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casum / casum.py
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# 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}