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
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### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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### Direct Use
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## Bias, Risks, and Limitations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## More Information [optional]
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-classification
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language:
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- en
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tags:
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- claim-verification
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- fact-checking
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# DecomposeRL: Tiny Judge
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This model is a part of the **DecomposeRL** framework, as presented in the paper "[DecomposeRL: Learning to Ask Useful, Informative, and Diverse Questions for Semi-Supervised, Traceable Claim Verification](https://huggingface.co/papers/2605.27858)".
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## Model Description
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DecomposeRL frames claim verification as an RL policy that decomposes factual claims into sub-questions, answers them using evidence, and aggregates the results into a verdict. To address the high training costs of reinforcement learning (GRPO), the authors distilled large LLM judges into small, efficient ModernBERT-large classifiers known as **"Tiny Judges"**.
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This specific model is one of these classifiers, designed to provide fast reward computation (up to 100x faster than an LLM) for criteria such as atomicity, answerability, correctness, or coverage during training.
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- **Paper:** [DecomposeRL: Learning to Ask Useful, Informative, and Diverse Questions for Semi-Supervised, Traceable Claim Verification](https://huggingface.co/papers/2605.27858)
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- **Repository:** [https://github.com/dipta007/decomposerl](https://github.com/dipta007/decomposerl)
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- **Project Page:** [https://dipta007.github.io/DecomposeRL](https://dipta007.github.io/DecomposeRL)
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## Model Details
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- **Architecture:** ModernBERT-large
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- **Task:** Text Classification (Claim Verification)
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- **Labels:**
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- `0`: supported
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- `1`: refuted
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- `2`: not_enough_information
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## Citation
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```bibtex
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@article{dipta2025decomposerl,
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title={DecomposeRL: Traceable Claim Verification via RL-Trained Decomposition},
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author={Shubhashis Roy Dipta and Ankur Padia and Francis Ferraro},
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year={2025},
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eprint={2605.27858},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2605.27858},
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}
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```
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