---
library_name: transformers
tags:
- seq2seq
license: apache-2.0
datasets:
- Helsinki-NLP/europarl
- Helsinki-NLP/opus-100
language:
- en
- it
base_model:
- bigscience/mt0-small
pipeline_tag: translation
metrics:
- bleu
---
```html
   ___                     _         _    __                _   _       
  / _ \   _  _   __ _   __| |  _ _  (_)  / _|  ___   __ _  | | (_)  ___ 
 | (_) | | || | / _` | / _` | | '_| | | |  _| / _ \ / _` | | | | | / _ \
  \__\_\  \_,_| \__,_| \__,_| |_|   |_| |_|   \___/ \__, | |_| |_| \___/
                                                    |___/               
```
## 🍀 Quadrifoglio - A small model for English -> Italian translation

Quadrifoglio is an encoder-decoder transformer model for English-Italian text translation based on `bigscience/mt0-small`. It was trained on the `en-it` section of `Helsinki-NLP/opus-100` and `Helsinki-NLP/europarl`.


## Usage

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Load model and tokenizer from checkpoint directory
tokenizer = AutoTokenizer.from_pretrained("LeonardPuettmann/mt0-Quadrifoglio-mt-en-it")
model = AutoModelForSeq2SeqLM.from_pretrained("LeonardPuettmann/mt0-Quadrifoglio-mt-en-it")

def generate_response(input_text):
    input_ids = tokenizer("translate English to Italian:" + input_text, return_tensors="pt").input_ids
    output = model.generate(input_ids, max_new_tokens=256)
    return tokenizer.decode(output[0], skip_special_tokens=True)

text_to_translate = "I would like a cup of green tea, please."
response = generate_response(text_to_translate)
print(response)
```

## Evaluation
Done on the Opus 100 test set.

### BLEU
|              | Quadrifoglio (this model)     | mt0-small| DeepL  |
|--------------|-------------------------------|----------|--------|
| BLEU Score   | 0.4816                        | 0.0159   | 0.5210 |
| Precision 1  | 0.7305                        | 0.2350   | 0.7613 | 
| Precision 2  | 0.5413                        | 0.0290   | 0.5853 | 
| Precision 3  | 0.4289                        | 0.0076   | 0.4800 |
| Precision 4  | 0.3417                        | 0.0013   | 0.3971 |