Datasets:
Tasks:
Summarization
Languages:
Catalan
Multilinguality:
monolingual
Size Categories:
unknown
Language Creators:
expert-generated
Annotations Creators:
machine-generated
ArXiv:
License:
| # Loading script for the CaSum dataset. | |
| import json | |
| import datasets | |
| logger = datasets.logging.get_logger(__name__) | |
| _CITATION = """@misc{degibert2022sequencetosequence, | |
| title={Sequence-to-Sequence Resources for Catalan}, | |
| author={Ona de Gibert and Ksenia Kharitonova and Blanca Calvo Figueras and Jordi Armengol-Estapé and Maite Melero}, | |
| year={2022}, | |
| eprint={2202.06871}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CL} | |
| }""" | |
| _DESCRIPTION = """CaSum is a summarization dataset. It is extracted from a newswire corpus crawled from the Catalan News Agency. The corpus consists of 217,735 instances that are composed by the headline and the body. | |
| """ | |
| _HOMEPAGE = """https://github.com/TeMU-BSC/seq-to-seq-catalan""" | |
| _URL = "https://huggingface.co/datasets/projecte-aina/casum/resolve/main/" | |
| _TRAIN_FILE = "train.jsonl" | |
| _VALID_FILE = "valid.jsonl" | |
| _TEST_FILE = "test.jsonl" | |
| class CaSumConfig(datasets.BuilderConfig): | |
| """ Builder config for the CaSum dataset """ | |
| def __init__(self, **kwargs): | |
| """BuilderConfig for CaSum. | |
| Args: | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(CaSumConfig, self).__init__(**kwargs) | |
| class CaSum(datasets.GeneratorBasedBuilder): | |
| """CaSum Dataset.""" | |
| BUILDER_CONFIGS = [ | |
| CaSumConfig( | |
| name="CaSum", | |
| version=datasets.Version("1.0.0"), | |
| description="CaSum dataset" | |
| ), | |
| ] | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "summary": datasets.Value("string"), | |
| "text": datasets.Value("string") | |
| } | |
| ), | |
| supervised_keys=None, | |
| homepage=_HOMEPAGE, | |
| citation=_CITATION | |
| ) | |
| def _split_generators(self, dl_manager): | |
| """Returns SplitGenerators.""" | |
| urls_to_download = { | |
| "train": f"{_URL}{_TRAIN_FILE}", | |
| "valid": f"{_URL}{_VALID_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["valid"]}), | |
| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), | |
| ] | |
| def _generate_examples(self, filepath): | |
| """This function returns the examples in the raw (text) form.""" | |
| logger.info("generating examples from = %s", filepath) | |
| with open(filepath) as f: | |
| for id_, row in enumerate(f): | |
| article = json.loads(row) | |
| text = article['text'] | |
| summary = article['summary'] | |
| yield id_, { "summary": summary,"text": text} |