Search Results for author: Ona de Gibert Bonet

Found 5 papers, 2 papers with code

The Catalan Language CLUB

no code implementations3 Dec 2021 Carlos Rodriguez-Penagos, Carme Armentano-Oller, Marta Villegas, Maite Melero, Aitor Gonzalez, Ona de Gibert Bonet, Casimiro Carrino Pio

The Catalan Language Understanding Benchmark (CLUB) encompasses various datasets representative of different NLU tasks that enable accurate evaluations of language models, following the General Language Understanding Evaluation (GLUE) example.

Spanish Biomedical Crawled Corpus: A Large, Diverse Dataset for Spanish Biomedical Language Models

no code implementations16 Sep 2021 Casimiro Pio Carrino, Jordi Armengol-Estapé, Ona de Gibert Bonet, Asier Gutiérrez-Fandiño, Aitor Gonzalez-Agirre, Martin Krallinger, Marta Villegas

We introduce CoWeSe (the Corpus Web Salud Espa\~nol), the largest Spanish biomedical corpus to date, consisting of 4. 5GB (about 750M tokens) of clean plain text.

On the Multilingual Capabilities of Very Large-Scale English Language Models

1 code implementation30 Aug 2021 Jordi Armengol-Estapé, Ona de Gibert Bonet, Maite Melero

Generative Pre-trained Transformers (GPTs) have recently been scaled to unprecedented sizes in the history of machine learning.

Few-Shot Learning Language Modelling +2

Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan

no code implementations Findings (ACL) 2021 Jordi Armengol-Estapé, Casimiro Pio Carrino, Carlos Rodriguez-Penagos, Ona de Gibert Bonet, Carme Armentano-Oller, Aitor Gonzalez-Agirre, Maite Melero, Marta Villegas

For this, we: (1) build a clean, high-quality textual Catalan corpus (CaText), the largest to date (but only a fraction of the usual size of the previous work in monolingual language models), (2) train a Transformer-based language model for Catalan (BERTa), and (3) devise a thorough evaluation in a diversity of settings, comprising a complete array of downstream tasks, namely, Part of Speech Tagging, Named Entity Recognition and Classification, Text Classification, Question Answering, and Semantic Textual Similarity, with most of the corresponding datasets being created ex novo.

Language Modelling named-entity-recognition +5

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