OPUS-MT-tiny-ita-eng
Distilled model from a Tatoeba-MT Teacher: OPUS-MT-models/it-en/opus-2019-12-18, which has been trained on the Tatoeba dataset.
We used the OpusDistillery to train new a new student with the tiny architecture, with a regular transformer decoder. For training data, we used Tatoeba. The configuration file fed into OpusDistillery can be found here.
How to run
>>> from transformers import pipeline
>>> pipe = pipeline("translation", model="Helsinki-NLP/opus-mt_tiny_ita-eng", max_length=256)
>>> pipe("Ciao, come sta?")
Benchmarks
Teacher
| testset | BLEU | chr-F | COMET |
|---|---|---|---|
| Flores+ | 29.4 | 60.0 | 0.8416 |
| Bouquet | 52.4 | 70.3 | 0.8824 |
Student
| testset | BLEU | chr-F | COMET |
|---|---|---|---|
| Flores+ | - | - | - |
| Bouquet | - | - | - |
Marian models
We also provide Marian-compatible versions of this model. To use them, compile Marian and run decoding with marian-decoder, for example:
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