Datasets:
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"""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,
} |