dipta007
/

answer-judge-balanced

@@ -1,199 +1,129 @@
 ---
 library_name: transformers
-tags: []
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 ---
 library_name: transformers
+license: apache-2.0
+base_model: answerdotai/ModernBERT-large
+pipeline_tag: text-classification
+language:
+  - en
+datasets:
+  - dipta007/decomposeRL-tiny-judge
+tags:
+  - fact-verification
+  - claim-verification
+  - reward-model
+  - llm-as-a-judge
+  - distillation
+  - modernbert
+  - text-classification
+  - decomposition
+  - faithfulness
+  - answer-verification
 ---
+# DecomposeRL Tiny-Judge: Answer Correctness Judge
+<p align="center">
+  <a href="https://arxiv.org/abs/2605.27858v1">
+    <img src="https://img.shields.io/badge/%F0%9F%93%84_Paper-arXiv-b12a00?style=for-the-badge&labelColor=ffb300" alt="Paper">
+  </a>
+</p>
+[![Paper](https://img.shields.io/badge/arXiv-2605.27858-red)](https://arxiv.org/abs/2605.27858v1)
+[![Project Page](https://img.shields.io/badge/Project-Page-green)](https://dipta007.github.io/DecomposeRL/)
+[![Dataset](https://img.shields.io/badge/HuggingFace-Dataset-yellow)](https://huggingface.co/datasets/dipta007/decomposeRL-tiny-judge)
+[![Collection](https://img.shields.io/badge/HuggingFace-Collection-blueviolet)](https://huggingface.co/collections/dipta007/decomposerl)
+[![GitHub](https://img.shields.io/badge/GitHub-Code-blue)](https://github.com/dipta007/DecomposeRL)
+A ModernBERT-large classifier that scores whether an answer is **faithful to the evidence document** (no contradictions, no extrinsic information) — the **answer correctness** sub-signal of DecomposeRL's joint multiplicative quality reward.
+It is part of the **DecomposeRL tiny-judge stack** — eight task-specific LoRA classifier heads on a shared `ModernBERT-large` backbone that *distill* a `Qwen3-32B` LLM judge into small, fast reward models. Swapping the 32B judge for this ~400M-parameter stack cuts GRPO judge compute by ~80% (240 → 48 GPU-hours) while retaining ~99% of in-domain accuracy.
+## Model Overview
+| Property | Value |
+|----------|-------|
+| **Model Type** | `ModernBertForSequenceClassification` (sequence classification) |
+| **Base Model** | `answerdotai/ModernBERT-large` (~400M params) |
+| **Training** | LoRA (r=64, α=128), merged into the base before release |
+| **Labels** | 2-way: `no` / `yes` |
+| **Distilled from** | `Qwen/Qwen3-32B` judge labels |
+| **Dataset / config** | [`dipta007/decomposeRL-tiny-judge`](https://huggingface.co/datasets/dipta007/decomposeRL-tiny-judge) · `answer_correctness` |
+| **Train split** | `train_balanced` (class-balanced); selected on macro-F1 |
+| **Language** | English |
+## What it judges
+Provides the **answer correctness** sub-signal (`R_corr`) of the joint multiplicative quality reward. For honest abstentions (*"I don't know"*) this factor is dropped so the question is scored on answerability and atomicity alone.
+### Input format
+Evidence document + sub-question + the policy's answer:
+```
+Document: {document}
+Question: {question}
+Answer: {answer}
+```
+### Label space
+| Label | Name | Meaning |
+|------:|------|---------|
+| `0` | `no`  | the answer contradicts the document or adds information not grounded in it |
+| `1` | `yes` | the answer is faithful to the document |
+## Quickstart
+```python
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+repo = "dipta007/answer-judge-balanced"
+tokenizer = AutoTokenizer.from_pretrained(repo)
+model = AutoModelForSequenceClassification.from_pretrained(repo).eval()
+text = (
+    'Document: ## Managerial career\\n'
+    'He brought along Rube Foster and a number of American black players, but the team lost five of its first six games, and White and most of his players were released...\\n'
+    'Question: How many of its first six games did the team lose?\\n'
+    'Answer: The team won five of its first six games.'
+)
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=8192)
+with torch.no_grad():
+    logits = model(**inputs).logits
+pred = int(logits.argmax(-1))
+print(pred, model.config.id2label[pred])
+# expected: 0 -> no
+```
+## Training Data
+Trained on the `answer_correctness` config of [`dipta007/decomposeRL-tiny-judge`](https://huggingface.co/datasets/dipta007/decomposeRL-tiny-judge), whose labels are distilled from `Qwen3-32B` judge calls made during DecomposeRL reward computation. The model is fine-tuned with LoRA on the class-balanced `train_balanced` split, validated on the natural `validation` split, and the best checkpoint is chosen by macro-F1. LoRA adapters are merged into the backbone before release, so the model loads with a plain `from_pretrained` (no PEFT required).
+## Role in DecomposeRL
+DecomposeRL trains a claim-verification policy with GRPO over a seven-reward ensemble. Five of those rewards are scored by an LLM judge, which dominates training-time GPU cost. The tiny-judge stack replaces that 32B judge with eight small distilled heads so reward scoring runs on the same single GPU as training. See the [paper](https://arxiv.org/abs/2605.27858v1) (tiny-judge ablation) and the [DecomposeRL-7B model](https://huggingface.co/dipta007/decomposeRL-7b) for the full reward design.
+## Intended Use
+- **In-scope**: serving as a fast reward / scoring model inside the DecomposeRL training loop, or as a standalone classifier for the specific judgment above on claim-decomposition traces.
+- **Out-of-scope**: general-purpose fact-checking, use on inputs that do not follow the input format above, or as a standalone end-to-end claim verifier (use [DecomposeRL-7B](https://huggingface.co/dipta007/decomposeRL-7b) for that).
+## Citation
+```bibtex
+@article{dipta2025decomposerl,
+  title={DecomposeRL: Learning to Ask Useful, Informative, and Diverse Questions for Semi-Supervised, Traceable Claim Verification},
+  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/2605.27858v1},
+}
+```
+## License
+Released under the Apache 2.0 License.