metadata
language:
- ca
license: '???'
tags:
- catalan
- semantic textual similarity
- sts-ca
- CaText
- Catalan Textual Corpus
datasets:
- projecte-aina/sts-ca
metrics:
- pearson
model-index:
- name: roberta-base-ca-cased-sts
results:
- task:
type: text-classification
dataset:
type: projecte-aina/sts-ca
name: sts-ca
metrics:
- type: pearson
value: 0.8120486139447483
widget:
- text: M'agrades. T'estimo.
- text: M'agrada el sol i la calor. A la Garrotxa plou molt.
- text: El llibre va caure per la finestra. El llibre va sortir volant.
- text: El meu aniversari és el 23 de maig. Faré anys a finals de maig.
Catalan BERTa (RoBERTa-base) finetuned for Semantic Textual Similarity.
The roberta-base-ca-cased-sts is a Semantic Textual Similarity (STS) model for the Catalan language fine-tuned from the BERTa model, a RoBERTa base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the BERTa model card for more details).
Datasets
We used the TE dataset in Catalan called STS-ca for training and evaluation.
Evaluation and results
We evaluated the roberta-base-ca-cased-sts on the STS-ca test set against standard multilingual and monolingual baselines:
Model | STS-ca (Pearson) |
---|---|
roberta-base-ca-cased-sts | 81.20 |
mBERT | 76.34 |
XLM-RoBERTa | 75.40 |
WikiBERT-ca | 77.18 |
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
TODO
Disclaimer
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