OpenMLPhDResearch/TextSpire-Character-Level-Transformer

Developing a TextSpire-inspired character-level Transformer model for text generation in low-resource languages, optimized with custom Byte Pair Encoding (BPE), leveraging the XTREME Benchmark for evaluation. # TextSpire: Character-Level Transformer Models for Text Generation in Low-Resource Languages This repository focuses on developing a **TextSpire-inspired** character-level Transformer model, specifically designed for **text generation in low-resource languages**. Building on ideas from the **makemore** framework, this project optimizes the text generation process using custom **Byte Pair Encoding (BPE)** techniques to enhance model efficiency and performance. Our goal is to improve accessibility and usability for low-resource languages by leveraging state-of-the-art Transformer models. ## 🚀 Research Focus Traditional language models often rely on word-level tokenization, which can be challenging for low-resource languages due to the limited amount of training data. This project aims to: - **Character-Level Modeling**: Develop a Transformer-based model that generates text at the character level, allowing it to handle languages with sparse datasets more effectively. - **Custom BPE Optimization**: Enhance the tokenization process using a custom BPE algorithm designed to adapt to low-resource languages. - **Cross-Lingual Evaluation**: Leverage the **XTREME Benchmark** to evaluate model performance across various languages, particularly focusing on low-resource settings. ## 🌍 Why It Matters Low-resource languages face significant challenges in NLP, as they lack large-scale annotated datasets. Character-level models offer a promising approach to mitigate these issues by: - **Reducing Vocabulary Size**: By working at the character level, the model can process languages with small or no pre-built lexicons. - **Improving Model Robustness**: Character-level models are better suited to handle languages with complex morphological structures. - **Supporting Low-Resource Languages**: This research is focused on improving NLP tools for underrepresented languages, providing opportunities for equitable AI development. ## 📚 Dataset This project uses the **XTREME Benchmark** …