Instructions to use dipta007/question-judge-balanced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dipta007/question-judge-balanced with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dipta007/question-judge-balanced")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dipta007/question-judge-balanced") model = AutoModelForSequenceClassification.from_pretrained("dipta007/question-judge-balanced") - Notebooks
- Google Colab
- Kaggle
Add model card for DecomposeRL Tiny Judge
#1
by nielsr HF Staff - opened
Hi! I'm Niels from the community science team at Hugging Face. This PR improves the model card for the DecomposeRL Tiny Judge classifier.
Specifically, I have:
- Added the
text-classificationpipeline tag andtransformerslibrary name. - Included metadata for the base model (
ModernBERT-large) and the associated dataset. - Linked the model to the original paper, GitHub repository, and project page.
- Provided a description of the model's function as a distilled reward judge for claim verification.