Word embedding models are numerical representations of words, capturing semantic relationships and meaning within the complexity of language. They are widely used in natural language processing (NLP) tasks such as machine translation, sentiment analysis, and named entity recognition. Word embeddings enables computers to understand and process human language more effectively, and thus they are crucial for large language models (LLMs).
The provided models were trained using fastText from the corpora available in Sketch Engine using the SkipGram model with dimension 100. You can browse the models using our web frontend at https://embeddings.sketchengine.eu/