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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.
from pathlib import Path
from typing import Iterable, List
import datasets
from .bigbiohub import kb_features
from .bigbiohub import BigBioConfig
from .bigbiohub import Tasks
from .bigbiohub import parse_brat_file
from .bigbiohub import brat_parse_to_bigbio_kb
_DATASETNAME = "bionlp_st_2013_gro"
_DISPLAYNAME = "BioNLP 2013 GRO"
_SOURCE_VIEW_NAME = "source"
_UNIFIED_VIEW_NAME = "bigbio"
_LANGUAGES = ['English']
_PUBMED = True
_LOCAL = False
_CITATION = """\
@inproceedings{kim-etal-2013-gro,
title = "{GRO} Task: Populating the Gene Regulation Ontology with events and relations",
author = "Kim, Jung-jae and
Han, Xu and
Lee, Vivian and
Rebholz-Schuhmann, Dietrich",
booktitle = "Proceedings of the {B}io{NLP} Shared Task 2013 Workshop",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W13-2007",
pages = "50--57",
}
"""
_DESCRIPTION = """\
GRO Task: Populating the Gene Regulation Ontology with events and
relations. A data set from the bio NLP shared tasks competition from 2013
"""
_HOMEPAGE = "https://github.com/openbiocorpora/bionlp-st-2013-gro"
_LICENSE = 'GENIA Project License for Annotated Corpora'
_URLs = {
"train": "data/train.zip",
"validation": "data/devel.zip",
"test": "data/test.zip",
}
_SUPPORTED_TASKS = [
Tasks.EVENT_EXTRACTION,
Tasks.NAMED_ENTITY_RECOGNITION,
Tasks.RELATION_EXTRACTION,
]
_SOURCE_VERSION = "1.0.0"
_BIGBIO_VERSION = "1.0.0"
class bionlp_st_2013_gro(datasets.GeneratorBasedBuilder):
"""GRO Task: Populating the Gene Regulation Ontology with events and
relations. A data set from the bio NLP shared tasks competition from 2013"""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
BUILDER_CONFIGS = [
BigBioConfig(
name="bionlp_st_2013_gro_source",
version=SOURCE_VERSION,
description="bionlp_st_2013_gro source schema",
schema="source",
subset_id="bionlp_st_2013_gro",
),
BigBioConfig(
name="bionlp_st_2013_gro_bigbio_kb",
version=BIGBIO_VERSION,
description="bionlp_st_2013_gro BigBio schema",
schema="bigbio_kb",
subset_id="bionlp_st_2013_gro",
),
]
DEFAULT_CONFIG_NAME = "bionlp_st_2013_gro_source"
def _info(self):
"""
- `features` defines the schema of the parsed data set. The schema depends on the
chosen `config`: If it is `_SOURCE_VIEW_NAME` the schema is the schema of the
original data. If `config` is `_UNIFIED_VIEW_NAME`, then the schema is the
canonical KB-task schema defined in `biomedical/schemas/kb.py`.
"""
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("string"),
"document_id": datasets.Value("string"),
"text": datasets.Value("string"),
"text_bound_annotations": [ # T line in brat, e.g. type or event trigger
{
"offsets": datasets.Sequence([datasets.Value("int32")]),
"text": datasets.Sequence(datasets.Value("string")),
"type": datasets.Value("string"),
"id": datasets.Value("string"),
}
],
"events": [ # E line in brat
{
"trigger": datasets.Value(
"string"
), # refers to the text_bound_annotation of the trigger,
"id": datasets.Value("string"),
"type": datasets.Value("string"),
"arguments": datasets.Sequence(
{
"role": datasets.Value("string"),
"ref_id": datasets.Value("string"),
}
),
}
],
"relations": [ # R line in brat
{
"id": datasets.Value("string"),
"head": {
"ref_id": datasets.Value("string"),
"role": datasets.Value("string"),
},
"tail": {
"ref_id": datasets.Value("string"),
"role": datasets.Value("string"),
},
"type": datasets.Value("string"),
}
],
"equivalences": [ # Equiv line in brat
{
"id": datasets.Value("string"),
"ref_ids": datasets.Sequence(datasets.Value("string")),
}
],
"attributes": [ # M or A lines in brat
{
"id": datasets.Value("string"),
"type": datasets.Value("string"),
"ref_id": datasets.Value("string"),
"value": datasets.Value("string"),
}
],
"normalizations": [ # N lines in brat
{
"id": datasets.Value("string"),
"type": datasets.Value("string"),
"ref_id": datasets.Value("string"),
"resource_name": datasets.Value(
"string"
), # Name of the resource, e.g. "Wikipedia"
"cuid": datasets.Value(
"string"
), # ID in the resource, e.g. 534366
"text": datasets.Value(
"string"
), # Human readable description/name of the entity, e.g. "Barack Obama"
}
],
},
)
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: datasets.DownloadManager
) -> List[datasets.SplitGenerator]:
data_files = dl_manager.download_and_extract(_URLs)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"data_files": dl_manager.iter_files(data_files["train"])},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"data_files": dl_manager.iter_files(data_files["validation"])},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"data_files": dl_manager.iter_files(data_files["test"])},
),
]
def _generate_examples(self, data_files: Iterable[str]):
if self.config.schema == "source":
guid = 0
for data_file in data_files:
txt_file = Path(data_file)
if txt_file.suffix != ".txt":
continue
example = parse_brat_file(txt_file)
example["id"] = str(guid)
yield guid, example
guid += 1
elif self.config.schema == "bigbio_kb":
guid = 0
for data_file in data_files:
txt_file = Path(data_file)
if txt_file.suffix != ".txt":
continue
example = brat_parse_to_bigbio_kb(
parse_brat_file(txt_file)
)
example["id"] = str(guid)
yield guid, example
guid += 1
else:
raise ValueError(f"Invalid config: {self.config.name}")
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