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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.
# 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
import datasets
class BioLang(datasets.GeneratorBasedBuilder):
"""BioLang: a dataset to train language models in biology."""
_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@Unpublished{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",
}
VERSION = datasets.Version("0.0.1")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="SEQ2SEQ", version="0.0.1", description="Control dataset with no masking for seq2seq task."),
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."),
datasets.BuilderConfig(name="NOUN", version="0.0.1", description="Dataset for part-of-speech (nouns) 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", "NOUN", "NULL"]:
features = datasets.Features({
"input_ids": datasets.Sequence(feature=datasets.Value("int32")),
"tag_mask": datasets.Sequence(feature=datasets.Value("int8")),
})
elif self.config.name == "SEQ2SEQ":
features = datasets.Features({
"input_ids": datasets.Sequence(feature=datasets.Value("int32")),
"labels": datasets.Sequence(feature=datasets.Value("int32"))
})
return datasets.DatasetInfo(
description=self._DESCRIPTION,
features=features, # Here we define them above because they are different between the two configurations
supervised_keys=('input_ids', 'pos_mask'),
homepage=self._HOMEPAGE,
license=self._LICENSE,
citation=self._CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
if self.config.data_dir:
data_dir = self.config.data_dir
else:
url = self._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']
}
# else Part of Speech tags based on
# Universal POS tags https://universaldependencies.org/u/pos/
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,
}
elif self.config.name == "NOUN":
pos_mask = [0] * len(data['input_ids'])
for idx, label in enumerate(data['label_ids']):
if label in ['NOUN']:
pos_mask[idx] = 1
yield id_, {
"input_ids": data['input_ids'],
"tag_mask": pos_mask,
}
elif self.config.name == "SEQ2SEQ":
"Seq2seq training needs the input_ids as labels, no masking"
pos_mask = [0] * len(data['input_ids'])
yield id_, {
"input_ids": data['input_ids'],
"labels": data['input_ids']
}
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