PLOD-filtered.py

import os

import datasets


logger = datasets.logging.get_logger(__name__)


_CITATION = """
"""

_DESCRIPTION = """
This is the dataset repository for PLOD Dataset accepted to be published at LREC 2022.
The dataset can help build sequence labelling models for the task Abbreviation Detection.
"""

_TRAINING_FILE_URL = "https://huggingface.co/datasets/surrey-nlp/PLOD-filtered/resolve/main/data/PLOS-train70-filtered-pos_bio.json"
_DEV_FILE_URL = "https://huggingface.co/datasets/surrey-nlp/PLOD-filtered/resolve/main/data/PLOS-val15-filtered-pos_bio.json"
_TEST_FILE_URL = "https://huggingface.co/datasets/surrey-nlp/PLOD-filtered/resolve/main/data/PLOS-test15-filtered-pos_bio.json"

_TRAINING_FILE = "PLOS-train70-filtered-pos_bio.json"
_DEV_FILE = "PLOS-val15-filtered-pos_bio.json"
_TEST_FILE = "PLOS-test15-filtered-pos_bio.json"

class PLODfilteredConfig(datasets.BuilderConfig):
    """BuilderConfig for Conll2003"""

    def __init__(self, **kwargs):
        """BuilderConfig forConll2003.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(PLODfilteredConfig, self).__init__(**kwargs)


class PLODfilteredConfig(datasets.GeneratorBasedBuilder):
    """PLOD Filtered dataset."""

    BUILDER_CONFIGS = [
        PLODfilteredConfig(name="PLODfiltered", version=datasets.Version("0.0.2"), description="PLOD filtered dataset"),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "pos_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                "ADJ",
                                "ADP",
                                "ADV",
                                "AUX",
                                "CONJ",
                                "CCONJ",
                                "DET",
                                "INTJ",
                                "NOUN",
                                "NUM",
                                "PART",
                                "PRON",
                                "PROPN",
                                "PUNCT",
                                "SCONJ",
                                "SYM",
                                "VERB",
                                "X",
                                "SPACE"
                            ]
                        )
                    ),
                    "ner_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                "B-O",
                                "B-AC",
                                "I-AC",
                                "B-LF",
                                "I-LF"
                            ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
            homepage="https://github.com/surrey-nlp/PLOD-AbbreviationDetection",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        downloaded_train = dl_manager.download_and_extract(_TRAINING_FILE_URL)
        downloaded_val = dl_manager.download_and_extract(_DEV_FILE_URL)
        downloaded_test = dl_manager.download_and_extract(_TEST_FILE_URL)

        data_files = {
            "train": _TRAINING_FILE,
            "dev": _DEV_FILE,
            "test": _TEST_FILE,
        }

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files["dev"]}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_files["test"]}),
        ]

    def _generate_examples(self, filepath):
        logger.info("⏳ Generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            guid = 0
            tokens = []
            pos_tags = []
            ner_tags = []
            for line in f:
                if line.startswith("-DOCSTART-") or line == "" or line == "\n":
                    if tokens:
                        yield guid, {
                            "id": str(guid),
                            "tokens": tokens,
                            "pos_tags": pos_tags,
                            "ner_tags": ner_tags,
                        }
                        guid += 1
                        tokens = []
                        pos_tags = []
                        ner_tags = []
                else:
                    # conll2003 tokens are space separated
                    splits = line.split(" ")
                    tokens.append(splits[0])
                    pos_tags.append(splits[1].strip())
                    ner_tags.append(splits[2].strip())
            # last example
            yield guid, {
                "id": str(guid),
                "tokens": tokens,
                "pos_tags": pos_tags,
                "ner_tags": ner_tags,
            }