Comments (3)
Certainly! Get your hands on some training data (Looks like you can get some from here or here), run build_dictionary_char.py
on it to learn the vocabulary (here the source and target vocabularies would be the same), start training and there you go. your character-level conversational model.
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Thanks for the help. Great work on this project!
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Cheers!
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Related Issues (20)
- output? HOT 1
- Decode on CPU HOT 6
- Using custom datasets HOT 1
- error when run on cpu HOT 4
- Preprocessing for other datasets HOT 2
- Possible bug in `*/nmt[_many].py` HOT 2
- Per-instance weighted loss HOT 7
- where and what is the preprocessing code exactly? HOT 1
- Where and what is the preprocessing code exactly?
- What is wmts in the code? HOT 2
- Use your own parallel corpus for training and translation HOT 1
- f_init gets stuck during translation
- One question about highway HOT 1
- Request on Theano and pygpu version HOT 2
- Using char-to-char model for Hindi-English translation task and working on Google Colab
- Why save the best model only when saveFreq != validFreq ? HOT 6
- Some Errors HOT 11
- No module named mixer HOT 1
- Training-Question HOT 2
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