Datasets:

bigbio
/

bio_simlex

	# coding=utf-8
	# Copyright 2022 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.

	"""
	Bio-SimLex enables intrinsic evaluation of word representations. This evaluation
	can serve as a predictor of performance on various downstream tasks in the
	biomedical domain. The results on Bio-SimLex using standard word representation
	models highlight the importance of developing dedicated evaluation resources for
	NLP in biomedicine for particular word classes (e.g. verbs).
	"""

	from typing import Dict, List, Tuple

	import datasets

	from .bigbiohub import pairs_features
	from .bigbiohub import BigBioConfig
	from .bigbiohub import Tasks

	_LANGUAGES = ['English']
	_PUBMED = True
	_LOCAL = False
	_CITATION = """\
	@article{article,
	title = {
	Bio-SimVerb and Bio-SimLex: Wide-coverage evaluation sets of word
	similarity in biomedicine
	},
	author = {Chiu, Billy and Pyysalo, Sampo and Vulić, Ivan and Korhonen, Anna},
	year = 2018,
	month = {02},
	journal = {BMC Bioinformatics},
	volume = 19,
	pages = {},
	doi = {10.1186/s12859-018-2039-z}
	}
	"""

	_DATASETNAME = "bio_simlex"
	_DISPLAYNAME = "Bio-SimLex"

	_DESCRIPTION = """\
	Bio-SimLex enables intrinsic evaluation of word representations. This evaluation \
	can serve as a predictor of performance on various downstream tasks in the \
	biomedical domain. The results on Bio-SimLex using standard word representation \
	models highlight the importance of developing dedicated evaluation resources for \
	NLP in biomedicine for particular word classes (e.g. verbs).
	"""

	_HOMEPAGE = "https://github.com/cambridgeltl/bio-simverb"

	_LICENSE = 'License information unavailable'

	_URLS = {
	_DATASETNAME: "https://github.com/cambridgeltl/bio-simverb/blob/master/wvlib/word-similarities/\
	bio-simlex/Bio-SimLex.txt?raw=true"
	}

	_SUPPORTED_TASKS = [Tasks.SEMANTIC_SIMILARITY]

	_SOURCE_VERSION = "1.0.0"
	_BIGBIO_VERSION = "1.0.0"


	class BioSimlexDataset(datasets.GeneratorBasedBuilder):
	"""
	Bio-SimLex enables intrinsic evaluation of word representations. Config schema
	as source gives score between 0-10 for pairs of words. The source schema casts
	labels as `float`, but the bigbio schema casts them as `str`.
	"""

	SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
	BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)

	BUILDER_CONFIGS = [
	BigBioConfig(
	name="bio_simlex_source",
	version=SOURCE_VERSION,
	description="bio_simlex source schema",
	schema="source",
	subset_id="bio_simlex",
	),
	BigBioConfig(
	name="bio_simlex_bigbio_pairs",
	version=BIGBIO_VERSION,
	description="bio_simlex BigBio schema",
	schema="bigbio_pairs",
	subset_id="bio_simlex",
	),
	]

	DEFAULT_CONFIG_NAME = "bio_simlex_source"

	def _info(self) -> datasets.DatasetInfo:

	if self.config.schema == "source":
	features = datasets.Features(
	{
	"text_1": datasets.Value("string"),
	"text_2": datasets.Value("string"),
	"score": datasets.Value("float32"),
	}
	)

	elif self.config.schema == "bigbio_pairs":
	features = pairs_features

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	homepage=_HOMEPAGE,
	license=str(_LICENSE),
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
	"""Returns SplitGenerators."""

	url = _URLS[_DATASETNAME]
	data_dir = dl_manager.download_and_extract(url)

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

	def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]:
	"""Yields examples as (key, example) tuples."""
	with open(filepath, "r", encoding="utf-8") as f:
	for id_, line in enumerate(f):
	word1, word2, score = line.split("\t")
	if self.config.schema == "source":
	yield id_, {
	"text_1": word1,
	"text_2": word2,
	"score": float(score),
	}

	elif self.config.schema == "bigbio_pairs":
	yield id_, {
	"id": str(id_),
	"document_id": str(id_),
	"text_1": word1,
	"text_2": word2,
	"label": str(score.strip()),
	}