laser/tasks/SentimentAnalysis/README.md

# Laser Encoder: Sentiment Analysis

## Overview

This project demonstrates the application of the Laser Encoder tool for creating sentence embeddings in the context of sentiment analysis. The Laser Encoder is used to encode text data, and a sentiment analysis model is trained to predict the sentiment of the text.

## Getting Started

To run the notebook in Google Colab, click the "Open in Colab" button below:

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NIXBLACK11/LASER-fork/blob/Sentiment-analysis-laser/tasks/SentimentAnalysis/SentimentAnalysis.ipynb)

Also, check out the hugging face space with the button below:

[![Open In Hugging Face Space](https://img.shields.io/badge/Open%20In-Hugging%20Face%20Space-blue?logo=huggingface)](https://huggingface.co/spaces/NIXBLACK/SentimentAnalysis_LASER_)


## Example Usage

Run the Example Notebook:
    Execute the provided Jupyter Notebook SentimentAnalysis.ipynb

        jupyter notebook SentimentAnalysis.ipynb


## Customization

- Modify the model architecture, hyperparameters, and training settings in the neural network model section based on your requirements.
- Customize the sentiment mapping and handling of unknown sentiments in the data preparation section.

## Additional Notes
- Feel free to experiment with different models, embeddings, and hyperparameters to optimize performance.
- Ensure that the dimensions of embeddings and model inputs are compatible.
Adapt the code based on your specific dataset and use case.