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README.md

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+# Text Embedding Visualizer
+
+This project generates embeddings for short sentences and visualizes them in 2D using PCA and t-SNE.  
+It works on **both CPU and GPU laptops** with the same dependencies.
+
+## Install
+```bash
+## create a virtual environment 
+python -m venv venv
+## activate the venv
+source ./venv/bin/activate  # Windows: ./venv/Scripts/activate
+
+pip install -r requirements.txt
+
+***How it Works***
+## Loads a small dataset of sentences.
+## Generates embeddings with all-MiniLM-L6-v2.
+## Reduces dimensions using PCA and t-SNE.
+## Visualizes them on a 2D plot.
+
+
+## Example Output
+
+When running the script, you will see:
+followed by **two interactive plots**:
+
+1. **PCA Visualization**
+   - Each dot represents a sentence.
+   - Sentences with similar meaning appear closer together.
+   - Example:  
+     - "The Eiffel Tower is in France"  
+       and "The capital of France is Paris"  
+       are positioned near each other.
+
+2. **t-SNE Visualization**
+   - Another dimensionality reduction method that shows natural clusters.
+   - Example:
+     - "Cats are amazing pets" and "Dogs are loyal companions"  
+       appear together in one cluster, away from unrelated topics.
+
+The plots help you **see how AI models understand meaning** in text.
+
+---
+
+## Sample Sentences Used
+- Artificial intelligence is transforming the world.
+- Cats are amazing pets.
+- The capital of France is Paris.
+- The Eiffel Tower is in France.
+- Deep learning enables image recognition.
+- Dogs are loyal companions.
+- The sun rises in the east.
+- The moon orbits the Earth.

requirements.txt

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+torch==2.2.2
+transformers==4.41.2
+sentence-transformers==3.0.1
+scikit-learn==1.5.0
+matplotlib==3.9.0
+numpy==1.26.4