portuguese_benchmark.py

import textwrap
import datasets
from typing import Dict, List, Optional, Union

import xml.etree.ElementTree as ET

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",
        "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/",
)

# 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",
        "dev": "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
)


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 _get_rte_dataset_info(config):
    return datasets.DatasetInfo(
            description=config.description,
            homepage=config.url,
            citation=config.citation,
            features=datasets.Features(
                {
                    "id": datasets.Value("int32"),
                    "sentence1": datasets.Value("string"),
                    "sentence2": datasets.Value("string"),
                    "label": datasets.features.ClassLabel(names=config.label_classes),
                }
            )
        )

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,
        }

def _assin2_rte_generator(file_path):
    """Yields examples."""
    id_ = 0

    with open(file_path, "rb") as f:

        tree = ET.parse(f)
        root = tree.getroot()

        for pair in root:

            yield id_, {
                "id": int(pair.attrib.get("id")),
                "sentence1": pair.find(".//t").text,
                "sentence2": pair.find(".//h").text,
                #"relatedness_score": float(pair.attrib.get("similarity")),
                "label": pair.attrib.get("entailment").upper(),
            }

            id_ += 1


class PTBenchmark(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        PTBenchmarkConfig(
            **_LENERBR_KWARGS
        ),
        PTBenchmarkConfig(
            **_ASSIN2_RTE_KWARGS
        )
    ]

    def _info(self) -> datasets.DatasetInfo:
        if self.config.task_type == "ner":
            return _get_ner_dataset_info(self.config)
        elif self.config.task_type == "rte":
            return _get_rte_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)
        elif self.config.task_type == "rte":
            if "assin2" in self.config.name:
                yield from _assin2_rte_generator(file_path)