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--- |
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language: |
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- en |
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- el |
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tags: |
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- translation |
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widget: |
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- 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. " |
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license: apache-2.0 |
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metrics: |
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- bleu |
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--- |
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## Greek to English NMT |
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## By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC) |
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* source languages: el |
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* target languages: en |
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* licence: apache-2.0 |
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* dataset: Opus, CCmatrix |
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* model: transformer(fairseq) |
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* pre-processing: tokenization + BPE segmentation |
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* metrics: bleu, chrf |
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### Model description |
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Trained using the Fairseq framework, transformer_iwslt_de_en architecture.\\ |
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BPE segmentation (20k codes).\\ |
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Mixed-case model. |
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### How to use |
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``` |
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from transformers import FSMTTokenizer, FSMTForConditionalGeneration |
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mname = "lighteternal/SSE-TUC-mt-el-en-cased" |
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tokenizer = FSMTTokenizer.from_pretrained(mname) |
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model = FSMTForConditionalGeneration.from_pretrained(mname) |
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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 ." |
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encoded = tokenizer.encode(text, return_tensors='pt') |
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outputs = model.generate(encoded, num_beams=5, num_return_sequences=5, early_stopping=True) |
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for i, output in enumerate(outputs): |
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i += 1 |
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print(f"{i}: {output.tolist()}") |
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decoded = tokenizer.decode(output, skip_special_tokens=True) |
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print(f"{i}: {decoded}") |
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``` |
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## Training data |
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Consolidated corpus from Opus and CC-Matrix (~6.6GB in total) |
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## Eval results |
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Results on Tatoeba testset (EL-EN): |
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| BLEU | chrF | |
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| ------ | ------ | |
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| 79.3 | 0.795 | |
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Results on XNLI parallel (EL-EN): |
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| BLEU | chrF | |
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| ------ | ------ | |
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| 66.2 | 0.623 | |
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### BibTeX entry and citation info |
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Dimitris Papadopoulos, et al. "PENELOPIE: Enabling Open Information Extraction for the Greek Language through Machine Translation." (2021). Accepted at EACL 2021 SRW |
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### Acknowledgement |
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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) |
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