I want to experiment with different embeddings such Word2Vec, ELMo, and BERT but I'm a little confused about whether to use the word embeddings or sentence embeddings, and why. I'm using the embeddings as features input to SVM classifier.
Thank you.
Training SVM classifier (word embeddings vs. sentence embeddings)
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Though both approaches can prove efficient for different datasets, as a rule of thumb I would advice you to use word embeddings when your input is of a few words, and sentence embeddings when your input in longer (e.g. large paragraphs).