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, answer = "Explain nuclear fusion like I am five", "Nuclear fusion is the process by which two or more protons and neutrons combine to form a single nucleus. It is a very important process in the universe, as it is the source of energy for stars and galaxies. Nuclear fusion is also a key process in the production of energy for nuclear power plants."
inputs = tokenizer(question, answer, return_tensors='pt')
score = rank_model(**inputs).logits[0].cpu().detach()
print(score)

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])