This project is an auto-filling text program implemented in Python using N-gram models. The program suggests the next word based on the input given by the user. It utilizes N-gram models, specifically Trigrams and Bigrams, to generate predictions.

Artificial Intelligence Course 4th Project: Implementing Bigram and Unigram models for filtering comments

It's a python based n-gram langauage model which calculates bigrams, probability and smooth probability (laplace) of a sentence using bi-gram and perplexity of the model.

NLP-persian-poet-identification

This project is meant to generate a Local Language Model based on textual input.

Implementation of Handwritten Text Recognition Systems using TensorFlow

Detection of language of a written text using bigram letter model.

Implementation of lightweight transformer model for character level text generation

A basic application with necessary steps for filtering spam messages using bigram model with python language.

Detect the text language automatically using a bigram model, Support Vector Machines, and Artifical Neural Networks. The model is trained using the WiLI-2018 benchmark dataset, and the highest accuracy achieved on the test dataset is 99.7% with paragraph text.

Artifacts for CS-539 Natural Language Processing course

We designed an Information Retrieval system based on Vector Space model in python. We Also have implemented Bi gram Indices for Phrasal query search and Champion List retrieval. We also compared time of whole retrieving in our project report.

Explored various NLP techniques across different applications

A python library to train and store a word2vec model trained on wiki data. Model includes most common bigrams.

Implementation of unigram/bigram language models, noisy channel and pointwise mutual information for natural language processing.

For any given query, an Information Retrieval (IR) system is used to obtain and rank relevant word documents from the data collection of interest. The most basic IR system uses Term Frequency Inverse Document Frequency (TF-IDF) to represent documents and queries as vectors, and then uses measures like cosine similarity to assess the relevance of…

Classifying texts using unigram and bigram models

GPT Model from scratch

An LLM Based on Harry Porter Series

A replication of an experiment by Reali and Christiansen (2005) disputing the basic assumptions of Chomsky's Poverty of Stimulus theory.