Comments (1)
If you are training the re-ranker with M2 metric, you need to use an M2 format file (released with CoNLL-2014 & 2013 test sets). This is similar to how dev.m2 prepared by the released scripts looks like (format is available in the README.md https://github.com/nusnlp/m2scorer). The reason for "Levenshtein distance" error while training is because we use an external implementation of M2 in Moses framework for training, which skips distance source/target sentences possibly for efficiency reasons and prints the error you got. Alternatively, we've recently implemented and released another version of n-best reranker (https://github.com/nusnlp/crosentgec/blob/master/tools/nbest-reranker/) that has an in-built M2 implementation that does not skip sentence pairs and can compute M2 efficiently. We noticed this may slightly improve results also.
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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
- 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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