I've been using a few big word embedding models like word2vec & FastText, and they work very well on most problems. I am now adressing a new kind of data, on which they perform quite poorly, and I found out that it is possible to train your own model.
- Why would you do that ? does it make it more domain-specific ?
- How much data examples would I need to feed the model to achieve a decent result, knowing that my documents are very short (3-4 words max) ?
Is it possible to get an estimation of the training time ? say for a million data examples of 3 words each.
Bonus : is continuing the training of a pre-trained model a good strategy ? will it be able to capture the "essence" of the new data even if there are way less data examples than during the first, original training ?
Thank you so much !