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# coding=utf-8
# Copyright 2020 HuggingFace Datasets Authors.
#
# 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.
# Lint as: python3
"""INSERT TITLE"""
import logging
import datasets
_CITATION = """\
*REDO*
"""
_DESCRIPTION = """\
**REWRITE*
"""
_URL = "https://huggingface.co/datasets/wzkariampuzha/EpiClassifySet/raw/main/"
_TRAINING_FILE = "epi_classify_train.tsv"
_VAL_FILE = "epi_classify_val.tsv"
_TEST_FILE = "epi_classify_test.tsv"
class EpiSetConfig(datasets.BuilderConfig):
"""BuilderConfig for Conll2003"""
def __init__(self, **kwargs):
"""BuilderConfig forConll2003.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(EpiSetConfig, self).__init__(**kwargs)
class EpiSet(datasets.GeneratorBasedBuilder):
"""EpiSet4NER by GARD."""
BUILDER_CONFIGS = [
EpiSetConfig(name="EpiSet4NER", version=datasets.Version("1.0.0"), description="EpiSet4NER by NIH NCATS GARD"),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"idx": datasets.Value("string"),
#"abstracts": datasets.Value("string"),
"abstracts": datasets.Sequence(datasets.Value("string")),
'''
"labels": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O", #(0)
"B-LOC", #(1)
"I-LOC", #(2)
"B-EPI", #(3)
"I-EPI", #(4)
"B-STAT", #(5)
"I-STAT", #(6)
]
)
),
'''
"labels": datasets.features.ClassLabel(
names=[
"1 = Epi Abstract",
"2 = Not Epi Abstract",
]
),
}
),
supervised_keys=None,
homepage="https://github.com/ncats/epi4GARD/tree/master/Epi4GARD#epi4gard",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
urls_to_download = {
"train": f"{_URL}{_TRAINING_FILE}",
"val": f"{_URL}{_VAL_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["val"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
]
def _generate_examples(self, filepath):
logging.info("⏳ Generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
data = csv.reader(f, delimiter="\t", quoting=csv.QUOTE_NONNUMERIC)
next(data)
for id_, row in enumerate(data):
yield id_, {
"text": row[0],
"label": int(row[1]),
}
'''
with open(filepath, encoding="utf-8") as f:
guid = 0
abstracts = []
labels = []
for line in f:
if line.startswith("-DOCSTART-") or line == "" or line == "\n" or line == "abstract\tlabel\n":
if abstracts:
yield guid, {
"idx": str(guid),
"abstracts": abstracts,
"labels": labels,
}
guid += 1
abstracts = []
labels = []
else:
# EpiSet abstracts are space separated
splits = line.split("\t")
abstracts.append(splits[0])
labels.append(splits[1].rstrip())
# last example
if tokens:
yield guid, {
"idx": str(guid),
"abstracts": abstracts,
"labels": labels,
}
''' |