language:
- ca
license: apache-2.0
tags:
- catalan
- text classification
- WikiCAT_ca
- CaText
- Catalan Textual Corpus
datasets:
- projecte-aina/WikiCAT_ca
metrics:
- f1
model-index:
- name: roberta-base-ca-v2-cased-wikicat-ca
results:
- task:
type: text-classification
dataset:
type: projecte-aina/WikiCAT_ca
name: WikiCAT_ca
metrics:
- name: F1
type: f1
value: 77.823
widget:
- text: >-
La ressonància magnètica és una prova diagnòstica clau per a moltes
malalties.
- text: >-
Les tres idees bàsiques del noümen són l'ànima, el món i Déu, i és una
continuació de les tres substàncies de Descartes (tot i que el francès
anomenava jo o ment l'ànima).
Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for Viquipedia-based Text Classification.
Table of Contents
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Model description
The roberta-base-ca-v2-cased-wikicat-ca is a Text Classification 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). Dataset used is https://huggingface.co/datasets/projecte-aina/WikiCAT_ca, automatically created from Wikipedia and Wikidata sources
Intended uses and limitations
roberta-base-ca-v2-cased-wikicat-ca 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="roberta-base-ca-v2-cased-wikicat-ca")
example = "La ressonància magnètica és una prova diagnòstica clau per a moltes malalties."
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 WikiCAT_ca for training and evaluation.
Training procedure
The model was trained with a batch size of 16 and three learning rates (1e-5, 3e-5, 5e-5) for 10 epochs. We then selected the best learning rate (3e-5) and checkpoint (epoch 3, step 1857) using the downstream task metric in the corresponding development set.
Evaluation
Variable and metrics
This model was finetuned maximizing F1 (weighted) score.
Evaluation results
We evaluated the roberta-base-ca-v2-cased-wikicat-ca on the WikiCAT_ca dev set:
| Model | WikiCAT_ca (F1) |
|---|---|
| roberta-base-ca-v2-cased-wikicat-ca | 77.823 |
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 ([email protected])
Contact information
For further information, send an email to [email protected]
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.
Disclaimer
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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.