Sentence Similarity
sentence-transformers
Safetensors
English
mpnet
text2sql
schema-linking
aap-sql
text-embeddings-inference
Instructions to use TommyPanLab/AAP-SQL-Column-Retriever with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use TommyPanLab/AAP-SQL-Column-Retriever with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("TommyPanLab/AAP-SQL-Column-Retriever") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Align model card with AAP-SQL GitHub workflow
Browse files
README.md
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- aap-sql
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# AAP-SQL
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AAP-SQL
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AAP-SQL
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## Model details
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- Base model:
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- Training objective:
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- Training seed:
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- Training data: column-retrieval examples derived from the BIRD training split and schema descriptions
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- Expected library:
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## Use with AAP-SQL
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Download this repository into the path expected by the final runner:
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hf download TommyPanLab/AAP-SQL-
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Direct loading:
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("TommyPanLab/AAP-SQL-
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embeddings = model.encode(["table.column: column description"])
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The complete pipeline, required BIRD directory layout, and Gemini 3.1 result are documented in the [AAP-SQL publication branch](https://github.com/Tommyweige/AAP-SQL/tree/codex/final-aap-sql-experiment/AAP-SQL-Original).
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## Data and license notice
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The training examples were derived from the BIRD benchmark. Review the [BIRD project terms](https://bird-bench.github.io/) before using the model. No additional license has been declared for these fine-tuned weights; the upstream model and dataset terms still apply.
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- aap-sql
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# AAP-SQL column retriever
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AAP-SQL 欄位檢索器是完整 AAP-SQL 設定中的雙編碼模型。它從完整資料庫結構與欄位描述中召回最多 50 個候選欄位,供後續重排序器選出核心欄位。
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AAP-SQL column retriever is the bi-encoder used in the full AAP-SQL workflow. It performs the first stage of schema retrieval and returns up to 50 candidate columns for candidate reranking.
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## Model details
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- Base model: sentence-transformers/all-mpnet-base-v2
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- Training objective: MultipleNegativesRankingLoss
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- Training seed: 42
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- Training data: column-retrieval examples derived from the BIRD training split and schema descriptions
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- Expected library: sentence-transformers>=5.1.2
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## AAP-SQL publication branch
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The complete AAP-SQL workflow, research method terminology, BIRD directory layout, and reproduction instructions are maintained in the [GitHub publication branch](https://github.com/Tommyweige/AAP-SQL/tree/codex/final-aap-sql-experiment/AAP-SQL-Original).
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## Use with AAP-SQL
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Download this repository into the path expected by the final runner:
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~~~powershell
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hf download TommyPanLab/AAP-SQL-Column-Retriever --local-dir models/column_retriever_v3_paper_earlystop
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~~~
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Direct loading:
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~~~python
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("TommyPanLab/AAP-SQL-Column-Retriever")
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embeddings = model.encode(["table.column: column description"])
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~~~
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## Data and license notice
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The training examples were derived from the BIRD benchmark. Review the [BIRD project terms](https://bird-bench.github.io/) before using the model. No additional license has been declared for these fine-tuned weights; the upstream model and dataset terms still apply.
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