ngram-trie 1.2.1

A Rust-based n-gram trie library for Python

ngram-trie

ngram-trie is a Rust library designed to efficiently handle n-gram data structures using a trie-based approach. It provides functionalities for fitting, saving, loading, and querying n-gram models, with support for various smoothing techniques.

Installation Rust

1. Include it in the Cargo.toml:

   [dependencies]
   ngram-trie = { git = "https://github.com/behappiness/ngram-trie" }
   

Installation Python

1. Install from pip:

   pip install ngram-trie
   

Example Usage

from ngram_trie import PySmoothedTrie

trie = PySmoothedTrie(n_gram_max_length=7, root_capacity=None)

trie.fit(tokenized_data, n_gram_max_length=7, root_capacity=None, max_tokens=None)

trie.set_rule_set(["++++++", "+++++", "++++", "+++", "++", "+"])

trie.fit_smoothing()

trie.get_prediction_probabilities(tokenized_context)

Specify the smoothing

trie.fit_smoothing("modified_kneser_ney"/"stupid_backoff")

Unsmoothed

from ngram_trie import PySmoothedTrie

trie = PySmoothedTrie(n_gram_max_length=7, root_capacity=None)

trie.fit(tokenized_data, n_gram_max_length=7, root_capacity=None, max_tokens=None)

trie.set_rule_set(rules)

trie.get_unsmoothed_probabilities(tokenized_context)

Dev

cargo add pyo3 --features extension-module

Build wheel

maturin build