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AFQMC

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AFQMC / AFQMC.py

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INIT: add afqmc dataset

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	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# 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.
	"""AFQMC"""


	import os
	import json
	import datasets


	_CITATION = """\
	"""

	_DESCRIPTION = """\
	Download from https://www.cluebenchmarks.com/introduce.html
	"""

	_LICENSE = "apache-license-2.0"
	_HOMEPAGE = "https://github.com/IDEA-CCNL/Fengshenbang-LM"


	class AFQMCConfig(datasets.BuilderConfig):
	"""BuilderConfig for AFQMCConfig"""

	def __init__(self, **kwargs):
	super().__init__(**kwargs)
	"""
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""


	class AFQMCConfig(datasets.GeneratorBasedBuilder):

	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIG_CLASS = AFQMCConfig
	BUILDER_CONFIGS = [
	AFQMCConfig(description=_DESCRIPTION)
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features({
	"sentence1": datasets.Value("string"),
	"sentence2": datasets.Value("string"),
	"label": datasets.ClassLabel(num_classes=2, names=['not similar', 'similar']),
	}),
	homepage=_HOMEPAGE,
	citation=_CITATION,
	license=_LICENSE
	)

	def _split_generators(self, dl_manager):

	files = {
	"test": os.path.join("afqmc_public", f"test.json"),
	"validation": os.path.join("afqmc_public", f"dev.json"),
	"train": os.path.join("afqmc_public", f"train.json"),
	}
	data_dir = dl_manager.download_and_extract(files)

	output = []
	test = datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": data_dir["test"]
	}
	)
	output.append(test)

	# if os.path.exists(data_dir["validation"]):
	valid = datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": data_dir["validation"]
	}
	)
	output.append(valid)

	train = datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": data_dir["train"]
	}
	)
	output.append(train)

	return output

	def _generate_examples(self, filepath):
	"""Yields examples."""
	with open(filepath, encoding="utf-8") as f:
	lines = f.readlines()
	for id_, line in enumerate(lines):
	data = json.loads(line)
	s = {
	'sentence1': data['sentence1'],
	'sentence2': data['sentence2'],
	'label': 'not similar',
	}
	if 'label' in data:
	s['label'] = 'not similar' if data['label'] == '0' else 'similar'
	yield id_, s