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jnlpba

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jnlpba / jnlpba.py

gabrielaltay

upload hubscripts/jnlpba_hub.py to hub from bigbio repo

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	# 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.

	"""
	The data came from the GENIA version 3.02 corpus (Kim et al., 2003).
	This was formed from a controlled search on MEDLINE using the MeSH terms human, blood cells and transcription factors.
	From this search 2,000 abstracts were selected and hand annotated according to a small taxonomy of 48 classes based on
	a chemical classification. Among the classes, 36 terminal classes were used to annotate the GENIA corpus.
	"""

	from typing import Dict, List, Tuple

	import datasets

	from .bigbiohub import kb_features
	from .bigbiohub import BigBioConfig
	from .bigbiohub import Tasks

	_LANGUAGES = ['English']
	_PUBMED = True
	_LOCAL = False

	# TODO: Add BibTeX citation
	_CITATION = """\
	@inproceedings{collier-kim-2004-introduction,
	title = "Introduction to the Bio-entity Recognition Task at {JNLPBA}",
	author = "Collier, Nigel and Kim, Jin-Dong",
	booktitle = "Proceedings of the International Joint Workshop
	on Natural Language Processing in Biomedicine and its Applications
	({NLPBA}/{B}io{NLP})",
	month = aug # " 28th and 29th", year = "2004",
	address = "Geneva, Switzerland",
	publisher = "COLING",
	url = "https://aclanthology.org/W04-1213",
	pages = "73--78",
	}
	"""

	_DATASETNAME = "jnlpba"
	_DISPLAYNAME = "JNLPBA"

	_DESCRIPTION = """\
	NER For Bio-Entities
	"""

	_HOMEPAGE = "http://www.geniaproject.org/shared-tasks/bionlp-jnlpba-shared-task-2004"

	_LICENSE = 'Creative Commons Attribution 3.0 Unported'

	_URLS = {
	_DATASETNAME: "http://www.nactem.ac.uk/GENIA/current/Shared-tasks/JNLPBA/Train/Genia4ERtraining.tar.gz",
	}

	# TODO: add supported task by dataset. One dataset may support multiple tasks
	_SUPPORTED_TASKS = [
	Tasks.NAMED_ENTITY_RECOGNITION
	] # example: [Tasks.TRANSLATION, Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION]

	# TODO: set this to a version that is associated with the dataset. if none exists use "1.0.0"
	# This version doesn't have to be consistent with semantic versioning. Anything that is
	# provided by the original dataset as a version goes.
	_SOURCE_VERSION = "3.2.0"

	_BIGBIO_VERSION = "1.0.0"


	class JNLPBADataset(datasets.GeneratorBasedBuilder):
	"""
	The data came from the GENIA version 3.02 corpus
	(Kim et al., 2003).
	This was formed from a controlled search on MEDLINE
	using the MeSH terms human, blood cells and transcription factors.
	From this search 2,000 abstracts were selected and hand annotated
	according to a small taxonomy of 48 classes based on
	a chemical classification.
	Among the classes, 36 terminal classes were used to annotate the GENIA corpus.
	"""

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

	BUILDER_CONFIGS = [
	BigBioConfig(
	name="jnlpba_source",
	version=SOURCE_VERSION,
	description="jnlpba source schema",
	schema="source",
	subset_id="jnlpba",
	),
	BigBioConfig(
	name="jnlpba_bigbio_kb",
	version=BIGBIO_VERSION,
	description="jnlpba BigBio schema",
	schema="bigbio_kb",
	subset_id="jnlpba",
	),
	]

	DEFAULT_CONFIG_NAME = "jnlpba_source"

	def _info(self) -> datasets.DatasetInfo:

	if self.config.schema == "source":
	features = datasets.load_dataset("jnlpba", split="train").features

	elif self.config.schema == "bigbio_kb":
	features = kb_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."""
	data = datasets.load_dataset("jnlpba")

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# Whatever you put in gen_kwargs will be passed to _generate_examples
	gen_kwargs={"data": data["train"]},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"data": data["validation"]},
	),
	]

	def _generate_examples(self, data: datasets.Dataset) -> Tuple[int, Dict]:
	"""Yields examples as (key, example) tuples."""
	uid = 0

	if self.config.schema == "source":
	for key, sample in enumerate(data):
	yield key, sample

	elif self.config.schema == "bigbio_kb":
	for i, sample in enumerate(data):
	feature_dict = {
	"id": uid,
	"document_id": "NULL",
	"passages": [],
	"entities": [],
	"relations": [],
	"events": [],
	"coreferences": [],
	}

	uid += 1
	offset_start = 0
	for token, tag in zip(sample["tokens"], sample["ner_tags"]):
	offset_start += len(token) + 1
	feature_dict["entities"].append(
	{
	"id": uid,
	"offsets": [[offset_start, offset_start + len(token)]],
	"text": [token],
	"type": tag,
	"normalized": [],
	}
	)
	uid += 1

	# entities
	yield i, feature_dict