readme: ---
  language:
  - en
  - el
  tags:
  - translation
  widget:
  - text: "Not all those who wander are lost."
  license: apache-2.0
  metrics:
  - bleu
  ---
  
  ## 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): 
  
  | BLEU | chrF  |
  | ------ | ------ |
  | 77.3 |  0.739 |
  
  
  Results on XNLI parallel (EN-EL): 
  
  | BLEU | chrF  |
  | ------ | ------ |
  | 66.1 |  0.606 |
  
  ### 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)
  