gpt2 bengali

Introduction

The Bengali GPT-2 model is part of Hugging Face's JAX/Flax community event. It is based on OpenAI's GPT-2 architecture, specifically adapted and pretrained on the Bengali corpus from the mC4 (multilingual C4) dataset. This model can generate text in Bengali and has been fine-tuned for specific tasks like generating Bengali song lyrics.

Architecture

The model is a causal (unidirectional) transformer, consistent with the original GPT-2 design. It was pretrained using language modeling on the Bengali subset of a large text corpus, similar to the original model's use of a 40 GB text dataset. The architecture supports text generation tasks in Bengali.

Training

• Dataset: mC4-bn
• Training Steps: 250,000
• Evaluation Metrics:
  • Eval Loss: 1.45
  • Eval Perplexity: 3.141

The training code is open-sourced and available for review and adaptation.

Guide: Running Locally

To use the Bengali GPT-2 model locally, you can utilize the Hugging Face Transformers library. Here’s a basic example:

1. Install Transformers: Ensure you have the transformers library installed.

   pip install transformers
   

2. Load the Model:

   from transformers import pipeline
   
   gpt2_bengali = pipeline('text-generation', model="flax-community/gpt2-bengali", tokenizer='flax-community/gpt2-bengali')
   

3. Generate Text: Use the pipeline to generate text in Bengali.

For enhanced performance, especially for training or fine-tuning tasks, consider using cloud GPUs such as those provided by AWS, GCP, or Azure.

License

This model is licensed under the MIT License, allowing for permissive use, distribution, and modification.