This is a diacritization model for Arabic language. This model was built/trained using the Tashkeela: the Arabic diacritization corpus on Kaggle

Official code for Group-Transformer (Scale down Transformer by Grouping Features for a Lightweight Character-level Language Model, COLING-2020).

A structured documentation hub for AI and ML concepts, based on Andrej Karpathy's 'Zero to Hero' series, featuring practical implementations and learning resources for language models and transformers.

Lyrics Generation:notes: using LSTM , word2vec Analysis and more

Build a character level language model to generate new dinosaur names

Text Article generator using using Character level LSTM network.

Sequence Models coding assignments

A causal intervention framework to learn robust and interpretable character representations inside subword-based language models

In this project, I worked with a small corpus consisting of simple sentences. I tokenized the words using n-grams from the NLTK library and performed word-level and character-level one-hot encoding. Additionally, I utilized the Keras Tokenizer to tokenize the sentences and implemented word embedding using the Embedding layer. For sentiment analysis

An implementation of "Character-level Convolutional Networks for Text Classification" in Tensorflow. See https://arxiv.org/pdf/1509.01626.pdf.

Notebooks of programming assignments of Sequence Models course of deeplearning.ai on coursera in May-2020

It aims to write new sentences by learning character units sentences using RNN. As training data, a collection of Shakespeare's novels was used.

Name generation using RNN. This model was trained for generating indian names. Made using keras.

This repository demonstrates a minimal character-level recurrent neural network language model built with PyTorch. It trains on the Tiny Shakespeare corpus popularized by Andrej Karpathy, providing both the original Google Colab notebook and an exported Python script for reference or interactive execution.

This repository contains the code and PLODv2 dataset to train character-level language models (CLM) for abbreviation and long-form detection released with our LREC-COLING 2024 publication

Character-level and token-based language models implemented in pure PyTorch.

retro style tokenization for language models

This repository contains the source code for our research on character-level models in Arabic Natural Language Processing (NLP).

Recurrent neural network for building a character-level language model and its application to generating new dinosaur names