this repo contains LLM and NLP applications starting from
- how tokenisers are applied, how embedings work,
- concepts of Masked Language Modelling(MLM),
- and practical applications of BERT, GPT and T5 using real world use cases such as: - Question Answering using BERT, - Text Generation using GPT-2, - Product Review using T5
For your comfortability, I have kept enough comments for code readability. Also to focus on concepts of embeddings, tokenisers and other concepts, they are created in .py format and practical implementation of LLM models are in .ipynb format I would suggest you to go with .py files first if you are dealing with NLP tasks for the first time.
The inspiration for my work is from the following course: https://www.udemy.com/course/llms-mastery-complete-guide-to-transformers-generative-ai/ This course is designed as a compact yet resourceful concepts of LLM models.
For any queries or suggestions, kindly reach me out to my LinkedIn or to my mail given in my bio. Happy Learning! I'll see you in the next one!