# 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.

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## 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.