Text Classification
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Reward model trained from human feedback

Reward model (RM) trained to predict which generated answer is better judged by a human, given a question.

RM are useful in these domain:

  • QA model evaluation

  • serves as reward score in RLHF

All models are train on these dataset with a same split seed across datasets (if validation split wasn't available)

How to use

from transformers import AutoModelForSequenceClassification, AutoTokenizer
reward_name = "OpenAssistant/reward-model-deberta-v3-base"
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()


Validation split accuracy

Model WebGPT Summary SytheticGPT
electra-large-discriminator 59.30 68.66 99.85
deberta-v3-large 61.13 72.23 99.94
deberta-v3-base 59.07 66.84 99.85

Its likely SytheticGPT has somekind of surface pattern on the choosen-rejected pair which makes it trivial to differentiate between better the answer.

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Datasets used to train OpenAssistant/reward-model-deberta-v3-base