Datasets:
Languages:
Catalan
Multilinguality:
monolingual
Size Categories:
unknown
Language Creators:
found
Annotations Creators:
expert-generated
ArXiv:
License:
# Loading script for the Ancora NER dataset. | |
import datasets | |
logger = datasets.logging.get_logger(__name__) | |
_CITATION = """ """ | |
_DESCRIPTION = """AnCora Catalan NER. | |
This is a dataset for Named Eentity Reacognition (NER) from Ancora corpus adapted for | |
Machine Learning and Language Model evaluation purposes. | |
Since multiwords (including Named Entites) in the original Ancora corpus are aggregated as | |
a single lexical item using underscores (e.g. "Ajuntament_de_Barcelona") | |
we splitted them to align with word-per-line format, and added conventional Begin-Inside-Outside (IOB) | |
tags to mark and classify Named Entites. | |
We did not filter out the different categories of NEs from Ancora (weak and strong). | |
We did 6 minor edits by hand. | |
AnCora corpus is used under [CC-by] (https://creativecommons.org/licenses/by/4.0/) licence. | |
This dataset was developed by BSC TeMU as part of the AINA project, and to enrich the Catalan Language Understanding Benchmark (CLUB). | |
""" | |
_HOMEPAGE = """https://zenodo.org/record/4762031""" | |
_URL = "https://huggingface.co/datasets/projecte-aina/ancora-ca-ner/resolve/main/" | |
_TRAINING_FILE = "train.conll" | |
_DEV_FILE = "dev.conll" | |
_TEST_FILE = "test.conll" | |
class AncoraCaNerConfig(datasets.BuilderConfig): | |
""" Builder config for the Ancora Ca NER dataset """ | |
def __init__(self, **kwargs): | |
"""BuilderConfig for AncoraCaNer. | |
Args: | |
**kwargs: keyword arguments forwarded to super. | |
""" | |
super(AncoraCaNerConfig, self).__init__(**kwargs) | |
class AncoraCaNer(datasets.GeneratorBasedBuilder): | |
""" AncoraCaNer dataset.""" | |
BUILDER_CONFIGS = [ | |
AncoraCaNerConfig( | |
name="AncoraCaNer", | |
version=datasets.Version("2.0.0"), | |
description="AncoraCaNer 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=[ | |
"B-LOC", | |
"B-MISC", | |
"B-ORG", | |
"B-PER", | |
"I-LOC", | |
"I-MISC", | |
"I-ORG", | |
"I-PER", | |
"O" | |
] | |
) | |
), | |
} | |
), | |
supervised_keys=None, | |
homepage=_HOMEPAGE, | |
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 = [] | |
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: | |
# AncoraCaNer tokens are space 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, | |
} | |