fine-tuning-with-huggingface.md

Fine-tuning with HuggingFace

• In this lab we will practice fine-tuning an OpenAI model

• A practical example of fine-tuning would be using the Hugging Face Transformers library to fine-tune the GPT-2 model on a custom dataset. In this example, we'll fine-tune GPT-2 on a dataset of movie reviews to generate movie review-like text.

Lab Goals:

STEP 0) Login to the server

• Each student is provided their individual server and credentials

• (Instructor: use our ubuntu AMI, t2.large or t2.xlarge instances

STEP 1) Prepare your dataset:

• For this example, you can use the IMDb dataset available from TensorFlow Datasets. The dataset contains 50,000 movie reviews: https://www.tensorflow.org/datasets/catalog/imdb_reviews

STEP 2) Set up the environment:

• Install the Hugging Face Transformers library (https://github.com/huggingface/transformers) and other necessary dependencies using pip:

STEP 3) Install the packages in the shell

pip install transformers
pip install tensorflow
pip install torch

STEP 4) Preprocess the data:

• Tokenize the movie reviews using the GPT-2 tokenizer, and convert them into input tensors.

STEP 5) Fine-tune GPT-2:

• Use the Trainer class from Hugging Face Transformers to fine-tune the GPT-2 model. Set appropriate training parameters like learning rate, batch size, and the number of epochs. Here's a link to a detailed tutorial on fine-tuning GPT-2 using Hugging Face Transformers: https://huggingface.co/blog/how-to-generate

STEP 6) Evaluate and test the model:

• After fine-tuning, evaluate the model on a separate dataset to ensure it generalizes well to new data. Test the model by generating new movie reviews using the generate() method from the Hugging Face Transformers library.

STEP 7) This example provides a high-level overview of the fine-tuning process.

• You may need to modify the code and adjust hyperparameters based on your specific requirements and resources.

STEP 8) Congrats!