devaminta/amharic_n_gram_model

# N-Gram Language Model Evaluation This repository contains the implementation and evaluation of N-Gram Language Models (Unigrams, Bigrams, and Trigrams) for a given corpus. The models are evaluated using both intrinsic and extrinsic methods to assess their performance. ## Table of Contents - Overview - Installation - Usage - Evaluation - Intrinsic Evaluation - Extrinsic Evaluation - Results - Contributing - License ## Overview This project involves the creation of N-Gram models (Unigrams, Bigrams, and Trigrams) and their evaluation using two primary methods: ### Task 1 - Creation of N-Gram Models: 1. **Unigrams**: A unigram model calculates the probability of each word in the corpus independently. 2. **Bigrams**: A bigram model calculates the probability of a word given the previous word. 3. **Trigrams**: A trigram model calculates the probability of a word given the previous two words. ### Task 2 - Intrinsic Evaluation: - **Perplexity**: Measures the model's ability to predict a test dataset by calculating the inverse probability of test data normalized by the number of words. ### Task 3 - Extrinsic Evaluation: - The models are evaluated using an extrinsic task, such as a classification task, and evaluated using metrics like accuracy, precision, recall, and F1-score. ### Language Models Implemented: - **Unigrams**: Single word probabilities based on frequency in the corpus. - **Bigrams**: Two-word sequence probabilities. - **Trigrams**: Three-word sequence probabilities. ## Installation 1. Clone the repository: ```bash git clone github.com cd ngram-language-model