Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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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+---
+base_model: sentence-transformers/all-mpnet-base-v2
+datasets: []
+language: []
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:300000
+- loss:CoSENTLoss
+widget:
+- source_sentence: SELECT DISTINCT count(alias3.col1) , alias1.col2 FROM table1 AS
+    alias1 JOIN table2 AS alias2 ON alias1.col2 = alias2.col2 JOIN table3 AS alias3
+    ON alias1.col1 = alias3.col1 WHERE alias2.col3 = str AND alias3.year = num GROUP
+    BY alias1.col2
+  sentences:
+  - SELECT col1 , avg(col2) FROM table1 WHERE col3 LIKE str GROUP BY col1
+  - SELECT col1 , col2 FROM table1 WHERE col3 LIKE str GROUP BY col1 ORDER BY count(*)
+    DESC LIMIT num
+  - SELECT col1 , avg(col2) FROM table1 GROUP BY col1 ORDER BY avg(col2)
+- source_sentence: SELECT alias2.year FROM table1 AS alias1 JOIN table2 AS alias2
+    ON alias1.col1 = alias2.col2 WHERE alias1.alias1 = str
+  sentences:
+  - SELECT alias1.col1 , alias2.col2 FROM table1 AS alias1 JOIN table2 AS alias2 ON
+    alias1.col3 = alias2.col3
+  - SELECT DISTINCT alias1.col1 FROM table1 AS alias1 JOIN table2 AS alias2 ON alias2.col2
+    = alias1.col3 JOIN table3 AS alias3 ON alias2.col4 = alias3.col3 WHERE alias3.col5
+    > num
+  - SELECT col1 FROM table1 ORDER BY col2 LIMIT num
+- source_sentence: SELECT DISTINCT count(alias2.col1) FROM table1 AS alias1 JOIN table2
+    AS alias2 ON alias1.col2 = alias2.col2 WHERE alias1.col3 = str
+  sentences:
+  - SELECT alias3.col1 FROM table1 AS alias1 JOIN table2 AS alias2 ON alias1.col2
+    = alias2.col2 JOIN table3 AS alias3 ON alias2.col3 = alias3.col3 WHERE alias1.col4
+    = str AND alias1.col5 = str
+  - SELECT count(DISTINCT col1) FROM table1 WHERE col1 NOT IN ( SELECT col2 FROM table2
+    )
+  - SELECT count(*) FROM table1 WHERE col1 = str AND col2 < num
+- source_sentence: SELECT alias1.col1 FROM table1 AS alias1 JOIN table2 AS alias2
+    ON alias1.col2 = alias2.col2 WHERE alias2.col3 LIKE str
+  sentences:
+  - SELECT col1 FROM table1 ORDER BY col2 DESC
+  - SELECT col1 FROM table1 WHERE col2 NOT IN (SELECT col2 FROM table2)
+  - SELECT alias1.col1 , alias1.col2 , alias1.col3 FROM table1 AS alias1 JOIN table2
+    AS alias2 ON alias1.col4 = alias2.col5 ORDER BY alias2.col6 LIMIT num
+- source_sentence: SELECT alias1.col1 FROM table1 AS alias1 JOIN table2 AS alias2
+    ON alias1.col2 = alias2.col2 JOIN table3 AS alias3 ON alias2.col3 = alias3.col3
+    WHERE alias3.col4 = str INTERSECT SELECT alias1.col1 FROM table1 AS alias1 JOIN
+    table2 AS alias2 ON alias1.col2 = alias2.col2 JOIN table3 AS alias3 ON alias2.col3
+    = alias3.col3 WHERE alias3.col4 = str
+  sentences:
+  - SELECT count(*) FROM table1
+  - SELECT count(DISTINCT col1) FROM table1
+  - SELECT count(col1) FROM table1 WHERE col2 = num
+---
+
+# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 84f2bcc00d77236f9e89c8a360a00fb1139bf47d -->
+- **Maximum Sequence Length:** 384 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
+  (1): Pooling({'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})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("s2593817/sft-sql-embedding")
+# Run inference
+sentences = [
+    'SELECT alias1.col1 FROM table1 AS alias1 JOIN table2 AS alias2 ON alias1.col2 = alias2.col2 JOIN table3 AS alias3 ON alias2.col3 = alias3.col3 WHERE alias3.col4 = str INTERSECT SELECT alias1.col1 FROM table1 AS alias1 JOIN table2 AS alias2 ON alias1.col2 = alias2.col2 JOIN table3 AS alias3 ON alias2.col3 = alias3.col3 WHERE alias3.col4 = str',
+    'SELECT count(col1) FROM table1 WHERE col2 = num',
+    'SELECT count(DISTINCT col1) FROM table1',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 300,000 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                          | sentence2                                                                          | score                                                           |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             | float                                                           |
+  | details | <ul><li>min: 8 tokens</li><li>mean: 38.49 tokens</li><li>max: 189 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 37.44 tokens</li><li>max: 153 tokens</li></ul> | <ul><li>min: 0.04</li><li>mean: 0.36</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                                                                                                                                                                                                                                                                                                    | sentence2                                                                                                                                                                                                 | score                            |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
+  | <code>SELECT DISTINCT count(DISTINCT alias4.col1) , alias3.col2 FROM table1 AS alias1 JOIN table2 AS alias2 ON alias1.col3 = alias2.col3 JOIN table3 AS alias3 ON alias3.col4 = alias1.col4 JOIN table4 AS alias4 ON alias3.col4 = alias4.col5 WHERE alias2.col6 = str GROUP BY alias3.col2 ORDER BY count(DISTINCT alias4.col1) DESC</code> | <code>SELECT count(*) FROM table1 WHERE col1 = str</code>                                                                                                                                                 | <code>0.14221014492753623</code> |
+  | <code>SELECT DISTINCT count(alias2.col1) FROM table1 AS alias1 JOIN table2 AS alias2 ON alias1.col2 = alias2.col2 WHERE alias1.col3 = str</code>                                                                                                                                                                                             | <code>SELECT alias3.col1 FROM table1 AS alias1 JOIN table2 AS alias2 ON alias1.col2 = alias2.col2 JOIN table3 AS alias3 ON alias2.col3 = alias3.col3 WHERE alias1.col4 = str AND alias1.col5 = str</code> | <code>0.5468686868686868</code>  |
+  | <code>SELECT count(*) FROM table1</code>                                                                                                                                                                                                                                                                                                     | <code>SELECT count(*) FROM table1 WHERE col1 LIKE str</code>                                                                                                                                              | <code>0.6269230769230769</code>  |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 160
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 8
+- `warmup_ratio`: 0.2
+- `fp16`: True
+- `dataloader_num_workers`: 16
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 160
+- `per_device_eval_batch_size`: 8
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 8
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.2
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 16
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step  | Training Loss |
+|:------:|:-----:|:-------------:|
+| 0.0533 | 100   | 12.0379       |
+| 0.1067 | 200   | 9.2042        |
+| 0.16   | 300   | 8.6521        |
+| 0.2133 | 400   | 8.5353        |
+| 0.2667 | 500   | 8.4472        |
+| 0.32   | 600   | 8.4105        |
+| 0.3733 | 700   | 8.3927        |
+| 0.4267 | 800   | 8.3553        |
+| 0.48   | 900   | 8.3326        |
+| 0.5333 | 1000  | 8.3168        |
+| 0.5867 | 1100  | 8.2941        |
+| 0.64   | 1200  | 6.0021        |
+| 0.6933 | 1300  | 5.3802        |
+| 0.7467 | 1400  | 5.3282        |
+| 0.8    | 1500  | 5.2365        |
+| 0.8533 | 1600  | 5.0198        |
+| 0.9067 | 1700  | 4.899         |
+| 0.96   | 1800  | 4.8887        |
+| 1.0133 | 1900  | 4.7603        |
+| 1.0667 | 2000  | 4.6292        |
+| 1.12   | 2100  | 4.4811        |
+| 1.1733 | 2200  | 4.2841        |
+| 1.2267 | 2300  | 4.2251        |
+| 1.28   | 2400  | 4.0261        |
+| 1.3333 | 2500  | 3.8628        |
+| 1.3867 | 2600  | 3.8404        |
+| 1.44   | 2700  | 3.6471        |
+| 1.4933 | 2800  | 3.6673        |
+| 1.5467 | 2900  | 3.5626        |
+| 1.6    | 3000  | 3.5391        |
+| 1.6533 | 3100  | 3.5629        |
+| 1.7067 | 3200  | 3.4787        |
+| 1.76   | 3300  | 3.4401        |
+| 1.8133 | 3400  | 3.491         |
+| 1.8667 | 3500  | 3.3358        |
+| 1.92   | 3600  | 3.3555        |
+| 1.9733 | 3700  | 3.161         |
+| 2.0267 | 3800  | 3.1708        |
+| 2.08   | 3900  | 3.1678        |
+| 2.1333 | 4000  | 3.1348        |
+| 2.1867 | 4100  | 2.9159        |
+| 2.24   | 4200  | 2.8359        |
+| 2.2933 | 4300  | 2.8359        |
+| 2.3467 | 4400  | 2.796         |
+| 2.4    | 4500  | 2.8483        |
+| 2.4533 | 4600  | 2.7774        |
+| 2.5067 | 4700  | 2.7766        |
+| 2.56   | 4800  | 2.7185        |
+| 2.6133 | 4900  | 2.778         |
+| 2.6667 | 5000  | 2.7114        |
+| 2.72   | 5100  | 2.6623        |
+| 2.7733 | 5200  | 2.5093        |
+| 2.8267 | 5300  | 2.4835        |
+| 2.88   | 5400  | 2.2851        |
+| 2.9333 | 5500  | 2.1488        |
+| 2.9867 | 5600  | 2.2175        |
+| 3.04   | 5700  | 2.0813        |
+| 3.0933 | 5800  | 2.1489        |
+| 3.1467 | 5900  | 2.1337        |
+| 3.2    | 6000  | 2.2258        |
+| 3.2533 | 6100  | 2.1601        |
+| 3.3067 | 6200  | 1.9266        |
+| 3.36   | 6300  | 1.8427        |
+| 3.4133 | 6400  | 1.8434        |
+| 3.4667 | 6500  | 1.917         |
+| 3.52   | 6600  | 1.8204        |
+| 3.5733 | 6700  | 2.0209        |
+| 3.6267 | 6800  | 1.7852        |
+| 3.68   | 6900  | 1.9566        |
+| 3.7333 | 7000  | 1.852         |
+| 3.7867 | 7100  | 1.8562        |
+| 3.84   | 7200  | 1.7595        |
+| 3.8933 | 7300  | 1.4295        |
+| 3.9467 | 7400  | 1.2669        |
+| 4.0    | 7500  | 1.2029        |
+| 4.0533 | 7600  | 1.3074        |
+| 4.1067 | 7700  | 1.435         |
+| 4.16   | 7800  | 1.5712        |
+| 4.2133 | 7900  | 1.2366        |
+| 4.2667 | 8000  | 1.526         |
+| 4.32   | 8100  | 1.2565        |
+| 4.3733 | 8200  | 1.4546        |
+| 4.4267 | 8300  | 1.374         |
+| 4.48   | 8400  | 1.3387        |
+| 4.5333 | 8500  | 1.3776        |
+| 4.5867 | 8600  | 1.3984        |
+| 4.64   | 8700  | 1.3577        |
+| 4.6933 | 8800  | 1.2393        |
+| 4.7467 | 8900  | 1.4125        |
+| 4.8    | 9000  | 1.6127        |
+| 4.8533 | 9100  | 1.6897        |
+| 4.9067 | 9200  | 1.1217        |
+| 4.96   | 9300  | 1.406         |
+| 5.0133 | 9400  | 1.4641        |
+| 5.0667 | 9500  | 1.48          |
+| 5.12   | 9600  | 1.3367        |
+| 5.1733 | 9700  | 1.4681        |
+| 5.2267 | 9800  | 1.4628        |
+| 5.28   | 9900  | 1.32          |
+| 5.3333 | 10000 | 1.448         |
+| 5.3867 | 10100 | 1.2516        |
+| 5.44   | 10200 | 1.4421        |
+| 5.4933 | 10300 | 1.2542        |
+| 5.5467 | 10400 | 1.4545        |
+| 5.6    | 10500 | 1.1441        |
+| 5.6533 | 10600 | 1.251         |
+| 5.7067 | 10700 | 1.3396        |
+| 5.76   | 10800 | 1.0305        |
+| 5.8133 | 10900 | 1.0155        |
+| 5.8667 | 11000 | 0.9871        |
+| 5.92   | 11100 | 1.074         |
+| 5.9733 | 11200 | 0.4534        |
+| 6.0267 | 11300 | 0.1965        |
+| 6.08   | 11400 | 0.1822        |
+| 6.1333 | 11500 | 0.2101        |
+| 6.1867 | 11600 | 0.2326        |
+| 6.24   | 11700 | 0.4126        |
+| 6.2933 | 11800 | 0.4871        |
+| 6.3467 | 11900 | 0.2012        |
+| 6.4    | 12000 | 0.2113        |
+| 6.4533 | 12100 | 0.1788        |
+| 6.5067 | 12200 | 0.2271        |
+| 6.56   | 12300 | 0.1685        |
+| 6.6133 | 12400 | 0.3347        |
+| 6.6667 | 12500 | 0.123         |
+| 6.72   | 12600 | 0.155         |
+| 6.7733 | 12700 | 0.2476        |
+| 6.8267 | 12800 | 0.1926        |
+| 6.88   | 12900 | 0.1394        |
+| 6.9333 | 13000 | 0.1683        |
+| 6.9867 | 13100 | 0.2484        |
+| 7.04   | 13200 | 0.1338        |
+| 7.0933 | 13300 | 0.1568        |
+| 7.1467 | 13400 | 0.1206        |
+| 7.2    | 13500 | 0.1683        |
+| 7.2533 | 13600 | 0.1831        |
+| 7.3067 | 13700 | 0.3077        |
+| 7.36   | 13800 | 0.3533        |
+| 7.4133 | 13900 | 0.1165        |
+| 7.4667 | 14000 | 0.2128        |
+| 7.52   | 14100 | 0.236         |
+| 7.5733 | 14200 | 0.3616        |
+| 7.6267 | 14300 | 0.2989        |
+| 7.68   | 14400 | 0.2416        |
+| 7.7333 | 14500 | 0.2105        |
+| 7.7867 | 14600 | 0.1575        |
+| 7.84   | 14700 | 0.224         |
+| 7.8933 | 14800 | 0.1593        |
+| 7.9467 | 14900 | 0.1293        |
+| 8.0    | 15000 | 0.0985        |
+
+</details>
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.0.1
+- Transformers: 4.42.4
+- PyTorch: 2.3.1+cu121
+- Accelerate: 0.33.0
+- Datasets: 2.20.0
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### CoSENTLoss
+```bibtex
+@online{kexuefm-8847,
+    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
+    author={Su Jianlin},
+    year={2022},
+    month={Jan},
+    url={https://kexue.fm/archives/8847},
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,24 @@
+{
+  "_name_or_path": "models/sft-sql-embedding",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
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+}

config_sentence_transformers.json

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+{
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+    "transformers": "4.41.2",
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+  },
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+  "similarity_fn_name": null
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model.safetensors

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+size 437967672

modules.json

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+    "name": "0",
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+    "type": "sentence_transformers.models.Transformer"
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+    "type": "sentence_transformers.models.Normalize"
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+]

sentence_bert_config.json

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+{
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+  "do_lower_case": false
+}

special_tokens_map.json

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tokenizer_config.json

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+{
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+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}