Text Classification
Transformers
Safetensors
English
Chinese
qwen3
reward-model
text-embeddings-inference
Instructions to use bmbgsj/ProRAG_PRM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bmbgsj/ProRAG_PRM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bmbgsj/ProRAG_PRM")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bmbgsj/ProRAG_PRM") model = AutoModelForSequenceClassification.from_pretrained("bmbgsj/ProRAG_PRM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba81adcfb1d839a1972ec98a71a40cbbd854c8fcf197e115cbb3e21dc6d8fcd4
- Size of remote file:
- 11.4 MB
- SHA256:
- f4b2ca3759ff8654c268c431ba48bd8b2afb30aed4be69b793316603b693c989
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