Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Indonesian
Size:
10K<n<100K
License:
| 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. |