Add model card

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

README.md

@@ -0,0 +1,78 @@
+---
+library_name: pytorch
+base_model: microsoft/deberta-v3-large
+tags:
+- retrieval-augmented-generation
+- reranking
+- robust-retrieval
+- evidence-critic
+- corm-rag
+- arxiv:2605.01302
+---
+
+# CoRM-RAG Evidence Critic
+
+This repository hosts the released Evidence Critic checkpoint for **CoRM-RAG**:
+
+**Beyond Semantic Relevance: Counterfactual Risk Minimization for Robust Retrieval-Augmented Generation**  
+Peiyang Liu, Qiang Yan, Ziqiang Cui, Di Liang, Xi Wang, Wei Ye  
+arXiv: <https://arxiv.org/abs/2605.01302>
+
+Code: <https://github.com/PeiYangLiu/CoRM-RAG>
+
+## Model Description
+
+CoRM-RAG aligns retrieval with decision safety rather than semantic similarity alone. The Evidence Critic is a lightweight reranking model trained to score whether a document remains useful under cognitively biased query perturbations, such as false premises, confirmation bias, and distracting assumptions.
+
+The released checkpoint uses a `microsoft/deberta-v3-large` backbone and outputs a robustness score for a `(query, document)` pair. It is intended to be used inside the CoRM-RAG pipeline for evidence reranking and risk-aware retrieval.
+
+## Files
+
+```text
+critic-v12-mixed/checkpoint-latest/state.pt
+```
+
+This file is a PyTorch checkpoint consumed by the CoRM-RAG codebase.
+
+## Usage
+
+Install the code from GitHub and download the checkpoint:
+
+```bash
+git clone https://github.com/PeiYangLiu/CoRM-RAG.git
+cd CoRM-RAG
+
+huggingface-cli download PeiyangLiu/CoRM-RAG \
+  critic-v12-mixed/checkpoint-latest/state.pt \
+  --local-dir checkpoints/hf
+```
+
+Run evaluation by pointing `CRITIC_PATH` to the downloaded checkpoint:
+
+```bash
+CRITIC_PATH=checkpoints/hf/critic-v12-mixed/checkpoint-latest/state.pt bash src/run_eval.sh
+```
+
+For training-data construction, critic training, and end-to-end evaluation details, see the GitHub repository.
+
+## Intended Use
+
+This checkpoint is intended for research on robust retrieval-augmented generation, evidence reranking, and risk-aware retrieval under biased or perturbed user queries. It is not a standalone generative model.
+
+## Limitations
+
+The critic score reflects robustness patterns learned from the CoRM-RAG training pipeline and should be interpreted within that retrieval setting. Performance may vary across domains, corpora, retrievers, and perturbation distributions.
+
+## Citation
+
+```bibtex
+@misc{liu2026cormrag,
+  title={Beyond Semantic Relevance: Counterfactual Risk Minimization for Robust Retrieval-Augmented Generation},
+  author={Peiyang Liu and Qiang Yan and Ziqiang Cui and Di Liang and Xi Wang and Wei Ye},
+  year={2026},
+  eprint={2605.01302},
+  archivePrefix={arXiv},
+  primaryClass={cs.CL},
+  url={https://arxiv.org/abs/2605.01302}
+}
+```