Dataset Summary The IndoNLU benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems for Bahasa Indonesia (Indonesian language). There are 12 datasets in IndoNLU benchmark for Indonesian natural language understanding. 1. `EmoT`: An emotion classification dataset collected from the social media platform Twitter. The dataset consists of around 4000 Indonesian colloquial language tweets, covering five different emotion labels: anger, fear, happy, love, and sadness 2. `SmSA`: This sentence-level sentiment analysis dataset is a collection of comments and reviews in Indonesian obtained from multiple online platforms. The text was crawled and then annotated by several Indonesian linguists to construct this dataset. There are three possible sentiments on the `SmSA` dataset: positive, negative, and neutral 3. `CASA`: An aspect-based sentiment analysis dataset consisting of around a thousand car reviews collected from multiple Indonesian online automobile platforms. The dataset covers six aspects of car quality. We define the task to be a multi-label classification task, where each label represents a sentiment for a single aspect with three possible values: positive, negative, and neutral. 4. `HoASA`: An aspect-based sentiment analysis dataset consisting of hotel reviews collected from the hotel aggregator platform, [AiryRooms](https://github.com/annisanurulazhar/absa-playground). The dataset covers ten different aspects of hotel quality. Similar to the `CASA` dataset, each review is labeled with a single sentiment label for each aspect. There are four possible sentiment classes for each sentiment label: positive, negative, neutral, and positive-negative. The positivenegative label is given to a review that contains multiple sentiments of the same aspect but for different objects (e.g., cleanliness of bed and toilet). 5. `WReTE`: The Wiki Revision Edits Textual Entailment dataset consists of 450 sentence pairs constructed from Wikipedia revision history. The dataset contains pairs of sentences and binary semantic relations between the pairs. The data are labeled as entailed when the meaning of the second sentence can be derived from the first one, and not entailed otherwise. 6. `POSP`: This Indonesian part-of-speech tagging (POS) dataset is collected from Indonesian news websites. The dataset consists of around 8000 sentences with 26 POS tags. The POS tag labels follow the [Indonesian Association of Computational Linguistics (INACL) POS Tagging Convention](http://inacl.id/inacl/wp-content/uploads/2017/06/INACL-POS-Tagging-Convention-26-Mei.pdf). 7. `BaPOS`: This POS tagging dataset contains about 1000 sentences, collected from the [PAN Localization Project](http://www.panl10n.net/). In this dataset, each word is tagged by one of [23 POS tag classes](https://bahasa.cs.ui.ac.id/postag/downloads/Tagset.pdf). Data splitting used in this benchmark follows the experimental setting used by [Kurniawan and Aji (2018)](https://arxiv.org/abs/1809.03391). 8. `TermA`: This span-extraction dataset is collected from the hotel aggregator platform, [AiryRooms](https://github.com/jordhy97/final_project). The dataset consists of thousands of hotel reviews, which each contain a span label for aspect and sentiment words representing the opinion of the reviewer on the corresponding aspect. The labels use Inside-Outside-Beginning (IOB) tagging representation with two kinds of tags, aspect and sentiment. 9. `KEPS`: This keyphrase extraction dataset consists of text from Twitter discussing banking products and services and is written in the Indonesian language. A phrase containing important information is considered a keyphrase. Text may contain one or more keyphrases since important phrases can be located at different positions. The dataset follows the IOB chunking format, which represents the position of the keyphrase. 10. `NERGrit`: This NER dataset is taken from the [Grit-ID repository](https://github.com/grit-id/nergrit-corpus), and the labels are spans in IOB chunking representation. The dataset consists of three kinds of named entity tags, PERSON (name of person), PLACE (name of location), and ORGANIZATION (name of organization). 11. `NERP`: This NER dataset (Hoesen and Purwarianti, 2018) contains texts collected from several Indonesian news websites. There are five labels available in this dataset, PER (name of person), LOC (name of location), IND (name of product or brand), EVT (name of the event), and FNB (name of food and beverage). Similar to the `TermA` dataset, the `NERP` dataset uses the IOB chunking format. 12. `FacQA`: The goal of the FacQA dataset is to find the answer to a question from a provided short passage from a news article. Each row in the FacQA dataset consists of a question, a short passage, and a label phrase, which can be found inside the corresponding short passage. There are six categories of questions: date, location, name, organization, person, and quantitative. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages Indonesian ## Dataset Structure ### Data Instances 1. `EmoT` dataset A data point consists of `tweet` and `label`. An example from the train set looks as follows: ``` { 'tweet': 'Ini adalah hal yang paling membahagiakan saat biasku foto bersama ELF #ReturnOfTheLittlePrince #HappyHeeChulDay' 'label': 4, } ``` 2. `SmSA` dataset A data point consists of `text` and `label`. An example from the train set looks as follows: ``` { 'text': 'warung ini dimiliki oleh pengusaha pabrik tahu yang sudah puluhan tahun terkenal membuat tahu putih di bandung . tahu berkualitas , dipadu keahlian memasak , dipadu kretivitas , jadilah warung yang menyajikan menu utama berbahan tahu , ditambah menu umum lain seperti ayam . semuanya selera indonesia . harga cukup terjangkau . jangan lewatkan tahu bletoka nya , tidak kalah dengan yang asli dari tegal !' 'label': 0, } ``` 3. `CASA` dataset A data point consists of `sentence` and multi-label `feature`, `machine`, `others`, `part`, `price`, and `service`. An example from the train set looks as follows: ``` { 'sentence': 'Saya memakai Honda Jazz GK5 tahun 2014 ( pertama meluncur ) . Mobil nya bagus dan enak sesuai moto nya menyenangkan untuk dikendarai', 'fuel': 1, 'machine': 1, 'others': 2, 'part': 1, 'price': 1, 'service': 1 } ``` 4. `HoASA` dataset A data point consists of `sentence` and multi-label `ac`, `air_panas`, `bau`, `general`, `kebersihan`, `linen`, `service`, `sunrise_meal`, `tv`, and `wifi`. An example from the train set looks as follows: ``` { 'sentence': 'kebersihan kurang...', 'ac': 1, 'air_panas': 1, 'bau': 1, 'general': 1, 'kebersihan': 0, 'linen': 1, 'service': 1, 'sunrise_meal': 1, 'tv': 1, 'wifi': 1 } ``` 5. `WreTE` dataset A data point consists of `premise`, `hypothesis`, `category`, and `label`. An example from the train set looks as follows: ``` { 'premise': 'Pada awalnya bangsa Israel hanya terdiri dari satu kelompok keluarga di antara banyak kelompok keluarga yang hidup di tanah Kanan pada abad 18 SM .', 'hypothesis': 'Pada awalnya bangsa Yahudi hanya terdiri dari satu kelompok keluarga di antara banyak kelompok keluarga yang hidup di tanah Kanan pada abad 18 SM .' 'category': 'menolak perubahan teks terakhir oleh istimewa kontribusi pengguna 141 109 98 87 141 109 98 87 dan mengembalikan revisi 6958053 oleh johnthorne', 'label': 0, } ``` 6. `POSP` dataset A data point consists of `tokens` and `pos_tags`. An example from the train set looks as follows: ``` { 'tokens': ['kepala', 'dinas', 'tata', 'kota', 'manado', 'amos', 'kenda', 'menyatakan', 'tidak', 'tahu', '-', 'menahu', 'soal', 'pencabutan', 'baliho', '.', 'ia', 'enggan', 'berkomentar', 'banyak', 'karena', 'merasa', 'bukan', 'kewenangannya', '.'], 'pos_tags': [11, 6, 11, 11, 7, 7, 7, 9, 23, 4, 21, 9, 11, 11, 11, 21, 3, 2, 4, 1, 19, 9, 23, 11, 21] } ``` 7. `BaPOS` dataset A data point consists of `tokens` and `pos_tags`. An example from the train set looks as follows: ``` { 'tokens': ['Kera', 'untuk', 'amankan', 'pesta', 'olahraga'], 'pos_tags': [27, 8, 26, 27, 30] } ``` 8. `TermA` dataset A data point consists of `tokens` and `seq_label`. An example from the train set looks as follows: ``` { 'tokens': ['kamar', 'saya', 'ada', 'kendala', 'di', 'ac', 'tidak', 'berfungsi', 'optimal', '.', 'dan', 'juga', 'wifi', 'koneksi', 'kurang', 'stabil', '.'], 'seq_label': [1, 1, 1, 1, 1, 4, 3, 0, 0, 1, 1, 1, 4, 2, 3, 0, 1] } ``` 9. `KEPS` dataset A data point consists of `tokens` and `seq_label`. An example from the train set looks as follows: ``` { 'tokens': ['Setelah', 'melalui', 'proses', 'telepon', 'yang', 'panjang', 'tutup', 'sudah', 'kartu', 'kredit', 'bca', 'Ribet'], 'seq_label': [0, 1, 1, 2, 0, 0, 1, 0, 1, 2, 2, 1] } ``` 10. `NERGrit` dataset A data point consists of `tokens` and `ner_tags`. An example from the train set looks as follows: ``` { 'tokens': ['Kontribusinya', 'terhadap', 'industri', 'musik', 'telah', 'mengumpulkan', 'banyak', 'prestasi', 'termasuk', 'lima', 'Grammy', 'Awards', ',', 'serta', 'dua', 'belas', 'nominasi', ';', 'dua', 'Guinness', 'World', 'Records', ';', 'dan', 'penjualannya', 'diperkirakan', 'sekitar', '64', 'juta', 'rekaman', '.'], 'ner_tags': [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]} ``` 11. `NERP` dataset A data point consists of `tokens` and `ner_tags`. An example from the train set looks as follows: ``` { 'tokens': ['kepala', 'dinas', 'tata', 'kota', 'manado', 'amos', 'kenda', 'menyatakan', 'tidak', 'tahu', '-', 'menahu', 'soal', 'pencabutan', 'baliho', '.', 'ia', 'enggan', 'berkomentar', 'banyak', 'karena', 'merasa', 'bukan', 'kewenangannya', '.'], 'ner_tags': [9, 9, 9, 9, 2, 7, 0, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9] } ``` 12. `FacQA` dataset A data point consists of `question`, `passage`, and `seq_label`. An example from the train set looks as follows: ``` { 'passage': ['Lewat', 'telepon', 'ke', 'kantor', 'berita', 'lokal', 'Current', 'News', 'Service', ',', 'Hezb-ul', 'Mujahedeen', ',', 'kelompok', 'militan', 'Kashmir', 'yang', 'terbesar', ',', 'menyatakan', 'bertanggung', 'jawab', 'atas', 'ledakan', 'di', 'Srinagar', '.'], 'question': ['Kelompok', 'apakah', 'yang', 'menyatakan', 'bertanggung', 'jawab', 'atas', 'ledakan', 'di', 'Srinagar', '?'], 'seq_label': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] } ``` ### Data Fields 1. `EmoT` dataset - `tweet`: a `string` feature. - `label`: an emotion label, with possible values including `sadness`, `anger`, `love`, `fear`, `happy`. 2. `SmSA` dataset - `text`: a `string` feature. - `label`: a sentiment label, with possible values including `positive`, `neutral`, `negative`. 3. `CASA` dataset - `sentence`: a `string` feature. - `fuel`: a sentiment label, with possible values including `negative`, `neutral`, `positive`. - `machine`: a sentiment label, with possible values including `negative`, `neutral`, `positive`. - `others`: a sentiment label, with possible values including `negative`, `neutral`, `positive`. - `part`: a sentiment label, with possible values including `negative`, `neutral`, `positive`. - `price`: a sentiment label, with possible values including `negative`, `neutral`, `positive`. - `service`: a sentiment label, with possible values including `negative`, `neutral`, `positive`. 4. `HoASA` dataset - `sentence`: a `string` feature. - `ac`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`. - `air_panas`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`. - `bau`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`. - `general`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`. - `kebersihan`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`. - `linen`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`. - `service`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`. - `sunrise_meal`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`. - `tv`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`. - `wifi`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`. 5. `WReTE` dataset - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `category`: a `string` feature. - `label`: a classification label, with possible values including `NotEntail`, `Entail_or_Paraphrase`. 6. `POSP` dataset - `tokens`: a `list` of `string` features. - `pos_tags`: a `list` of POS tag labels, with possible values including `B-PPO`, `B-KUA`, `B-ADV`, `B-PRN`, `B-VBI`. The POS tag labels follow the [Indonesian Association of Computational Linguistics (INACL) POS Tagging Convention](http://inacl.id/inacl/wp-content/uploads/2017/06/INACLPOS-Tagging-Convention-26-Mei.pdf). 7. `BaPOS` dataset - `tokens`: a `list` of `string` features. - `pos_tags`: a `list` of POS tag labels, with possible values including `B-PR`, `B-CD`, `I-PR`, `B-SYM`, `B-JJ`. The POS tag labels from [Tagset UI](https://bahasa.cs.ui.ac.id/postag/downloads/Tagset.pdf). 8. `TermA` dataset - `tokens`: a `list` of `string` features. - `seq_label`: a `list` of classification labels, with possible values including `I-SENTIMENT`, `O`, `I-ASPECT`, `B-SENTIMENT`, `B-ASPECT`. 9. `KEPS` dataset - `tokens`: a `list` of `string` features. - `seq_label`: a `list` of classification labels, with possible values including `O`, `B`, `I`. The labels use Inside-Outside-Beginning (IOB) tagging. 10. `NERGrit` dataset - `tokens`: a `list` of `string` features. - `ner_tags`: a `list` of NER tag labels, with possible values including `I-PERSON`, `B-ORGANISATION`, `I-ORGANISATION`, `B-PLACE`, `I-PLACE`. The labels use Inside-Outside-Beginning (IOB) tagging. 11. `NERP` dataset - `tokens`: a `list` of `string` features. - `ner_tags`: a `list` of NER tag labels, with possible values including `I-PPL`, `B-EVT`, `B-PLC`, `I-IND`, `B-IND`. 12. `FacQA` dataset - `question`: a `list` of `string` features. - `passage`: a `list` of `string` features. - `seq_label`: a `list` of classification labels, with possible values including `O`, `B`, `I`. ### Data Splits The data is split into a training, validation and test set. \| \| dataset \| Train \| Valid \| Test \| \|----\|---------\|-------\|-------\|------\| \| 1 \| EmoT \| 3521 \| 440 \| 440 \| \| 2 \| SmSA \| 11000 \| 1260 \| 500 \| \| 3 \| CASA \| 810 \| 90 \| 180 \| \| 4 \| HoASA \| 2283 \| 285 \| 286 \| \| 5 \| WReTE \| 300 \| 50 \| 100 \| \| 6 \| POSP \| 6720 \| 840 \| 840 \| \| 7 \| BaPOS \| 8000 \| 1000 \| 1029 \| \| 8 \| TermA \| 3000 \| 1000 \| 1000 \| \| 9 \| KEPS \| 800 \| 200 \| 247 \| \| 10 \| NERGrit \| 1672 \| 209 \| 209 \| \| 11 \| NERP \| 6720 \| 840 \| 840 \| \| 12 \| FacQA \| 2495 \| 311 \| 311 \| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information The licensing status of the IndoNLU benchmark datasets is under MIT License. ### Citation Information IndoNLU citation ``` @inproceedings{wilie2020indonlu, title={IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding}, author={Bryan Wilie and Karissa Vincentio and Genta Indra Winata and Samuel Cahyawijaya and X. Li and Zhi Yuan Lim and S. Soleman and R. Mahendra and Pascale Fung and Syafri Bahar and A. Purwarianti}, booktitle={Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing}, year={2020} } ``` `EmoT` dataset citation ``` @inproceedings{saputri2018emotion, title={Emotion Classification on Indonesian Twitter Dataset}, author={Mei Silviana Saputri, Rahmad Mahendra, and Mirna Adriani}, booktitle={Proceedings of the 2018 International Conference on Asian Language Processing(IALP)}, pages={90--95}, year={2018}, organization={IEEE} } ``` `SmSA` dataset citation ``` @inproceedings{purwarianti2019improving, title={Improving Bi-LSTM Performance for Indonesian Sentiment Analysis Using Paragraph Vector}, author={Ayu Purwarianti and Ida Ayu Putu Ari Crisdayanti}, booktitle={Proceedings of the 2019 International Conference of Advanced Informatics: Concepts, Theory and Applications (ICAICTA)}, pages={1--5}, year={2019}, organization={IEEE} } ``` `CASA` dataset citation ``` @inproceedings{ilmania2018aspect, title={Aspect Detection and Sentiment Classification Using Deep Neural Network for Indonesian Aspect-based Sentiment Analysis}, author={Arfinda Ilmania, Abdurrahman, Samuel Cahyawijaya, Ayu Purwarianti}, booktitle={Proceedings of the 2018 International Conference on Asian Language Processing(IALP)}, pages={62--67}, year={2018}, organization={IEEE} } ``` `HoASA` dataset citation ``` @inproceedings{azhar2019multi, title={Multi-label Aspect Categorization with Convolutional Neural Networks and Extreme Gradient Boosting}, author={A. N. Azhar, M. L. Khodra, and A. P. Sutiono} booktitle={Proceedings of the 2019 International Conference on Electrical Engineering and Informatics (ICEEI)}, pages={35--40}, year={2019} } ``` `WReTE` dataset citation ``` @inproceedings{setya2018semi, title={Semi-supervised Textual Entailment on Indonesian Wikipedia Data}, author={Ken Nabila Setya and Rahmad Mahendra}, booktitle={Proceedings of the 2018 International Conference on Computational Linguistics and Intelligent Text Processing (CICLing)}, year={2018} } ``` `POSP` dataset citation ``` @inproceedings{hoesen2018investigating, title={Investigating Bi-LSTM and CRF with POS Tag Embedding for Indonesian Named Entity Tagger}, author={Devin Hoesen and Ayu Purwarianti}, booktitle={Proceedings of the 2018 International Conference on Asian Language Processing (IALP)}, pages={35--38}, year={2018}, organization={IEEE} } ``` `BaPOS` dataset citation ``` @inproceedings{dinakaramani2014designing, title={Designing an Indonesian Part of Speech Tagset and Manually Tagged Indonesian Corpus}, author={Arawinda Dinakaramani, Fam Rashel, Andry Luthfi, and Ruli Manurung}, booktitle={Proceedings of the 2014 International Conference on Asian Language Processing (IALP)}, pages={66--69}, year={2014}, organization={IEEE} } @inproceedings{kurniawan2018toward, title={Toward a Standardized and More Accurate Indonesian Part-of-Speech Tagging}, author={Kemal Kurniawan and Alham Fikri Aji}, booktitle={Proceedings of the 2018 International Conference on Asian Language Processing (IALP)}, pages={303--307}, year={2018}, organization={IEEE} } ``` `TermA` dataset citation ``` @article{winatmoko2019aspect, title={Aspect and Opinion Term Extraction for Hotel Reviews Using Transfer Learning and Auxiliary Labels}, author={Yosef Ardhito Winatmoko, Ali Akbar Septiandri, Arie Pratama Sutiono}, journal={arXiv preprint arXiv:1909.11879}, year={2019} } @article{fernando2019aspect, title={Aspect and Opinion Terms Extraction Using Double Embeddings and Attention Mechanism for Indonesian Hotel Reviews}, author={Jordhy Fernando, Masayu Leylia Khodra, Ali Akbar Septiandri}, journal={arXiv preprint arXiv:1908.04899}, year={2019} } ``` `KEPS` dataset citation ``` @inproceedings{mahfuzh2019improving, title={Improving Joint Layer RNN based Keyphrase Extraction by Using Syntactical Features}, author={Miftahul Mahfuzh, Sidik Soleman, and Ayu Purwarianti}, booktitle={Proceedings of the 2019 International Conference of Advanced Informatics: Concepts, Theory and Applications (ICAICTA)}, pages={1--6}, year={2019}, organization={IEEE} } ``` `NERGrit` dataset citation ``` @online{nergrit2019, title={NERGrit Corpus}, author={NERGrit Developers}, year={2019}, url={https://github.com/grit-id/nergrit-corpus} } ``` `NERP` dataset citation ``` @inproceedings{hoesen2018investigating, title={Investigating Bi-LSTM and CRF with POS Tag Embedding for Indonesian Named Entity Tagger}, author={Devin Hoesen and Ayu Purwarianti}, booktitle={Proceedings of the 2018 International Conference on Asian Language Processing (IALP)}, pages={35--38}, year={2018}, organization={IEEE} } ``` `FacQA` dataset citation ``` @inproceedings{purwarianti2007machine, title={A Machine Learning Approach for Indonesian Question Answering System}, author={Ayu Purwarianti, Masatoshi Tsuchiya, and Seiichi Nakagawa}, booktitle={Proceedings of Artificial Intelligence and Applications }, pages={573--578}, year={2007} } ``` ### Contributions Thanks to [@yasirabd](https://github.com/yasirabd) for adding this dataset.
inquisitive_qg	--- pretty_name: InquisitiveQg annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text2text-generation task_ids: [] paperswithcode_id: inquisitive tags: - question-generation dataset_info: features: - name: id dtype: int32 - name: article_id dtype: int32 - name: article dtype: string - name: sentence_id dtype: int32 - name: sentence dtype: string - name: span dtype: string - name: question dtype: string - name: span_start_position dtype: int32 - name: span_end_position dtype: int32 config_name: plain_text splits: - name: train num_bytes: 66099232 num_examples: 15931 - name: validation num_bytes: 8904329 num_examples: 1991 - name: test num_bytes: 7167203 num_examples: 1894 download_size: 7085941 dataset_size: 82170764 --- # Dataset Card for InquisitiveQg ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Add homepage URL here if available (unless it's a GitHub repository)]() - Repository: [If the dataset is hosted on github or has a github homepage, add URL here]() - Paper: [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]() - Leaderboard: [If the dataset supports an active leaderboard, add link here]() - Point of Contact: [If known, name and email of at least one person the reader can contact for questions about the dataset.]() ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.
interpress_news_category_tr	--- annotations_creators: - found language_creators: - found language: - tr license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: [] pretty_name: Interpress Turkish News Category Dataset (270K) tags: - news-category-classification dataset_info: features: - name: id dtype: int32 - name: title dtype: string - name: content dtype: string - name: category dtype: class_label: names: '0': aktuel '1': bilisim '2': egitim '3': ekonomi '4': gida '5': iletisim '6': kultursanat '7': magazin '8': saglik '9': savunma '10': seyahat '11': siyasi '12': spor '13': teknoloji '14': ticaret '15': turizm '16': yasam - name: categorycode dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' '5': '5' '6': '6' '7': '7' '8': '8' '9': '9' '10': '10' '11': '11' '12': '12' '13': '13' '14': '14' '15': '15' '16': '16' - name: publishdatetime dtype: string config_name: 270k splits: - name: train num_bytes: 736098052 num_examples: 218880 - name: test num_bytes: 184683629 num_examples: 54721 download_size: 354802486 dataset_size: 920781681 --- # Dataset Card for Interpress Turkish News Category Dataset (270K) ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Interpress](https://www.interpress.com/) - Point of Contact: [Yavuz Komecoglu](mailto:yavuz.komecoglu@kodiks.com) ### Dataset Summary Turkish News Category Dataset (270K) is a Turkish news data set consisting of 273601 news in 17 categories, compiled from printed media and news websites between 2010 and 2017 by the Interpress (https://www.interpress.com/) media monitoring company. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset is based on Turkish. ## Dataset Structure ### Data Instances A text classification dataset with 17 different news category. ``` {'id': 301365715, 'title': 'BİR SİHİRBAZ', 'content': 'NİANG, TAKIM ARKADAŞI FERNANDES E ÖVGÜLER YAĞDIRDI FUTBOL OYNARKEN EĞLENİYORUM YÜZDE 701E OYNUYORUM LİDERLE ARAMIZDA SADECE 5 PUAN VAR, ŞAMPİYONLUK ŞANSIMIZ YÜKSEK 4 j Fernandes le birlikte oynamayı seviyorum, adam adeta sihirbaz gibi J Frank Ribery, futbol hayatımda oynamaktan en çok zevk aldığım isim ı Abartılacak bir ] sonuç almadık ı .BAHÇE derbisinde Kartal ın ilk golünü atan, üçüncü golün de asistini yapan Mamadou Niang, TRT Spor da Futbol Keyfi programında özel açıklamalar yaptı. Senegalli forvet şampiyonluk şanslarının yüksek olduğunu dile getirirken, Portekizli yıldız Fernandes için Onunla oynamayı seviyorum, adeta bir sihirbaz gibi ifadesini kullandı. Frank Ribery nin futbol hayatında oynamaktan en çok zevk aldığım isim olduğunu ifade eden Niang, Moussa Sow ve Burak Yılmaz ın da Süper Lig deki en iyi forvetler olduğunu, ikisinin de tarzını beğendiğini söyledi. Senegalli yıldız şampiyonluk şansları için, Çok yüksek. Çünkü liderle aramızda 5 puan fark var ve bunu kapatacak güçteyiz yorumunu yaptı. NİANG şöyle devam etti: t.f En zorlandığım stoper İbrahim Toraman dır. Neyse ki şu an onunla takım arkadaşıyım. Almeida sakatlıktan döndükten sonra nasıl bir diziliş olur bilemiyorum. Onunla beraber oynayabiliriz, Holosko ile de oynayabiliriz. Türkiye, .. O NİANG, şu anda gerçek performansının yüzde 70 i ile oynadığını söyledi. İyi bir seviyede olmadığını kabul ettiğini belirten Senegalli yıldız, Sahada savaşan oyuncularla birlikte olmayı seviyorum. Bizim takımda Olcay ve Oğuzhan gibi bu yapıda isimler var. Tabii ki şansın da önemi var diye konuştu. zor bir lig. Eskiden arkadaşlarıma Türkiye Ligi nin iyi olduğunu söylediğimde inanmazlardı. Şimdi Didier Drogba, VVesley Sneijder, Sovvgibi oyuncuların burada olması ilgiyi artırdı. Futbol oynarken eğleniyorum ve şu an emekli olmayı düşünmüyorum. Açılış törenine, yönetici Metin Albayrak ile birlikte futbolcular Necip Uysal, McGregor ve Mehmet Akyüz de katıldı. BEŞİKTAŞLI Necip Uysal, +f başkan Fikret Orman gibi F.Bahçe galibiyetinin abartılmaması gerektiğini söyledi. Pazar günü İnönü Stadı nda güzel bir zafer elde ettiklerini vurgulayan genç yıldız, 10 karşılaşmaya daha çıkacağız. Her maçımız final, ayaklarımızın yere sağlam basması gerekiyor. Maçlara tek tek bakacağız ve hepsini kazanmak için oynayacağız yorumunu yaptı. Trabzon un her zaman zor deplasman olduğunu ifade eden Necip, Kolay olmayacağını biliyoruz ama şampiyonluk şansımızın sürmesi için kesinlikle üç puanla dönmeye mecburuz dedi. sflPa', 'category': 12, 'categorycode': 12, 'publishdatetime': '2013-03-07T00:00:00Z'} ``` ### Data Fields - `id` - `title` - `content` - `category` - `categorycode` - `publishdatetime` ### Data Splits The data is split into a training and testing. The split is organized as the following \| \| train \| test \| \|------------\|--------:\|-------:\| \| data split \| 218,880 \| 54,721 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization Downloaded over 270,000 news from the printed media and news websites between 2010 and 2017 by the Interpress (https://www.interpress.com/) media monitoring company. This data collection compiled from print media and internet news is presented in its raw form. For this reason, it is appropriate to use it with careful pre-processing steps regarding various OCR errors and typos. #### Who are the source language producers? Turkish printed news sources and online news sites. ### Annotations The dataset does not contain any additional annotations. #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information https://www.interpress.com/ ### Contributions Thanks to [@basakbuluz](https://github.com/basakbuluz) for adding this dataset.
interpress_news_category_tr_lite	--- annotations_creators: - found language_creators: - found language: - tr license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - extended\|interpress_news_category_tr task_categories: - text-classification task_ids: [] pretty_name: Interpress Turkish News Category Dataset (270K - Lite Version) tags: - news-category-classification dataset_info: features: - name: content dtype: string - name: category dtype: class_label: names: '0': kültürsanat '1': ekonomi '2': siyaset '3': eğitim '4': dünya '5': spor '6': teknoloji '7': magazin '8': sağlık '9': gündem config_name: 270k_10class splits: - name: train num_bytes: 721110711 num_examples: 218880 - name: test num_bytes: 179348267 num_examples: 54721 download_size: 342920336 dataset_size: 900458978 --- # Dataset Card for Interpress Turkish News Category Dataset (270K - Lite Version) ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Interpress](https://www.interpress.com/) - Point of Contact: [Yavuz Komecoglu](mailto:yavuz.komecoglu@kodiks.com) ### Dataset Summary Turkish News Category Dataset (270K - Lite Version) is a Turkish news data set consisting of 273601 news in 10 categories ("kültürsanat", "ekonomi", "siyaset", "eğitim", "dünya", "spor", "teknoloji", "magazin", "sağlık", "gündem"), compiled from printed media and news websites between 2010 and 2017 by the Interpress (https://www.interpress.com/) media monitoring company. It has been rearranged as easily separable and with fewer classes. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset is based on Turkish. ## Dataset Structure ### Data Instances A text classification dataset with 10 different news category. Here is an example from the dataset: ``` { 'category': 0, 'content': 'Tarihten Sınıfta Kaldık Bugün tarihe damgasını vuran Osmanlı İmparatorluğu nun kuruluş yıldönümü. Adına dizilerin çekildiği tarihimizi ne kadar biliyoruz? Gerekçeler faklı; ama sonuç aynı çıktı. Tarihten sınıfta kaldık. Sayfa 5r 1 Bugün tarihe damgasını vuran Osmanlı İmparatorluğumun kuruluş yıldönümü. Adına dizilerin çekildiği tarihimizi ne kadar biliyoruz? Gerekçeler faklı; ama sonuç aynı çıktı. Tarihten sınıfta kaldık 7 Ocak 1299... Kıtalara dağılan ücüyle, ülkeler arasında gördüğü aygıyla tarihe damgasını vuran anlı devletin kuruluş tarihi. Peki, anlı tarihimizi ne kadar biliyoruz? on zamanlarda tarihimizi anlatan izilere ilgi nasıl? Bu dizilerde anlatanlar ne kadar sağlıklı? İşte sokaın değerlendirmesi; levlüdiye Karaman (42-Ev lamım): Bir bilgim yok. Tarihle izla ilgilenmiyorum. Eşim daha ilgilidir bu konuda. Evde anlatır, ndan duyduklarımla yetiniyorum esem yalan olmaz. Osmanlı döeminde yaşamak isterdim. Tarih izileri izlerim Muhteşem Yüzyıl izisini çok izledim; hatta hiç kaırmazdım. Ama tarihimiz bu değil. Sunuün bilincindeyim. Muhteşem üzyıl dizisi genelde haremiyle ön landaydı. Onun için tarihi diziden ğrenmeyi de doğru bulmuyorum. )kullarda verilen tarih dersleri yeisiz. Daha çok tanıtabilirler. Görel anlatım yapılsın çocuklarımız aten okumak istemiyor. En azman eğlenceli hale getirip bu şekilde ilgilendirebilirler. erdi Üstün (22-Saatçi): Bu gün Osmanlı Devleti nin kuruluş yıldönümü olduğunu bilmiyordum. O dönemde yaşamak isterdim. Tarih yazılmış neden yaşamak istemeyim ki. Tarihime yeterince hakim olduğumu düşünüyorum. Araştırmalar yapıyorum. Merak ediyorum. Okullarda verilen tarih dersleri yeterli. Tarih dizisi izlemem, televizyondan tarihimi öğrenmek bana mantıklı gelmiyor. Yeterli olabilir; ama hikayeleştiriliyor. Sonuçta olduğu gibi anlatılsa daha iyi olur. Songül Karabacak (40-Ev Hanımı): Kuruluş yıldönümü olduğunu bilmiyordum. Tarih bilgim çok azdır. Zaten biz yaşadığımız dönemde tarih yazıyoruz. Osmanlı Dönemi nde yaşamak istemezdim. Sebebini bilmiyorum; ama hayatımdan memnunum, dönemden de memnunum. Dizileri takip etmiyorum. Ama mutlaka dizilerde tarihimiz doğru yansıtılıyor ki insanlar sürekli takip ediyor. Benim televizyonla pek aram yoktur. Ertuğrul Şahin (47-Çalışmıyor): Kuruluş yıldönümü olduğunu bilmiyordum. Sizden öğrendim. O dönemde yaşamak isterdim. Tarih sonuçta merak ederim. Tarihle ilgili çok bilgim yok. Okumadım, zaten şartlar el vermedi. Okullarda verilen eğitim yeterli değil. Örnek vermek gerekirse; 20 yaşında oğlum var Atatürk ün doğum yılını soruyorum yüzüme bakıyor. Verilen eğitim belli. Konu belirliyorlar onun dışına çıkmıyorlar. Daha fazla bilgi verilebilir. Tabi gençlerimizde de suç var bize baksınlar tarihimizi bilmiyoruz. Onlar araştırma yapsınlar her gün internette geziyorlar faydasız bir şeye bakacaklarına ecdatlarını okusunlar. Tarih dizlerini izlerim. Ama doğru yansıtılıyor mu orasını bilmiyorum sadece izleyiciyim. Ama önceden Süleyman Şah ı duyardım. Büyüklerimiz anlatırdı bunu diziden teyit ettim mesela. Ahmet Efe (22-Muhasebeci): Kuruluş yıldönümü olduğuyla ilgili bir bilgim yok. O dönemde yaşamak isterdim. Aldığımız bilgiler sonucunda illa ki bir özenme oluyor. Tam anlamıyla tarih bilgisine sahip olduğumu düşünmüyorum. Tarihe merakım var aslında; ama çok kısıtlı araştırma yapıyorum. Okullarda verilen tarih dersi yeterli değil. Çünkü şuradan birkaç çocuğu çevirip sorsanız size yeterli bilgi vermez. Veremez onun da bilgisi yok sonuçta. Zaten kısıtlı bilgiler veriliyor. Tarih dizilerini kılıç kalkan kuşanıp izliyorum. Doğru yansıtılıyor bundan dolayı da biraz insanlar tarihini öğrenmeye başladı desek yalan olmaz. Bu ne kadar doğru derseniz de bilgiyi doğru verdikten sonra tabi diziden de tarih öğrenilebilir. Mehmet Ak (28-Satış Danışmanı): Kuruluşunun bugün olduğunu bilmiyordum. O dönemde yaşamak isterdim. Yeterli bilgim yok bence kim tarihi tam anlamıyla öğrenebilir ki zaten. Ama tabi tarih kitapları okuyorum, araştırıyorum. Okullarda verilen tarih derslerini yeterli bulmuyorum; ama daha fazla neler yapılabilir, tarih küçüklere nasıl anlatılır bilmiyorum tek bildiğim yeterli olmadığı. Tarih dizileri gerçeği yüzde 75 yansıtıyor. Bu konuda araştırma yaptım yüzeysel anlatılıyor; fakat yine de bilgi edinilebilecek diziler. En azından rutinleşmiş dizi konularından uzak. Aile ile rahat rahat izleyebilirsin. Hasan Çalık (65-Emekli): Kuruluş yıldönümü olduğunu biliyorum. Araştırma yaparım. O dönemde yaşamak istemezdim Cumhuriyet döneminde yaşamayı daha çok isterdim. Okullarda verilen dersler yeterli. Film ya da dizi okumak yerine kitap okumayı tercih ederim. Bir insan ancak kitap okuyarak aydınlanabilir. Bu şekilde kendini geliştirebilir. Bir ömre ne kadar kitap sığdırırsan o kadar aydın bir insan olursun. Konusu fark etmez ister tarih olsun, ister roman okumak her zaman kazanç sağlar. Bir diziden tarihi ne kadar yeterli öğrenebilirsin ki ya da ne kadar doğru anlatılabilir. Bence diziyi bırakıp kitaplara yönelsinler. Nuray Çelik' } ``` ### Data Fields - category : Indicates to which category the news text belongs. (Such as "kültürsanat" (0), "ekonomi" (1), "siyaset" (2), "eğitim" (3), "dünya" (4), "spor" (5), "teknoloji" (6), "magazin" (7), "sağlık" (8), "gündem" (9)) - content : Contains the text of the news. ### Data Splits The data is split into a training and testing. The split is organized as the following \| \| train \| test \| \|------------\|--------:\|-------:\| \| data split \| 218,880 \| 54,721 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization Downloaded over 270,000 news from the printed media and news websites between 2010 and 2017 by the Interpress (https://www.interpress.com/) media monitoring company. This data collection compiled from print media and internet news is presented in its raw form. For this reason, it is appropriate to use it with careful pre-processing steps regarding various OCR errors and typos. #### Who are the source language producers? Turkish printed news sources and online news sites. ### Annotations The dataset does not contain any additional annotations. #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information https://www.interpress.com/ ### Contributions Thanks to [@basakbuluz](https://github.com/basakbuluz) & [@yavuzkomecoglu](https://github.com/yavuzkomecoglu) & [@serdarakyol](https://github.com/serdarakyol/) for adding this dataset.
irc_disentangle	--- annotations_creators: - expert-generated language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: [] paperswithcode_id: irc-disentanglement pretty_name: IRC Disentanglement tags: - conversation-disentanglement dataset_info: - config_name: ubuntu features: - name: id dtype: int32 - name: raw dtype: string - name: ascii dtype: string - name: tokenized dtype: string - name: date dtype: string - name: connections sequence: int32 splits: - name: train num_bytes: 56012854 num_examples: 220616 - name: validation num_bytes: 3081479 num_examples: 12510 - name: test num_bytes: 3919900 num_examples: 15010 download_size: 118470210 dataset_size: 63014233 - config_name: channel_two features: - name: id dtype: int32 - name: raw dtype: string - name: ascii dtype: string - name: tokenized dtype: string - name: connections sequence: int32 splits: - name: dev num_bytes: 197505 num_examples: 1001 - name: pilot num_bytes: 92663 num_examples: 501 - name: test num_bytes: 186823 num_examples: 1001 - name: pilot_dev num_bytes: 290175 num_examples: 1501 - name: all_ num_bytes: 496524 num_examples: 2602 download_size: 118470210 dataset_size: 1263690 --- # Dataset Card for IRC Disentanglement ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) - [Acknowledgments](#acknowledgments) ## Dataset Description - Homepage: https://jkk.name/irc-disentanglement/ - Repository: https://github.com/jkkummerfeld/irc-disentanglement/tree/master/data - Paper: https://aclanthology.org/P19-1374/ - Leaderboard: NA - Point of Contact: jkummerf@umich.edu ### Dataset Summary Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets. This new dataset of 77,563 messages manually annotated with reply-structure graphs that both disentangle conversations and define internal conversation structure. The dataset is 16 times larger than all previously released datasets combined, the first to include adjudication of annotation disagreements, and the first to include context. Note, the Github repository for the dataset also contains several useful tools for: - Conversion (e.g. extracting conversations from graphs) - Evaluation - Preprocessing - Word embeddings trained on the full Ubuntu logs in 2018 ### Supported Tasks and Leaderboards Conversational Disentanglement ### Languages English (en) ## Dataset Structure ### Data Instances For Ubuntu: data["train"][1050] ``` { 'ascii': "[03:57] <Xophe> (also, I'm guessing that this isn't a good place to report minor but annoying bugs... what is?)", 'connections': [1048, 1054, 1055, 1072, 1073], 'date': '2004-12-25', 'id': 1050, 'raw': "[03:57] <Xophe> (also, I'm guessing that this isn't a good place to report minor but annoying bugs... what is?)", 'tokenized': "<s> ( also , i 'm guessing that this is n't a good place to report minor but annoying bugs ... what is ?) </s>" } ``` For Channel_two: data["train"][50] ``` { 'ascii': "[01:04] <Felicia> Chanel: i don't know off hand sorry", 'connections': [49, 53], 'id': 50, 'raw': "[01:04] <Felicia> Chanel: i don't know off hand sorry", 'tokenized': "<s> <user> : i do n't know off hand sorry </s>" } ``` ### Data Fields 'id' : The id of the message, this is the value that would be in the 'connections' of associated messages. 'raw' : The original message from the IRC log, as downloaded. 'ascii' : The raw message converted to ascii (unconvertable characters are replaced with a special word). 'tokenized' : The same message with automatic tokenisation and replacement of rare words with placeholder symbols. 'connections' : The indices of linked messages. (only ubuntu) 'date' : The date the messages are from. The labelling for each date only start after the first 1000 messages of that date. ### Data Splits The dataset has 4 parts: \| Part \| Number of Annotated Messages \| \| ------------- \| ------------------------------------------- \| \| Train \| 67,463 \| \| Dev \| 2,500 \| \| Test \| 5,000 \| \| Channel 2 \| 2,600 \| ## Dataset Creation ### Curation Rationale IRC is a synchronous chat setting with a long history of use. Several channels log all messages and make them publicly available. The Ubuntu channel is particularly heavily used and has been the subject of several academic studies. Data was selected from the channel in order to capture the diversity of situations in the channel (e.g. when there are many users or very few users). For full details, see the [annotation information page](https://github.com/jkkummerfeld/irc-disentanglement/blob/master/data/READ.history.md). ### Source Data #### Initial Data Collection and Normalization Data was collected from the Ubuntu IRC channel logs, which are publicly available at [https://irclogs.ubuntu.com/](https://irclogs.ubuntu.com/). The raw files are included, as well as two other versions: - ASCII, converted using the script [make_txt.py](https://github.com/jkkummerfeld/irc-disentanglement/blob/master/tools/preprocessing/make-txt.py) - Tok, tokenised text with rare words replaced by UNK using the script [dstc8-tokenise.py](https://github.com/jkkummerfeld/irc-disentanglement/blob/master/tools/preprocessing/dstc8-tokenise.py) The raw channel two data is from prior work [(Elsner and Charniak, 2008)](https://www.aclweb.org/anthology/P08-1095.pdf)]. #### Who are the source language producers? The text is from a large group of internet users asking questions and providing answers related to Ubuntu. ### Annotations #### Annotation process The data is expert annotated with: - Training, one annotation per line in general, a small portion is double-annotated and adjudicated - Dev, Channel 2, double annotated and adjudicated - Test, triple annotated and adjudicated \| Part \| Annotators \| Adjudication? \| \| ------------- \| --------------- \| ------------------------------------- \| \| Train \| 1 or 2 per file \| For files with 2 annotators (only 10) \| \| Dev \| 2 \| Yes \| \| Test \| 3 \| Yes \| \| Channel 2 \| 2 \| Yes \| #### Who are the annotators? Students and a postdoc at the University of Michigan. Everyone involved went through a training process with feedback to learn the annotation guidelines. ### Personal and Sensitive Information No content is removed or obfuscated. There is probably personal information in the dataset from users. ## Considerations for Using the Data ### Social Impact of Dataset The raw data is already available online and the annotations do not significantly provide additional information that could have a direct social impact. ### Discussion of Biases The data is mainly from a single technical domain (Ubuntu tech support) that probably has a demographic skew of some sort. Given that users are only identified by their self-selected usernames, it is difficult to know more about the authors. ### Other Known Limitations Being focused on a single language and a single channel means that the data is likely capturing a particular set of conventions in communication. Those conventions may not apply to other channels, or beyond IRC. ## Additional Information ### Dataset Curators Jonathan K. Kummerfeld ### Licensing Information Creative Commons Attribution 4.0 ### Citation Information ``` @inproceedings{kummerfeld-etal-2019-large, title = "A Large-Scale Corpus for Conversation Disentanglement", author = "Kummerfeld, Jonathan K. and Gouravajhala, Sai R. and Peper, Joseph J. and Athreya, Vignesh and Gunasekara, Chulaka and Ganhotra, Jatin and Patel, Siva Sankalp and Polymenakos, Lazaros C and Lasecki, Walter", booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P19-1374", doi = "10.18653/v1/P19-1374", pages = "3846--3856", arxiv = "https://arxiv.org/abs/1810.11118", software = "https://jkk.name/irc-disentanglement", data = "https://jkk.name/irc-disentanglement", abstract = "Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets. We created a new dataset of 77,563 messages manually annotated with reply-structure graphs that both disentangle conversations and define internal conversation structure. Our data is 16 times larger than all previously released datasets combined, the first to include adjudication of annotation disagreements, and the first to include context. We use our data to re-examine prior work, in particular, finding that 89{\%} of conversations in a widely used dialogue corpus are either missing messages or contain extra messages. Our manually-annotated data presents an opportunity to develop robust data-driven methods for conversation disentanglement, which will help advance dialogue research.", } ``` ### Contributions Thanks to [@dhruvjoshi1998](https://github.com/dhruvjoshi1998) for adding this dataset. Thanks to [@jkkummerfeld](https://github.com/jkkummerfeld) for improvements to the documentation. ### Acknowledgments This material is based in part upon work supported by IBM under contract 4915012629. Any opinions, findings, conclusions or recommendations expressed are those of the authors and do not necessarily reflect the views of IBM.
isixhosa_ner_corpus	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - xh license: - other multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: IsixhosaNerCorpus license_details: Creative Commons Attribution 2.5 South Africa License dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': OUT '1': B-PERS '2': I-PERS '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC config_name: isixhosa_ner_corpus splits: - name: train num_bytes: 2414995 num_examples: 6284 download_size: 14513302 dataset_size: 2414995 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [IsiXhosa Ner Corpus Homepage](https://repo.sadilar.org/handle/20.500.12185/312) - Repository: - Paper: - Leaderboard: - Point of Contact: [Martin Puttkammer](mailto:Martin.Puttkammer@nwu.ac.za) ### Dataset Summary The isiXhosa Ner Corpus is a Xhosa dataset developed by [The Centre for Text Technology (CTexT), North-West University, South Africa](http://humanities.nwu.ac.za/ctext). The data is based on documents from the South African goverment domain and crawled from gov.za websites. It was created to support NER task for Xhosa language. The dataset uses CoNLL shared task annotation standards. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The language supported is Xhosa. ## Dataset Structure ### Data Instances A data point consists of sentences seperated by empty line and tab-seperated tokens and tags. {'id': '0', 'ner_tags': [7, 8, 5, 6, 0], 'tokens': ['Injongo', 'ye-website', 'yaseMzantsi', 'Afrika', 'kukuvelisa'] } ### Data Fields - `id`: id of the sample - `tokens`: the tokens of the example text - `ner_tags`: the NER tags of each token The NER tags correspond to this list: ``` "OUT", "B-PERS", "I-PERS", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC", ``` The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and miscellaneous names (MISC). (OUT) is used for tokens not considered part of any named entity. ### Data Splits The data was not split. ## Dataset Creation ### Curation Rationale The data was created to help introduce resources to new language - Xhosa. [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The data is based on South African government domain and was crawled from gov.za websites. [More Information Needed] #### Who are the source language producers? The data was produced by writers of South African government websites - gov.za [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The data was annotated during the NCHLT text resource development project. [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The annotated data sets were developed by the Centre for Text Technology (CTexT, North-West University, South Africa). See: [more information](http://www.nwu.ac.za/ctext) ### Licensing Information The data is under the [Creative Commons Attribution 2.5 South Africa License](http://creativecommons.org/licenses/by/2.5/za/legalcode) ### Citation Information ``` @inproceedings{isixhosa_ner_corpus, author = { K. Podile and Roald Eiselen}, title = {NCHLT isiXhosa Named Entity Annotated Corpus}, booktitle = {Eiselen, R. 2016. Government domain named entity recognition for South African languages. Proceedings of the 10th Language Resource and Evaluation Conference, Portorož, Slovenia.}, year = {2016}, url = {https://repo.sadilar.org/handle/20.500.12185/312}, } ``` ### Contributions Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset.
isizulu_ner_corpus	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - zu license: - other multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: Isizulu Ner Corpus license_details: Creative Commons Attribution 2.5 South Africa dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': OUT '1': B-PERS '2': I-PERS '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC config_name: isizulu_ner_corpus splits: - name: train num_bytes: 4038876 num_examples: 10956 download_size: 25097584 dataset_size: 4038876 --- # Dataset Card for Isizulu Ner Corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Isizulu Ner Corpus Homepage](https://repo.sadilar.org/handle/20.500.12185/319) - Repository: - Paper: - Leaderboard: - Point of Contact: [Martin Puttkammer](mailto:Martin.Puttkammer@nwu.ac.za) ### Dataset Summary The isizulu Ner Corpus is a Zulu dataset developed by [The Centre for Text Technology (CTexT), North-West University, South Africa](http://humanities.nwu.ac.za/ctext). The data is based on documents from the South African goverment domain and crawled from gov.za websites. It was created to support NER task for Zulu language. The dataset uses CoNLL shared task annotation standards. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The language supported is Zulu. ## Dataset Structure ### Data Instances A data point consists of sentences seperated by empty line and tab-seperated tokens and tags. {'id': '0', 'ner_tags': [7, 8, 0, 0, 0], 'tokens': ['Lesi', 'sigaba', 'se-website', ',', 'esikhonjiswe'] } ### Data Fields - `id`: id of the sample - `tokens`: the tokens of the example text - `ner_tags`: the NER tags of each token The NER tags correspond to this list: ``` "OUT", "B-PERS", "I-PERS", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC", ``` The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and miscellaneous names (MISC). (OUT) is used for tokens not considered part of any named entity. ### Data Splits The data was not split. ## Dataset Creation ### Curation Rationale The data was created to help introduce resources to new language - zulu. [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The data is based on South African government domain and was crawled from gov.za websites. #### Who are the source language producers? The data was produced by writers of South African government websites - gov.za ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The data was annotated during the NCHLT text resource development project. [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The annotated data sets were developed by the Centre for Text Technology (CTexT, North-West University, South Africa). See: [more information](http://www.nwu.ac.za/ctext) ### Licensing Information The data is under the [Creative Commons Attribution 2.5 South Africa License](http://creativecommons.org/licenses/by/2.5/za/legalcode) ### Citation Information ``` @inproceedings{isizulu_ner_corpus, author = {A.N. Manzini and Roald Eiselen}, title = {NCHLT isiZulu Named Entity Annotated Corpus}, booktitle = {Eiselen, R. 2016. Government domain named entity recognition for South African languages. Proceedings of the 10th Language Resource and Evaluation Conference, Portorož, Slovenia.}, year = {2016}, url = {https://repo.sadilar.org/handle/20.500.12185/319}, } ``` ### Contributions Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset.
iwslt2017	--- annotations_creators: - crowdsourced language: - ar - de - en - fr - it - ja - ko - nl - ro - zh language_creators: - expert-generated license: - cc-by-nc-nd-4.0 multilinguality: - translation pretty_name: IWSLT 2017 size_categories: - 1M<n<10M source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: iwslt-2017 dataset_info: - config_name: iwslt2017-en-it features: - name: translation dtype: translation: languages: - en - it splits: - name: train num_bytes: 46647925 num_examples: 231619 - name: test num_bytes: 305246 num_examples: 1566 - name: validation num_bytes: 200023 num_examples: 929 download_size: 329391132 dataset_size: 47153194 - config_name: iwslt2017-en-nl features: - name: translation dtype: translation: languages: - en - nl splits: - name: train num_bytes: 42843933 num_examples: 237240 - name: test num_bytes: 311646 num_examples: 1777 - name: validation num_bytes: 197814 num_examples: 1003 download_size: 329391132 dataset_size: 43353393 - config_name: iwslt2017-en-ro features: - name: translation dtype: translation: languages: - en - ro splits: - name: train num_bytes: 44129950 num_examples: 220538 - name: test num_bytes: 316790 num_examples: 1678 - name: validation num_bytes: 205028 num_examples: 914 download_size: 329391132 dataset_size: 44651768 - config_name: iwslt2017-it-en features: - name: translation dtype: translation: languages: - it - en splits: - name: train num_bytes: 46647925 num_examples: 231619 - name: test num_bytes: 305246 num_examples: 1566 - name: validation num_bytes: 200023 num_examples: 929 download_size: 329391132 dataset_size: 47153194 - config_name: iwslt2017-it-nl features: - name: translation dtype: translation: languages: - it - nl splits: - name: train num_bytes: 43033168 num_examples: 233415 - name: test num_bytes: 309725 num_examples: 1669 - name: validation num_bytes: 197774 num_examples: 1001 download_size: 329391132 dataset_size: 43540667 - config_name: iwslt2017-it-ro features: - name: translation dtype: translation: languages: - it - ro splits: - name: train num_bytes: 44485169 num_examples: 217551 - name: test num_bytes: 314974 num_examples: 1643 - name: validation num_bytes: 204989 num_examples: 914 download_size: 329391132 dataset_size: 45005132 - config_name: iwslt2017-nl-en features: - name: translation dtype: translation: languages: - nl - en splits: - name: train num_bytes: 42843933 num_examples: 237240 - name: test num_bytes: 311646 num_examples: 1777 - name: validation num_bytes: 197814 num_examples: 1003 download_size: 329391132 dataset_size: 43353393 - config_name: iwslt2017-nl-it features: - name: translation dtype: translation: languages: - nl - it splits: - name: train num_bytes: 43033168 num_examples: 233415 - name: test num_bytes: 309725 num_examples: 1669 - name: validation num_bytes: 197774 num_examples: 1001 download_size: 329391132 dataset_size: 43540667 - config_name: iwslt2017-nl-ro features: - name: translation dtype: translation: languages: - nl - ro splits: - name: train num_bytes: 41338738 num_examples: 206920 - name: test num_bytes: 320952 num_examples: 1680 - name: validation num_bytes: 202380 num_examples: 913 download_size: 329391132 dataset_size: 41862070 - config_name: iwslt2017-ro-en features: - name: translation dtype: translation: languages: - ro - en splits: - name: train num_bytes: 44129950 num_examples: 220538 - name: test num_bytes: 316790 num_examples: 1678 - name: validation num_bytes: 205028 num_examples: 914 download_size: 329391132 dataset_size: 44651768 - config_name: iwslt2017-ro-it features: - name: translation dtype: translation: languages: - ro - it splits: - name: train num_bytes: 44485169 num_examples: 217551 - name: test num_bytes: 314974 num_examples: 1643 - name: validation num_bytes: 204989 num_examples: 914 download_size: 329391132 dataset_size: 45005132 - config_name: iwslt2017-ro-nl features: - name: translation dtype: translation: languages: - ro - nl splits: - name: train num_bytes: 41338738 num_examples: 206920 - name: test num_bytes: 320952 num_examples: 1680 - name: validation num_bytes: 202380 num_examples: 913 download_size: 329391132 dataset_size: 41862070 - config_name: iwslt2017-ar-en features: - name: translation dtype: translation: languages: - ar - en splits: - name: train num_bytes: 56481059 num_examples: 231713 - name: test num_bytes: 2014296 num_examples: 8583 - name: validation num_bytes: 241206 num_examples: 888 download_size: 27748780 dataset_size: 58736561 - config_name: iwslt2017-de-en features: - name: translation dtype: translation: languages: - de - en splits: - name: train num_bytes: 42608380 num_examples: 206112 - name: test num_bytes: 1608474 num_examples: 8079 - name: validation num_bytes: 210975 num_examples: 888 download_size: 16758320 dataset_size: 44427829 - config_name: iwslt2017-en-ar features: - name: translation dtype: translation: languages: - en - ar splits: - name: train num_bytes: 56481059 num_examples: 231713 - name: test num_bytes: 2014296 num_examples: 8583 - name: validation num_bytes: 241206 num_examples: 888 download_size: 29333173 dataset_size: 58736561 - config_name: iwslt2017-en-de features: - name: translation dtype: translation: languages: - en - de splits: - name: train num_bytes: 42608380 num_examples: 206112 - name: test num_bytes: 1608474 num_examples: 8079 - name: validation num_bytes: 210975 num_examples: 888 download_size: 16758334 dataset_size: 44427829 - config_name: iwslt2017-en-fr features: - name: translation dtype: translation: languages: - en - fr splits: - name: train num_bytes: 49273286 num_examples: 232825 - name: test num_bytes: 1767465 num_examples: 8597 - name: validation num_bytes: 207579 num_examples: 890 download_size: 27699724 dataset_size: 51248330 - config_name: iwslt2017-en-ja features: - name: translation dtype: translation: languages: - en - ja splits: - name: train num_bytes: 48204987 num_examples: 223108 - name: test num_bytes: 1809007 num_examples: 8469 - name: validation num_bytes: 208124 num_examples: 871 download_size: 26983602 dataset_size: 50222118 - config_name: iwslt2017-en-ko features: - name: translation dtype: translation: languages: - en - ko splits: - name: train num_bytes: 51678043 num_examples: 230240 - name: test num_bytes: 1869793 num_examples: 8514 - name: validation num_bytes: 219295 num_examples: 879 download_size: 19364776 dataset_size: 53767131 - config_name: iwslt2017-en-zh features: - name: translation dtype: translation: languages: - en - zh splits: - name: train num_bytes: 44271004 num_examples: 231266 - name: test num_bytes: 1605527 num_examples: 8549 - name: validation num_bytes: 202537 num_examples: 879 download_size: 27597071 dataset_size: 46079068 - config_name: iwslt2017-fr-en features: - name: translation dtype: translation: languages: - fr - en splits: - name: train num_bytes: 49273286 num_examples: 232825 - name: test num_bytes: 1767465 num_examples: 8597 - name: validation num_bytes: 207579 num_examples: 890 download_size: 26880731 dataset_size: 51248330 - config_name: iwslt2017-ja-en features: - name: translation dtype: translation: languages: - ja - en splits: - name: train num_bytes: 48204987 num_examples: 223108 - name: test num_bytes: 1809007 num_examples: 8469 - name: validation num_bytes: 208124 num_examples: 871 download_size: 26190859 dataset_size: 50222118 - config_name: iwslt2017-ko-en features: - name: translation dtype: translation: languages: - ko - en splits: - name: train num_bytes: 51678043 num_examples: 230240 - name: test num_bytes: 1869793 num_examples: 8514 - name: validation num_bytes: 219295 num_examples: 879 download_size: 19364733 dataset_size: 53767131 - config_name: iwslt2017-zh-en features: - name: translation dtype: translation: languages: - zh - en splits: - name: train num_bytes: 44271004 num_examples: 231266 - name: test num_bytes: 1605527 num_examples: 8549 - name: validation num_bytes: 202537 num_examples: 879 download_size: 26849290 dataset_size: 46079068 --- # Dataset Card for IWSLT 2017 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://sites.google.com/site/iwsltevaluation2017/TED-tasks](https://sites.google.com/site/iwsltevaluation2017/TED-tasks) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [Overview of the IWSLT 2017 Evaluation Campaign](https://aclanthology.org/2017.iwslt-1.1/) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 4.24 GB - Size of the generated dataset: 1.14 GB - Total amount of disk used: 5.38 GB ### Dataset Summary The IWSLT 2017 Multilingual Task addresses text translation, including zero-shot translation, with a single MT system across all directions including English, German, Dutch, Italian and Romanian. As unofficial task, conventional bilingual text translation is offered between English and Arabic, French, Japanese, Chinese, German and Korean. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### iwslt2017-ar-en - Size of downloaded dataset files: 27.75 MB - Size of the generated dataset: 58.74 MB - Total amount of disk used: 86.49 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "translation": "{\"ar\": \"لقد طرت في \\\"القوات الجوية \\\" لمدة ثمان سنوات. والآن أجد نفسي مضطرا لخلع حذائي قبل صعود الطائرة!\", \"en\": \"I flew on Air ..." } ``` #### iwslt2017-de-en - Size of downloaded dataset files: 16.76 MB - Size of the generated dataset: 44.43 MB - Total amount of disk used: 61.18 MB An example of 'train' looks as follows. ``` { "translation": { "de": "Es ist mir wirklich eine Ehre, zweimal auf dieser Bühne stehen zu dürfen. Tausend Dank dafür.", "en": "And it's truly a great honor to have the opportunity to come to this stage twice; I'm extremely grateful." } } ``` #### iwslt2017-en-ar - Size of downloaded dataset files: 29.33 MB - Size of the generated dataset: 58.74 MB - Total amount of disk used: 88.07 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "translation": "{\"ar\": \"لقد طرت في \\\"القوات الجوية \\\" لمدة ثمان سنوات. والآن أجد نفسي مضطرا لخلع حذائي قبل صعود الطائرة!\", \"en\": \"I flew on Air ..." } ``` #### iwslt2017-en-de - Size of downloaded dataset files: 16.76 MB - Size of the generated dataset: 44.43 MB - Total amount of disk used: 61.18 MB An example of 'validation' looks as follows. ``` { "translation": { "de": "Die nächste Folie, die ich Ihnen zeige, ist eine Zeitrafferaufnahme was in den letzten 25 Jahren passiert ist.", "en": "The next slide I show you will be a rapid fast-forward of what's happened over the last 25 years." } } ``` #### iwslt2017-en-fr - Size of downloaded dataset files: 27.69 MB - Size of the generated dataset: 51.24 MB - Total amount of disk used: 78.94 MB An example of 'validation' looks as follows. ``` { "translation": { "en": "But this understates the seriousness of this particular problem because it doesn't show the thickness of the ice.", "fr": "Mais ceci tend à amoindrir le problème parce qu'on ne voit pas l'épaisseur de la glace." } } ``` ### Data Fields The data fields are the same among all splits. #### iwslt2017-ar-en - `translation`: a multilingual `string` variable, with possible languages including `ar`, `en`. #### iwslt2017-de-en - `translation`: a multilingual `string` variable, with possible languages including `de`, `en`. #### iwslt2017-en-ar - `translation`: a multilingual `string` variable, with possible languages including `en`, `ar`. #### iwslt2017-en-de - `translation`: a multilingual `string` variable, with possible languages including `en`, `de`. #### iwslt2017-en-fr - `translation`: a multilingual `string` variable, with possible languages including `en`, `fr`. ### Data Splits \| name \|train \|validation\|test\| \|---------------\|-----:\|---------:\|---:\| \|iwslt2017-ar-en\|231713\| 888\|8583\| \|iwslt2017-de-en\|206112\| 888\|8079\| \|iwslt2017-en-ar\|231713\| 888\|8583\| \|iwslt2017-en-de\|206112\| 888\|8079\| \|iwslt2017-en-fr\|232825\| 890\|8597\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information Creative Commons BY-NC-ND See the (TED Talks Usage Policy)[https://www.ted.com/about/our-organization/our-policies-terms/ted-talks-usage-policy]. ### Citation Information ``` @inproceedings{cettolo-etal-2017-overview, title = "Overview of the {IWSLT} 2017 Evaluation Campaign", author = {Cettolo, Mauro and Federico, Marcello and Bentivogli, Luisa and Niehues, Jan and St{\"u}ker, Sebastian and Sudoh, Katsuhito and Yoshino, Koichiro and Federmann, Christian}, booktitle = "Proceedings of the 14th International Conference on Spoken Language Translation", month = dec # " 14-15", year = "2017", address = "Tokyo, Japan", publisher = "International Workshop on Spoken Language Translation", url = "https://aclanthology.org/2017.iwslt-1.1", pages = "2--14", } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@Narsil](https://github.com/Narsil) for adding this dataset.