Pretrained embeddings
type: Dataset
distribution: Landing page for the dataset
title: Förtränade inbäddningar @sv
description: Embeddings (mappings of linguistic units, such as words, sentences, characters, to vectors of real numbers) are playing an extremely important role in modern language technology. Training the embedding models is often costly, which is why pretrained embeddings are widely used. On this page we provide lists of various pretrained embeddings for Swedish and of studies that focus on evaluating Swedish embeddings. If you have suggestions or comments, please contact us. Embeddings Facebook FastText models: Common Crawl + Wiki, Wiki, Wiki with cross-lingual alignment NLPL repository: Word2Vec Continuous Skipgram (CoNLL17 corpus); ELMO (CoNLL17 corpus), ELMO (Wiki) NLPLAB at Linköping University: a pretrained Word2Vec model (trained on a Göteborgs-Posten corpus); a script for training both cbow and sgns Word2Vec; a paper comparing Word2Vec and GloVe to Saldo The National Library's (Kungliga bibliotekets) models: BERT, BERT fine-tuned for NER, ALBERT The Public Employment Service's (Arbetsförmedlingens )models: BERT Polyglot Kyubyong Park's models: trained on Wiki, Word2Vec and FastText Flair models. See also our Flair model for Swedish POS tagging. Språkbanken Text diachronic embeddings. Evaluation studies Sahlgren, Magnus, and Fredrik Olsson. 2016. Gender Bias in Pretrained Swedish Embeddings. Proceedings of the 22nd Nordic Conference on Computational Linguistics. Fallgren, Per, Jesper Segeblad, and Marco Kuhlmann. 2016. Towards a standard dataset of swedish word vectors. Sixth Swedish Language Technology Conference (SLTC). Holmer, Daniel. 2020. Context matters: Classifying Swedish texts using BERT's deep bidirectional word embeddings. Bachelor thesis at Linköping University. Adewumi, Tosin, Foteini Liwicki and Marcus Liwicki. 2020. Exploring Swedish & English fastText Embeddings with the Transformer Adewumi, Tosin, Foteini Liwicki and Marcus Liwicki. 2020. Corpora Compared: The Case of the Swedish Gigaword & Wikipedia Corpora
modified: 2025-01-01T00:00:00Z
issued: 2025-01-01T00:00:00Z
publisher: University of Gothenburg
qualified attribution: Attribution
  had role: author
  agent: Språkbanken Text
    type: Agent
    type: Academia-ScientificOrganisation
keyword: Language Technology (Computational Linguistics)
keyword: model
language: SWE

Landing page for the dataset
type: Distribution
title: Landningssida för datasetet @sv
access URL: Pretrained embeddings
availability: stable

University of Gothenburg
type: Agent
name: Göteborgs universitet @sv
type: Academia-ScientificOrganisation
source: 01tm6cn81