Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
named-entity-recognition
Languages:
Spanish
Size:
10K - 100K
License:
File size: 3,438 Bytes
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import datasets
logger = datasets.logging.get_logger(__name__)
_LICENSE = "Creative Commons Attribution 4.0 International"
_VERSION = "1.1.0"
_URL = "https://huggingface.co/datasets/plncmm/clinical_trials/resolve/main/"
_TRAINING_FILE = "train.conll"
_DEV_FILE = "dev.conll"
_TEST_FILE = "test.conll"
class ClinicalTrialsConfig(datasets.BuilderConfig):
"""BuilderConfig for ClinicalTrials dataset."""
def __init__(self, **kwargs):
super(ClinicalTrialsConfig, self).__init__(**kwargs)
class ClinicalTrials(datasets.GeneratorBasedBuilder):
"""ClinicalTrials dataset."""
BUILDER_CONFIGS = [
ClinicalTrialsConfig(
name="ClinicalTrials",
version=datasets.Version(_VERSION),
description="ClinicalTrials dataset"),
]
def _info(self):
return datasets.DatasetInfo(
features=datasets.Features(
{
"id": datasets.Value("string"),
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-ANAT",
"B-CHEM",
"B-DISO",
"B-PROC",
"I-ANAT",
"I-CHEM",
"I-DISO",
"I-PROC",
]
)
),
}
),
supervised_keys=None,
)
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 = []
pos_tags = []
ner_tags = []
for line in f:
if line == "":
if tokens:
yield guid, {
"id": str(guid),
"tokens": tokens,
"ner_tags": ner_tags,
}
guid += 1
tokens = []
ner_tags = []
else:
splits = line.split(" ")
tokens.append(splits[0])
ner_tags.append(splits[-1].rstrip())
# last example
yield guid, {
"id": str(guid),
"tokens": tokens,
"ner_tags": ner_tags,
}
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