Text Classification
Transformers
Safetensors
deberta-v2
process-reward-model
reasoning-verification
step-verification
crp
context-relay-protocol
Eval Results (legacy)
text-embeddings-inference
Instructions to use AutoCyberAI/crp-prm-deberta-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AutoCyberAI/crp-prm-deberta-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AutoCyberAI/crp-prm-deberta-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AutoCyberAI/crp-prm-deberta-v1") model = AutoModelForSequenceClassification.from_pretrained("AutoCyberAI/crp-prm-deberta-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 943ece167286e9ef6f405453da1fa81d598425f4ed00dc51b663a528e64f7383
- Size of remote file:
- 5.37 kB
- SHA256:
- a46903763a4254908b5970e58ba37d43acdd8a48a27012eb0cd967a6cd79a8a6
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