s2593817/sft-sql-embedding

SentenceTransformer based on sentence-transformers/all-mpnet-base-v2

This is a sentence-transformers model finetuned from 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
• Maximum Sequence Length: 384 tokens
• Output Dimensionality: 768 tokens
• Similarity Function: Cosine Similarity

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

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]

Training Details

Training Dataset

Unnamed Dataset

• Size: 300,000 training samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 8 tokens mean: 38.49 tokens max: 189 tokens	min: 7 tokens mean: 37.44 tokens max: 153 tokens	min: 0.04 mean: 0.36 max: 1.0

• Samples:

  sentence1	sentence2	score
  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	SELECT count(*) FROM table1 WHERE col1 = str	0.14221014492753623
  SELECT DISTINCT count(alias2.col1) FROM table1 AS alias1 JOIN table2 AS alias2 ON alias1.col2 = alias2.col2 WHERE alias1.col3 = str	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	0.5468686868686868
  SELECT count(*) FROM table1	SELECT count(*) FROM table1 WHERE col1 LIKE str	0.6269230769230769

• Loss: CoSENTLoss with these parameters:

  {
      "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

Click to expand

• 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

Training Logs

Click to expand

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

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

@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

@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},
}