CoarseWSD-20.py

import json
import datasets
import os

_CITATION = """\
@misc{loureiro2021analysis,
      title={Analysis and Evaluation of Language Models for Word Sense Disambiguation}, 
      author={Daniel Loureiro and Kiamehr Rezaee and Mohammad Taher Pilehvar and Jose Camacho-Collados},
      year={2021},
      eprint={2008.11608},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
"""

_DESCRIPTION = """\
The CoarseWSD-20 dataset is a coarse-grained sense disambiguation built from Wikipedia 
(nouns only) targetting 2 to 5 senses of 20 ambiguous words. It was specifically designed 
to provide an ideal setting for evaluating WSD models (e.g. no senses in test sets missing 
from training), both quantitavely and qualitatively.
"""

_URL = "https://huggingface.co/datasets/kiamehr74/CoarseWSD-20/raw/main/data"

_WORDS = ["apple", "arm", "bank", "bass",
         "bow", "chair", "club", "crane",
         "deck", "digit", "hood", "java",
         "mole", "pitcher", "pound", "seal",
         "spring", "square", "trunk", "yard"]


class CWSD20(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    # TODO: Set up version.
    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [datasets.BuilderConfig(name=word, description=_DESCRIPTION) for word in _WORDS]

    def _info(self):
      dl_manager = datasets.DownloadManager()
      cmap_path = dl_manager.download_and_extract(
                url_or_urls=os.path.join(_URL, self.config.name,
                "classes_map.txt")
                )        
      with open(cmap_path) as cmap_f:
        cmap = json.load(cmap_f)
      label_classes = [cmap[str(k)] for k in range(len(cmap))]
      
      # Specifies the datasets.DatasetInfo object
      return datasets.DatasetInfo(
          # This is the description that will appear on the datasets page.
          description=_DESCRIPTION,
          # datasets.features.FeatureConnectors
          features=datasets.Features(
              {
                  "idx": datasets.Value("int32"),
                  "sentence": datasets.Value("string"),
                  "label": datasets.features.ClassLabel(names=label_classes)
              }
          ),
          # If there's a common (input, target) tuple from the features,
          # specify them here. They'll be used if as_supervised=True in
          # builder.as_dataset.
          supervised_keys=None,
          # Homepage of the dataset for documentation
          homepage="https://github.com/danlou/bert-disambiguation.git",
          citation=_CITATION,
      )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        dl = {
            "train_ex":dl_manager.download_and_extract(
                url_or_urls=os.path.join(_URL, self.config.name,
                "train.data.txt")
                ),
            "train_lb":dl_manager.download_and_extract(
                url_or_urls=os.path.join(_URL, self.config.name,
                "train.gold.txt")
                ),
            "test_ex":dl_manager.download_and_extract(
                url_or_urls=os.path.join(_URL, self.config.name,
                "test.data.txt")
                ),
            "test_lb":dl_manager.download_and_extract(
                url_or_urls=os.path.join(_URL, self.config.name,
                "test.gold.txt")
                )
        }
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, 
                gen_kwargs={"ex": dl["train_ex"], "lb":dl["train_lb"] }),
                datasets.SplitGenerator(name=datasets.Split.TEST, 
                gen_kwargs={"ex": dl["test_ex"], "lb":dl["test_lb"] })]

    def _generate_examples(self, ex, lb):
      """Yields examples."""
      with open(ex, encoding="utf-8") as exf:
        with open(lb, encoding="utf-8") as lbf:

          for id_, (exi, lbi) in enumerate(zip(exf, lbf)):
            example = {}
            parts = exi.split("\t")
            idx = parts[0]
            sent = parts[1]
            example["sentence"] = sent.strip()
            example["idx"] = idx
            example["label"] = int(lbi)

            yield id_, example