SSE-TUC-mt-en-el-lowercase

English to Greek NMT (lower-case output)

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 + lower-casing + BPE segmentation
• metrics: bleu, chrf
• output: lowercase only, for mixed-cased model use this: https://huggingface.co/lighteternal/SSE-TUC-mt-en-el-cased

Model description

Trained using the Fairseq framework, transformer_iwslt_de_en architecture.\ BPE segmentation (10k codes).\ Lower-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 = "Not all those who wander are lost."

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):

Results on XNLI parallel (EN-EL):

BibTeX entry and citation info

Dimitris Papadopoulos, et al. "PENELOPIE: Enabling Open Information Extraction for the Greek Language through Machine Translation." (2021). Accepted at EACL 2021 SRW

Acknowledgement

The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the HFRI PhD Fellowship grant (Fellowship Number:50, 2nd call)