readme: # Finetuned DialoGPT model for Eng-Spa translation
  
  DialoGPT-small model was used and finetuned on English to Spanish translations, extracted from http://storage.googleapis.com/download.tensorflow.org/data/spa-eng.zip
  
  some examples of translations
  
  | Role  | Response  | 
  | :---: |------------------------| 
  | User  | please, sing me a song | 
  | Bot   | Por favor, canta una canción. | 
  | User  | I really want to go to China | 
  | Bot   | Realmente quiero ir a China. | 
  | User  | Can you do me a favor? | 
  | Bot   | ¿Me puedes hacer un favor? |
  | User  | I don't know what you are talking about | 
  | Bot   | No sé de qué estás hablando. |
  | User  | I don't want to go to China | 
  | Bot   | No quiero ir a China. |
   
  
  # Using the model
  example code for trying out the model
  
  ```python
  from transformers import AutoModelWithLMHead, AutoTokenizer
  
  tokenizer = AutoTokenizer.from_pretrained('microsoft/DialoGPT-small')
  model = AutoModelWithLMHead.from_pretrained('OscarNav/dialoGPT_translate')
  
  # Let's traslate 5 sentences
  for step in range(5):
      # encode the new user input, add the eos_token and return a tensor in Pytorch
      new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
  
      # generated a response while limiting the total chat history to 1000 tokens, 
      chat_history_ids = model.generate(
          new_user_input_ids, max_length=1000,
          pad_token_id=tokenizer.eos_token_id,
          top_p=0.92, top_k = 50
      )
      
      # pretty print last ouput tokens from bot
      print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, new_user_input_ids.shape[-1]:][0], skip_special_tokens=True)))
  ```