| --- |
| 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¢er=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> |
