Datasets:

Linguateca
/

harem

{"default": {"description": "\nThe HAREM is a Portuguese language corpus commonly used for Named Entity Recognition tasks. It includes about 93k words, from 129 different texts,\nfrom several genres, and language varieties. The split of this dataset version follows the division made by [1], where 7% HAREM\ndocuments are the validation set and the miniHAREM corpus (with about 65k words) is the test set. There are two versions of the dataset set,\na version that has a total of 10 different named entity classes (Person, Organization, Location, Value, Date, Title, Thing, Event,\nAbstraction, and Other) and a \"selective\" version with only 5 classes (Person, Organization, Location, Value, and Date).\n\nIt's important to note that the original version of the HAREM dataset has 2 levels of NER details, namely \"Category\" and \"Sub-type\".\nThe dataset version processed here ONLY USE the \"Category\" level of the original dataset.\n\n[1] Souza, F\u00e1bio, Rodrigo Nogueira, and Roberto Lotufo. \"BERTimbau: Pretrained BERT Models for Brazilian Portuguese.\" Brazilian Conference on Intelligent Systems. Springer, Cham, 2020.\n", "citation": "\n@inproceedings{santos2006harem,\n title={Harem: An advanced ner evaluation contest for portuguese},\n author={Santos, Diana and Seco, Nuno and Cardoso, Nuno and Vilela, Rui},\n 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)},\n year={2006}\n}\n", "homepage": "https://www.linguateca.pt/primeiroHAREM/harem_coleccaodourada_en.html", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "ner_tags": {"feature": {"num_classes": 21, "names": ["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"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "harem", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1506373, "num_examples": 121, "dataset_name": "harem"}, "test": {"name": "test", "num_bytes": 1062714, "num_examples": 128, "dataset_name": "harem"}, "validation": {"name": "validation", "num_bytes": 51318, "num_examples": 8, "dataset_name": "harem"}}, "download_checksums": {"https://raw.githubusercontent.com/neuralmind-ai/portuguese-bert/master/ner_evaluation/data/FirstHAREM-total-train.json": {"num_bytes": 1060674, "checksum": "3542944e1e56145c5d1f4df1750df8ec81d0f9e0a7cc0c3e74b0b26df5869763"}, "https://raw.githubusercontent.com/neuralmind-ai/portuguese-bert/master/ner_evaluation/data/FirstHAREM-total-dev.json": {"num_bytes": 47603, "checksum": "a08705c45caef5bdb82b7fe394491de114edb24d14e007a9f3978be030219537"}, "https://raw.githubusercontent.com/neuralmind-ai/portuguese-bert/master/ner_evaluation/data/MiniHAREM-total.json": {"num_bytes": 779004, "checksum": "9a31f28df9664d7de4ceab6f2ec427ad1761463348083d35c7fa97cae87505db"}}, "download_size": 1887281, "post_processing_size": null, "dataset_size": 2620405, "size_in_bytes": 4507686}, "selective": {"description": "\nThe HAREM is a Portuguese language corpus commonly used for Named Entity Recognition tasks. It includes about 93k words, from 129 different texts,\nfrom several genres, and language varieties. The split of this dataset version follows the division made by [1], where 7% HAREM\ndocuments are the validation set and the miniHAREM corpus (with about 65k words) is the test set. There are two versions of the dataset set,\na version that has a total of 10 different named entity classes (Person, Organization, Location, Value, Date, Title, Thing, Event,\nAbstraction, and Other) and a \"selective\" version with only 5 classes (Person, Organization, Location, Value, and Date).\n\nIt's important to note that the original version of the HAREM dataset has 2 levels of NER details, namely \"Category\" and \"Sub-type\".\nThe dataset version processed here ONLY USE the \"Category\" level of the original dataset.\n\n[1] Souza, F\u00e1bio, Rodrigo Nogueira, and Roberto Lotufo. \"BERTimbau: Pretrained BERT Models for Brazilian Portuguese.\" Brazilian Conference on Intelligent Systems. Springer, Cham, 2020.\n", "citation": "\n@inproceedings{santos2006harem,\n title={Harem: An advanced ner evaluation contest for portuguese},\n author={Santos, Diana and Seco, Nuno and Cardoso, Nuno and Vilela, Rui},\n 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)},\n year={2006}\n}\n", "homepage": "https://www.linguateca.pt/primeiroHAREM/harem_coleccaodourada_en.html", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "ner_tags": {"feature": {"num_classes": 11, "names": ["O", "B-PESSOA", "I-PESSOA", "B-ORGANIZACAO", "I-ORGANIZACAO", "B-LOCAL", "I-LOCAL", "B-TEMPO", "I-TEMPO", "B-VALOR", "I-VALOR"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "harem", "config_name": "selective", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1506373, "num_examples": 121, "dataset_name": "harem"}, "test": {"name": "test", "num_bytes": 1062714, "num_examples": 128, "dataset_name": "harem"}, "validation": {"name": "validation", "num_bytes": 51318, "num_examples": 8, "dataset_name": "harem"}}, "download_checksums": {"https://raw.githubusercontent.com/neuralmind-ai/portuguese-bert/master/ner_evaluation/data/FirstHAREM-selective-train.json": {"num_bytes": 969734, "checksum": "afb49c5d11116ff297d7abb7657f524917cb5704b221d5e3fb687e064a71e494"}, "https://raw.githubusercontent.com/neuralmind-ai/portuguese-bert/master/ner_evaluation/data/FirstHAREM-selective-dev.json": {"num_bytes": 38988, "checksum": "2ea2d350c587d35b08a86d067f6de27df6e3587339e80d10df787c7443fca7f3"}, "https://raw.githubusercontent.com/neuralmind-ai/portuguese-bert/master/ner_evaluation/data/MiniHAREM-selective.json": {"num_bytes": 707151, "checksum": "7a5d88cf1319ddae1940a02d3fde7dd5841863e05e616cfb2b574613407b7f37"}}, "download_size": 1715873, "post_processing_size": null, "dataset_size": 2620405, "size_in_bytes": 4336278}}