Sentiment Analysis Tutorial using laser

tasks/SentimentAnalysis/README.md

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+# 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.