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# coding=utf-8
#
# 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
"""Introduction to the Biobert NER Shared Task: Named Entity Recognition"""
import datasets
import json
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """\
Este es un dataset biomédico Biobert para el español con 29 etiquetas"""
_URL="data56/"
_TRAINING_FILE = "train.json"
_DEV_FILE = "valid.json"
_TEST_FILE = "test.json"
class Biobert_json_Config(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(Biobert_json_Config, self).__init__(**kwargs)
class Conll2003(datasets.GeneratorBasedBuilder):
"""Conll2003 dataset."""
BUILDER_CONFIGS = [
Biobert_json_Config(name="Biobert_json", version=datasets.Version("1.0.0"), description="Biobert_json dataset"),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
# "id": datasets.Value("string"),
"sentencia": datasets.Sequence(datasets.Value("string")),
"tag": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"B_CANCER_CONCEPT",
"B_CHEMOTHERAPY",
"B_DATE",
"B_DRUG",
"B_FAMILY",
"B_FREQ",
"B_IMPLICIT_DATE",
"B_INTERVAL",
"B_METRIC",
"B_OCURRENCE_EVENT",
"B_QUANTITY",
"B_RADIOTHERAPY",
"B_SMOKER_STATUS",
"B_STAGE",
"B_SURGERY",
"B_TNM",
"I_CANCER_CONCEPT",
"I_DATE",
"I_DRUG",
"I_FAMILY",
"I_FREQ",
"I_IMPLICIT_DATE",
"I_INTERVAL",
"I_METRIC",
"I_OCURRENCE_EVENT",
"I_SMOKER_STATUS",
"I_STAGE",
"I_SURGERY",
"I_TNM",
"O",
]
)
),
}
),
supervised_keys=None,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
urls_to_download = {
"train": f"{_URL}{_TRAINING_FILE}",
"val": 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["val"]}),
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
for line in f:
record = json.loads(line)
yield guid, record
guid += 1
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