Spmencode

Spmencode is the name commonly used for the command-line tool that encodes text into subword units using a pre-trained SentencePiece model. It is part of the SentencePiece project, an open-source text tokenizer and detokenizer designed to be language-agnostic. The tool is typically invoked as spm_encode (the spelling spmencode is a frequent variant in informal references) and is used to convert raw text into a sequence of subword tokens suitable for neural language models and machine translation systems.

How it works: spmencode loads a trained model file generated by the SentencePiece training process and applies the segmentation rules defined by that model to input text. The output is a sequence of tokens either as textual pieces or as numeric IDs, depending on the chosen format. It can read input from standard input or from a file and produce one line of encoded tokens per input line. Users can control the output format, and in some configurations may specify how special tokens are handled.

Usage and integration: spmencode is commonly used in NLP data processing pipelines to prepare data for training or inference with models that rely on subword representations. It is frequently combined with spm_train, the model-building tool, in end-to-end workflows. The tool is available as part of the SentencePiece distribution and is usable from both the command line and through language bindings, such as Python, enabling integration into preprocessing scripts and model training workflows.

Licensing and availability: SentencePiece, including the spmencode tool, is open-source and widely adopted in research and industry. It supports multiple model types, such as unigram and BPE-based tokenization, and is designed to handle diverse languages and writing systems.