SentimentAnalysis-DistilBERT

Sentiment Analysis with DistilBERT

This repository contains a Jupyter Notebook for performing sentiment analysis using the DistilBERT model. The notebook demonstrates the process of fine-tuning DistilBERT for text classification tasks, specifically for sentiment analysis.

Project Overview

Sentiment analysis is a common task in natural language processing (NLP) where the goal is to determine the sentiment expressed in a piece of text. This notebook leverages the pre-trained DistilBERT model, which is a smaller, faster, and lighter version of BERT (Bidirectional Encoder Representations from Transformers).

Notebook Contents

The notebook includes the following sections:

1. Introduction: Brief overview of the project and objectives.
2. Setup: Installation and import of necessary libraries and packages.
3. Data Loading and Preprocessing: Loading the dataset and preprocessing the text data for model training.
4. Model Setup: Initializing the DistilBERT model and preparing it for fine-tuning.
5. Training: Fine-tuning the DistilBERT model on the sentiment analysis dataset.
6. Evaluation: Evaluating the performance of the model on the validation/test dataset.
7. Inference: Making predictions on new text data using the trained model.

How to Use

To run the notebook, you can use the following Google Colab link:

Prerequisites

• Python 3.x
• Jupyter Notebook
• Required Python packages (can be installed via requirements.txt if provided)

Installation

1. Clone this repository:

   git clone https://github.com/yourusername/sentiment-analysis-distilbert.git
   cd sentiment-analysis-distilbert

2. Install the required packages:

git clone https://github.com/yourusername/sentiment-analysis-distilbert.git
cd sentiment-analysis-distilbert

3. Running the Notebook

• Open the notebook:

  jupyter notebook distilbert_base_uncased_new_lora_text_classification.ipynb

• Follow the instructions in the notebook to run each cell and perform sentiment analysis.

Results

• After fine-tuning the model, you will be able to evaluate its performance on the test dataset. The notebook provides various evaluation metrics such as accuracy, precision, recall, and F1 score.

Contributing

• If you would like to contribute to this project, please fork the repository and create a pull request with your changes. Contributions are welcome!

License

• This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgements

Hugging Face for providing the pre-trained DistilBERT model and the transformers library. Google Colab for providing a free and convenient environment for running Jupyter Notebooks.