Embeddings with the Catalan Textual Corpus The embeddings have been trained with a Catalan textual corpus of more than 34GB of data using floret with the following hyperparameters: mode: str = "floret", model: str = "cbow", dim: int = 300, mincount: int = 10, minn: int = 5, maxn: int = 6, neg: int = 10, hashcount: int = 2, bucket: int = 50000, thread: int = 128, The Catalan Textual Corpus used to train this embeddings, is the extended version of the initial available corpora described in Armengol-Estapé et al. (2021). This new version includes: Corpus Size in GB CaCrawlat 13.00 Wikipedia 1.10 DOGC 0.78 Catalan Open Subtitles 0.02 Catalan Oscar 4.00 CaWaC 3.60 Cat. General Crawling 2.50 Cat. Goverment Crawling 0.24 ACN 0.42 Padicat 0.63 RacoCatalà 8.10 NacióDigital 0.42 VilaWeb 0.06 From the new corpora, VilaWeb and NacióDigital come from digital newspapers, Padicat is composed of crawlings of the Biblioteca de Catalunya, and CaCrawlat comes from the Biblioteca Nacional de España (BNE). The processing took place on an HPC node equipped with an AMD EPYC 7742 (@ 2.250GHz) processor with 128 threads. How to use First initialize the spacy vectors from the floret table (.floret file): spacy init vectors ca floret_embeddings_ca.floret floret_embeddings_ca --mode floret import spacy # Load the floret vectors floret_embeddings = spacy.load("floret_embeddings_ca") # Get the embeddings of some words castanyes = floret_embeddings.vocab["castanyes"] flors = floret_embeddings.vocab["flors"] primavera = floret_embeddings.vocab["primavera"] tardor = floret_embeddings.vocab["tardor"] # Get some similarities print(flors.similarity(tardor)) print(flors.similarity(primavera)) # flors should be more similar to primavera than tardor. print(castanyes.similarity(primavera)) print(castanyes.similarity(tardor)) # castanyes should be more similar to tardor than primavera. Intended Uses and Limitations At the time of submission, no measures have been taken to estimate the bias and toxicity 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 card will be updated. Authors The Text Mining Unit from Barcelona Supercomputing Center. Contact Information For further information, send an email to aina@bsc.es. 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. Copyright Copyright (c) 2022 Text Mining Unit - Barcelona Supercomputing Center.