Text Classification
Transformers
PyTorch
English
deberta-v2
reward-model
reward_model
RLHF
text-embeddings-inference
Instructions to use OpenAssistant/reward-model-deberta-v3-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenAssistant/reward-model-deberta-v3-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="OpenAssistant/reward-model-deberta-v3-large")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("OpenAssistant/reward-model-deberta-v3-large") model = AutoModelForSequenceClassification.from_pretrained("OpenAssistant/reward-model-deberta-v3-large") - Notebooks
- Google Colab
- Kaggle
Commit ·
1f543c0
1
Parent(s): d93adaf
Python formatting (#2)
Browse files- Python formatting (00793a4cfb8d5d41497d5b45ca9a6044d9c2ac12)
Co-authored-by: Leandro von Werra <lvwerra@users.noreply.huggingface.co>
README.md
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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"
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rank_model, tokenizer = AutoModelForSequenceClassification.from_pretrained(reward_name), AutoTokenizer.from_pretrained(reward_name)
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# How to use
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```python
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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reward_name = "OpenAssistant/reward-model-deberta-v3-large"
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rank_model, tokenizer = AutoModelForSequenceClassification.from_pretrained(reward_name), AutoTokenizer.from_pretrained(reward_name)
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