Add model card and metadata

README.md

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----
-license: mit
----
+---
+license: mit
+library_name: transformers
+base_model: Qwen/Qwen3-Reranker-0.6B
+pipeline_tag: text-generation
+tags:
+- code
+- context-pruning
+---
+
+# SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents
+
+SWE-Pruner is a self-adaptive context pruning framework specifically designed for coding agents. It addresses the challenges of long interaction contexts, such as high API costs and latency, by performing task-aware adaptive pruning.
+
+- **Paper:** [SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents](https://huggingface.co/papers/2601.16746)
+- **Repository:** [https://github.com/Ayanami1314/swe-pruner](https://github.com/Ayanami1314/swe-pruner)
+
+## Description
+Inspired by how human programmers selectively skim code, SWE-Pruner enables agents to formulate explicit goals (e.g., "focus on error handling") which guide a lightweight neural skimmer (0.6B parameters). This skimmer dynamically selects relevant lines from the surrounding context, preserving critical implementation details while significantly reducing token usage.
+
+Evaluations across benchmarks show that SWE-Pruner achieves 23-54% token reduction on agent tasks like SWE-Bench Verified and up to 14.84x compression on single-turn tasks like LongCodeQA with minimal performance impact.
+
+## Citation
+If you find SWE-Pruner useful in your research, please cite:
+```bibtex
+@misc{wang2026sweprunerselfadaptivecontextpruning,
+      title={SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents}, 
+      author={Yuhang Wang and Yuling Shi and Mo Yang and Rongrui Zhang and Shilin He and Heng Lian and Yuting Chen and Siyu Ye and Kai Cai and Xiaodong Gu},
+      year={2026},
+      eprint={2601.16746},
+      archivePrefix={arXiv},
+      primaryClass={cs.SE},
+      url={https://arxiv.org/abs/2601.16746},
+}
+```