Text Classification
Transformers
Safetensors
Portuguese
bert
reward model
alignment
preference model
RLHF
text-embeddings-inference
Instructions to use nicholasKluge/Harmless-RewardModelPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nicholasKluge/Harmless-RewardModelPT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nicholasKluge/Harmless-RewardModelPT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nicholasKluge/Harmless-RewardModelPT") model = AutoModelForSequenceClassification.from_pretrained("nicholasKluge/Harmless-RewardModelPT") - Notebooks
- Google Colab
- Kaggle
Ctrl+K