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Spirax/DialoGPT-medium-sheldon
DialoGPT-medium-sheldon is a text generation model from Spirax. 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 is an instance of microsoft/DialoGPT-medium trained on a TV series character, Sheldon from The Big Bang Theory. The data comes from a Kaggle TV series script dataset.
Downloads · 30 days
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From the Hugging Face model README
This is an instance of microsoft/DialoGPT-medium trained on a TV series character, Sheldon from The Big Bang Theory. The data comes from a Kaggle TV series script dataset.
Chat with the model:
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("spirax/DialoGPT-medium-sheldon")
model = AutoModelWithLMHead.from_pretrained("spirax/DialoGPT-medium-sheldon")
# Let's chat for 4 lines
for step in range(4):
# 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')
# print(new_user_input_ids)
# 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=200,
pad_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=3,
do_sample=True,
top_k=100,
top_p=0.7,
temperature=0.8
)
# pretty print last ouput tokens from bot
print("SheldorBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))