README.md

metadata

license: mit
datasets:
  - openai/webgpt_comparisons
  - openai/summarize_from_feedback
  - Dahoas/instruct-synthetic-prompt-responses
language:
  - en
metrics:
  - accuracy
tags:
  - reward-model
  - reward_model
  - RLHF

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)

• webgpt_comparisons

• summarize_from_feedback

• synthetic-instruct-gptj-pairwise

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()
print(score)

Performance

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.