Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- Helsinki-NLP/tatoeba
+- openlanguagedata/flores_plus
+- facebook/bouquet
+language:
+- en
+- ru
+metrics:
+- bleu
+- comet
+- chrf
+pipeline_tag: translation
+---
+
+# OPUS-MT-tiny-rus-eng
+
+Distilled model from a Tatoeba-MT Teacher: [OPUS-MT-models/ru-en/opus-2020-02-26](https://object.pouta.csc.fi/OPUS-MT-models/ru-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.ru-en.yml).
+
+## How to run
+```python
+from transformers import MarianMTModel, MarianTokenizer
+model_name = "Helsinki-NLP/opus-mt_tiny_rus-eng"
+tokenizer = MarianTokenizer.from_pretrained(model_name)
+model = MarianMTModel.from_pretrained(model_name)
+tok = tokenizer("Это привело к тому, что два вида рыб вымерли, а два других, в том числе горбатый голавль, попали под угрозу исчезновения.", 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+ 	| 29.4 | 57.7| 0.8172 |
+| Bouquet 	|33.4 	| 56.6 |0.8235 |
+
+### Student
+
+| testset               | BLEU  | chr-F | COMET |
+|-----------------------|-------|-------|-------|
+| Flores+ 	| 28.4 	| 56.8 | 0.8220 |
+| Bouquet 	|31.1|54.1| 0.8151 |
+
+