Add model card documenting negative result

README.md

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+---
+language:
+- en
+- code
+library_name: sentence-transformers
+pipeline_tag: text-classification
+tags:
+- cross-encoder
+- reranker
+- code-search
+- onnx
+- bert
+datasets:
+- code_search_net
+license: apache-2.0
+---
+
+# code-reranker-v1
+
+A cross-encoder reranker for code search, trained on CodeSearchNet pairs. **Experimental — does not improve retrieval in our benchmarks.** Published for reproducibility.
+
+## Status: Negative Result
+
+This reranker **regresses** retrieval quality on our hard eval (55 confusable function pairs):
+
+| Config | Recall@1 | Delta |
+|--------|----------|-------|
+| No reranker | 90.9% | — |
+| Web-trained cross-encoder | 80.0% | **-10.9pp** |
+| **This model (code-trained)** | **9.1%** | **-81.8pp** |
+
+**Root cause:** Trained with random same-language negatives, which are too easy for cross-encoders. The model learns surface-level language patterns instead of semantic code discrimination. A V2 with BM25 hard negatives may fix this.
+
+## Training
+
+- **Architecture:** Cross-encoder (BERT-base)
+- **Data:** 50,000 CodeSearchNet pairs + 7,500 docstring pairs
+- **Epochs:** 3
+- **Negatives:** Random same-language (this was the mistake)
+
+## Usage (if you want to experiment)
+
+```bash
+# In cqs — NOT default, opt-in only
+CQS_RERANKER_MODEL=jamie8johnson/code-reranker-v1 cqs "query" --rerank
+```
+
+## License
+
+Apache 2.0.