Datasets:
Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
named-entity-recognition
Languages:
Spanish
Size:
10K - 100K
License:
File size: 4,025 Bytes
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# Loading script for the Cantemist NER dataset.
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@inproceedings{miranda2020named,
title={Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results},
author={Miranda-Escalada, A and Farr{\'e}, E and Krallinger, M},
booktitle={Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings},
year={2020}
}"""
_DESCRIPTION = """\
https://temu.bsc.es/cantemist/
"""
_URL = "https://huggingface.co/datasets/PlanTL-GOB-ES/cantemist-ner/resolve/main/"
# _URL = "./"
_TRAINING_FILE = "train.conll"
_DEV_FILE = "dev.conll"
_TEST_FILE = "test.conll"
class CantemistNerConfig(datasets.BuilderConfig):
"""BuilderConfig for Cantemist Ner dataset"""
def __init__(self, **kwargs):
"""BuilderConfig for CantemistNer.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(CantemistNerConfig, self).__init__(**kwargs)
class CantemistNer(datasets.GeneratorBasedBuilder):
"""Cantemist Ner dataset."""
BUILDER_CONFIGS = [
CantemistNerConfig(
name="CantemistNer",
version=datasets.Version("1.0.0"),
description="CantemistNer dataset"),
]
def _info(self):
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=[
"O",
"B-MORFOLOGIA_NEOPLASIA",
"I-MORFOLOGIA_NEOPLASIA",
]
)
),
}
),
supervised_keys=None,
homepage="https://temu.bsc.es/cantemist/",
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 = []
pos_tags = []
ner_tags = []
for line in f:
if line.startswith("-DOCSTART-") or line == "" or line == "\n":
if tokens:
yield guid, {
"id": str(guid),
"tokens": tokens,
"ner_tags": ner_tags,
}
guid += 1
tokens = []
ner_tags = []
else:
# Cantemist tokens are tab separated
splits = line.split("\t")
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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