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from __future__ import absolute_import, division, print_function |
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import logging |
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import datasets |
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_CITATION = """ |
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MiniNLP Data |
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""" |
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_DESCRIPTION = """ |
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MiniNLP Data |
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""" |
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_URLS = { |
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"train": "train.tsv", |
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"dev": "dev.tsv", |
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"test": "test.tsv", |
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} |
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class MiniNLPConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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""" |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super().__init__(**kwargs) |
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class MiniNLP(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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MiniNLPConfig( |
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name="MiniNLP", |
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version=datasets.Version("1.0.0", ""), |
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description="MiniNLP Dataset For Models", |
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), |
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] |
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def _info(self): |
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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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"id": datasets.Value("int32"), |
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"num": datasets.Value("int32"), |
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"query": datasets.Value("string"), |
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"doc": datasets.Value("string"), |
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"label": datasets.Value("string"), |
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"score": datasets.Value("float32"), |
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} |
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), |
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supervised_keys=None, |
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homepage="https://fuliucansheng.github.io/", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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downloaded_files = dl_manager.download_and_extract(_URLS) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"filepath": downloaded_files["train"]}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={"filepath": downloaded_files["dev"]}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"filepath": downloaded_files["test"]}, |
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), |
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] |
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def _generate_examples(self, filepath): |
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"""This function returns the examples in the raw (text) form.""" |
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logging.info("generating examples from = %s", filepath) |
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names = ["num", "query", "doc", "label", "score"] |
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with open(filepath, encoding="utf-8") as f: |
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for id_, line in enumerate(f): |
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values = line.strip("\n").split("\t") |
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row_dict = dict(zip(names, values)) |
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yield id_, { |
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"id": id_, |
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"num": int(row_dict.get("num")), |
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"query": row_dict.get("query"), |
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"doc": row_dict.get("doc"), |
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"label": row_dict.get("label"), |
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"score": float(row_dict.get("score")), |
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} |
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