By default, OpenNMT-tf expects and generates tokenized text. The users are thus responsible to tokenize the input and detokenize the output with the tool of their choice.
However, OpenNMT-tf provides tokenization tools based on the C++ OpenNMT Tokenizer that can be used in 2 ways:
offline: use the provided scripts to manually tokenize the text files before the execution and detokenize the output for evaluation
online: configure the execution to apply tokenization and detokenization on-the-fly
pyonmttok package is only supported on Linux as of now.
YAML files are used to set the tokenizer options to ensure consistency during data preparation and training. For example, this configuration defines a simple word-based tokenization using the OpenNMT tokenizer:
type: OpenNMTTokenizer params: mode: aggressive joiner_annotate: true segment_numbers: true segment_alphabet_change: true
For a complete list of available options, see the Tokenizer documentation).
OpenNMT-tf also defines additional tokenizers:
You can invoke the
onmt-tokenize-text script directly and pass the tokenizer configuration:
$ echo "Hello world!" | onmt-tokenize-text --tokenizer_config config/tokenization/aggressive.yml Hello world ￭!
A key feature is the possibility to tokenize the data on-the-fly during the training. This avoids the need of storing tokenized files and also increases the consistency of your preprocessing pipeline.
Here is an example workflow:
1. Build the vocabularies with the custom tokenizer, e.g.:
onmt-build-vocab --tokenizer_config config/tokenization/aggressive.yml --size 50000 --save_vocab data/enfr/en-vocab.txt data/enfr/en-train.txt onmt-build-vocab --tokenizer_config config/tokenization/aggressive.yml --size 50000 --save_vocab data/enfr/fr-vocab.txt data/enfr/fr-train.txt
The text files are only given as examples and are not part of the repository.
2. Reference the tokenizer configurations in the data configuration, e.g.:
data: source_tokenization: config/tokenization/aggressive.yml target_tokenization: config/tokenization/aggressive.yml
Only TensorFlow ops can be exported to graphs and used for serving. When a tokenizer is not implemented in terms of TensorFlow ops such as the OpenNMT tokenizer, it will not be part of the exported graph. The model will then expects tokenized inputs during serving.
text (1D string tensor)
tokens (2D string tensor),
length (1D int32 tensor)
(*) During model export, tokenization resources used by the OpenNMT tokenizer (configuration, subword models, etc.) are registered as additional assets in the