finetuning

Fine-Tuning DistilBERT for Text Classification

Welcome to the Jupyter notebook project for fine-tuning the DistilBERT model from Hugging Face for text classification tasks using TensorFlow and Keras. This project serves as a practical guide for understanding the process of fine-tuning pre-trained models and encourages you to contribute by experimenting with different models and datasets. Overview

In this project, we explore the fine-tuning process for text classification using the DistilBERT model. DistilBERT is a distilled version of BERT, designed for efficiency while maintaining competitive performance. You can adapt this project to various text classification tasks, such as sentiment analysis, topic classification, or custom NLP problems. Getting Started Prerequisites

Before running the Jupyter notebook, ensure you have the following dependencies installed:

Python 3.x
TensorFlow
Hugging Face Transformers
Jupyter Notebook

You can install these dependencies using pip:

pip install tensorflow transformers jupyter

Clone the Repository

Clone this repository to your local machine:

git clone https://github.com/Vishakh2012/finetuning.git

Notebook

To run the fine-tuning Jupyter notebook, navigate to the project directory and open the Jupyter notebook:

cd finetune-distilbert
jupyter notebook sarcasm_detector.ipynb

Follow the instructions within the notebook to execute the code and fine-tune the DistilBERT model on your specific classification task. Contributing

We encourage contributions from the community to make this project a valuable resource for learning about fine-tuning various NLP models. Here are some ways you can contribute:

1. Experiment with Different Models: Try fine-tuning other pre-trained models like RoBERTa, GPT-3, or XLNet for different text classification tasks and share your findings.

2. Add Datasets: Include additional datasets for various NLP tasks and document how to fine-tune models using these datasets.

3. Performance Analysis: Conduct comprehensive performance analysis by evaluating models on different evaluation metrics and benchmark datasets.

4. Code Improvements: If you identify any issues or have ideas for improving the code or documentation, please submit pull requests.

5. Tutorials and Guides: Create tutorials or documentation on fine-tuning models, explaining key concepts, and helping newcomers understand the process.

Get Involved

Join the community and contribute to this project. Collaboration and shared knowledge are vital for advancing the field of natural language processing and deep learning. If you have questions or need assistance, feel free to open an issue.

Let's work together to make this project a valuable resource for fine-tuning NLP models and advancing your understanding of deep learning!

Happy experimenting! 😄🤖📚