average_checkpoints run type can be used to average the parameters of several checkpoints, usually increasing the model performance. For example:
onmt-main \ --config config/my_config.yml --auto_config \ average_checkpoints \ --output_dir run/baseline-enfr/avg \ --max_count 5
will average the parameters of the 5 latest checkpoints from the model directory configured in
config/my_config.yml and save a new checkpoint in the directory
Then, execute the inference by setting the
--checkpoint_path option, e.g.:
onmt-main \ --config config/my_config.yml --auto_config \ --checkpoint_path run/baseline-enfr/avg/ckpt-200000 \ infer --features_file newstest2014.en.tok --predictions_file newstest2014.en.tok.out
To control the saving of checkpoints during the training, configure the following options in your configuration file:
train: # (optional) Save a checkpoint every this many steps. save_checkpoints_steps: 5000 # (optional) How many checkpoints to keep on disk. keep_checkpoint_max: 10
Sampling predictions from the output distribution can be an effective decoding strategy for back-translation, as described by Edunov et al. 2018. To enable this feature, you should configure the parameter
sampling_topk. Possible values are:
k, sample from the
kmost likely tokens
0, sample from the full output distribution
1, no sampling (default)
params: beam_width: 1 sampling_topk: 0 sampling_temperature: 1
Noising the decoded output is also a possible decoding strategy for back-translation, as described in Edunov et al. 2018. 3 types of noise are currently implemented:
dropout: randomly drop words in the sequence
replacement: randomly replace words by a filler token
permutation: randomly permute words with a maximum distance
which can be combined in sequence, e.g.:
params: decoding_subword_token: ▁ decoding_noise: - dropout: 0.1 - replacement: [0.1, ｟unk｠] - permutation: 3
decoding_subword_token (here set to the SentencePiece spacer) is useful to apply noise at the word level instead of the subword level.
A n-best list can be generated for models using beam search. You can configure it in your configuration file:
infer: n_best: 5
With this option, each input line will simply generate N consecutive lines in the output, ordered from best to worst.
Note that N can not be greater than the configured
The main OpenNMT-tf script can also be used to score existing translations via the
score run type. It requires 2 command line options to be set:
--features_file, the input labels;
--predictions_file, the translations to score.
onmt-main \ --config config/my_config.yml --auto_config \ score --features_file newstest2014.en.tok \ --predictions_file newstest2014.en.tok.out
The command will write on the standard output the score generated for each line in the following format:
<score> ||| <translation>
<score> is the negative log likelihood of the provided translation.
Tip: combining the n-best list generation and the scoring can be used for reranking translations.