---
license: apache-2.0
datasets:
- Helsinki-NLP/tatoeba
- openlanguagedata/flores_plus
- facebook/bouquet
language:
- en
- fr
metrics:
- bleu
- comet
- chrf
pipeline_tag: translation
---

# OPUS-MT-tiny-fra-eng

Distilled model from a Tatoeba-MT Teacher: [OPUS-MT-models/fr-en/opus-2020-02-26](https://object.pouta.csc.fi/OPUS-MT-models/fr-en/opus-2020-02-26.zip), which has been trained on the [Tatoeba](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/data) dataset.

We used the [OpusDistillery](https://github.com/Helsinki-NLP/OpusDistillery) to train new a new student with the tiny architecture, with a regular transformer decoder.
For training data, we used [Tatoeba](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/data).
The configuration file fed into OpusDistillery can be found [here](https://github.com/Helsinki-NLP/OpusDistillery/blob/main/configs/opustranslate_hf/config.op.fr-en.yml).

## How to run
```python
from transformers import MarianMTModel, MarianTokenizer
model_name = "Helsinki-NLP/opus-mt_tiny_fra-eng"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
tok = tokenizer("Les efforts visant à trouver le lieu de l’accident sont restreints par des intempéries et le terrain accidenté.", return_tensors="pt").input_ids
output = model.generate(tok)[0]
tokenizer.decode(output, skip_special_tokens=True)
```

## Benchmarks
### Teacher
| testset               | BLEU  | chr-F | COMET| 
|-----------------------|-------|-------|-------|
| Flores+ 	| 41.8	| 66.9 | 0.8689 |
| Bouquet 	| 43.5 	| 64.5 | 0.875 |

### Student

| testset               | BLEU  | chr-F | COMET |
|-----------------------|-------|-------|-------|
| Flores+ 	| 40.4 	| 65.9 | 0.8734 |
| Bouquet 	| 39.9 	| 61.9 | 0.8551 |

## Marian models

We also provide Marian-compatible versions of this model. To use them, compile [Marian](https://marian-nmt.github.io/quickstart/) and run decoding with `marian-decoder`, for example:

```bash
marian-decoder \
  -i input.txt \
  -c final.model.npz.best-perplexity.npz.decoder.yml \
  -m final.model.npz.best-perplexity.npz \
  -v vocab.spm vocab.spm