Text Classification
Transformers
Safetensors
English
deberta-v2
reward-model
scpo
cultural-alignment
canada
text-embeddings-inference
Instructions to use MulticulturalAlignment/scpo-reward-model-canada with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MulticulturalAlignment/scpo-reward-model-canada with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MulticulturalAlignment/scpo-reward-model-canada")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MulticulturalAlignment/scpo-reward-model-canada") model = AutoModelForSequenceClassification.from_pretrained("MulticulturalAlignment/scpo-reward-model-canada", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 326 Bytes
dd9391b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"country": "Canada",
"shuffle_seed": 0,
"learning_rate": 1e-06,
"tau": 0.7,
"beta": 1.1,
"batch_size": 8,
"max_steps": 1024,
"method": "filtered_weighted",
"model_name": "OpenAssistant/reward-model-deberta-v3-base",
"split_seed": 0,
"model_seed": 0,
"trainer_seed": 42,
"trainer_data_seed": null
}
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