SentenceTransformer

This is a sentence-transformers model trained. It maps sentences & paragraphs to a 1024-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
Maximum Sequence Length: 8192 tokens
Output Dimensionality: 1024 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': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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})
)

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("strongpear/M3-retriever-Vi-Text2SQL")
# Run inference
sentences = [
    "Năm 2011 là bao nhiêu khi năm 2009 là 'MỘT'?",
    'CREATE TABLE table 61807("Giải đấu" text,"2009" text,"2010" text,"2011" text,"2012" text)',
    'CREATE TABLE table name 27(điểm VARCHAR,đội khách VARCHAR,date VARCHAR)',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

Unnamed Dataset

Size: 1,312,839 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 6 tokens mean: 22.66 tokens max: 323 tokens	min: 12 tokens mean: 56.47 tokens max: 159 tokens	min: 11 tokens mean: 41.05 tokens max: 621 tokens

Samples:

anchor	positive	negative
`Chủ đề StackOverflow vào tháng 5 năm 2010. Sẽ thật tuyệt nếu có một biểu đồ, tôi biết :)`	`CREATE TABLE Bài viết(Id number,PostTypeId number,AcceptedAnswerId number,ParentId number,CreationDate time,DeletionDate time,Score number,ViewCount number,Nội dung text,OwnerUserId number,OwnerDisplayName text,LastEditorUserId number,LastEditorDisplayName text,LastEditDate time,LastActivityDate time,Title text,Tags text,AnswerCount number,CommentCount number,FavoriteCount number,ClosedDate time,CommunityOwnedDate time,ContentLince text);`	`CREATE TABLE PostTypes(Id number,Name text)`
`sao2 của bệnh nhân 31854 trong lần khám tại bệnh viện hiện tại có bình thường không?`	`CREATE TABLE icustays(row id number,subject id number,hadm id number,icustay id number,first careunit text,last careunit text,first wardid number,last wardid number,intime time,outtime time);`	`CREATE TABLE inputevents cv(row id number,subject id number,hadm id number,icustay id number,charttime time,itemid number,amount number)`
`chuyến bay nào từ PITTSBURGH đến ATLANTA vào sáng thứ Tư phục vụ BỮA SÁNG`	`CREATE TABLE sân bay(airport code varchar,airport name text,airport location text,state code varchar,country name varchar,time zone code varchar,minimum connect time int);`	`CREATE TABLE máy bay(aircraft code varchar,aircraft description varchar,nhà sản xuất varchar,basic type varchar,engines int,động cơ varchar,thân rộng varchar,sải cánh int,chiều dài int,trọng lượng int,công suất int,tải trả int,tốc độ bay int,phạm vi dặm int,có áp suất varchar)`

Loss: TripletLoss with these parameters:

{
    "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
    "triplet_margin": 5
}

Evaluation Dataset

Unnamed Dataset

Size: 69,098 evaluation samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 8 tokens mean: 23.11 tokens max: 323 tokens	min: 7 tokens mean: 57.77 tokens max: 181 tokens	min: 12 tokens mean: 42.41 tokens max: 207 tokens

Samples:

anchor	positive	negative
`Đã bao nhiêu ngày kể từ lần cuối bệnh nhân 1561 nhận được một lượng lactate ringer trong lần thăm khám tại phòng chăm sóc đặc biệt hiện tại?`	`CREATE TABLE inputevents cv(row id number,subject id number,hadm id number,icustay id number,charttime time,itemid number,amount number);`	`CREATE TABLE d icd diagnoses(row id number,icd9 code text,short title text,tiêu đề dài text)`
`Có nhiều giáo sư dạy ECON 631 trong kỳ Xuân Hè 2003 không?`	`CREATE TABLE giảng viên(instructor id int,name varchar,tên uniq varchar);`	`CREATE TABLE học kỳ(semester id int,semester varchar,year int)`
`bệnh nhân 011-55642 có được chẩn đoán mắc bệnh gì trong lần khám tại bệnh viện hiện tại không?`	CREATE TABLE bệnh nhân(uniquepid text,bệnh nhânhealthsystemstayid number,bệnh nhân đơn vị ở lạiid number,giới tính text,tuổi text,dân tộc text,bệnh viện number,khu bệnh number,chiều cao nhập viện number,cân nặng nhập viện number,cân nặng xuất viện number,thời gian nhập viện time,nguồn nhập viện text,thời gian nhập viện đơn vị time,thời gian nhập viện time,thời gian xuất viện time,trạng thái xuất viện text)	`CREATE TABLE inputoutput(intakeoutputid number,Patientunitstayid number,cellpath text,celllabel text,cellvaluenumeric number,intakeoutputtime time)`

Loss: TripletLoss with these parameters:

{
    "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
    "triplet_margin": 5
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
gradient_accumulation_steps: 4
learning_rate: 1e-05
weight_decay: 0.1
num_train_epochs: 1
warmup_steps: 500
fp16: True
remove_unused_columns: False

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 8
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 4
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 1e-05
weight_decay: 0.1
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
warmup_steps: 500
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: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: False
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
eval_use_gather_object: False
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss
0.0244	1000	0.165	0.1212
0.0487	2000	0.1242	0.1124
0.0731	3000	0.1189	0.1007
0.0975	4000	0.102	0.0905
0.1219	5000	0.0941	0.0911
0.1462	6000	0.0992	0.0845
0.1706	7000	0.0915	0.0846
0.1950	8000	0.0913	0.0787
0.2194	9000	0.0879	0.0750
0.2437	10000	0.0842	0.0733
0.2681	11000	0.0803	0.0696
0.2925	12000	0.0856	0.0672
0.3169	13000	0.0742	0.0674
0.3412	14000	0.0757	0.0690
0.3656	15000	0.076	0.0667
0.3900	16000	0.0684	0.0678
0.4144	17000	0.0729	0.0633
0.4387	18000	0.0641	0.0611
0.4631	19000	0.0678	0.0603
0.4875	20000	0.0637	0.0578
0.5119	21000	0.0671	0.0578
0.5362	22000	0.0651	0.0558
0.5606	23000	0.0596	0.0553
0.5850	24000	0.0577	0.0552
0.6094	25000	0.0639	0.0539
0.6337	26000	0.0559	0.0526
0.6581	27000	0.06	0.0509
0.6825	28000	0.0555	0.0507
0.7069	29000	0.0595	0.0501
0.7312	30000	0.0569	0.0503
0.7556	31000	0.0565	0.0487
0.7800	32000	0.0512	0.0476
0.8044	33000	0.0515	0.0469
0.8287	34000	0.0489	0.0461
0.8531	35000	0.051	0.0463
0.8775	36000	0.0518	0.0460
0.9019	37000	0.0491	0.0454
0.9262	38000	0.0485	0.0453
0.9506	39000	0.0489	0.0447
0.9750	40000	0.0432	0.0445
0.9994	41000	0.0509	0.0444

Framework Versions

Python: 3.9.19
Sentence Transformers: 3.0.1
Transformers: 4.44.2
PyTorch: 2.4.0+cu121
Accelerate: 0.34.2
Datasets: 2.21.0
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",
}

TripletLoss

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification}, 
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

strongpear
/

M3-retriever-Vi-Text2SQL

SentenceTransformer

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Training Details

Training Dataset

Unnamed Dataset

Evaluation Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

TripletLoss

Model tree for strongpear/M3-retriever-Vi-Text2SQL