Welcome to the "AI4Thai Tutorial on Step by Step to Build Your Own Language Model" repository. This tutorial is designed to guide you through the process of building your own language model (LLM), leveraging the AI4Thai resources and tools. Whether you're a beginner or an experienced developer in the field of artificial intelligence, this tutorial aims to provide you with a comprehensive understanding and practical skills to develop a language model tailored to the Thai language.
AI4Thai is an initiative aimed at promoting and developing artificial intelligence technologies specifically for the Thai language. It provides a range of tools, datasets, and resources to support developers and researchers in creating AI models that understand and generate Thai language effectively.
Before diving into the tutorial, make sure you have the following prerequisites covered:
- Basic understanding of Python programming
- Familiarity with natural language processing (NLP) concepts
- An environment to run Python code (e.g., Jupyter notebook, Python script)
- Python Environment: Ensure you have Python 3.6 or later installed on your machine.
- Dependencies: Install all required libraries using pip:
pip install -r requirements.txtThis command will install all necessary Python packages listed in requirements.txt, including NLP libraries and AI4Thai API clients.
This tutorial is divided into several sections, each designed to walk you through different stages of building a language model:
- Introduction to Language Models: Understand what language models are and their significance in NLP.
- Setting Up AI4Thai API: Learn how to set up and authenticate with the AI4Thai API.
- Data Preparation: Guidelines on preparing your dataset for training a language model.
- Model Training: Step-by-step instructions on how to train your language model using the prepared dataset.
- Evaluation and Testing: Methods to evaluate the performance of your model and test it with real-world examples.
- Deployment: Tips on deploying your model for applications and services.
The repository includes example scripts and notebooks that demonstrate each step of the process. You can find these examples in the examples directory.