Text Classification
Transformers
TensorBoard
Safetensors
deberta-v2
trl
reward-trainer
Generated from Trainer
text-embeddings-inference
Instructions to use MahmoudMohamed/Reward_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MahmoudMohamed/Reward_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MahmoudMohamed/Reward_Model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MahmoudMohamed/Reward_Model") model = AutoModelForSequenceClassification.from_pretrained("MahmoudMohamed/Reward_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8900ba0e7fd6dcf53b8d25909b4a89ce17a7f98a32493c161f45be61f0eda9fe
- Size of remote file:
- 738 MB
- SHA256:
- 78b76091172e3b2db6bb8bfa5a6208e14e172cf0b2ab7f62d5bc23515e5ec7cc
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