Comments (1)
Yes, after Fairseq training finishes (using the early stopping criteria based on learning rate), the checkpoint that has the highest F0.5 on the development data is selected as the final model.
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Related Issues (20)
- Using interactive instead of generate to evaluate HOT 1
- Python error: <stdin> is a directory, cannot continue HOT 2
- the size of training dataset? HOT 2
- How to use language model (94Bcclm.trie)? HOT 2
- TypeError: iter() returned non-iterator of type 'NBestList' HOT 9
- An error about Compile and install PyTorch(0.2.0) HOT 5
- Accuracy of trained model? HOT 12
- Fairseq-py Installation Issue HOT 5
- About training data
- reranker error HOT 6
- pytorch version? HOT 3
- pre-trainined embeddings. HOT 1
- ImportError: cannot import name 'libbleu' HOT 4
- 'Levenshtein greater than source size' when training re-ranker HOT 1
- Output directory error while running run.sh in google colab HOT 1
- ImportError: cannot import name 'libbleu' from 'fairseq' HOT 3
- Requesting NUCLE dataset 3.2
- Error loading state_size in FConvModel : size mismatch in encoder-decoder weights
- General question: GEC data preprocessing
- Can this model be used for any language?
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