readme: ### Finetuned on annual report sentence pair
  This marianMT has been further finetuned on annual report sentence pairs
  
  ## Test out at huggingface spaces!
  https://huggingface.co/spaces/wolfrage89/finance_domain_translation_marianMT
  
  ## Sample colab notebook
  https://colab.research.google.com/drive/1H57vwiah7n1JXvXYMqJ8dklrIuU6Cljb?usp=sharing
  
  ## How to use
  
  ```python
  !pip install transformers
  !pip install sentencepiece
  
  
  from transformers import MarianMTModel, MarianTokenizer
  
  tokenizer = MarianTokenizer.from_pretrained("wolfrage89/annual_report_translation_id_en")
  model = MarianMTModel.from_pretrained("wolfrage89/annual_report_translation_id_en")
  
  #tokenizing bahasa sentence
  bahasa_sentence = "Interpretasi ini merupakan interpretasi atas PSAK 46: Pajak Penghasilan yang bertujuan untuk mengklarifikasi dan memberikan panduan dalam merefleksikan ketidakpastian perlakuan pajak penghasilan dalam laporan keuangan."
  tokenized_bahasa_sentence = tokenizer([bahasa_sentence], return_tensors='pt', max_length=104, truncation=True)
  
  #feeding tokenized sentence into model, the max_legnth have been set to 104 as the model was trained mostly on sentences with this length
  translated_tokens = model.generate(**tokenized_bahasa_sentence, max_length=104)[0]
  
  ## decoding the tokens to get english sentence
  english_sentence = tokenizer.decode(translated_tokens, skip_special_tokens=True)
  
  print(english_sentence)
  # This interpretation is an interpretation of PSAK 46: Income Tax that aims to clarify and provide guidance in reflecting the uncertainty of income tax treatments in the financial statements.
  
  
  ```
  
  
  
  
  ### opus-mt-id-en (original model)
  
  * source languages: id
  * target languages: en
  *  OPUS readme: [id-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/id-en/README.md)
  