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import datasets
logger = datasets.logging.get_logger(__name__)
_HOMEPAGE = "https://www.google.com"
_URL = f'https://huggingface.co./datasets/chintagunta85/pv_dataset/raw/main/'
_TRAINING_FILE = "pv_train.tsv"
_DEV_FILE = "pv_val.tsv"
_TEST_FILE = "pv_test.tsv"
class PVDatasetConfig(datasets.BuilderConfig):
"""BuilderConfig for Bc2gmCorpus"""
def __init__(self, **kwargs):
"""BuilderConfig for Bc2gmCorpus.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(PVDatasetConfig, self).__init__(**kwargs)
class PVDataset(datasets.GeneratorBasedBuilder):
"""Bc2gmCorpus dataset."""
BUILDER_CONFIGS = [
PVDatasetConfig(name="PVDatasetCorpus", version=datasets.Version("1.0.0"), description="PVDataset"),
]
def _info(self):
custom_names = ['O','B-GENE','I-GENE','B-CHEMICAL','I-CHEMICAL','B-DISEASE','I-DISEASE','B-SPECIES', 'I-SPECIES']
return datasets.DatasetInfo(
description='abhi',
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='cite me',
)
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):
shift = 0
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:
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"):
ner_tags.append("B-SPECIES")
elif(splits[1].rstrip()=="I"):
ner_tags.append("I-SPECIES")
elif(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(splits[1].rstrip())
# last example
yield guid, {
"id": str(guid),
"tokens": tokens,
"ner_tags": ner_tags,
}
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