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import json |
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import datasets |
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import os |
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_CITATION = """\ |
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@misc{loureiro2021analysis, |
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title={Analysis and Evaluation of Language Models for Word Sense Disambiguation}, |
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author={Daniel Loureiro and Kiamehr Rezaee and Mohammad Taher Pilehvar and Jose Camacho-Collados}, |
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year={2021}, |
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eprint={2008.11608}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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""" |
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_DESCRIPTION = """\ |
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The CoarseWSD-20 dataset is a coarse-grained sense disambiguation built from Wikipedia |
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(nouns only) targetting 2 to 5 senses of 20 ambiguous words. It was specifically designed |
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to provide an ideal setting for evaluating WSD models (e.g. no senses in test sets missing |
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from training), both quantitavely and qualitatively. |
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""" |
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_URL = "https://huggingface.co/datasets/kiamehr74/CoarseWSD-20/raw/main/data" |
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_WORDS = ["apple", "arm", "bank", "bass", |
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"bow", "chair", "club", "crane", |
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"deck", "digit", "hood", "java", |
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"mole", "pitcher", "pound", "seal", |
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"spring", "square", "trunk", "yard"] |
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class CWSD20(datasets.GeneratorBasedBuilder): |
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"""TODO: Short description of my dataset.""" |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = [datasets.BuilderConfig(name=word, description=_DESCRIPTION) for word in _WORDS] |
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def _info(self): |
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dl_manager = datasets.DownloadManager() |
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cmap_path = dl_manager.download_and_extract( |
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url_or_urls=os.path.join(_URL, self.config.name, |
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"classes_map.txt") |
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) |
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with open(cmap_path) as cmap_f: |
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cmap = json.load(cmap_f) |
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label_classes = [cmap[str(k)] for k in range(len(cmap))] |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"idx": datasets.Value("int32"), |
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"sentence": datasets.Value("string"), |
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"label": datasets.features.ClassLabel(names=label_classes) |
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} |
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), |
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supervised_keys=None, |
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homepage="https://github.com/danlou/bert-disambiguation.git", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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dl = { |
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"train_ex":dl_manager.download_and_extract( |
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url_or_urls=os.path.join(_URL, self.config.name, |
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"train.data.txt") |
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), |
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"train_lb":dl_manager.download_and_extract( |
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url_or_urls=os.path.join(_URL, self.config.name, |
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"train.gold.txt") |
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), |
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"test_ex":dl_manager.download_and_extract( |
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url_or_urls=os.path.join(_URL, self.config.name, |
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"test.data.txt") |
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), |
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"test_lb":dl_manager.download_and_extract( |
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url_or_urls=os.path.join(_URL, self.config.name, |
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"test.gold.txt") |
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) |
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} |
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return [datasets.SplitGenerator(name=datasets.Split.TRAIN, |
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gen_kwargs={"ex": dl["train_ex"], "lb":dl["train_lb"] }), |
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datasets.SplitGenerator(name=datasets.Split.TEST, |
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gen_kwargs={"ex": dl["test_ex"], "lb":dl["test_lb"] })] |
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def _generate_examples(self, ex, lb): |
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"""Yields examples.""" |
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with open(ex, encoding="utf-8") as exf: |
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with open(lb, encoding="utf-8") as lbf: |
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for id_, (exi, lbi) in enumerate(zip(exf, lbf)): |
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example = {} |
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parts = exi.split("\t") |
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idx = parts[0] |
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sent = parts[1] |
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example["sentence"] = sent.strip() |
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example["idx"] = idx |
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example["label"] = int(lbi) |
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yield id_, example |
