| |
| 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} |
