{"description": "\nPL-corpus is a Portuguese language dataset for named entity recognition applied to legislative documents. Its parte of the UlyssesBR-corpus, and consists entirely of manually annotated public bills texts (projetos de leis) and contains tags for persons, locations, date entities, organizations, legal foundation and bills.\n", "citation": "\nALBUQUERQUE2022,author=\"Albuquerque, Hidelberg O. and Costa, Rosimeire and Silvestre, Gabriel and Souza, Ellen and da Silva, N{'a}dia F. F. and Vit{'o}rio, Douglas and Moriyama, Gyovana and Martins, Lucas and Soezima, Luiza and Nunes, Augusto and Siqueira, Felipe and Tarrega, Jo{\\~a}o P. and Beinotti, Joao V. and Dias, Marcio and Silva, Matheus and Gardini, Miguel and Silva, Vinicius and de Carvalho, Andr{'e} C. P. L. F. and Oliveira, Adriano L. I.\", title=\"{UlyssesNER-Br}: A Corpus of Brazilian Legislative Documents for Named Entity Recognition\", booktitle=\"Computational Processing of the Portuguese Language\", year=\"2022\", pages=\"3--14\",@inproceedings{inPress, PROPOR2022}\n", "homepage": "https://github.com/Convenio-Camara-dos-Deputados/ulyssesner-br-propor", "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": 15, "names": ["O", "B-ORGANIZACAO", "I-ORGANIZACAO", "B-PESSOA", "I-PESSOA", "B-DATA", "I-DATA", "B-LOCAL", "I-LOCAL", "B-FUNDAMENTO", "I-FUNDAMENTO", "B-PRODUTODELEI", "I-PRODUTODELEI", "B-EVENTO", "I-EVENTO"], "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "pl_corpus", "config_name": "pl-corpus", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1179261, "num_examples": 4770, "dataset_name": "pl_corpus"}, "validation": {"name": "validation", "num_bytes": 618518, "num_examples": 2479, "dataset_name": "pl_corpus"}, "test": {"name": "test", "num_bytes": 573681, "num_examples": 2277, "dataset_name": "pl_corpus"}}, "download_checksums": {"https://raw.githubusercontent.com/bergoliveira/assessment-of-deep-learning-models-icann/main/pl-corpus/train.conll": {"num_bytes": 564558, "checksum": "a30f4d06d1a71506c3dc186ec4e2de03dc38a7b87e7eb130708fcecf392e1ef4"}, "https://raw.githubusercontent.com/bergoliveira/assessment-of-deep-learning-models-icann/main/pl-corpus/dev.conll": {"num_bytes": 293377, "checksum": "03e7a1b7089621ab654d3fe3c4f909e5da021d588db5007310cea7f9eeaf0715"}, "https://raw.githubusercontent.com/bergoliveira/assessment-of-deep-learning-models-icann/main/pl-corpus/test.conll": {"num_bytes": 266510, "checksum": "f6f462ecdfc1f9cd4e6b5c127ff80ef7942977b67b868b47e2ee90ae30b6d073"}}, "download_size": 1124445, "post_processing_size": null, "dataset_size": 2371460, "size_in_bytes": 3495905}