readme: ---
  language: 
    - et
  widget:
  - text: "te olete ka noh, noh, päris korralikult ka Rahvusringhäälingu teatud mõttes sellisesse keerulisse olukorda pannud,"
  - text: "Et, et, et miks mitte olla siis tasakaalus, ma noh, hüpoteetiliselt viskan selle palli üles,"
  
  ---
  
  Dataset must be processed as following:
  
  ```
  
  def preprocess_function_with_seconds(ds):
  
      inputs = ds['generated']
      targets =  ds['subtitle']
  
      model_inputs = tokenizer(inputs, truncation=True, max_length=128, padding=True, return_tensors="np")
      secs = list(map(lambda x: "{:.1f}".format(x), ds["seconds"]))
      sec_inputs = tokenizer(secs, truncation=True, max_length=128, padding=True, return_tensors="np")
  
      model_inputs['input_ids'] = np.concatenate((sec_inputs['input_ids'][:,1:2], model_inputs['input_ids']), 1)
      model_inputs['attention_mask'] = np.concatenate((sec_inputs['attention_mask'][:,1:2], model_inputs['attention_mask']), 1)
  
      with tokenizer.as_target_tokenizer():
          labels = tokenizer(targets, truncation=True, max_length=128, padding=True, return_tensors="np")
  
      model_inputs["labels"] = labels["input_ids"]
      return model_inputs    
  ```
      
  Importing the model and tokenizer:
  
  ```
  tokenizer = MBart50Tokenizer.from_pretrained("IljaSamoilov/MBART-estonian-subtitles-with-seconds", src_lang="et_EE", tgt_lang="et_EE")
  model = MBartForConditionalGeneration.from_pretrained("IljaSamoilov/MBART-estonian-subtitles-with-seconds")
  ```