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
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
BLEU Evaluation
Language Overview
Highlights
| Direction | BLEU |
|---|---|
| 🇬🇧 → 🇫🇷 | 32.43 |
| 🇩🇪 → 🇬🇧 | 16.93 |
| 🇬🇧 → 🇪🇸 | 6.51 |
| 🇫🇷 → 🇬🇧 | 5.13 |
| 🇬🇧 → 🇵🇹 | 5.05 |
| 🇬🇧 → 🇮🇹 | 4.15 |
Speed
⚡ ~8,151 Tokens / Second
Fast enough for lightweight multilingual applications while remaining compact.
See speed.txt for the complete benchmark.
Examples
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
WhirlwindAI
Efficient AI • Practical Research • Open Models