reward-model-deberta-v3-large-v2

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license:mit
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OpenAssistant
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Quick Summary

AI model with specialized capabilities.

Code Examples

texttransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer
reward_name = "OpenAssistant/reward-model-deberta-v3-large-v2"
rank_model, tokenizer = AutoModelForSequenceClassification.from_pretrained(reward_name), AutoTokenizer.from_pretrained(reward_name)

question = "I just came out of from jail, any suggestion of my future?"
helpful = "It's great to hear that you have been released from jail."
bad = "Go back to jail you scum"

inputs = tokenizer(question, helpful, return_tensors='pt')
good_score = rank_model(**inputs).logits[0].cpu().detach()

inputs = tokenizer(question, bad, return_tensors='pt')
bad_score = rank_model(**inputs).logits[0].cpu().detach()
print(good_score > bad_score) # tensor([True])

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