Datasets:
amttl

	# coding=utf-8
	# Copyright 2020 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
	"""Introduction to AMTTL CWS Dataset"""

	import datasets


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@inproceedings{xing2018adaptive,
	title={Adaptive multi-task transfer learning for Chinese word segmentation in medical text},
	author={Xing, Junjie and Zhu, Kenny and Zhang, Shaodian},
	booktitle={Proceedings of the 27th International Conference on Computational Linguistics},
	pages={3619--3630},
	year={2018}
	}
	"""

	_DESCRIPTION = """\
	Chinese word segmentation (CWS) trained from open source corpus faces dramatic performance drop
	when dealing with domain text, especially for a domain with lots of special terms and diverse
	writing styles, such as the biomedical domain. However, building domain-specific CWS requires
	extremely high annotation cost. In this paper, we propose an approach by exploiting domain-invariant
	knowledge from high resource to low resource domains. Extensive experiments show that our mode
	achieves consistently higher accuracy than the single-task CWS and other transfer learning
	baselines, especially when there is a large disparity between source and target domains.

	This dataset is the accompanied medical Chinese word segmentation (CWS) dataset.
	The tags are in BIES scheme.

	For more details see https://www.aclweb.org/anthology/C18-1307/
	"""

	_URL = "https://raw.githubusercontent.com/adapt-sjtu/AMTTL/master/medical_data/"
	_TRAINING_FILE = "forum_train.txt"
	_DEV_FILE = "forum_dev.txt"
	_TEST_FILE = "forum_test.txt"


	class AmttlConfig(datasets.BuilderConfig):
	"""BuilderConfig for AMTTL"""

	def __init__(self, **kwargs):
	"""BuilderConfig for AMTTL.

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


	class Amttl(datasets.GeneratorBasedBuilder):
	"""AMTTL Chinese Word Segmentation dataset."""

	BUILDER_CONFIGS = [
	AmttlConfig(
	name="amttl",
	version=datasets.Version("1.0.0"),
	description="AMTTL medical Chinese word segmentation dataset",
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"tokens": datasets.Sequence(datasets.Value("string")),
	"tags": datasets.Sequence(
	datasets.features.ClassLabel(
	names=[
	"B",
	"I",
	"E",
	"S",
	]
	)
	),
	}
	),
	supervised_keys=None,
	homepage="https://www.aclweb.org/anthology/C18-1307/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	urls_to_download = {
	"train": f"{_URL}{_TRAINING_FILE}",
	"dev": f"{_URL}{_DEV_FILE}",
	"test": f"{_URL}{_TEST_FILE}",
	}
	downloaded_files = dl_manager.download_and_extract(urls_to_download)

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
	]

	def _generate_examples(self, filepath):
	logger.info("⏳ Generating examples from = %s", filepath)
	with open(filepath, encoding="utf-8") as f:
	guid = 0
	tokens = []
	tags = []
	for line in f:
	line_stripped = line.strip()
	if line_stripped == "":
	if tokens:
	yield guid, {
	"id": str(guid),
	"tokens": tokens,
	"tags": tags,
	}
	guid += 1
	tokens = []
	tags = []
	else:
	splits = line_stripped.split("\t")
	if len(splits) == 1:
	splits.append("O")
	tokens.append(splits[0])
	tags.append(splits[1])
	# last example
	yield guid, {
	"id": str(guid),
	"tokens": tokens,
	"tags": tags,
	}