Python implementation of n-gram language models from scratch and using the NLTK library. I also provide slides from my NLP course to understand n-grams.
-
Updated
May 30, 2024
Python implementation of n-gram language models from scratch and using the NLTK library. I also provide slides from my NLP course to understand n-grams.
Tools for Arabic language processing using the MADAR dataset. Includes Next Word Prediction with an n-gram model and Dialect Identification with a BERT model. Features an interactive UI with Streamlit and comprehensive text preprocessing for Arabic.
NGram with basic smoothing
Ngrams with basic smoothing.
Ngrams with Basic Smoothings
Ngrams with Basic Smoothings
Ngrams with Basic Smoothings
Ngrams with Basic Smoothings
Ngrams with Basic Smoothings
Next-token prediction in JavaScript — build fast language and diffusion models.
a probabilistic language identification system that identifies the language of a sentence
This demo accompanies the poster presentation "Limitations of the entropy measure in n-gram language modelling".
A simple NLP project for word prediction using N-grams.
Fit an n-gram Markov-Model to WhatsApp chat history
Academic project centered around n-grams and their application in developing a spelling corrector with contextual awareness.
Implemented a collection of Ngram language models on brown corpus from scratch
Basic Language Models , Bag of Words, Ngram Models Etc NLP modelling and associated tasks
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.
Word/Image/Audio Embedding models, Tokenizer models, Ngram language models, MatrixModels, Corpus building, Vocabulary Building, Language modelling
Character-level ngram language model implemented in Python
Add a description, image, and links to the ngram-language-model topic page so that developers can more easily learn about it.
To associate your repository with the ngram-language-model topic, visit your repo's landing page and select "manage topics."