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 release-settings.json from MulticulturalAlignment/scpo-reward-model-canada: direct link, hf CLI and curl.
- Browser
- Download file 326 Bytes
-
https://huggingface.co/MulticulturalAlignment/scpo-reward-model-canada/resolve/main/release-settings.json
- Command line
-
hf download hf://MulticulturalAlignment/scpo-reward-model-canada/release-settings.json
-
curl -L -o release-settings.json https://huggingface.co/MulticulturalAlignment/scpo-reward-model-canada/resolve/main/release-settings.json
326 Bytes
| { | |
| "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 | |
| } | |