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

	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