This project focuses on fine-tuning the pre-trained GPT-2 model from OpenAI to improve its text generation capabilities using a custom dataset. The goal is to adapt the model to produce more relevant and context-specific text based on the input data provided.
The project involves the following key steps:
Data Preparation: Collecting and preparing the dataset for training. The dataset used in this project consists of text data tailored to the desired output style and context.
Tokenization: Utilizing the GPT-2 tokenizer to preprocess the text data. This step ensures that the input text is properly formatted and tokenized for the model.
Model Configuration: Setting up the GPT-2 model for fine-tuning. The Hugging Face Transformers library is used to load the pre-trained GPT-2 model and configure it for further training.
Training: Fine-tuning the model on the prepared dataset. This involves training the model over several epochs, adjusting hyperparameters such as learning rate, batch size, and the number of epochs to optimize performance.
Evaluation: Assessing the performance of the fine-tuned model. The model's ability to generate text is evaluated using various metrics and qualitative analysis.
The fine-tuned GPT-2 model demonstrates improved text generation capabilities tailored to the specific context of the training data. The results showcase the model's ability to produce coherent and contextually relevant text.