This article provides an overview of embeddings in NLP and highlights their importance in capturing the meaning of words. It discusses various applications of embeddings in NLP, such as machine translation, text summarization, and question answering. The article also lists popular embedding models in the market, including GloVe, word2vec, FastText, BERT, RoBERTa, XLNet, and others. It compares the pros and cons of these models and provides guidance on choosing the right model based on specific needs and considerations. Overall, embeddings are seen as a critical tool in NLP that improve the performance of models and enable more effective tasks.
Table of contents
NLP, Embeddings -Embedding Models and ComparisonIn this article, we will look at embeddings, purpose of embeddings, most popular embedding models available in market and a comparison of these models.1. NLP and Embeddings Overview2. Application of Embeddings in NLP3. Most Popular Embedding Models available in market4. Comparison — Pros and Cons of the Embedding Models5. How would you Choose, Which Embedding Model to use ?Sort: