Biomedical Spanish CBOW Word Embeddings in Floret The embeddings have been trained with a biomedical Spanish corpus 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 embeddings were trained on the concatenation of all corpora from the Spanish biomedical corpus that includes Spanish data from various sources for a total of 1.1B tokens across 2,5M documents. Source No. tokens Medical crawler 903,558,136 Clinical cases misc. 102,855,267 EHRs documents* 95,267,204 Scielo 60,007,289 BARR2 Background 24,516,442 Wikipedia (Life Sciences) 13,890,501 Patents 13,463,387 EMEA 5,377,448 Mespen (MedlinePlus) 4,166,077 PubMed 1,858,966 More information about the corpus can be found here https://aclanthology.org/2022.bionlp-1.19/ and here https://arxiv.org/abs/2109.07765 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 es floret_embeddings_bio_es.floret floret_embeddings_bio_es --mode floret import spacy # Load the floret vectors floret_embeddings = spacy.load("floret_embeddings_bio_es") # Get the embeddings of some words diabetes = floret_embeddings.vocab["diabetes"] insulina = floret_embeddings.vocab["insulina"] radiografia = floret_embeddings.vocab["radiografia"] # Get some similarities print(diabetes.similarity(insulina)) print(diabetes.similarity(radiografia)) # diabetes should be more similar to insuline than radiografia 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 plantl-gob-es@bsc.es Funding This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL. Copyright Copyright (c) 2022 Secretaría de Estado de Digitalización e Inteligencia Artificial

Funded by the Plan de Impulso de las Tecnologías del Lenguaje (Plan-TL).