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
Download validation.json from MulticulturalAlignment/scpo-reward-model-canada: direct link, hf CLI and curl.
- Browser
- Download file 252 Bytes
-
https://huggingface.co/MulticulturalAlignment/scpo-reward-model-canada/resolve/main/validation.json
- Command line
-
hf download hf://MulticulturalAlignment/scpo-reward-model-canada/validation.json
-
curl -L -o validation.json https://huggingface.co/MulticulturalAlignment/scpo-reward-model-canada/resolve/main/validation.json
252 Bytes
| { | |
| "checkpoint": "scpo-deberta-v3-base-canada", | |
| "device": "cpu", | |
| "scores": [ | |
| [ | |
| -2.664663314819336 | |
| ], | |
| [ | |
| 0.41405123472213745 | |
| ] | |
| ], | |
| "deterministic": true, | |
| "sample_pair_vs_concat_ids_equal": [ | |
| true, | |
| true | |
| ] | |
| } | |