Add Ulysses-C, brazillian_court_decisions and HAREM

portuguese_benchmark.py

@@ -1,4 +1,5 @@
 import datasets
+from datasets import ClassLabel
 from typing import Dict, List, Optional, Union, Callable
 import json
 import textwrap
@@ -158,9 +159,6 @@ _ULYSSESNER_META_KWARGS = dict(
  from legislation."""
  ),
  task_type="ner",
- label_classes=['DATA', 'EVENTO', 'FUNDapelido', 'FUNDlei', 'FUNDprojetodelei', 'LOCALconcreto', 'LOCALvirtual', \
- 'ORGgovernamental', 'ORGnaogovernamental', 'ORGpartido', 'PESSOAcargo', 'PESSOAgrupocargo', 'PESSOAindividual', \
- 'PRODUTOoutros', 'PRODUTOprograma', 'PRODUTOsistema'],
  citation=textwrap.dedent(
  """\
  @InProceedings{10.1007/978-3-030-98305-5_1,
@@ -235,21 +233,138 @@ _ULYSSESNER_META_KWARGS = dict(
 )
 _ULYSSESNER_PL_KWARGS = dict(
  name = "UlyssesNER-Br-PL",
+ 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",
+ 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-tipos",
  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-tipos",
+ 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",
+ hf_config_name = "default",
+ label_classes = ["PESSOA", "ORGANIZACAO", "LOCAL", "TEMPO", "VALOR", "ABSTRACCAO", "ACONTECIMENTO", "COISA", "OBRA", "OUTRO"],
+ **HAREM_BASE_KWARGS
+)
+HAREM_SELECTIVE_KWARGS = dict(
+ name = "harem-selective",
+ hf_config_name = "selective",
+ label_classes = ["PESSOA", "ORGANIZACAO", "LOCAL", "TEMPO", "VALOR"],
+ **HAREM_BASE_KWARGS
+)
+
 class PTBenchmarkConfig(datasets.BuilderConfig):
  """BuilderConfig for PTBenchmark."""
 
  def __init__(
  self,
  task_type: str,
- data_urls: Dict[str, str],
+ data_urls: Union[str, Dict[str, str]],
  citation: str,
  url: str,
  label_classes: Optional[List[Union[str, int]]] = None,
@@ -257,6 +372,7 @@ class PTBenchmarkConfig(datasets.BuilderConfig):
  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: Optional[Callable[[str], str]] = lambda x: x,
+ hf_config_name = "default",
  **kwargs,
  ):
  """BuilderConfig for GLUE.
@@ -287,6 +403,7 @@ class PTBenchmarkConfig(datasets.BuilderConfig):
  self.text_and_label_columns = text_and_label_columns
  self.indexes_url = indexes_url
  self.process_label = process_label
+ self.hf_config_name = hf_config_name
 
 def _get_classification_features(config: PTBenchmarkConfig):
  return datasets.Features(
@@ -421,20 +538,13 @@ def _assin2_generator(file_path, config: PTBenchmarkConfig):
 class PTBenchmark(datasets.GeneratorBasedBuilder):
  BUILDER_CONFIGS = [
  PTBenchmarkConfig(
- **_LENERBR_KWARGS
- ),
- PTBenchmarkConfig(
- **_ASSIN2_RTE_KWARGS
- ),
- PTBenchmarkConfig(
- **_ASSIN2_STS_KWARGS
- ),
- PTBenchmarkConfig(
- **_HATEBR_KWARGS
- ),
- PTBenchmarkConfig(
- **_ULYSSESNER_PL_KWARGS
- )
+ **CONFIG_KWARGS
+ ) \
+ for CONFIG_KWARGS in \
+ [_LENERBR_KWARGS, _ASSIN2_RTE_KWARGS, _ASSIN2_STS_KWARGS, _HATEBR_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]
  ]
 
  def _info(self) -> datasets.DatasetInfo:
@@ -457,6 +567,14 @@ class PTBenchmark(datasets.GeneratorBasedBuilder):
  )
  
  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
@@ -500,6 +618,10 @@ class PTBenchmark(datasets.GeneratorBasedBuilder):
  split: Optional[str] = None
  ):
  logger.info("⏳ Generating examples from = %s", file_path)
+ if self.config.file_type == "hf_dataset":
+ dataset = datasets.load_dataset(self.config.data_urls, self.config.hf_config_name, split=split)
+ text_col, label_col = self.config.text_and_label_columns
+
  if self.config.task_type == "classification":
  if self.config.file_type == "csv":
  yield from _csv_generator(
@@ -508,8 +630,32 @@ class PTBenchmark(datasets.GeneratorBasedBuilder):
  indexes_path=indexes_path,
  split=split
  )
+ elif self.config.file_type == "hf_dataset":
+ for id, item in enumerate(dataset):
+ label = item[label_col]
+ if label not in self.config.label_classes:
+ continue # filter out invalid classes to construct ClassLabel
+ if isinstance(dataset.features[label_col], ClassLabel):
+ label = dataset.features[label_col].int2str(label)
+ yield id, {
+ "idx": id,
+ "sentence": item[text_col],
+ "label": self.config.process_label(label),
+ }
  elif self.config.task_type == "ner":
- yield from _conll_ner_generator(file_path, self.config)
+ if self.config.file_type == "hf_dataset":
+ for id, item in enumerate(dataset):
+ tags = item[label_col]
+ if isinstance(dataset.features[label_col], ClassLabel):
+ for i in range(len(tags)):
+ tags[i] = self.config.process_label(dataset.features[label_col].int2str(tags[i]))
+ yield id, {
+ "idx": id,
+ "tokens": item[text_col],
+ "ner_tags": tags,
+ }
+ else:
+ 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)