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s2593817
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sft-sql-embedding

Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:300000
loss:CoSENTLoss
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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.*
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## 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.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## 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},
}
```
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