language:
- ca
tags:
- catalan
- text classification
- tecla
- CaText
- Catalan Textual Corpus
datasets:
- projecte-aina/tecla
metrics:
- accuracy
model-index:
- name: roberta-base-ca-v2-cased-tc
results:
- task:
type: text-classification
dataset:
name: TeCla
type: projecte-aina/tecla
metrics:
- name: Accuracy
type: accuracy
value: 0.8034
widget:
- text: Els Pets presenten el seu nou treball al Palau Sant Jordi.
- text: >-
Els barcelonins incrementen un 23% l’ús del cotxe des de l’inici de la
pandèmia.
- text: >-
Retards a quatre línies de Rodalies per una avaria entre Sants i plaça de
Catalunya.
- text: >-
Majors de 60 anys i sanitaris començaran a rebre la tercera dosi de la
vacuna covid els propers dies.
- text: Els cinemes Verdi estrenen Verdi Classics, un nou canal de televisió.
Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for TeCla-based Text Classification.
Table of Contents
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Model description
The roberta-base-ca-v2-cased-tc is a Text Classification (TC) model for the Catalan language fine-tuned from the roberta-base-ca-v2 model, a RoBERTa base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the roberta-base-ca-v2 model card for more details).
The previous version of this model, which was trained on the old TeCla dataset (v1), can still be accessed through the "v1" tag.
Intended uses and limitations
roberta-base-ca-v2-cased-tc model can be used to classify texts. The model is limited by its training dataset and may not generalize well for all use cases.
How to use
Here is how to use this model:
from transformers import pipeline
from pprint import pprint
nlp = pipeline("text-classification", model="projecte-aina/roberta-base-ca-v2-cased-tc")
example = "Retards a quatre línies de Rodalies per una avaria entre Sants i plaça de Catalunya."
tc_results = nlp(example)
pprint(tc_results)
Limitations and bias
At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.
Training
Training data
We used the TC dataset in Catalan called TeCla for training and evaluation. Although TeCla includes a coarse-grained ('label1') and a fine-grained categorization ('label2'), only the last one, with 53 classes, was used for the training.
Training procedure
The model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding development set and then evaluated it on the test set.
Evaluation
Variable and metrics
This model was finetuned maximizing F1 (weighted).
Evaluation results
We evaluated the roberta-base-ca-v2-cased-tc on the TeCla test set against standard multilingual and monolingual baselines. The results for 'label1' categories were obtained through a mapping from the fine-grained category ('label2') to the corresponding coarse-grained one ('label1').
| Model | TeCla - label1 (Accuracy) | TeCla - label2 (Accuracy) |
|---|---|---|
| roberta-base-ca-v2 | 96.31 | 80.34 |
| roberta-large-ca-v2 | 96.51 | 80.68 |
| mBERT | 95.72 | 78.47 |
| XLM-RoBERTa | 95.66 | 78.01 |
For more details, check the fine-tuning and evaluation scripts in the official GitHub repository.
Additional information
Author
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)
Contact information
For further information, send an email to aina@bsc.es
Copyright
Copyright (c) 2022 Text Mining Unit at Barcelona Supercomputing Center
Licensing information
Funding
This work was funded by the Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya within the framework of Projecte AINA.
Citation Information
If you use any of these resources (datasets or models) in your work, please cite our latest paper:
@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",
}
Disclaimer
Click to expand
The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.
When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
In no event shall the owner and creator of the models (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.