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OLD/README.md

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+---
+YAML tags:
+annotations_creators:
+- auromatically-generated
+language_creators:
+- found
+language:
+- ca
+license:
+- cc-by-sa-3.0
+multilinguality:
+- monolingual
+pretty_name: wikicat_ca
+size_categories:
+- unknown
+source_datasets: []
+task_categories:
+- text-classification
+task_ids:
+- multi-class-classification
+---
+
+# WikiCAT_ca: Catalan Text Classification dataset
+
+## Dataset Description
+- **Homepage** [Projecte AINA](https://projecteaina.cat/tech/)
+- **Repository** [HuggingFace](https://huggingface.co/projecte-aina)
+- **Point of Contact** [email protected]
+
+
+**Repository**
+
+https://github.com/TeMU-BSC/WikiCAT
+
+
+### Dataset Summary
+
+WikiCAT_ca is a Catalan corpus for thematic Text Classification tasks. It is created automagically from Wikipedia and Wikidata sources, and contains 13201 articles from the Viquipedia classified under 13 different categories.
+
+This dataset was developed by BSC TeMU as part of the AINA project, and intended as an evaluation of LT capabilities to generate useful synthetic corpus.
+
+This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/">Attribution-ShareAlike 4.0 International</a>.
+
+
+### Supported Tasks and Leaderboards
+
+Text classification, Language Model
+
+### Languages
+
+The dataset is in Catalan (ca-ES).
+
+## Dataset Structure
+
+### Data Instances
+
+Two json files, one for each split.
+
+### Data Fields
+
+We used a simple model with the article text and associated labels, without further metadata.
+
+#### Example:
+
+<pre>
+{"version": "1.1.0",
+ "data":
+   [
+    {
+     'sentence': ' Celsius és conegut com l\'inventor de l\'escala centesimal del termòmetre. Encara que aquest instrument és un invent molt antic, la història de la seva gradació és molt més capritxosa. Durant el segle xvi era graduat com "fred" col·locant-lo (...)', 
+    'label': 'Ciència'
+    },
+    .
+    .
+    .
+  ]
+}
+
+
+</pre>
+
+#### Labels
+
+'Ciència_i_Tecnologia', 'Dret', 'Economia', 'Enginyeria', 'Entreteniment', 'Esport', 'Filosofia', 'Història', 'Humanitats', 'Matemàtiques', 'Música', 'Política', 'Religió'
+
+### Data Splits
+
+* dev_ca.json: 2484 label-document pairs
+* train_ca.json:  9907 label-document pairs
+
+
+## Dataset Creation
+
+### Methodology
+
+
+“Category” starting pages are chosen to represent the topics in each language.
+
+We extract, for each category, the main pages, as well as the subcategories ones, and the individual pages under this first level.
+For each page, the "summary" provided by Wikipedia is also extracted as the representative text.
+
+### Curation Rationale
+
+
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The source data are thematic categories in the different Wikipedias
+
+#### Who are the source language producers?
+
+
+### Annotations
+
+#### Annotation process
+Automatic annotation
+
+#### Who are the annotators?
+
+[N/A]
+
+### Personal and Sensitive Information
+
+No personal or sensitive information included.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+We hope this corpus contributes to the development of language models in Catalan, a low-resource language.
+
+### Discussion of Biases
+
+We are aware that this data might contain biases. We have not applied any steps to reduce their impact.
+
+### Other Known Limitations
+
+[N/A]
+
+## Additional Information
+
+### Dataset Curators
+
+Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected])
+
+This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
+
+### Licensing Information
+
+This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/">Attribution-ShareAlike 4.0 International</a>.
+
+### Contributions
+
+[N/A]

OLD/WikiCAT_ca.py

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+# Loading script for the TeCla dataset.
+import json
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+_CITATION = """
+
+            """
+
+_DESCRIPTION = """
+                   WikiCAT: Text Classification Catalan dataset from the Viquipedia
+
+               """
+
+_HOMEPAGE = """   """
+
+# TODO: upload datasets to github
+_URL = "https://huggingface.co/datasets/projecte-aina/WikiCAT_ca/raw/main/"
+_TRAINING_FILE = "train_ca.json"
+_DEV_FILE = "dev_ca.json"
+#_TEST_FILE = "test.json"
+
+
+class wikiCAT_caConfig(datasets.BuilderConfig):
+    """ Builder config for the Topicat dataset """
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for WikiCAT_ca.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(wikiCAT_caConfig, self).__init__(**kwargs)
+
+
+class wikiCAT_ca(datasets.GeneratorBasedBuilder):
+    """ WikiCAT_ca Dataset """
+
+    BUILDER_CONFIGS = [
+        wikiCAT_caConfig(
+            name="wikiCAT_ca",
+            version=datasets.Version("1.1.0"),
+            description="wikiCAT_ca",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel
+                        (names= ['Ciència_i_Tecnologia', 'Dret', 'Economia', 'Enginyeria', 'Entreteniment', 'Esport', 'Filosofia', 'Història', 'Humanitats', 'Matemàtiques', 'Música', 'Política', 'Religió']
+                    ),
+                }
+            ),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = {
+            "train": f"{_URL}{_TRAINING_FILE}",
+            "dev": f"{_URL}{_DEV_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)
+        print("filepath:",filepath)
+        with open(filepath, encoding="utf-8") as f:
+            wikicat_ca = json.load(f)
+            for id_, article in enumerate(wikicat_ca["data"]):
+                text = article["text"]
+                label = article["target"]
+                yield id_, {
+                    "text": text,
+                    "label": label,
+                }