SDU-test.py

import os

import datasets
from typing import List
import json

logger = datasets.logging.get_logger(__name__)


_CITATION = """
"""

_DESCRIPTION = """
This is the dataset repository for SDU Dataset from SDU workshop at AAAI22.
The dataset can help build sequence labelling models for the task Abbreviation Detection.
"""

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

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


class SDUtestConfig(datasets.GeneratorBasedBuilder):
    """SDU Filtered dataset."""

    BUILDER_CONFIGS = [
        SDUtestConfig(name="SDUtest", version=datasets.Version("0.0.2"), description="SDU test dataset"),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "ner_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                "B-O",
                                "B-AC",
                                "I-AC",
                                "B-LF",
                                "I-LF"
                            ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
            homepage="",
            citation=_CITATION,
        )

    _URL = "https://huggingface.co/datasets/surrey-nlp/SDU-test/raw/main/"
    _URLS = {
        "train+dev": _URL + "sdu_data_trunc.json",
        #"dev": _URL + "PLOS-val15-filtered-pos_bio.json",
        #"test": _URL + "PLOS-test15-filtered-pos_bio.json"
    }

    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
        urls_to_download = self._URLS
        downloaded_files = dl_manager.download_and_extract(urls_to_download)

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

    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form."""
        logger.info("generating examples from = %s", filepath)
        with open(filepath) as f:
            plod = json.load(f)
            for object in plod:
                id_ = int(object['id'])
                yield id_, {
                    "id": str(id_),
                    "tokens": object['tokens'],
                    #"pos_tags": object['pos_tags'],
                    "ner_tags": object['ner_tags'],
                }