Datasets:

EMBO
/

biolang

	# 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.


	# template from : https://github.com/huggingface/datasets/blob/master/templates/new_dataset_script.py

	"""Loading script for the biolang dataset for language modeling in biology."""

	from __future__ import absolute_import, division, print_function

	import json
	from pathlib import Path
	import datasets
	import shutil

	_CITATION = """\
	@Unpublished{
	huggingface: dataset,
	title = {biolang},
	authors={Thomas Lemberger, EMBO},
	year={2021}
	}
	"""

	_DESCRIPTION = """\
	This dataset is based on abstracts from the open access section of EuropePubMed Central to train language models in the domain of biology.
	"""

	_HOMEPAGE = "https://europepmc.org/downloads/openaccess"

	_LICENSE = "CC BY 4.0"

	_URLs = {
	"biolang": "https://huggingface.co/datasets/EMBO/biolang/resolve/main/oapmc_abstracts_figs.zip",
	}


	class BioLang(datasets.GeneratorBasedBuilder):
	"""BioLang: a dataset to train language models in biology."""

	VERSION = datasets.Version("0.0.1")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="MLM", version="0.0.1", description="Dataset for general masked language model."),
	datasets.BuilderConfig(name="DET", version="0.0.1", description="Dataset for part-of-speech (determinant) masked language model."),
	datasets.BuilderConfig(name="VERB", version="0.0.1", description="Dataset for part-of-speech (verbs) masked language model."),
	datasets.BuilderConfig(name="SMALL", version="0.0.1", description="Dataset for part-of-speech (determinants, conjunctions, prepositions, pronouns) masked language model."),
	]

	DEFAULT_CONFIG_NAME = "MLM" # It's not mandatory to have a default configuration. Just use one if it make sense.

	def _info(self):
	if self.config.name == "MLM":
	features = datasets.Features(
	{
	"input_ids": datasets.Sequence(feature=datasets.Value("int32")),
	"special_tokens_mask": datasets.Sequence(feature=datasets.Value("int8")),
	}
	)
	elif self.config.name in ["DET", "VERB", "SMALL"]:
	features = datasets.Features({
	"input_ids": datasets.Sequence(feature=datasets.Value("int32")),
	"tag_mask": datasets.Sequence(feature=datasets.Value("int8")),
	})

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features, # Here we define them above because they are different between the two configurations
	supervised_keys=('input_ids', 'pos_mask'),
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	if self.config.data_dir:
	data_dir = self.config.data_dir
	else:
	url = _URLs["biolang"]
	data_dir = dl_manager.download_and_extract(url)
	data_dir += "/oapmc_abstracts_figs"
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": data_dir + "/train.jsonl",
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": data_dir + "/test.jsonl",
	"split": "test"
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": data_dir + "/eval.jsonl",
	"split": "eval",
	},
	),
	]

	def _generate_examples(self, filepath, split):
	""" Yields examples. """
	with open(filepath, encoding="utf-8") as f:
	for id_, row in enumerate(f):
	data = json.loads(row)
	if self.config.name == "MLM":
	yield id_, {
	"input_ids": data["input_ids"],
	"special_tokens_mask": data['special_tokens_mask']
	}
	elif self.config.name == "DET":
	pos_mask = [0] * len(data['input_ids'])
	for idx, label in enumerate(data['label_ids']):
	if label == 'DET':
	pos_mask[idx] = 1
	yield id_, {
	"input_ids": data['input_ids'],
	"tag_mask": pos_mask,
	}
	elif self.config.name == "VERB":
	pos_mask = [0] * len(data['input_ids'])
	for idx, label in enumerate(data['label_ids']):
	if label == 'VERB':
	pos_mask[idx] = 1
	yield id_, {
	"input_ids": data['input_ids'],
	"tag_mask": pos_mask,
	}
	elif self.config.name == "SMALL":
	pos_mask = [0] * len(data['input_ids'])
	for idx, label in enumerate(data['label_ids']):
	if label in ['DET', 'CCONJ', 'SCONJ', 'ADP', 'PRON']:
	pos_mask[idx] = 1
	yield id_, {
	"input_ids": data['input_ids'],
	"tag_mask": pos_mask,
	}