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:
- bc6c5f8bb58fd98e0a6eb57e35a56d3b1bcd2d14095843a72d5c36b9ded03a29
- Size of remote file:
- 4.09 kB
- SHA256:
- c5ef9405a9d75dbc3d85f18ad44a184070e8187d74f7026a41a46fb38b61a3ad
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