Unity ML-Agents | Pretrain an LLM from Scratch with Sentence Transformers | Part 4
0*Welcome back to our Tau LLM series! 🌟*
In this episode, we're taking our project to the next level with some exciting new developments. Our highlights include:
**Encoding Training Data**: Learn how to get our encoder to transform the training dataset into embeddings, setting the foundation for effective model training.
**Teacher Forcing Algorithm**: Discover the implementation of a basic Teacher Forcing algorithm to teach our model auto-regression, improving its ability to generate coherent responses.
**Evaluation System**: See how we build an evaluation system to score our model's results after training an epoch (1 million steps), ensuring we can measure and refine its performance accurately.
**Query Functionality Testing**: Watch as we test the query functionality of our newly created vector database, verifying its efficiency in handling and retrieving relevant embeddings.
Whether you're a beginner or an experienced developer, this episode will provide valuable insights into the intricate process of fine-tuning and evaluating an LLM using Unity, ML-Agents, and NLP techniques.