---
language:
- en
- el
tags:
- translation
widget:
- text: "'Katerina', is the best name for a girl."
license: apache-2.0
metrics:
- bleu
---

## English to Greek NMT from Hellenic Army Academy (SSE) and Technical University of Crete (TUC)

* source languages: en
* target languages: el
* licence: apache-2.0
* dataset: Opus, CCmatrix
* model: transformer(fairseq)
* pre-processing: tokenization + BPE segmentation
* metrics: bleu, chrf

### Model description

Trained using the Fairseq framework, transformer_iwslt_de_en architecture.\
BPE segmentation (20k codes).\
Mixed-case model. 

### How to use

```
from transformers import FSMTTokenizer, FSMTForConditionalGeneration

mname = " <your_downloaded_model_folderpath_here> "

tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)

text = " 'Katerina', is the best name for a girl."

encoded = tokenizer.encode(text, return_tensors='pt')

outputs = model.generate(encoded, num_beams=5, num_return_sequences=5, early_stopping=True)
for i, output in enumerate(outputs):
    i += 1
    print(f"{i}: {output.tolist()}")
    
    decoded = tokenizer.decode(output, skip_special_tokens=True)
    print(f"{i}: {decoded}")
```


## Training data

Consolidated corpus from Opus and CC-Matrix (~6.6GB in total)


## Eval results


Results on Tatoeba testset (EN-EL): 

| BLEU | chrF  |
| ------ | ------ |
| 76.9 |  0.733 |


Results on XNLI parallel (EN-EL): 

| BLEU | chrF  |
| ------ | ------ |
| 65.4 |  0.624 |

### BibTeX entry and citation info
TODO