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)