readme: ---
  tags:
  - conversational
  license: mit
  ---
  
  ## Finetuned DialoGPT model on Spanish Conversations
  
  This model was finetuned from the original [DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) model on subtitles from Spanish movies and telenovelas from the awesome [OpenSubtitle dataset](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/opensubtitles).
  
  DialoGPT paper: [https://arxiv.org/abs/1911.00536](https://arxiv.org/abs/1911.00536)
  
  Some example dialog from the model:
  
  |Role | Response |
  |---------|--------|
  |User | ¿Cómo está el clima hoy? |
  | Bot | Muy bien. |
  |User | Eso es bueno escuchar. ¿Vas a ir a la playa? |
  | Bot | No, también puedo regresar. |
  |User | Oh bien, bueno, iré a la playa más tarde. |
  | Bot | ¡No! |
  |User | ¿Qué está mal con eso? |
  | Bot | ¡Cómo que no! |
  |User | Estoy confundido, ¿por qué no puedo ir a la playa? |
  | Bot | ¡Cómo que no! |
  |User | Explícamelo por favor. |
  | Bot | ¡No! |
  
  ## Using the model
  
  Example code for trying out the model (taken directly from the [DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) model card): 
  ```python
  from transformers import AutoModelWithLMHead, AutoTokenizer
  import torch
  
  
  tokenizer = AutoTokenizer.from_pretrained("ncoop57/DiGPTame-medium")
  model = AutoModelWithLMHead.from_pretrained("ncoop57/DiGPTame-medium")
  
  # Let's chat for 5 lines
  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')
  
  	# append the new user input tokens to the chat history
  	bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
  
  	# generated a response while limiting the total chat history to 1000 tokens, 
  	chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
  
  	# pretty print last ouput tokens from bot
  	print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
  ```
  
  ## Training your own model
  
  If you would like to finetune your own model or finetune this Spanish model, please checkout my blog post on that exact topic!
  https://nathancooper.io/i-am-a-nerd/chatbot/deep-learning/gpt2/2020/05/12/chatbot-part-1.html