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Resume training by specifying the model you'd like to resume from using `--restore-file <path to checkpoint>`.

See original GitHub issue

Yes, you can resume training by specifying the model you’d like to resume from using --restore-file <path to checkpoint>.

_Originally posted by @lematt1991 in https://github.com/pytorch/fairseq/issues/1182#issuecomment-535507612_

First Model was trained on architecture LSTM and the second one was also LSTM with restore-file option. Both were being trained on separate data files (same language pair) Error: Architecture mismatch.

Issue Analytics

State:
Created 4 years ago
Comments:7 (2 by maintainers)

Top GitHub Comments

2reactions

aastha19commented, Dec 5, 2019

@echan00 The bin files were created separately and also a common dictionary was created. And this common dictionary was placed in the bin files for both the data sets replacing their actual dictionary created at the time of pre-processing. Somehow it worked for me!

2reactions

huihuifancommented, Sep 27, 2019

It’s not going to be possible to restore from a checkpoint where the vocabulary size is different… the input/output embedding matrices are going to be the wrong size. This is not a bug with the code. You need to decide how you want to handle this, most likely you want to re-process your second dataset with the same dictionary as the first.