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: 0.7426
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 Text Classification.
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).
Datasets
We used the TC dataset in Catalan called TeCla for training and evaluation.
Evaluation and results
We evaluated the roberta-base-ca-v2-cased-tc on the TeCla test set against standard multilingual and monolingual baselines:
Model | TeCla (Accuracy) |
---|---|
roberta-base-ca-v2-cased-tc | 74.26 |
roberta-base-ca-cased-tc | 73.65 |
mBERT | 69.90 |
XLM-RoBERTa | 70.14 |
For more details, check the fine-tuning and evaluation scripts in the official GitHub repository.
Citing
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",
}
Funding
This work was funded by the Catalan Government within the framework of the AINA project..