**Fine-Tuning GPT-2 and Using Transformer-Explainer for Analysis**

**Project Overview**
This project involves fine-tuning the GPT-2 model with the assigned language and analyzing its behavior using the Transformer-Explainer package. The objective is to observe how the fine-tuned model processes language inputs and generates outputs, gaining insights into its inner workings.

**Objectives**
1. Fine-tune GPT-2 with a specific language dataset.
2. Use the Transformer-Explainer package to analyze the behavior of the custom-trained GPT-2 model.
3. Generate 10 examples and observe the model’s performance using the explainer.

**Steps to Complete the Project**
1. Fine-Tuning GPT-2
   Used HuggingFace Transformers to fine-tune the GPT-2 model on finnish language.
   Prepared the dataset ensuring it represents the linguistic characteristics and diversity of the assigned language.
2. Running Transformer-Explainer
   Clone the Transformer-Explainer GitHub repository.
   Integrated the custom GPT-2 model with the explainer package.
3. Observed and analyzed the output on:
   Attention patterns.
   Token relationships.

**Resources**
1. Transformer-Explainer GitHub Repository
2. HuggingFace Transformers Documentation
3. Dataset in the assigned language