readme: ---
  language:
  - myv
  - ru
  - fi
  - de
  - es
  - en
  - hi
  - zh
  - tr
  - uk
  - fr
  - ar
  tags:
  - erzya
  - mordovian
  - translation
  license: cc-by-sa-4.0
  datasets:
    - slone/myv_ru_2022
    - yhavinga/ccmatrix
  ---
  
  This a model to translate texts from the Erzya language (`myv`, cyrillic script) to 11 other languages: `ru,fi,de,es,en,hi,zh,tr,uk,fr,ar`. See its [demo](https://huggingface.co/spaces/slone/myv-translation-2022-demo)!
  
  It is described in the paper [The first neural machine translation system for the Erzya language](https://arxiv.org/abs/2209.09368).
  
  This model is based on [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50), but with updated vocabulary and checkpoint:
  - Added an extra language token `myv_XX` and 19K new BPE tokens for the Erzya language;
  - Fine-tuned to translate to Erzya: first from Russian, then from all 11 languages. 
  
  The following code can be used to run translation using the model:
  
  ```Python
  from transformers import MBartForConditionalGeneration, MBart50Tokenizer
  
  
  def fix_tokenizer(tokenizer):
      """ Add a new language token to the tokenizer vocabulary (this should be done each time after its initialization) """
      old_len = len(tokenizer) - int('myv_XX' in tokenizer.added_tokens_encoder)
      tokenizer.lang_code_to_id['myv_XX'] = old_len-1
      tokenizer.id_to_lang_code[old_len-1] = 'myv_XX'
      tokenizer.fairseq_tokens_to_ids["<mask>"] = len(tokenizer.sp_model) + len(tokenizer.lang_code_to_id) + tokenizer.fairseq_offset
  
      tokenizer.fairseq_tokens_to_ids.update(tokenizer.lang_code_to_id)
      tokenizer.fairseq_ids_to_tokens = {v: k for k, v in tokenizer.fairseq_tokens_to_ids.items()}
      if 'myv_XX' not in tokenizer._additional_special_tokens:
          tokenizer._additional_special_tokens.append('myv_XX')
      tokenizer.added_tokens_encoder = {}
  
  
  def translate(text, model, tokenizer, src='ru_RU', trg='myv_XX', max_length='auto', num_beams=3, repetition_penalty=5.0, train_mode=False, n_out=None, **kwargs):
      tokenizer.src_lang = src
      encoded = tokenizer(text, return_tensors="pt", truncation=True, max_length=1024)
      if max_length == 'auto':
          max_length = int(32 + 1.5 * encoded.input_ids.shape[1])
      if train_mode:
          model.train()
      else:
          model.eval()
      generated_tokens = model.generate(
          **encoded.to(model.device),
          forced_bos_token_id=tokenizer.lang_code_to_id[trg], 
          max_length=max_length, 
          num_beams=num_beams,
          repetition_penalty=repetition_penalty,
          num_return_sequences=n_out or 1,
          **kwargs
      )
      out = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
      if isinstance(text, str) and n_out is None:
          return out[0]
      return out
      
  
  mname = 'slone/mbart-large-51-mul-myv-v1'
  model = MBartForConditionalGeneration.from_pretrained(mname)
  tokenizer = MBart50Tokenizer.from_pretrained(mname)
  fix_tokenizer(tokenizer)
  
  
  print(translate('Привет, собака!', model, tokenizer, src='ru_RU', trg='myv_XX'))
  # Шумбрат, киска!  # действительно, по-эрзянски собака именно так
  print(translate('Hello, doggy!', model, tokenizer, src='en_XX', trg='myv_XX'))
  # Шумбрат, киска!
  ```