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--- |
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license: mit |
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datasets: |
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- openai/summarize_from_feedback |
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- openai/webgpt_comparisons |
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- Dahoas/instruct-synthetic-prompt-responses |
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- Anthropic/hh-rlhf |
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language: |
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- en |
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metrics: |
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- accuracy |
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tags: |
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- reward-model |
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- reward_model |
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- RLHF |
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--- |
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# Reward model trained from human feedback |
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Reward model (RM) trained to predict which generated answer is better judged by a human, given a question. |
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RM are useful in these domain: |
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- QA model evaluation |
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- serves as reward score in RLHF |
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- detect potential toxic response via ranking |
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All models are train on these dataset with a same split seed across datasets (if validation split wasn't available) |
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- [webgpt_comparisons](https://huggingface.co/datasets/openai/webgpt_comparisons) |
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- [summarize_from_feedback](https://huggingface.co/datasets/openai/summarize_from_feedback) |
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- [synthetic-instruct-gptj-pairwise](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise) |
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- [anthropic_hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf) |
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# How to use |
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``` |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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reward_name = "OpenAssistant/reward-model-deberta-v3-large-v2" |
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rank_model, tokenizer = AutoModelForSequenceClassification.from_pretrained(reward_name), AutoTokenizer.from_pretrained(reward_name) |
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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." |
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inputs = tokenizer(question, answer, return_tensors='pt') |
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score = rank_model(**inputs).logits[0].cpu().detach() |
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print(score) |
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``` |
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**Toxic response detection** |
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``` |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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reward_name = "OpenAssistant/reward-model-deberta-v3-large-v2" |
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rank_model, tokenizer = AutoModelForSequenceClassification.from_pretrained(reward_name), AutoTokenizer.from_pretrained(reward_name) |
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question = "I just came out of from jail, any suggestion of my future?" |
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helpful = "It's great to hear that you have been released from jail." |
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bad = "Go back to jail you scum" |
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inputs = tokenizer(question, helpful, return_tensors='pt') |
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good_score = rank_model(**inputs).logits[0].cpu().detach() |
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inputs = tokenizer(question, bad, return_tensors='pt') |
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bad_score = rank_model(**inputs).logits[0].cpu().detach() |
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print(good_score > bad_score) # tensor([True]) |
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``` |
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# Performance |
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Validation split accuracy |
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| Model | [WebGPT](https://huggingface.co/datasets/openai/webgpt_comparisons) | [Summary](https://huggingface.co/datasets/openai/summarize_from_feedback) | [SytheticGPT](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise) | [Anthropic RLHF]() | |
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|---|---|---|---|---| |
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| [electra-large-discriminator](https://huggingface.co/OpenAssistant/reward-model-electra-large-discriminator) | 59.30 | 68.66 | 99.85 | 54.33 | |
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| **[deberta-v3-large-v2](https://huggingface.co/OpenAssistant/reward-model-deberta-v3-large-v2)** | **61.57** | 71.47 | 99.88 | **69.25** | |
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| [deberta-v3-large](https://huggingface.co/OpenAssistant/reward-model-deberta-v3-large) | 61.13 | 72.23 | **99.94** | 55.62 | |
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| [deberta-v3-base](https://huggingface.co/OpenAssistant/reward-model-deberta-v3-base) | 59.07 | 66.84 | 99.85 | 54.51 | |
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| deberta-v2-xxlarge | 58.67 | 73.27 | 99.77 | 66.74 | |
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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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