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:
- 0cead600440c47db2fd905e365db940233b00fe8f1aa7b1b8f59524777f7d28c
- Size of remote file:
- 872 MB
- SHA256:
- f8c3139a8c3e4279e2a9924bc864d843241a89c526bc8fdc6ea432a88c8d4e10
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