import datasets from datasets import ClassLabel from typing import Dict, List, Optional, Union, Callable import json import textwrap import xml.etree.ElementTree as ET import pandas as pd logger = datasets.logging.get_logger(__name__) # Extracted from: # - https://huggingface.co/datasets/lener_br # - https://github.com/peluz/lener-br # - https://teodecampos.github.io/LeNER-Br/ _LENERBR_KWARGS = dict( name = "LeNER-Br", description=textwrap.dedent( """\ LeNER-Br is a Portuguese language dataset for named entity recognition applied to legal documents. LeNER-Br consists entirely of manually annotated legislation and legal cases texts and contains tags for persons, locations, time entities, organizations, legislation and legal cases. To compose the dataset, 66 legal documents from several Brazilian Courts were collected. Courts of superior and state levels were considered, such as Supremo Tribunal Federal, Superior Tribunal de Justiça, Tribunal de Justiça de Minas Gerais and Tribunal de Contas da União. In addition, four legislation documents were collected, such as "Lei Maria da Penha", giving a total of 70 documents.""" ), task_type="ner", label_classes=["ORGANIZACAO", "PESSOA", "TEMPO", "LOCAL", "LEGISLACAO", "JURISPRUDENCIA"], data_urls={ "train": "https://raw.githubusercontent.com/peluz/lener-br/master/leNER-Br/train/train.conll", "validation": "https://raw.githubusercontent.com/peluz/lener-br/master/leNER-Br/dev/dev.conll", "test": "https://raw.githubusercontent.com/peluz/lener-br/master/leNER-Br/test/test.conll", }, citation=textwrap.dedent( """\ @InProceedings{luz_etal_propor2018, author = {Pedro H. {Luz de Araujo} and Te\'{o}filo E. {de Campos} and Renato R. R. {de Oliveira} and Matheus Stauffer and Samuel Couto and Paulo Bermejo}, title = {{LeNER-Br}: a Dataset for Named Entity Recognition in {Brazilian} Legal Text}, booktitle = {International Conference on the Computational Processing of Portuguese ({PROPOR})}, publisher = {Springer}, series = {Lecture Notes on Computer Science ({LNCS})}, pages = {313--323}, year = {2018}, month = {September 24-26}, address = {Canela, RS, Brazil}, doi = {10.1007/978-3-319-99722-3_32}, url = {https://teodecampos.github.io/LeNER-Br/}, }""" ), url="https://teodecampos.github.io/LeNER-Br/", ) # Extracted from: # - https://huggingface.co/datasets/assin2 # - https://sites.google.com/view/assin2 # - https://github.com/ruanchaves/assin _ASSIN2_BASE_KWARGS = dict( description=textwrap.dedent( """\ The ASSIN 2 corpus is composed of rather simple sentences. Following the procedures of SemEval 2014 Task 1. The training and validation data are composed, respectively, of 6,500 and 500 sentence pairs in Brazilian Portuguese, annotated for entailment and semantic similarity. Semantic similarity values range from 1 to 5, and text entailment classes are either entailment or none. The test data are composed of approximately 3,000 sentence pairs with the same annotation. All data were manually annotated.""" ), data_urls={ "train": "https://github.com/ruanchaves/assin/raw/master/sources/assin2-train-only.xml", "validation": "https://github.com/ruanchaves/assin/raw/master/sources/assin2-dev.xml", "test": "https://github.com/ruanchaves/assin/raw/master/sources/assin2-test.xml", }, citation=textwrap.dedent( """\ @inproceedings{real2020assin, title={The assin 2 shared task: a quick overview}, author={Real, Livy and Fonseca, Erick and Oliveira, Hugo Goncalo}, booktitle={International Conference on Computational Processing of the Portuguese Language}, pages={406--412}, year={2020}, organization={Springer} }""" ), url="https://sites.google.com/view/assin2", ) _ASSIN2_RTE_KWARGS = dict( name = "assin2-rte", task_type="rte", label_classes=["NONE", "ENTAILMENT"], **_ASSIN2_BASE_KWARGS ) _ASSIN2_STS_KWARGS = dict( name = "assin2-sts", task_type="sts", **_ASSIN2_BASE_KWARGS ) # Extracted from: # - https://huggingface.co/datasets/ruanchaves/hatebr # - https://github.com/franciellevargas/HateBR _HATEBR_META_KWARGS = dict( description=textwrap.dedent( """\ HateBR is the first large-scale expert annotated dataset of Brazilian Instagram comments for abusive language detection on the web and social media. The HateBR was collected from Brazilian Instagram comments of politicians and manually annotated by specialists. It is composed of 7,000 documents annotated according to three different layers: a binary classification (offensive versus non-offensive comments), offensiveness-level (highly, moderately, and slightly offensive messages), and nine hate speech groups (xenophobia, racism, homophobia, sexism, religious intolerance, partyism, apology for the dictatorship, antisemitism, and fatphobia). Each comment was annotated by three different annotators and achieved high inter-annotator agreement. Furthermore, baseline experiments were implemented reaching 85% of F1-score outperforming the current literature dataset baselines for the Portuguese language. We hope that the proposed expert annotated dataset may foster research on hate speech detection in the Natural Language Processing area.""" ), task_type="classification", file_type="csv", data_urls={ "train": "https://raw.githubusercontent.com/franciellevargas/HateBR/2d18c5b9410c2dfdd6d5394caa54d608857dae7c/dataset/HateBR.csv" }, citation=textwrap.dedent( """\ @inproceedings{vargas2022hatebr, title={HateBR: A Large Expert Annotated Corpus of Brazilian Instagram Comments for Offensive Language and Hate Speech Detection}, author={Vargas, Francielle and Carvalho, Isabelle and de G{\'o}es, Fabiana Rodrigues and Pardo, Thiago and Benevenuto, Fabr{\'\i}cio}, booktitle={Proceedings of the Thirteenth Language Resources and Evaluation Conference}, pages={7174--7183}, year={2022} }""" ), url="https://github.com/franciellevargas/HateBR", indexes_url="https://huggingface.co/datasets/ruanchaves/hatebr/raw/main/indexes.json" ) hatebr_level_map = { "0": "non-offensive", "1": "slightly", "2": "moderately", "3": "highly", } _HATEBR_LEVEL_KWARGS = dict( name = "HateBR_offensive_level", text_and_label_columns=["instagram_comments", "offensiveness_levels"], label_classes=["non-offensive", "slightly", "moderately", "highly"], process_label = lambda x: hatebr_level_map[x], **_HATEBR_META_KWARGS ) hatebr_binary_map = { "0": "non-offensive", "1": "offensive", } _HATEBR_BINARY_KWARGS = dict( name = "HateBR_offensive_binary", text_and_label_columns=["instagram_comments", "offensive_language"], label_classes=["non-offensive", "offensive"], process_label = lambda x: hatebr_binary_map[x], **_HATEBR_META_KWARGS ) # Extracted from: # - https://github.com/ulysses-camara/ulysses-ner-br _ULYSSESNER_META_KWARGS = dict( description=textwrap.dedent( """\ UlyssesNER-Br is a corpus of Brazilian Legislative Documents for NER with quality baselines. The presented corpus consists of bills and legislative consultations from Brazilian Chamber of Deputies. UlyssesNER-Br has seven semantic classes or categories. Based on HAREM, we defined five typical categories: person, location, organization, event and date. In addition, we defined two specific semantic classes for the legislative domain: law foundation and law product. The law foundation category makes reference to entities related to laws, resolutions, decrees, as well as to domain-specific entities such as bills, which are law proposals being discussed by the parliament, and legislative consultations, also known as job requests made by the parliamentarians. The law product entity refers to systems, programs, and other products created from legislation.""" ), task_type="ner", citation=textwrap.dedent( """\ @InProceedings{10.1007/978-3-030-98305-5_1, 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.", editor="Pinheiro, Vl{\'a}dia and Gamallo, Pablo and Amaro, Raquel and Scarton, Carolina and Batista, Fernando and Silva, Diego and Magro, Catarina and Pinto, Hugo", title="UlyssesNER-Br: A Corpus of Brazilian Legislative Documents for Named Entity Recognition", booktitle="Computational Processing of the Portuguese Language", year="2022", publisher="Springer International Publishing", address="Cham", pages="3--14", isbn="978-3-030-98305-5" } @InProceedings{10.1007/978-3-031-16474-3_62, author="Costa, Rosimeire and Albuquerque, Hidelberg Oliveira and Silvestre, Gabriel and Silva, N{\'a}dia F{\'e}lix F. and Souza, Ellen and Vit{\'o}rio, Douglas and Nunes, Augusto and Siqueira, Felipe and Pedro Tarrega, Jo{\~a}o and Vitor Beinotti, Jo{\~a}o and de Souza Dias, M{\'a}rcio and Pereira, Fab{\'i}ola S. F. and Silva, Matheus and Gardini, Miguel and Silva, Vinicius and de Carvalho, Andr{\'e} C. P. L. F. and Oliveira, Adriano L. I.", editor="Marreiros, Goreti and Martins, Bruno and Paiva, Ana and Ribeiro, Bernardete and Sardinha, Alberto", title="Expanding UlyssesNER-Br Named Entity Recognition Corpus with Informal User-Generated Text", booktitle="Progress in Artificial Intelligence", year="2022", publisher="Springer International Publishing", address="Cham", pages="767--779", isbn="978-3-031-16474-3" }""" ), url="https://github.com/ulysses-camara/ulysses-ner-br", ) _ULYSSESNER_PL_KWARGS = dict( name = "UlyssesNER-Br-PL-coarse", data_urls = { "train": "https://github.com/ulysses-camara/ulysses-ner-br/raw/main/annotated-corpora/PL_corpus_conll/pl_corpus_categorias/train.txt", "validation": "https://github.com/ulysses-camara/ulysses-ner-br/raw/main/annotated-corpora/PL_corpus_conll/pl_corpus_categorias/valid.txt", "test": "https://github.com/ulysses-camara/ulysses-ner-br/raw/main/annotated-corpora/PL_corpus_conll/pl_corpus_categorias/test.txt", }, label_classes = ['DATA', 'EVENTO', 'FUNDAMENTO', 'LOCAL', 'ORGANIZACAO', 'PESSOA', 'PRODUTODELEI'], **_ULYSSESNER_META_KWARGS ) _ULYSSESNER_C_KWARGS = dict( name = "UlyssesNER-Br-C-coarse", data_urls = { "train": "https://github.com/ulysses-camara/ulysses-ner-br/raw/main/annotated-corpora/C_corpus_conll/c_corpus_categorias/train.txt", "validation": "https://github.com/ulysses-camara/ulysses-ner-br/raw/main/annotated-corpora/C_corpus_conll/c_corpus_categorias/valid.txt", "test": "https://github.com/ulysses-camara/ulysses-ner-br/raw/main/annotated-corpora/C_corpus_conll/c_corpus_categorias/test.txt", }, label_classes = ['DATA', 'EVENTO', 'FUNDAMENTO', 'LOCAL', 'ORGANIZACAO', 'PESSOA', 'PRODUTODELEI'], **_ULYSSESNER_META_KWARGS ) _ULYSSESNER_PL_TIPOS_KWARGS = dict( name = "UlyssesNER-Br-PL-fine", data_urls = { "train": "https://github.com/ulysses-camara/ulysses-ner-br/raw/main/annotated-corpora/PL_corpus_conll/pl_corpus_tipos/train.txt", "validation": "https://github.com/ulysses-camara/ulysses-ner-br/raw/main/annotated-corpora/PL_corpus_conll/pl_corpus_tipos/valid.txt", "test": "https://github.com/ulysses-camara/ulysses-ner-br/raw/main/annotated-corpora/PL_corpus_conll/pl_corpus_tipos/test.txt", }, label_classes = ['DATA', 'EVENTO', 'FUNDapelido', 'FUNDlei', 'FUNDprojetodelei', 'LOCALconcreto', 'LOCALvirtual', \ 'ORGgovernamental', 'ORGnaogovernamental', 'ORGpartido', 'PESSOAcargo', 'PESSOAgrupocargo', 'PESSOAindividual', \ 'PRODUTOoutros', 'PRODUTOprograma', 'PRODUTOsistema'], **_ULYSSESNER_META_KWARGS ) _ULYSSESNER_C_TIPOS_KWARGS = dict( name = "UlyssesNER-Br-C-fine", data_urls = { "train": "https://github.com/ulysses-camara/ulysses-ner-br/raw/main/annotated-corpora/C_corpus_conll/c_corpus_tipos/train.txt", "validation": "https://github.com/ulysses-camara/ulysses-ner-br/raw/main/annotated-corpora/C_corpus_conll/c_corpus_tipos/valid.txt", "test": "https://github.com/ulysses-camara/ulysses-ner-br/raw/main/annotated-corpora/C_corpus_conll/c_corpus_tipos/test.txt", }, label_classes = ['DATA', 'EVENTO', 'FUNDapelido', 'FUNDlei', 'FUNDprojetodelei', 'LOCALconcreto', 'LOCALvirtual', \ 'ORGgovernamental', 'ORGnaogovernamental', 'ORGpartido', 'PESSOAcargo', 'PESSOAgrupocargo', 'PESSOAgrupoind', \ 'PESSOAindividual', 'PRODUTOoutros', 'PRODUTOprograma', 'PRODUTOsistema'], **_ULYSSESNER_META_KWARGS ) _BRAZILIAN_COURT_DECISIONS_JUDGMENT = dict( name = "brazilian_court_decisions_judgment", task_type = "classification", data_urls = "joelito/brazilian_court_decisions", text_and_label_columns = ["decision_description", "judgment_label"], file_type="hf_dataset", url = "https://github.com/lagefreitas/predicting-brazilian-court-decisions", description =textwrap.dedent( """\ The dataset is a collection of 4043 Ementa (summary) court decisions and their metadata from the Tribunal de Justiça de Alagoas (TJAL, the State Supreme Court of Alagoas (Brazil). The court decisions are labeled according to 7 categories and whether the decisions were unanimous on the part of the judges or not. The dataset supports the task of Legal Judgment Prediction.""" ), citation = textwrap.dedent( """\ @article{Lage-Freitas2022, author = {Lage-Freitas, Andr{\'{e}} and Allende-Cid, H{\'{e}}ctor and Santana, Orivaldo and Oliveira-Lage, L{\'{i}}via}, doi = {10.7717/peerj-cs.904}, issn = {2376-5992}, journal = {PeerJ. Computer science}, keywords = {Artificial intelligence,Jurimetrics,Law,Legal,Legal NLP,Legal informatics,Legal outcome forecast,Litigation prediction,Machine learning,NLP,Portuguese,Predictive algorithms,judgement prediction}, language = {eng}, month = {mar}, pages = {e904--e904}, publisher = {PeerJ Inc.}, title = {{Predicting Brazilian Court Decisions}}, url = {https://pubmed.ncbi.nlm.nih.gov/35494851 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044329/}, volume = {8}, year = {2022} }""" ), label_classes = ["no", "partial", "yes"] ) _BRAZILIAN_COURT_DECISIONS_UNANIMITY = { **_BRAZILIAN_COURT_DECISIONS_JUDGMENT, "name": "brazilian_court_decisions_unanimity", "text_and_label_columns": ["decision_description", "unanimity_label"], "label_classes": ["unanimity", "not-unanimity"], } HAREM_BASE_KWARGS = dict( description=textwrap.dedent( """\ 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.""" ), task_type="ner", data_urls="harem", file_type="hf_dataset", text_and_label_columns = ["tokens", "ner_tags"], citation=textwrap.dedent( """\ @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} }""" ), url="https://www.linguateca.pt/primeiroHAREM/harem_coleccaodourada_en.html", ) HAREM_DEFAULT_KWARGS = dict( name = "harem-default", extra_configs = {"name": "default"}, label_classes = ["PESSOA", "ORGANIZACAO", "LOCAL", "TEMPO", "VALOR", "ABSTRACCAO", "ACONTECIMENTO", "COISA", "OBRA", "OUTRO"], **HAREM_BASE_KWARGS ) HAREM_SELECTIVE_KWARGS = dict( name = "harem-selective", extra_configs = {"name": "selective"}, label_classes = ["PESSOA", "ORGANIZACAO", "LOCAL", "TEMPO", "VALOR"], **HAREM_BASE_KWARGS ) _MAPA_BASE_KWARGS = dict( task_type = "ner", data_urls = "joelito/mapa", file_type="hf_dataset", url = "", description =textwrap.dedent( """\ The dataset consists of 12 documents (9 for Spanish due to parsing errors) taken from EUR-Lex, a multilingual corpus of court decisions and legal dispositions in the 24 official languages of the European Union. The documents have been annotated for named entities following the guidelines of the MAPA project which foresees two annotation level, a general and a more fine-grained one. The annotated corpus can be used for named entity recognition/classification.""" ), citation = textwrap.dedent( """\ @article{DeGibertBonet2022, author = {{de Gibert Bonet}, Ona and {Garc{\'{i}}a Pablos}, Aitor and Cuadros, Montse and Melero, Maite}, journal = {Proceedings of the Language Resources and Evaluation Conference}, number = {June}, pages = {3751--3760}, title = {{Spanish Datasets for Sensitive Entity Detection in the Legal Domain}}, url = {https://aclanthology.org/2022.lrec-1.400}, year = {2022} }""" ) ) _MAPA_BASE_KWARGS['filter'] = lambda item: item["language"] == "pt" _MAPA_COARSE_KWARGS = dict( name = "mapa_pt_coarse", text_and_label_columns = ["tokens", "coarse_grained"], label_classes = ['ADDRESS', 'AMOUNT', 'DATE', 'ORGANISATION', 'PERSON', 'TIME'], **_MAPA_BASE_KWARGS ) _MAPA_FINE_KWARGS = dict( name = "mapa_pt_fine", text_and_label_columns = ["tokens", "fine_grained"], label_classes = ['AGE', 'BUILDING', 'CITY', 'COUNTRY', 'DAY', 'ETHNIC CATEGORY', 'FAMILY NAME', 'INITIAL NAME', 'MARITAL STATUS', 'MONTH', 'NATIONALITY', 'PLACE', 'PROFESSION', 'ROLE', 'STANDARD ABBREVIATION', 'TERRITORY', 'TITLE', 'TYPE', 'UNIT', 'URL', 'VALUE', 'YEAR'], **_MAPA_BASE_KWARGS ) _MULTIEURLEX_BASE_KWARGS = dict( name = "multi_eurlex_pt", task_type = "multilabel_classification", data_urls = "multi_eurlex", file_type="hf_dataset", extra_configs = {"language": "pt", "label_level": "level_1"}, text_and_label_columns = ["text", "labels"], url = "https://github.com/nlpaueb/MultiEURLEX/", description =textwrap.dedent( """\ MultiEURLEX comprises 65k EU laws in 23 official EU languages. Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU. Each EUROVOC label ID is associated with a label descriptor, e.g., [60, agri-foodstuffs], [6006, plant product], [1115, fruit]. The descriptors are also available in the 23 languages. Chalkidis et al. (2019) published a monolingual (English) version of this dataset, called EUR-LEX, comprising 57k EU laws with the originally assigned gold labels.""" ), citation = textwrap.dedent( """\ @InProceedings{chalkidis-etal-2021-multieurlex, author = {Chalkidis, Ilias and Fergadiotis, Manos and Androutsopoulos, Ion}, title = {MultiEURLEX -- A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer}, booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing}, year = {2021}, publisher = {Association for Computational Linguistics}, location = {Punta Cana, Dominican Republic}, url = {https://arxiv.org/abs/2109.00904} }""" ), label_classes = [ "100149","100160","100148","100147","100152","100143","100156", "100158","100154","100153","100142","100145","100150","100162", "100159","100144","100151","100157","100161","100146","100155" ] ) # Extracted from: # - https://huggingface.co/datasets/ruanchaves/hatebr # - https://github.com/franciellevargas/HateBR _PORTUGUESE_HATE_SPEECH_META_KWARGS = dict( description=textwrap.dedent( """\ The dataset is composed of 5,668 tweets. For its annotation, we defined two different schemes used by annotators with different levels of expertise. Firstly, non-experts annotated the tweets with binary labels (‘hate’ vs. ‘no-hate’). Secondly, expert annotators classified the tweets following a fine-grained hierarchical multiple label scheme with 81 hate speech categories in total. The inter-annotator agreement varied from category to category, which reflects the insight that some types of hate speech are more subtle than others and that their detection depends on personal perception. This hierarchical annotation scheme is the main contribution of the presented work, as it facilitates the identification of different types of hate speech and their intersections. """ ), task_type="classification", file_type="csv", citation=textwrap.dedent( """\ @inproceedings{fortuna-etal-2019-hierarchically, title = "A Hierarchically-Labeled {P}ortuguese Hate Speech Dataset", author = "Fortuna, Paula and Rocha da Silva, Jo{\~a}o and Soler-Company, Juan and Wanner, Leo and Nunes, S{\'e}rgio", editor = "Roberts, Sarah T. and Tetreault, Joel and Prabhakaran, Vinodkumar and Waseem, Zeerak", booktitle = "Proceedings of the Third Workshop on Abusive Language Online", month = aug, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/W19-3510", doi = "10.18653/v1/W19-3510", pages = "94--104" }""" ), url="https://github.com/paulafortuna/Portuguese-Hate-Speech-Dataset" ) portuguese_hate_binary_map = { "0": "no-hate", "1": "hate", } _PORTUGUESE_HATE_SPEECH_BINARY_KWARGS = dict( name = "Portuguese_Hate_Speech_binary", data_urls={ "train": "https://raw.githubusercontent.com/paulafortuna/Portuguese-Hate-Speech-Dataset/master/2019-05-28_portuguese_hate_speech_binary_classification.csv" }, text_and_label_columns=["text", "hatespeech_comb"], label_classes=["no-hate", "hate"], process_label = lambda x: portuguese_hate_binary_map[x], indexes_url="metadata/portuguese_hate_speech_binary_indexes.json", **_PORTUGUESE_HATE_SPEECH_META_KWARGS ) class PTBenchmarkConfig(datasets.BuilderConfig): """BuilderConfig for PTBenchmark.""" def __init__( self, task_type: str, data_urls: Union[str, Dict[str, str]], citation: str, url: str, label_classes: Optional[List[Union[str, int]]] = None, file_type: Optional[str] = None, #filetype (csv, tsc, jsonl) text_and_label_columns: Optional[List[str]] = None, #columns for train, dev and test for csv datasets indexes_url: Optional[str] = None, #indexes for train, dev and test for single file datasets process_label: Callable[[str], str] = lambda x: x, filter: Callable = lambda x: True, extra_configs: Dict = {}, **kwargs, ): """BuilderConfig for GLUE. Args: text_features: `dict[string, string]`, map from the name of the feature dict for each text field to the name of the column in the tsv file label_column: `string`, name of the column in the tsv file corresponding to the label data_url: `string`, url to download the zip file from data_dir: `string`, the path to the folder containing the tsv files in the downloaded zip citation: `string`, citation for the data set url: `string`, url for information about the data set label_classes: `list[string]`, the list of classes if the label is categorical. If not provided, then the label will be of type `datasets.Value('float32')`. process_label: `Function[string, any]`, function taking in the raw value of the label and processing it to the form required by the label feature **kwargs: keyword arguments forwarded to super. """ super(PTBenchmarkConfig, self).__init__(version=datasets.Version("1.0.3", ""), **kwargs) self.label_classes = label_classes self.task_type = task_type self.data_urls = data_urls self.citation = citation self.url = url self.file_type = file_type self.text_and_label_columns = text_and_label_columns self.indexes_url = indexes_url self.process_label = process_label self.filter = filter self.extra_configs = extra_configs def _get_classification_features(config: PTBenchmarkConfig): return datasets.Features( { "idx": datasets.Value("int32"), "sentence": datasets.Value("string"), "label": datasets.features.ClassLabel(names=config.label_classes), } ) def _get_multilabel_classification_features(config: PTBenchmarkConfig): return datasets.Features( { "idx": datasets.Value("int32"), "sentence": datasets.Value("string"), "labels": datasets.Sequence( datasets.features.ClassLabel(names=config.label_classes) ), } ) def _get_ner_features(config: PTBenchmarkConfig): bio_labels = ["O"] for label_name in config.label_classes: bio_labels.append("B-" + label_name) bio_labels.append("I-" + label_name) return datasets.Features( { "idx": datasets.Value("int32"), "tokens": datasets.Sequence(datasets.Value("string")), "ner_tags": datasets.Sequence( datasets.features.ClassLabel(names=bio_labels) ), } ) def _get_rte_features(config: PTBenchmarkConfig): return datasets.Features( { "idx": datasets.Value("int32"), "sentence1": datasets.Value("string"), "sentence2": datasets.Value("string"), "label": datasets.features.ClassLabel(names=config.label_classes), } ) def _get_sts_features(config: PTBenchmarkConfig = None): return datasets.Features( { "idx": datasets.Value("int32"), "sentence1": datasets.Value("string"), "sentence2": datasets.Value("string"), "label": datasets.Value("float32"), } ) def _csv_generator(file_path: str, config: PTBenchmarkConfig, indexes_path: Optional[str] = None, split: Optional[str] = None ): """Yields examples.""" df = pd.read_csv(file_path) columns = config.text_and_label_columns df = df[columns] with open(indexes_path, "r") as f: indexes= json.load(f) split_indexes = indexes[split] df_split = df.iloc[split_indexes] for id_, row in df_split.iterrows(): example = { "idx": id_, "sentence": str(row[columns[0]]), "label": config.process_label(str(row[columns[-1]])) } yield id_, example def _conll_ner_generator(file_path: str, config: PTBenchmarkConfig): with open(file_path, encoding="utf-8") as f: guid = 0 tokens = [] ner_tags = [] for line in f: if line == "" or line == "\n": if tokens: # Filter for Ulysses empty data if len(tokens) == 1 and tokens[0] == '.': guid += 1 tokens = [] ner_tags = [] continue yield guid, { "idx": guid, "tokens": tokens, "ner_tags": ner_tags, } guid += 1 tokens = [] ner_tags = [] else: splits = line.split(" ") tokens.append(splits[0]) ner_tags.append(config.process_label(splits[1].rstrip())) # last example yield guid, { "idx": guid, "tokens": tokens, "ner_tags": ner_tags, } def _assin2_generator(file_path, config: PTBenchmarkConfig): """Yields examples.""" id_ = 0 with open(file_path, "rb") as f: tree = ET.parse(f) root = tree.getroot() task_type = config.task_type for pair in root: example = { "idx": int(pair.attrib.get("id")), "sentence1": pair.find(".//t").text, "sentence2": pair.find(".//h").text } if task_type == "rte": example["label"] = pair.attrib.get("entailment").upper() elif task_type == "sts": example["label"] = float(config.process_label(pair.attrib.get("similarity"))) yield id_, example id_ += 1 def _hf_dataset_generator(split, config: PTBenchmarkConfig): dataset = datasets.load_dataset(config.data_urls, split=split, **config.extra_configs) feature_col, label_col = config.text_and_label_columns target_feature_col, target_label_col = feature_col, label_col if config.task_type == "classification": target_feature_col, target_label_col = "sentence", "label" elif config.task_type == "multilabel_classification": target_feature_col, target_label_col = "sentence", "labels" elif config.task_type == "ner": target_feature_col, target_label_col = "tokens", "ner_tags" for id, item in enumerate(dataset): #filter invalid items if not config.filter(item): continue label = item[label_col] #Convert label to original text if isinstance(dataset.features[label_col], ClassLabel): if isinstance(label, list): label = [dataset.features[label_col].int2str(l) for l in label] else: label = dataset.features[label_col].int2str(label) #Process label if isinstance(label, list): label = [config.process_label(l) for l in label] else: label = config.process_label(label) #Filter out invalid classes if config.task_type != "ner": if isinstance(label, list): invalid = False for i in range(len(label)): if label[i] not in config.label_classes: invalid = True break if invalid: continue else: if label not in config.label_classes: continue yield id, { "idx": id, target_feature_col: item[feature_col], target_label_col: label, } class PTBenchmark(datasets.GeneratorBasedBuilder): BUILDER_CONFIGS = [ PTBenchmarkConfig( **CONFIG_KWARGS ) \ for CONFIG_KWARGS in \ [_LENERBR_KWARGS, _ASSIN2_RTE_KWARGS, _ASSIN2_STS_KWARGS, _HATEBR_BINARY_KWARGS, _HATEBR_LEVEL_KWARGS, _ULYSSESNER_PL_KWARGS, _ULYSSESNER_C_KWARGS, _ULYSSESNER_PL_TIPOS_KWARGS, _ULYSSESNER_C_TIPOS_KWARGS, _BRAZILIAN_COURT_DECISIONS_JUDGMENT, _BRAZILIAN_COURT_DECISIONS_UNANIMITY, HAREM_DEFAULT_KWARGS, HAREM_SELECTIVE_KWARGS, _MULTIEURLEX_BASE_KWARGS, _MAPA_COARSE_KWARGS, _MAPA_FINE_KWARGS, _PORTUGUESE_HATE_SPEECH_BINARY_KWARGS] ] def _info(self) -> datasets.DatasetInfo: features = None if self.config.task_type == "classification": features = _get_classification_features(self.config) elif self.config.task_type == "multilabel_classification": features = _get_multilabel_classification_features(self.config) elif self.config.task_type == "ner": features = _get_ner_features(self.config) elif self.config.task_type == "rte": features = _get_rte_features(self.config) elif self.config.task_type == "sts": features = _get_sts_features(self.config) return datasets.DatasetInfo( description=self.config.description, homepage=self.config.url, citation=self.config.citation, supervised_keys=None, features=features ) def _split_generators(self, dl_manager: datasets.DownloadManager): if self.config.file_type == 'hf_dataset': return [ datasets.SplitGenerator( name=split, gen_kwargs={"split": split}, # These kwargs will be passed to _generate_examples ) for split in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST] ] data_urls = self.config.data_urls.copy() if self.config.indexes_url is not None: data_urls['indexes'] = self.config.indexes_url file_paths = dl_manager.download_and_extract(data_urls) if self.config.indexes_url is None: return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"file_path": file_paths["train"]}, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"file_path": file_paths["validation"]}, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"file_path": file_paths["test"]}, ) ] else: return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"file_path": file_paths["train"], "indexes_path": file_paths["indexes"], "split": "train"}, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"file_path": file_paths["train"], "indexes_path": file_paths["indexes"], "split": "validation"}, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"file_path": file_paths["train"], "indexes_path": file_paths["indexes"], "split": "test"}, ) ] def _generate_examples( self, file_path: Optional[str] = None, indexes_path: Optional[str] = None, split: Optional[str] = None ): logger.info("⏳ Generating examples from = %s", file_path) if self.config.file_type == "hf_dataset": yield from _hf_dataset_generator(split, self.config) return if self.config.task_type == "classification": if self.config.file_type == "csv": yield from _csv_generator( file_path, self.config, indexes_path=indexes_path, split=split ) elif self.config.task_type == "multilabel_classification": pass elif self.config.task_type == "ner": yield from _conll_ner_generator(file_path, self.config) elif self.config.task_type == "rte": if "assin2" in self.config.name: yield from _assin2_generator(file_path, self.config) elif self.config.task_type == "sts": if "assin2" in self.config.name: yield from _assin2_generator(file_path, self.config)