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<div align="center">

# ✦ Veytra ✦

**From words to vectors, from vectors to meaning.**

A lightweight, elegant **sentence embedding model**, built from scratch.

[![Params](https://img.shields.io/badge/params-3.3M-blue)](https://github.com/coderianx/veytra)
[![Embedding](https://img.shields.io/badge/embedding-64d-green)](https://github.com/coderianx/veytra)
[![Dataset](https://img.shields.io/badge/dataset-STS--B-orange)](https://huggingface.co/datasets/sentence-transformers/stsb)
[![GitHub](https://img.shields.io/badge/GitHub-coderian%2Fveytra-black?logo=github)](https://github.com/coderianx/veytra)

</div>

---

Trained with a Transformer architecture, Veytra maps sentences into **64-dimensional vectors** and measures the **semantic closeness** between two sentences via cosine similarity. Small yet ambitious — designed for those who believe in the power of simplicity.

<div align="center">

| ⚙️ Architecture | |
|---|---|
| **Total Parameters** | ~3.3M (3,316,544) |
| **Tokenizer** | GPT-2 (50,257 vocab) |
| **Model** | Transformer Encoder (2 layers, 4 heads) |
| **Embedding Dimension** | 64 |
| **Max Length** | 64 tokens |
| **Pooling** | Mean Pooling + L2 Normalization |

</div>

---

## ⚡ Usage

```bash
python3 train.py
```

<div align="center">

*Veytra — encoding meaning.*

</div>