Maximum Likelihood fit for N-grams

A small library for quickly deriving the Maximum Likelihood estimates and Neural Network training for N-grams.

Installation

pip install ngram-ml

Usage

from ngram_ml import *

Example

• Maximum Likelihood Estimator Example

mle = NGramMLEstimator(sentences=tokens, n_grams=2, label_smoothing=1)
mle.calculate_cross_entropy(tokens)
mle.calculate_cross_entropy([['<S>', 'the', 'cat', 'sat', 'on', 'the', 'mat', '</S>']])

mle.generate_sentence(30, initial_pre_seq= tuple([mle.word_to_idx['pencil']]))
mle.generate_most_probable_sentence(30, initial_pre_seq= tuple([mle.word_to_idx['book']]))

• Neural Network Example

# Neural Network Example
dataset = NGramDataset(sentences=tokens, n_grams=2)
NN = NGramNeuralNet(n_grams=2, in_size=dataset.n_unique_words, embed_size=200)
NN.train(dataset.x, dataset.y, n_epochs=100, lr=0.01)