projecte-aina / roberta-large-ca-paraphrase

Catalan BERTa (roberta-large-ca-v2) finetuned for Paraphrase Detection

Table of Contents

Model description

The roberta-large-ca-paraphrase is a Paraphrase Detection model for the Catalan language fine-tuned from the roberta-large-ca-v2 model, a RoBERTa base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers.

Intended uses and limitations

roberta-large-ca-paraphrase model can be used to detect if two sentences are in a paraphrase relation. 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-large-ca-paraphrase")
example = "Tinc un amic a Manresa. </s></s> A Manresa hi viu un amic meu."

paraphrase = nlp(example)
pprint(paraphrase)

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 Paraphase Detection dataset in Catalan Parafraseja for training and evaluation.

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 the combined_score.

Evaluation results

We evaluated the roberta-large-ca-paraphrase on the Parafraseja test set against standard multilingual and monolingual baselines:

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

Apache License, Version 2.0

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

NA

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