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emre/spanish-dialoGPT
spanish-dialoGPT is a text generation model from emre. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as mit.
This model was finetuned from the original DialoGPT-medium model on subtitles from Spanish movies and telenovelas from the awesome OpenSubtitle dataset.
Downloads · 30 days
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From the Hugging Face model README
This model was finetuned from the original DialoGPT-medium model on subtitles from Spanish movies and telenovelas from the awesome OpenSubtitle dataset.
DialoGPT paper: 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! |
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("emre/spanish-dialoGPT")
model = AutoModelWithLMHead.from_pretrained("emre/spanish-dialoGPT")
# 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)))