nli_zh.py

"""Natural Language Inference (NLI) Chinese Corpus.(nli_zh)"""


import csv
import os

import datasets


_DESCRIPTION = """\
常见中文语义匹配数据集，包含ATEC、BQ、LCQMC、PAWSX、STS-B共5个任务。
"""


class Nli_zh(datasets.GeneratorBasedBuilder):
    """The Natural Language Inference Chinese(NLI_zh) Corpus."""

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="ATEC",
            version=datasets.Version("1.0.0", ""),
            description="Plain text import of NLI_zh",
        )
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "sentence1": datasets.Value("string"),
                    "sentence2": datasets.Value("string"),
                    "label": datasets.Value("int32"),
                }
            ),
            supervised_keys=None,
            homepage="https://github.com/shibing624/text2vec",
        )

    def _split_generators(self, dl_manager):
        dl_dir = dl_manager.download_and_extract(_DATA_URL)
        data_dir = os.path.join(dl_dir, "nli_zh")
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, "snli_1.0_test.txt")}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(data_dir, "snli_1.0_dev.txt")}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir, "snli_1.0_train.txt")}
            ),
        ]

    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form."""
        with open(filepath, encoding="utf-8") as f:
            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
            for idx, row in enumerate(reader):
                label = -1 if row["gold_label"] == "-" else row["gold_label"]
                yield idx, {
                    "premise": row["sentence1"],
                    "hypothesis": row["sentence2"],
                    "label": label,
                }