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								curl -X POST \
-H "Authorization: Bearer YOUR_ORG_OR_USER_API_TOKEN" \
-H "Content-Type: application/json" \
-d '"json encoded string"' \
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ncoop57/DiGPTame-medium ncoop57/DiGPTame-medium
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Contributed by

ncoop57 Nathan Cooper
2 models

How to use this model directly from the 🤗/transformers library:

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from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("ncoop57/DiGPTame-medium") model = AutoModelWithLMHead.from_pretrained("ncoop57/DiGPTame-medium")
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Finetuned DialoGPT model on Spanish Conversations

This model was finetuned from the original DialoGPT-medium model on subtitles from Spanish movies and telenovelas from the awesome OpenSubtitle dataset.

DialoGPT paper:

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 model card):

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 =[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!