Text Classification
Transformers
Safetensors
English
deberta-v2
reward-model
scpo
cultural-alignment
australia
text-embeddings-inference
Instructions to use MulticulturalAlignment/scpo-reward-model-australia with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MulticulturalAlignment/scpo-reward-model-australia with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MulticulturalAlignment/scpo-reward-model-australia")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MulticulturalAlignment/scpo-reward-model-australia") model = AutoModelForSequenceClassification.from_pretrained("MulticulturalAlignment/scpo-reward-model-australia", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
2 days ago
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Use country-focused SCPO model names in title and loading example
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3 days ago
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Add paper citation and simplify result wording
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Use MIT license and simplify the reported results table
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Release reproduced country-specific SCPO DeBERTa checkpoint
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initial commit
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