upload dataset

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+train.json filter=lfs diff=lfs merge=lfs -text
+dev.json filter=lfs diff=lfs merge=lfs -text
+test.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+languages:
+- ca
+---
+# TeCla (Text Classification) Catalan dataset
+
+## BibTeX  citation
+
+If you use any of these resources (datasets or models) in your work, please cite our latest paper:
+
+```bibtex
+@inproceedings{armengol-estape-etal-2021-multilingual,
+    title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
+    author = "Armengol-Estap{\'e}, Jordi  and
+      Carrino, Casimiro Pio  and
+      Rodriguez-Penagos, Carlos  and
+      de Gibert Bonet, Ona  and
+      Armentano-Oller, Carme  and
+      Gonzalez-Agirre, Aitor  and
+      Melero, Maite  and
+      Villegas, Marta",
+    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
+    month = aug,
+    year = "2021",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.findings-acl.437",
+    doi = "10.18653/v1/2021.findings-acl.437",
+    pages = "4933--4946",
+}
+```
+
+
+## Digital Object Identifier (DOI) and access to dataset files
+
+https://doi.org/10.5281/zenodo.4627198
+
+
+## Introduction
+
+TeCla is a Catalan News corpus for thematic Text Classification tasks. It contains 153.265 articles classified under 30 different categories.
+
+The source data is crawled from the ACN (Catalan News Agency) site: [http://www.acn.cat], and used under CC-BY-NC-ND 4.0 licence. The dataset is released under the same licence, and is intended exclusively for training Machine Learning models.
+
+This dataset was developed by BSC TeMU as part of the AINA project, and intended as part of CLUB (Catalan Language Understanding Benchmark). It is part of the Catalan Language Understanding Benchmark (CLUB) as presented in: 
+
+Armengol-Estapé J., Carrino CP., Rodriguez-Penagos C., de Gibert Bonet O., Armentano-Oller C., Gonzalez-Agirre A., Melero M. and Villegas M.,Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan". Findings of ACL 2021 (ACL-IJCNLP 2021).
+
+
+### Supported Tasks and Leaderboards
+
+Text classification, Language Model
+
+### Languages
+
+CA- Catalan
+
+### Directory structure
+
+* **.gitattributes**
+* **README.md**
+* **dev.json** - json-formatted file with the dev split of the dataset
+* **tecla.py**
+* **test.json** - json-formatted file with the test split of the dataset
+* **train.json** - json-formatted file with the train split of the dataset
+
+## Dataset Structure
+
+### Data Instances
+
+Three 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": "1.0",
+ "data":
+   [
+    {
+     'sentence': 'L\\\\'editorial valenciana Media Vaca, Premi Nacional a la Millor Tasca Editorial Cultural del 2018. El jurat en destaca la cura "exquisida" del catàleg, la qualitat dels llibres i el "respecte" pels lectors. ACN Madrid.-L\\\\'editorial valenciana Media Vaca ha obtingut el Premi Nacional a la Millor Labor Editorial Cultural corresponent a l\\\\'any 2018 que atorga el Ministeri de Cultura i Esports. El guardó pretén distingir la tasca editorial d\\\\'una persona física o jurídica que hagi destacat per l\\\\'aportació a la vida cultural espanyola. El premi és de caràcter honorífic i no té dotació econòmica. En el cas de Media Vaca, fundada pel valencià Vicente Ferrer i la bilbaïna Begoña Lobo, el jurat n\\\\'ha destacat la cura "exquisida" del catàleg, la qualitat dels llibres i el "respecte" pels lectors i per la resta d\\\\'agents de la cadena del llibre. Media Vaca va publicar els primers llibres el desembre del 1998. El catàleg actual el componen 64 títols dividits en sis col·leccions, que barregen ficció i no ficció. Des del Ministeri de Cultura es destaca que la il·lustració té un pes "fonamental" als productes de l\\\\'editorial i que la majoria de projectes solen partir de propostes literàries i textos preexistents. L\\\\'editorial ha rebut quatre vegades el Bologna Ragazzi Award. És l\\\\'única editorial estatal que ha aconseguit el guardó que atorga la Fira del Llibre per a Nens de Bolonya, la més important del sector.', 
+    'label': 'Lletres'
+    },
+    .
+    .
+    .
+  ]
+}
+
+
+</pre>
+
+### Data Splits
+* train.json: 122587 article-label pairs
+* dev.json: 15339  article-label pairs
+* test.json: 15339  article-label pairs
+
+### Labels
+
+'Societat', 'Política', 'Turisme', 'Salut', 'Economia', 'Successos', 'Partits', 'Educació', 'Policial', 'Medi ambient', 'Parlament', 'Empresa', 'Judicial', 'Unió Europea', 'Comerç', 'Cultura', 'Cinema', 'Govern', 'Lletres', 'Infraestructures', 'Música', 'Festa i cultura popular', 'Teatre', 'Mobilitat', 'Govern espanyol', 'Equipaments i patrimoni', 'Meteorologia', 'Treball', 'Trànsit', 'Món'
+
+
+### Labels in the dataset by frequency
+
+train.json: 122587 articles
+
+| Label | Num art |% art |
+|:-----------------------|--------------:|------: |
+| Societat | 24975 | 20.37% |
+| Política | 18344 | 14.96% |
+| Partits | 10056 | 8.2% |
+| Successos | 7874 | 6.42% |
+| Judicial | 5788 | 4.72% |
+| Policial | 5557 | 4.53% |
+| Salut | 5430 | 4.43% |
+| Economia | 5032 | 4.1% |
+| Parlament | 4176 | 3.41% |
+| Medi_ambient | 3027 | 2.47% |
+| Música | 2872 | 2.34% |
+| Educació | 2757 | 2.25% |
+| Empresa | 2698 | 2.2% |
+| Cultura | 2495 | 2.04% |
+| Unió_Europea | 2064 | 1.68% |
+| Govern | 2039 | 1.66% |
+| Infraestructures | 1740 | 1.42% |
+| Treball | 1655 | 1.35% |
+| Mobilitat | 1624 | 1.32% |
+| Cinema | 1560 | 1.27% |
+| Teatre | 1492 | 1.22% |
+| Turisme | 1232 | 1.01% |
+| Equipaments_i_patrimoni | 1229 | 1.0% |
+| Lletres | 1180 | 0.96% |
+| Meteorologia | 1080 | 0.88% |
+| Comerç | 984 | 0.8% |
+| Govern_espanyol | 983 | 0.8% |
+| Món | 893 | 0.73% |
+| Festa_i_cultura_popular | 888 | 0.72% |
+| Trànsit | 863 | 0.7% |
+
+dev.json and test.json: 153265 articles each split
+
+| Label | Num art |% art |
+|:----------------------- | --------------:| ------: |
+| Societat | 3122 | 20.35% |
+| Política | 2294 | 14.96% |
+| Partits | 1257 | 8.19% |
+| Successos | 985 | 6.42% |
+| Judicial | 724 | 4.72% |
+| Policial | 695 | 4.53% |
+| Salut | 679 | 4.43% |
+| Economia | 630 | 4.11% |
+| Parlament | 523 | 3.41% |
+| Medi_ambient | 379 | 2.47% |
+| Música | 359 | 2.34% |
+| Educació | 345 | 2.25% |
+| Empresa | 338 | 2.2% |
+| Cultura | 312 | 2.03% |
+| Unió_Europea | 258 | 1.68% |
+| Govern | 256 | 1.67% |
+| Infraestructures | 218 | 1.42% |
+| Treball | 208 | 1.36% |
+| Mobilitat | 204 | 1.33% |
+| Cinema | 195 | 1.27% |
+| Teatre | 187 | 1.22% |
+| Turisme | 154 | 1.0% |
+| Equipaments_i_patrimoni | 154 | 1.0% |
+| Lletres | 148 | 0.96% |
+| Meteorologia | 135 | 0.88% |
+| Govern_espanyol | 124 | 0.81% |
+| Comerç | 123 | 0.8% |
+| Festa_i_cultura_popular | 112 | 0.73% |
+| Món | 112 | 0.73% |
+| Trànsit | 109 | 0.71% |
+
+
+## Dataset Creation
+
+### Methodology
+
+We crawled 219.586 articles from the Catalan News Agency (www.acn.cat) newswire archive, the latest from October 11, 2020.
+We used the "subsection" category as a classification label, after excluding territorial labels (see territorial_labels.txt file) and labels with less than 2000 occurrences. With this criteria compiled a total of 153.265 articles for this text classification dataset.
+
+### Curation Rationale
+
+We used the "subsection" category as a classification label, after excluding territorial labels (see territorial_labels.txt file) and labels with less than 2000 occurrences.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The source data are crawled articles from ACN (Catalan News Agency) site: www.acn.cat
+
+#### Who are the source language producers?
+
+The Catalan News Agency (CNA, in Catalan: Agència Catalana de Notícies (ACN)) is a news agency owned by the Catalan government via the public corporation Intracatalònia, SA. It is one of the first digital news agencies created in Europe and has been operating since 1999 (source: [https://en.wikipedia.org/wiki/Catalan_News_Agency])
+
+### Annotations
+
+#### Annotation process
+
+We used the "subsection" category as a classification label, after excluding territorial labels (see territorial_labels.txt file) and labels with less than 2000 occurrences.
+
+#### Who are the annotators?
+
+Editorial staff classified the articles under the different thematic sections, and we extracted these from metadata.
+
+### Dataset Curators
+
+Casimiro Pio Carrino, Carlos Rodríguez and Carme Armentano, from BSC-CNS
+
+### Personal and Sensitive Information
+
+No personal or sensitive information included.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+
+## Contact
+
+Carlos Rodríguez-Penagos ([email protected]) and Carme Armentano-Oller ([email protected])
+
+## License
+
+<a rel="license" href="https://creativecommons.org/licenses/by-nc-nd/4.0/"><img alt="Attribution-NonCommercial-NoDerivatives 4.0 International License" style="border-width:0" src="http://d2klr1ixr44jla.cloudfront.net/306/125/0.5-0.5/assets/images/55132bfeb13b7b027c000041.png" width="100"/></a><br />This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-nc-nd/4.0/">Attribution-NonCommercial-NoDerivatives 4.0 International License</a>.
+

dev.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1222f41063baa63ce8139691ab3e7021c19b8ccab3a6cb60d40c35eb93df81a2
+size 34946188

tecla.py

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+# Loading script for the TeCla dataset.
+import json
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+_CITATION = """
+               Carrino, Casimiro Pio, Rodriguez-Penagos, Carlos Gerardo, & Armentano-Oller, Carme. (2021). 
+               TeCla: Text Classification Catalan dataset (Version 1.0) [Data set]. 
+               Zenodo. http://doi.org/10.5281/zenodo.4627198
+            """
+
+_DESCRIPTION = """
+                   TeCla: Text Classification Catalan dataset
+                   Catalan News corpus for Text classification, crawled from ACN (Catalan News Agency) site: www.acn.cat
+                   Corpus de notícies en català per a classificació textual, extret del web de l'Agència Catalana de Notícies - www.acn.cat
+               """
+
+_HOMEPAGE = """https://zenodo.org/record/4761505"""
+
+# TODO: upload datasets to github
+_URL = "https://huggingface.co/datasets/bsc/tecla/resolve/main/"
+_TRAINING_FILE = "train.json"
+_DEV_FILE = "dev.json"
+_TEST_FILE = "test.json"
+
+
+class teclaConfig(datasets.BuilderConfig):
+    """ Builder config for the TeCla dataset """
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for TeCla.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(teclaConfig, self).__init__(**kwargs)
+
+
+class tecla(datasets.GeneratorBasedBuilder):
+    """ TeCla Dataset """
+
+    BUILDER_CONFIGS = [
+        teclaConfig(
+            name="tecla",
+            version=datasets.Version("1.0.1"),
+            description="tecla dataset",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel
+                        (names=
+                    [
+                        "Medi ambient",
+                        "Societat",
+                        "Policial",
+                        "Judicial",
+                        "Empresa",
+                        "Partits",
+                        "Pol\u00edtica",
+                        "Successos",
+                        "Salut",
+                        "Infraestructures",
+                        "Parlament",
+                        "M\u00fasica",
+                        "Govern",
+                        "Uni\u00f3 Europea",
+                        "Economia",
+                        "Mobilitat",
+                        "Treball",
+                        "Cultura",
+                        "Educaci\u00f3"
+                    ]
+                    ),
+                }
+            ),
+            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)
+        with open(filepath, encoding="utf-8") as f:
+            acn_ca = json.load(f)
+            for id_, article in enumerate(acn_ca["data"]):
+                text = article["sentence"]
+                label = article["label"]
+                yield id_, {
+                    "text": text,
+                    "label": label,
+                }

test.json

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+oid sha256:ecd080814ed5e466f33f5a5d64eb85c94c13efe367897174f4546491cade6df8
+size 34997197

train.json

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