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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""The Chinese Natural Language Inference (NLI-zh-all) Corpus.
upload： https://github.com/shibing624
"""
import csv
import os
import json
import datasets
_CITATION = """https://github.com/shibing624/text2vec"""
_DESCRIPTION = """\
The SNLI corpus (version 1.0) is a merged chinese sentence similarity dataset, supporting the task of natural language
inference (NLI), also known as recognizing textual entailment (RTE).
"""
_DATA_URL = "https://huggingface.co/datasets/shibing624/nli-zh-all/resolve/main/sampled_data"
class Nli(datasets.GeneratorBasedBuilder):
"""The Chinese Natural Language Inference (NLI-zh-all) Corpus."""
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="plain_text",
version=datasets.Version("1.0.0", ""),
description="Plain text import of NLI-zh-all",
)
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text1": datasets.Value("string"),
"text2": datasets.Value("string"),
"label": datasets.Value("int64"),
}
),
supervised_keys=None,
homepage="https://github.com/shibing624/text2vec",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
files = ['simclue-train-2k.jsonl',
'nli_zh-train-25k.jsonl',
'alpaca_gpt4-train-2k.jsonl',
'cmrc2018-train-2k.jsonl',
'snli_zh-train-5k.jsonl',
'chatmed_consult-train-500.jsonl',
'zhihu_kol-train-2k.jsonl',
'cblue_chip_sts-train-2k.jsonl',
'csl-train-500.jsonl',
'webqa-train-500.jsonl',
'xlsum-train-1k.jsonl',]
data_files = [f"{_DATA_URL}/{i}" for i in files]
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"filepath": dl_manager.download_and_extract(data_files)}
),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
id = 0
if isinstance(filepath, str):
filepath = [filepath]
for file in filepath:
with open(file, encoding="utf-8") as f:
for key, row in enumerate(f):
data = json.loads(row)
yield id, {
"text1": data["text1"],
"text2": data["text2"],
"label": data["label"]
}
id += 1 |