Publish AAP-SQL E2 column retriever

1_Pooling/config.json

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+{
+    "word_embedding_dimension": 768,
+    "pooling_mode_cls_token": false,
+    "pooling_mode_mean_tokens": true,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": false,
+    "include_prompt": true
+}

README.md

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+---
+language:
+- en
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+base_model: sentence-transformers/all-mpnet-base-v2
+tags:
+- sentence-transformers
+- text2sql
+- schema-linking
+- aap-sql
+---
+
+# AAP-SQL E2 column retriever
+
+AAP-SQL E2 是完整 AAP-SQL 設定中的雙編碼欄位檢索器。它先從資料庫 schema 中取回 50 個候選欄位，再交給 R1 cross-encoder 重排。
+
+AAP-SQL E2 is the bi-encoder column retriever used by the full AAP-SQL configuration. It retrieves 50 schema-column candidates before the R1 cross-encoder reranks them.
+
+## Model details
+
+- Base model: `sentence-transformers/all-mpnet-base-v2`
+- Training objective: `MultipleNegativesRankingLoss`
+- Training seed: `42`
+- Training data: column-retrieval examples derived from the BIRD training split and schema descriptions
+- Expected library: `sentence-transformers>=5.1.2`
+
+## Use with AAP-SQL
+
+Download this repository into the path expected by the final runner:
+
+```powershell
+hf download TommyPanLab/AAP-SQL-E2 --local-dir models/column_retriever_v3_paper_earlystop
+```
+
+Direct loading:
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("TommyPanLab/AAP-SQL-E2")
+embeddings = model.encode(["table.column: column description"])
+```
+
+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).
+
+## Data and license notice
+
+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.

config.json

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+{
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "transformers_version": "4.57.1",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "5.1.2",
+    "transformers": "4.57.1",
+    "pytorch": "2.4.0+cu124"
+  },
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+oid sha256:28f5610b924a7ef83849327c794072c0d27535d07e132fa99a4591b83cd50dcc
+size 437967672

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+    "max_seq_length": 384,
+    "do_lower_case": false
+}

special_tokens_map.json

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+{
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
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+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
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+  "padding_side": "right",
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+  "tokenize_chinese_chars": true,
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+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}