Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Fardan/rewardmodeling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fardan/rewardmodeling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Fardan/rewardmodeling")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Fardan/rewardmodeling") model = AutoModelForSequenceClassification.from_pretrained("Fardan/rewardmodeling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9c03c7326560c5ec0d590d32f8d151bff2436cf084657d1f353a7452e5f3bb69
- Size of remote file:
- 5.3 kB
- SHA256:
- 2dc5267fd04ec6d123dbd140fb513d11a804e758a728791e053d264d2257a424
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