Text Classification
Transformers
PyTorch
Safetensors
Portuguese
bert
reward model
alignment
preference model
RLHF
text-embeddings-inference
Instructions to use nicholasKluge/RewardModelPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nicholasKluge/RewardModelPT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nicholasKluge/RewardModelPT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nicholasKluge/RewardModelPT") model = AutoModelForSequenceClassification.from_pretrained("nicholasKluge/RewardModelPT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bc49bef6bd9703214a2709638f606c712f4df0e2eefea9027583c5e7c648938f
- Size of remote file:
- 436 MB
- SHA256:
- edd62695424c023985a0d6b34f7b764da509f9b6437b6d7af5274f07fc4c6cf6
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