metadata
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
By the Hellenic Army Academy (SSE) and the 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