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data/caBreu.py

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+# 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 } 
+                            } 
+                        }

data/train.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b6c7d6909f15429b4f94d60f9c81d517fc23fde93bdbe5070213f1fd35578ad6
+size 22825376