Delete caBreu.py

caBreu.py

@@ -1,137 +0,0 @@
-# Loading script for the CaSum dataset.
-import json
-import datasets
-
-logger = datasets.logging.get_logger(__name__)
-
-_CITATION = """"""
-
-_DESCRIPTION = """caBreu is a summarization dataset. 
-It consists of 3,000 articles, each averaging about 700 words in length, along with extreme, abstractive and extractive summaries, 
-manually generated by three annotators.
-
-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/)), 
- [VilaWeb](https://www.vilaweb.cat/) and [NacióDigital](https://www.naciodigital.cat/).
- """
-
-_HOMEPAGE = """https://github.com/TeMU-BSC/seq-to-seq-catalan"""
-
-_URL = "https://huggingface.co/datasets/projecte-aina/caBreu/resolve/main/"
-_TRAIN_FILE = "train.json"
-_VAL_FILE = "dev.json"
-_TEST_FILE = "test.json"
-
-class caBreuConfig(datasets.BuilderConfig):
-    """ Builder config for the caBreu dataset """
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for caBreu.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(caBreuConfig, self).__init__(**kwargs)
-
-
-class caBreu(datasets.GeneratorBasedBuilder):
-    """caBreu Dataset."""
-
-    BUILDER_CONFIGS = [
-        caBreuConfig(
-            name="caBreu",
-            version=datasets.Version("1.0.0"),
-            description="caBreu dataset"
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "title": datasets.Value("string"),
-                    "subtitle": datasets.Value("string"),
-                    "content": datasets.Value("string"),
-                    "category": datasets.Sequence(datasets.Value("string")),
-                    "source": datasets.Value("string"),
-                    "summaries":
-                        {
-                        "extreme": 
-                            {
-                                "a1": datasets.Value("string"),
-                                "a2": datasets.Value("string"),
-                                "a3": datasets.Value("string")
-                            },
-                        "abstractive":
-                            {
-                                "a1": datasets.Value("string"),
-                                "a2": datasets.Value("string"),
-                                "a3": datasets.Value("string")
-                            },
-                        "extractive":
-                            {
-                                "a1": datasets.Value("string"),
-                                "a2": datasets.Value("string"),
-                                "a3": 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}",
-            "dev": f"{_URL}{_VAL_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):
-        """This function returns the examples in the raw (text) form."""
-        logger.info("generating examples from = %s", filepath)
-        with open(filepath) as f:
-            data = json.load(f)
-            for article in data:
-                id_ = article['id']
-                title = article['title']
-                subtitle = article['subtitle']
-                content = article['content']
-                category = article['category']
-                if isinstance(category, str):
-                    category = []
-                source = article['source']
-                a1_extreme = article['summaries']['extreme']['a1']
-                a2_extreme = article['summaries']['extreme']['a2']
-                a3_extreme = article['summaries']['extreme']['a3']
-                a1_abstractive = article['summaries']['abstractive']['a1']
-                a2_abstractive = article['summaries']['abstractive']['a2']
-                a3_abstractive = article['summaries']['abstractive']['a3']
-                a1_extractive = article['summaries']['extractive']['a1']
-                a2_extractive = article['summaries']['extractive']['a2']
-                a3_extractive = article['summaries']['extractive']['a3']
-                yield id_, { 
-                    "id": id_,
-                    "title": title,
-                    "subtitle": subtitle,
-                    "content": content,
-                    "category": category,
-                    "source": source,
-                    "summaries":
-                        { 
-                            "extreme": { "a1": a1_extreme,"a2": a2_extreme,"a3": a3_extreme },
-                            "abstractive": { "a1": a1_abstractive,"a2": a2_abstractive,"a3": a3_abstractive },
-                            "extractive": { "a1": a1_extractive,"a2": a2_extractive,"a3": a3_extractive } 
-                            } 
-                        }