Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- Helsinki-NLP/tatoeba
+- openlanguagedata/flores_plus
+language:
+- es
+- eu
+metrics:
+- bleu
+- comet
+- chrf
+pipeline_tag: translation
+---
+
+# OPUS-MT-tiny-spa-eus
+
+Distilled model from a Tatoeba-MT Teacher: [Tatoeba-MT-models/itc-eus/opusTCv20210807_transformer-big_2022-07-23](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-eus/opusTCv20210807_transformer-big_2022-07-23.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.es-eu.yml).
+
+## How to run
+```python
+from transformers import MarianMTModel, MarianTokenizer
+model_name = "Helsinki-NLP/opus-mt_tiny_spa-eus"
+tokenizer = MarianTokenizer.from_pretrained(model_name)
+model = MarianMTModel.from_pretrained(model_name)
+tok = tokenizer("La gastronomía de Mayorca, como la de otras regiones similares del Mediterráneo, se basa en el pan, los vegetales y la carne (especialmente la porcina), y utiliza aceite de oliva en todas sus recetas.", 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+ 	| 13.3	| 52.5 | 0.8407 |
+
+### Student
+
+| testset               | BLEU  | chr-F | COMET |
+|-----------------------|-------|-------|-------|
+| Flores+ 	| 11.7 	| 51.6 | 0.824 |
+
+