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README.md

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+---
+base_model: Qwen/Qwen3-Reranker-4B
+library_name: transformers
+pipeline_tag: text-classification
+tags:
+  - text-to-sql
+  - sql
+  - template-matching
+  - nli
+  - paraphrase
+  - reranker
+  - qwen3
+language:
+  - en
+license: apache-2.0
+---
+
+# Qwen3-Reranker-4B — SQL Template Matcher
+
+Fine-tune of [`Qwen/Qwen3-Reranker-4B`](https://huggingface.co/Qwen/Qwen3-Reranker-4B) as a cross-encoder NLI classifier over pairs of natural-language questions. Given a user's question and a candidate question (with entity values masked), it predicts whether the user question is paraphrase of candidate question.
+
+### Inputs
+
+A pair of natural-language questions fed through the tokenizer as a standard cross-encoder input. Order matters — premise must be the masked candidate, hypothesis the raw user question:
+
+```
+Premise:    "Show movies released in _ sorted by popularity desc"
+Hypothesis: "What are the top films from 2010 by viewer count?"
+```
+
+Entity values in the premise are masked with a space-padded underscore `_`. All literal types (numbers, strings, dates) use the same token. Swapping the order or using a different masking convention will degrade performance.
+
+Training used the tokenizer's default max length with `truncation=True`; BIRD question pairs are typically short (~20–40 tokens each). Very long inputs are untested.
+
+### Outputs
+
+Three-class logits with this mapping:
+
+| id | label | Meaning in this task |
+|---:|---|---|
+| 0 | `entailment` | the two questions are similar (correspond to the same SQL template) |
+| 1 | `neutral` | unused at training time; logit is untrained |
+| 2 | `contradiction` | the two questions are not similar |
+
+Use `softmax(logits)[0]` as the match score (`p(entailment)`).
+
+## References
+
+- Base model: <https://huggingface.co/Qwen/Qwen3-Reranker-4B>
+- Training Data - BIRD Train Set: <https://bird-bench.github.io/>
+- Source repo: <https://github.com/SSLab-CSE-IITB/tecod>
+
+## License
+
+Apache 2.0