Text Classification
Transformers
PyTorch
English
deberta-v2
reward-model
reward_model
RLHF
Inference Endpoints
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Update README.md

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@@ -33,6 +33,19 @@ All models are train on these dataset with a same split seed across datasets (if
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  - [synthetic-instruct-gptj-pairwise](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise)
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  # Performance
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  Validation split accuracy
 
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  - [synthetic-instruct-gptj-pairwise](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise)
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+ # How to use
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+
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+ ```
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+ from transformer import AutoModelForSequenceClassification, AutoTokenizer
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+ reward_name = "OpenAssistant/reward-model-deberta-v3-base"
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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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+
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+
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  # Performance
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  Validation split accuracy