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import json |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """""" |
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_DESCRIPTION = """caBreu is a summarization dataset. |
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It consists of 3,000 articles, each averaging about 700 words in length, along with extreme, abstractive and extractive summaries, |
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manually generated by three annotators. |
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The source material for the articles was gathered from various Catalan news sources, including the Catalan News Agency ([Agència Catalana de Notícies; ACN](https://www.acn.cat/)), |
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[VilaWeb](https://www.vilaweb.cat/) and [NacióDigital](https://www.naciodigital.cat/). |
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""" |
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_HOMEPAGE = """https://github.com/TeMU-BSC/seq-to-seq-catalan""" |
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_URL = "https://huggingface.co/datasets/projecte-aina/caBreu/resolve/main/" |
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_TRAIN_FILE = "train.json" |
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_VAL_FILE = "dev.json" |
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_TEST_FILE = "test.json" |
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class caBreuConfig(datasets.BuilderConfig): |
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""" Builder config for the caBreu dataset """ |
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def __init__(self, **kwargs): |
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"""BuilderConfig for caBreu. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(caBreuConfig, self).__init__(**kwargs) |
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class caBreu(datasets.GeneratorBasedBuilder): |
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"""caBreu Dataset.""" |
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BUILDER_CONFIGS = [ |
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caBreuConfig( |
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name="caBreu", |
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version=datasets.Version("1.0.0"), |
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description="caBreu dataset" |
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), |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"title": datasets.Value("string"), |
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"subtitle": datasets.Value("string"), |
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"content": datasets.Value("string"), |
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"category": datasets.Sequence(datasets.Value("string")), |
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"source": datasets.Value("string"), |
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"summaries": |
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{ |
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"extreme": |
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{ |
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"a1": datasets.Value("string"), |
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"a2": datasets.Value("string"), |
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"a3": datasets.Value("string") |
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}, |
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"abstractive": |
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{ |
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"a1": datasets.Value("string"), |
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"a2": datasets.Value("string"), |
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"a3": datasets.Value("string") |
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}, |
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"extractive": |
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{ |
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"a1": datasets.Value("string"), |
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"a2": datasets.Value("string"), |
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"a3": datasets.Value("string") |
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} |
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} |
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} |
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), |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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citation=_CITATION |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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urls_to_download = { |
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"train": f"{_URL}{_TRAIN_FILE}", |
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"dev": f"{_URL}{_VAL_FILE}", |
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"test": f"{_URL}{_TEST_FILE}" |
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} |
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downloaded_files = dl_manager.download_and_extract(urls_to_download) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), |
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), |
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] |
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def _generate_examples(self, filepath): |
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"""This function returns the examples in the raw (text) form.""" |
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logger.info("generating examples from = %s", filepath) |
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with open(filepath) as f: |
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data = json.load(f) |
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for article in data: |
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id_ = article['id'] |
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title = article['title'] |
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subtitle = article['subtitle'] |
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content = article['content'] |
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category = article['category'] |
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if isinstance(category, str): |
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category = [] |
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source = article['source'] |
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a1_extreme = article['summaries']['extreme']['a1'] |
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a2_extreme = article['summaries']['extreme']['a2'] |
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a3_extreme = article['summaries']['extreme']['a3'] |
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a1_abstractive = article['summaries']['abstractive']['a1'] |
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a2_abstractive = article['summaries']['abstractive']['a2'] |
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a3_abstractive = article['summaries']['abstractive']['a3'] |
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a1_extractive = article['summaries']['extractive']['a1'] |
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a2_extractive = article['summaries']['extractive']['a2'] |
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a3_extractive = article['summaries']['extractive']['a3'] |
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yield id_, { |
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"id": id_, |
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"title": title, |
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"subtitle": subtitle, |
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"content": content, |
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"category": category, |
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"source": source, |
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"summaries": |
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{ |
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"extreme": { "a1": a1_extreme,"a2": a2_extreme,"a3": a3_extreme }, |
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"abstractive": { "a1": a1_abstractive,"a2": a2_abstractive,"a3": a3_abstractive }, |
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"extractive": { "a1": a1_extractive,"a2": a2_extractive,"a3": a3_extractive } |
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} |
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} |
