Upload CodeCompass-Embed v2 — #1 on CSN-Python (NDCG@10=0.979), 12-task CoIR eval

README.md

@@ -189,10 +189,10 @@ For optimal performance, use these instruction prefixes for queries:
 Training followed a two-stage approach:
 
 **Stage 1 — Embedding Conversion** (8.8M samples):
-Converted Qwen2.5-Coder-0.5B from a causal language model to a bidirectional embedding model. Trained on 8.8M samples spanning CoRNStack (Python, Java, JavaScript, Go, Ruby, PHP), CoderPile, StackOverflow, and synthetic SQL data with mined hard negatives.
+Converted Qwen2.5-Coder-0.5B from a causal language model to a bidirectional embedding model. Trained on 8.8M samples spanning CoRNStack (Python, Java, JavaScript, Go, Ruby, PHP), CoderPile, StackOverflow, and synthetic data with mined hard negatives.
 
 **Stage 2 — Hard Negative Refinement** (100K samples):
-Continued fine-tuning on a curated 100K-sample subset with up to 8 hard negatives per sample.
+Continued fine-tuning on a curated 100K-sample subset with hard negatives.
 
 - **Base Model**: [Qwen2.5-Coder-0.5B](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B)
 - **Architecture**: Bidirectional attention across all 24 layers, mean pooling, L2 normalization