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I got it. So if I want to use the model to encode the text to get its embeddings representation, can I directly use the embeddings.txt I trained?
Or is it similar to BERT used as an encoder? which can I fine-tuned on my tasks or using the best model you mentioned in ISSUE #16 as an encoder when I want to get the vector representation of texts?
If I pre-train a model, how can I use to process the text? Is it same as word2vec or Glove?
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