Text Classification
Transformers
Safetensors
English
modernbert
fact-verification
claim-verification
reward-model
llm-as-a-judge
distillation
decomposition
coverage
text-embeddings-inference
Instructions to use dipta007/coverage-judge-balanced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dipta007/coverage-judge-balanced with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dipta007/coverage-judge-balanced")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dipta007/coverage-judge-balanced") model = AutoModelForSequenceClassification.from_pretrained("dipta007/coverage-judge-balanced") - Notebooks
- Google Colab
- Kaggle
Improve model card with metadata and paper references
#1
by nielsr HF Staff - opened
This PR improves the model card for the DecomposeRL "Tiny Judge" model. It adds:
- Relevant metadata (
pipeline_tag,library_name,license). - Links to the research paper, the official GitHub repository, and the project page.
- A description of the model as a distilled ModernBERT classifier for claim verification.
This ensures the model is correctly categorized on the Hugging Face Hub and provides necessary context for users.
updated the model card with relevant information
dipta007 changed pull request status to closed