Add LeNER-Br dataset

portuguese_benchmark.py

@@ -0,0 +1,171 @@
+import textwrap
+import datasets
+from typing import Dict, List, Optional, Union
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_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",
+ "dev": "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/",
+)
+
+class PTBenchmarkConfig(datasets.BuilderConfig):
+ """BuilderConfig for PTBenchmark."""
+
+ def __init__(
+ self,
+ task_type,
+ data_urls,
+ citation,
+ url,
+ label_classes=None,
+ process_label=lambda x: x,
+ **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.process_label = process_label
+
+def _get_ner_dataset_info(config):
+ bio_labels = ["O"]
+ for label_name in config.label_classes:
+ bio_labels.append("B-" + label_name)
+ bio_labels.append("I-" + label_name)
+ return datasets.DatasetInfo(
+ description=config.description,
+ homepage=config.url,
+ citation=config.citation,
+ features=datasets.Features(
+ {
+ "id": datasets.Value("string"),
+ "tokens": datasets.Sequence(datasets.Value("string")),
+ "ner_tags": datasets.Sequence(
+ datasets.features.ClassLabel(names=bio_labels)
+ ),
+ }
+ )
+ )
+
+def _conll_ner_generator(file_path):
+ 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:
+ yield guid, {
+ "id": str(guid),
+ "tokens": tokens,
+ "ner_tags": ner_tags,
+ }
+ guid += 1
+ tokens = []
+ ner_tags = []
+ else:
+ splits = line.split(" ")
+ tokens.append(splits[0])
+ ner_tags.append(splits[1].rstrip())
+
+ # last example
+ yield guid, {
+ "id": str(guid),
+ "tokens": tokens,
+ "ner_tags": ner_tags,
+ }
+
+
+class PTBenchmark(datasets.GeneratorBasedBuilder):
+ BUILDER_CONFIGS = [
+ PTBenchmarkConfig(
+ **_LENERBR_KWARGS
+ )
+ ]
+
+ def _info(self) -> datasets.DatasetInfo:
+ if self.config.task_type == "ner":
+ return _get_ner_dataset_info(self.config)
+ 
+ def _split_generators(self, dl_manager: datasets.DownloadManager):
+ file_paths = dl_manager.download_and_extract(self.config.data_urls)
+ 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["dev"]},
+ ),
+ datasets.SplitGenerator(
+ name=datasets.Split.TEST,
+ gen_kwargs={"file_path": file_paths["test"]},
+ )
+ ]
+ 
+ def _generate_examples(
+ self,
+ file_path: Optional[str] = None
+ ):
+ logger.info("⏳ Generating examples from = %s", file_path)
+ if self.config.task_type == "ner":
+ yield from _conll_ner_generator(file_path)