Add model card for DecomposeRL Tiny Judge

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-classification` pipeline tag and `transformers` library 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.

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 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### 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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 ## Training Details
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 ---
+license: apache-2.0
 library_name: transformers
+pipeline_tag: text-classification
+base_model: answerdotai/ModernBERT-large
+datasets:
+- dipta007/DecomposeRL
+tags:
+- claim-verification
+- reinforcement-learning
+- grpo
 ---
+# DecomposeRL: Tiny Judge (ModernBERT-large)
+This model is part of the **DecomposeRL** framework, introduced in the paper [DecomposeRL: Learning to Ask Useful, Informative, and Diverse Questions for Semi-Supervised, Traceable Claim Verification](https://huggingface.co/papers/2605.27858).
+## Model Description
+DecomposeRL frames claim verification as a reinforcement learning task where claims are decomposed into sub-questions to provide inspectable traces. This specific repository contains a **Tiny Judge**—a distilled ModernBERT classifier designed to compute reward signals during Group Relative Policy Optimization (GRPO).
+These "tiny judges" replace large LLM-based judges (like Qwen3-32B) during training to provide a ~100x speedup in reward computation while running locally on a single GPU. Depending on the specific checkpoint, these classifiers evaluate criteria such as atomicity, answerability, correctness, and coverage.
+- **Developed by:** Shubhashis Roy Dipta, Ankur Padia, and Francis Ferraro
+- **Model type:** ModernBERT for Sequence Classification
+- **Task:** Claim Verification (Reward Judge)
+- **Repository:** [https://github.com/dipta007/decomposerl](https://github.com/dipta007/decomposerl)
+- **Project Page:** [https://dipta007.github.io/DecomposeRL](https://dipta007.github.io/DecomposeRL)
+- **Paper:** [arXiv:2605.27858](https://huggingface.co/papers/2605.27858)
 ## Training Details
+The model was distilled from an LLM judge (Qwen3-32B) cache to classify the quality of claim decompositions. It was trained using the [dipta007/DecomposeRL](https://huggingface.co/datasets/dipta007/DecomposeRL) dataset.
+## Citation
+```bibtex
+@article{dipta2025decomposerl,
+  title={DecomposeRL: Traceable Claim Verification via RL-Trained Decomposition},
+  author={Shubhashis Roy Dipta and Ankur Padia and Francis Ferraro},
+  year={2025},
+  eprint={2605.27858},
+  archivePrefix={arXiv},
+  primaryClass={cs.CL},
+  url={https://arxiv.org/abs/2605.27858},
+}
+```