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import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@article{krallinger2015chemdner,
title={The CHEMDNER corpus of chemicals and drugs and its annotation principles},
author={Krallinger, Martin and Rabal, Obdulia and Leitner, Florian and Vazquez, Miguel and Salgado, David and Lu, Zhiyong and Leaman, Robert and Lu, Yanan and Ji, Donghong and Lowe, Daniel M and others},
journal={Journal of cheminformatics},
volume={7},
number={1},
pages={1--17},
year={2015},
publisher={BioMed Central}
}
"""
_DESCRIPTION = """\
"""
_HOMEPAGE = ""
_URL = "https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/BC5CDR-IOB/"
_TRAINING_FILE = "train.tsv"
_DEV_FILE = "devel.tsv"
_TEST_FILE = "test.tsv"
class BC4CHEMDConfig(datasets.BuilderConfig):
"""BuilderConfig for BC4CHEMD"""
def __init__(self, **kwargs):
"""BuilderConfig for BC4CHEMD.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(BC4CHEMDConfig, self).__init__(**kwargs)
class BC4CHEMD(datasets.GeneratorBasedBuilder):
""" BC4CHEMD dataset."""
BUILDER_CONFIGS = [
BC4CHEMDConfig(name="BC5CDR-Disease", version=datasets.Version("1.0.0"), description=" BC5CDR-Disease dataset"),
]
def _info(self):
custom_names = ['O','B-GENE','I-GENE','B-CHEMICAL','I-CHEMICAL','B-DISEASE','I-DISEASE',
'B-DNA', 'I-DNA', 'B-RNA', 'I-RNA', 'B-CELL_LINE', 'I-CELL_LINE', 'B-CELL_TYPE', 'I-CELL_TYPE',
'B-PROTEIN', 'I-PROTEIN', 'B-SPECIES', 'I-SPECIES']
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=custom_names
)
),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
urls_to_download = {
"train": f"{_URL}{_TRAINING_FILE}",
"dev": f"{_URL}{_DEV_FILE}",
"test": f"{_URL}{_TEST_FILE}",
}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
]
def _generate_examples(self, filepath):
logger.info("⏳ Generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
guid = 0
tokens = []
ner_tags = []
for line in f:
if line == "" or line == "\n":
if tokens:
print(tokens)
yield guid, {
"id": str(guid),
"tokens": tokens,
"ner_tags": ner_tags,
}
guid += 1
tokens = []
ner_tags = []
else:
# tokens are tab separated
splits = line.split("\t")
tokens.append(splits[0])
if(splits[1].rstrip()=="B-Chemical"):
ner_tags.append("B-CHEMICAL")
elif(splits[1].rstrip()=="I-Chemical"):
ner_tags.append("I-CHEMICAL")
elif(splits[1].rstrip()=="B-Disease"):
ner_tags.append("B-DISEASE")
elif(splits[1].rstrip()=="I-Disease"):
ner_tags.append("I-DISEASE")
else:
ner_tags.append("O")
# ner_tags.append(splits[1].rstrip())
# last example
yield guid, {
"id": str(guid),
"tokens": tokens,
"ner_tags": ner_tags,
} |