---
base_model: Qwen/Qwen3-Embedding-0.6B
library_name: sentence-transformers
pipeline_tag: feature-extraction
tags: [text-to-sql, schema-linking, retrieval, embedding, spider2, bird]
---

# embedding-0.6b-spider2.0

Bi-encoder column retriever for text-to-SQL **schema linking** (Stage-I candidate retrieval).
Qwen3-Embedding-0.6B fine-tuned with **InfoNCE** (LoRA r=8/α=32, merged), `max_length=1024`, 1 epoch.

**Training data:** [`thanhdath/embedding-0.6b-spider2.0-data`](https://huggingface.co/datasets/thanhdath/embedding-0.6b-spider2.0-data)
— 39,238 (question, gold-columns, hard-negatives) groups from **BIRD train + Spider train +
Spider 2.0 synthetic (BigQuery/Snowflake + SQL-Gen)**. **No SynSQL.**

| source | rows |
|---|---|
| BIRD train | 9,356 |
| Spider train | 8,386 |
| Spider 2.0 synth (BQ/SF) | 17,693 |
| Spider 2.0 synth (SQL-Gen) | 3,803 |

## Results (column recall@K vs the previous embedding ckpt-3000)
**BIRD dev (n=1521), flat:** R@50 **0.959** (old 0.875), R@100 **0.995** (0.976), R@200 1.000.
**Spider 2.0-233q, two-stage top-50 tables → top-K cols:** R@300 **0.904** (old 0.876),
R@500 **0.930** (0.903), R@800 **0.956** (0.937).
**Spider 2.0-233q, flat (shard-collapsed):** R@500 **0.954** (0.934), R@800 **0.974** (0.959).
Beats the previous checkpoint on every operating point.

## Usage (vLLM embedding server)
```bash
vllm serve thanhdath/embedding-0.6b-spider2.0 --task embed --port 8001 --max-model-len 4096
```
Score = dot product between the question embedding and each column-description embedding
(`table.column ; Table meaning … ; Column meaning … ; type … ; has values …`); take top-K.