---
language:
- en
- es
- fr
- de
- it
- pt
- nl
- ru
- zh
- ja
- ko
- ar
- hi
- tr
- vi
license: apache-2.0
pipeline_tag: translation
tags:
- translation
- multilingual
- t5
- opus100
- whirlwindai
---

<p align="center">

<img src="https://capsule-render.vercel.app/api?type=venom&height=230&text=&animation=fadeIn&color=gradient&customColorList=12,20,24,30"/>

<img src="https://readme-typing-svg.demolab.com?font=Space+Grotesk&weight=700&size=28&pause=2500&color=00E7FF&center=true&vCenter=true&width=760&lines=Translate-15L;15+Languages.;Powered+by+WhirlwindAI"/>

</p>

<p align="center">
  <img src="https://img.shields.io/badge/Parameters-60M-4FC3F7?style=for-the-badge">
  <img src="https://img.shields.io/badge/Languages-15-8B5CF6?style=for-the-badge">
  <img src="https://img.shields.io/badge/Framework-Transformers-06B6D4?style=for-the-badge">
  <img src="https://img.shields.io/badge/License-Apache--2.0-10B981?style=for-the-badge">
</p>

---

# Overview

Translate-15L is a lightweight multilingual translation model trained on OPUS100.

Rather than maximizing parameter count, the model focuses on providing practical multilingual translation with fast inference and a compact footprint.

It supports translation between **English** and **14 additional languages** in both directions.

---

# Quick Start

```python
from transformers import AutoTokenizer, T5ForConditionalGeneration

model = T5ForConditionalGeneration.from_pretrained(
    "WhirlwindAI/Translate-15L"
)

tokenizer = AutoTokenizer.from_pretrained(
    "WhirlwindAI/Translate-15L"
)

text = "Hello, how are you?"

prompt = f"translate en to fr: {text}"

inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=50,
    num_beams=4
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

---

# Supported Languages

| Code | Language |
|------|----------|
| 🇬🇧 en | English |
| 🇪🇸 es | Spanish |
| 🇫🇷 fr | French |
| 🇩🇪 de | German |
| 🇮🇹 it | Italian |
| 🇵🇹 pt | Portuguese |
| 🇳🇱 nl | Dutch |
| 🇷🇺 ru | Russian |
| 🇨🇳 zh | Chinese |
| 🇯🇵 ja | Japanese |
| 🇰🇷 ko | Korean |
| 🇸🇦 ar | Arabic |
| 🇮🇳 hi | Hindi |
| 🇹🇷 tr | Turkish |
| 🇻🇳 vi | Vietnamese |

---

# Performance

<div align="center">

## BLEU Evaluation

  <img src="Images/bleu_separate.png" width="96%">

<br><br>

## Language Overview

  <img src="Images/radar_chart.png" width="70%">

</div>

---

# Highlights

| Direction | BLEU |
|-----------|------:|
| 🇬🇧 → 🇫🇷 | **32.43** |
| 🇩🇪 → 🇬🇧 | **16.93** |
| 🇬🇧 → 🇪🇸 | 6.51 |
| 🇫🇷 → 🇬🇧 | 5.13 |
| 🇬🇧 → 🇵🇹 | 5.05 |
| 🇬🇧 → 🇮🇹 | 4.15 |

---

# Speed

<div align="center">

# ⚡ ~8,151 Tokens / Second

Fast enough for lightweight multilingual applications while remaining compact.

See **speed.txt** for the complete benchmark.

</div>

---

# Examples

<div align="center">

<img src="https://huggingface.co/WhirlwindAI/Translate-15L/resolve/main/Images/download.png" width="48%">
<img src="https://huggingface.co/WhirlwindAI/Translate-15L/resolve/main/Images/download%20(1).png" width="48%">

</div>

---

# Notes

- English ↔ 14 Languages
- Optimized for compact deployment
- Trained on OPUS100
- Best performance on high-resource European languages
- Performance on low-resource languages remains limited

---

<div align="center">

### WhirlwindAI

Efficient AI • Practical Research • Open Models

</div>