A Rust implementation of the the Viterbi algorithm for part-of-speech tagging.
The executable this project produces has the capability to...
traina model, saves it to a file for future use, and optionally evaluates the model on some dataevaluatea pre-trained model on some datapredictthe POS tagging of some sentnces using a pre-trained model either from standard input or a file
These functionalities correspond to the train, evaluate, and predict subcommands, respectively. The following blocks indicate how to use each subcommand.
A Rust implementation of the the Viterbi algorithm for part-of-speech tagging.
Usage: pos-tagger <COMMAND>
Commands:
train Trains a model, saves it to a file for future use, and optionally evaluates the model on some data
evaluate Evaluate a pre-trained model on some data
predict Predict the POS tagging of some sentnces using a pre-trained model either from standard input or from a file
help Print this message or the help of the given subcommand(s)
Options:
-h, --help Print help
-V, --version Print version
Trains a model, saves it to a file for future use, and optionally evaluates the model on some data
Usage: pos-tagger train [OPTIONS] -d <DATA_FILES> -o <OUT_FILE>
Options:
-d <DATA_FILES> Paths to all of the data files used to train the model
-o <OUT_FILE> The path to save the trained model to
-e <EVAL_FILE> The path to a data file to evaluate the model
Evaluate a pre-trained model on some data
Usage: pos-tagger evaluate -m <MODEL_FILE> -e <EVAL_FILE>
Options:
-m <MODEL_FILE> The path to the saved pre-trained model
-e <EVAL_FILE> The path to a data file to evaluate the model
Predict the POS tagging of some sentnces using a pre-trained model either from standard input or from a file
Usage: pos-tagger predict [OPTIONS] -m <MODEL_FILE>
Options:
-m <MODEL_FILE> The path to the saved pre-trained model
-p <PREDICT_FILE> The path to a data file of sentences to predict with. Defaults to STDIN if not specified