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
File size: 255 Bytes
26d35c8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"checkpoint": "scpo-deberta-v3-base-australia",
"device": "cpu",
"scores": [
[
0.18036536872386932
],
[
0.8344672322273254
]
],
"deterministic": true,
"sample_pair_vs_concat_ids_equal": [
true,
true
]
}
|