lighteternal/SSE-TUC-mt-el-en-cased

Greek to English NMT

By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)

• source languages: el
• target languages: en
• 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 = "lighteternal/SSE-TUC-mt-el-en-cased"

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

text = "Ο όρος τεχνητή νοημοσύνη αναφέρεται στον κλάδο της πληροφορικής ο οποίος ασχολείται με τη σχεδίαση και την υλοποίηση υπολογιστικών συστημάτων που μιμούνται στοιχεία της ανθρώπινης συμπεριφοράς ."

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 (EL-EN):

BLEU	chrF
79.3	0.795

Results on XNLI parallel (EL-EN):

BLEU	chrF
66.2	0.623

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)