32 citations · 69 across the 11 of their papers we have counts for
7 papers · 1 filter
esCorpius: A Massive Spanish Crawling Corpus
Asier Gutiérrez-Fandiño, David Pérez-Fernández, Jordi Armengol-Estapé +2
In the recent years, transformer-based models have lead to significant advances in language modelling for natural language processing. However, they require a vast amount of data t…
FinEAS: Financial Embedding Analysis of Sentiment
Asier Gutiérrez-Fandiño, Miquel Noguer i Alonso, Petter Kolm +1
We introduce a new language representation model in finance called Financial Embedding Analysis of Sentiment (FinEAS). In financial markets, news and investor sentiment are signifi…
Spanish Legalese Language Model and Corpora
Asier Gutiérrez-Fandiño, Jordi Armengol-Estapé, Aitor Gonzalez-Agirre +1
There are many Language Models for the English language according to its worldwide relevance. However, for the Spanish language, even if it is a widely spoken language, there are v…
Biomedical and Clinical Language Models for Spanish: On the Benefits of Domain-Specific Pretraining in a Mid-Resource Scenario
Casimiro Pio Carrino, Jordi Armengol-Estapé, Asier Gutiérrez-Fandiño +4
This work presents biomedical and clinical language models for Spanish by experimenting with different pretraining choices, such as masking at word and subword level, varying the v…
Spanish Biomedical Crawled Corpus: A Large, Diverse Dataset for Spanish Biomedical Language Models
Casimiro Pio Carrino, Jordi Armengol-Estapé, Ona de Gibert Bonet +4
We introduce CoWeSe (the Corpus Web Salud Español), the largest Spanish biomedical corpus to date, consisting of 4.5GB (about 750M tokens) of clean plain text. CoWeSe is the result…
MarIA: Spanish Language Models
Asier Gutiérrez-Fandiño, Jordi Armengol-Estapé, Marc Pàmies +7
This work presents MarIA, a family of Spanish language models and associated resources made available to the industry and the research community. Currently, MarIA includes RoBERTa-…
Spanish Biomedical and Clinical Language Embeddings
Asier Gutiérrez-Fandiño, Jordi Armengol-Estapé, Casimiro Pio Carrino +3
We computed both Word and Sub-word Embeddings using FastText. For Sub-word embeddings we selected Byte Pair Encoding (BPE) algorithm to represent the sub-words. We evaluated the Bi…