The Idea

Translate‑25T is a multilingual translation model fine‑tuned from google/mt5‑small on the OPUS‑100 dataset. It supports translation between English and 18+ other languages in both directions, making it a practical tool for research, education, and lightweight multilingual applications. The focus is on balance: delivering solid translation quality while keeping inference fast and the model compact enough to run in resource‑constrained environments.

Quick Start

from transformers import MT5ForConditionalGeneration, AutoTokenizer

model = MT5ForConditionalGeneration.from_pretrained("WhirlwindAI/Translate-25L")
tokenizer = AutoTokenizer.from_pretrained("WhirlwindAI/Translate-25L")

def translate(text, src, tgt):
    prompt = f"translate {src} to {tgt}: {text}"
    inputs = tokenizer(prompt, return_tensors="pt", truncation=True)
    outputs = model.generate(**inputs, max_new_tokens=128, num_beams=4)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

print(translate("Hello, how are you?", "en", "fr"))

Supported Languages

Translate‑25T covers a diverse set of languages, including many high‑resource European languages, as well as several Asian and Middle Eastern languages.

Code	Language	Code	Language
🇬🇧 en	English	🇩🇪 de	German
🇫🇷 fr	French	🇪🇸 es	Spanish
🇷🇺 ru	Russian	🇨🇳 zh	Chinese
🇯🇵 ja	Japanese	🇰🇷 ko	Korean
🇸🇦 ar	Arabic	🇮🇹 it	Italian
🇵🇹 pt	Portuguese	🇳🇱 nl	Dutch
🇮🇳 hi	Hindi	🇮🇩 id	Indonesian
🇹🇷 tr	Turkish	🇻🇳 vi	Vietnamese
🇵🇱 pl	Polish	🇺🇦 uk	Ukrainian
🇷🇴 ro	Romanian	🇸🇪 sv	Swedish

Evaluation

We evaluated Translate‑25T on the OPUS‑100 test set using 50 samples per language pair. The results below show BLEU scores, inference speed, and a combined radar view.

BLEU Scores

Inference Speed

Radar Comparison

Sample Translation (French → English)

Input (French)	Output (English)
`Bonjour, comment allez‑vous aujourd'hui ?`	`Hello, how are you here now?`

Model Details

Property	Value
Base Model	google/mt5-small
Parameters	300 Million
Architecture	mT5 (Encoder‑Decoder)
Training Data	OPUS-100
Languages	18+
Framework	Hugging Face Transformers
License	Apache 2.0

Highlights

Multilingual: translate between English and 18+ languages.
Efficient: ~300M parameters – lightweight enough for many production scenarios.
Fast: average inference speed of 52.2 tokens/second on a T4 GPU.
Research‑friendly: open weights and Apache 2.0 license.
Practical: trained on a diverse set of parallel sentences from OPUS‑100.

Limitations

Performance varies across language pairs; high‑resource languages (e.g., English–French) achieve the best BLEU scores.
The model may struggle with domain‑specific terminology, slang, or very long documents.
Low‑resource languages are not covered; additional fine‑tuning is recommended for specialised use cases.