SentenceTransformer based on deepvk/USER-bge-m3

This is a sentence-transformers model finetuned from deepvk/USER-bge-m3. 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
Base model: deepvk/USER-bge-m3
Maximum Sequence Length: 512 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': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("Data-Lab/USER-bge-m3-embedder_distill-tg")
# Run inference
sentences = [
    'тушенка',
    'Говядина тушеная по-домашнему с пюре None, говядина, тушеное мясо, домашняя кухня, пюре, подливка, овощи, сливки, гарнир',
    'Томленая говядина с пюре из цветной капусты товар, гарнир, нежное мясо, томатный соус, запеченная морковь, пряная приправа, кокосовый крем, воздушное пюре, вегетарианское, безглютеновое',
]
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]

Evaluation

Metrics

Triplet

Dataset: dev
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9312
dot_accuracy	0.0688
manhattan_accuracy	0.9303
euclidean_accuracy	0.9312
max_accuracy	0.9312

Training Details

Training Dataset

Unnamed Dataset

Size: 10,189 training samples
Columns: sentence_0, sentence_1, sentence_2, and label

Approximate statistics based on the first 1000 samples:

	sentence_0	sentence_1	sentence_2	label
type	string	string	string	list
details	min: 3 tokens mean: 7.85 tokens max: 30 tokens	min: 21 tokens mean: 55.0 tokens max: 107 tokens	min: 21 tokens mean: 56.38 tokens max: 129 tokens	size: 3 elements

Samples:

sentence_0	sentence_1	sentence_2	label
`вареники картофель грибы`	`Вареники с картофелем, лисичками и трюфелем, 0.5 шт None, вареники, картофель, лисички, трюфель, гурманские, столовые закуски`	`Гёдза с цыпленком-бройлером, зам. None, гёдза, курица, капуста, замороженное, сладкий вкус, аромат, None`	[[0.05194865167140961, 0.029716061428189278, -0.015351681970059872, 0.03434691205620766, -0.015430141240358353, -0.01331735122948885, 0.025081496685743332, 0.024793783202767372, -0.03315753489732742, -0.029741337522864342, 0.01669594645500183, -0.014653450809419155, -0.03138551488518715, -0.015092355199158192, 0.05802172049880028, -0.031295765191316605, 0.010290372185409069, -0.011702042073011398, 0.017403237521648407, -0.059633176773786545, -0.02909679152071476, -4.078661004314199e-05, -0.04264095425605774, -0.03735298663377762, 0.013964444398880005, 0.01599659025669098, -0.030119849368929863, -0.008339658379554749, 0.014395869337022305, 0.001903194235637784, -0.04119347408413887, 0.011139851063489914, 0.0062087825499475, -0.06191807612776756, -0.017792701721191406, 0.006406228989362717, 0.009906291961669922, 0.0019379716832190752, -0.042057525366544724, 0.02383636124432087, 0.008834648877382278, 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`копченое мясо`	`Колбаса сырокопченая Родные места Брауншвейгская 300 г колбаса, сырокопченая, мясные изделия, нарезка, мясное ассорти, ГОСТ, свинина, говядина, специи, производитель Россия`	`Бизнес Ланч на весь день комбо-набор, три приёма пищи, завтрак, обед, ужин, блинчики, мясо, плов, лобио, курица, морс, ягодный, удобно, решение для занятых`	[[0.003859027521684766, -0.003908736165612936, -0.03974922373890877, 0.03655204549431801, -0.0021214208099991083, 0.026073791086673737, 0.05049542337656021, 0.030028151348233223, -0.020358067005872726, -0.013503949157893658, 0.004354887176305056, -0.010410487651824951, -0.008863752707839012, 0.02895798347890377, 0.03594919666647911, -0.027634678408503532, -0.00889184046536684, -0.012365046888589859, -0.031094608828425407, -0.05130459740757942, -0.027008699253201485, -0.008432094007730484, -0.04041757434606552, 0.00040848576463758945, -0.0066183777526021, 0.000903813517652452, -0.016931330785155296, -0.01620648428797722, -0.03479746729135513, 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`десерт для вечеринки, который можно подать с чаем`	`Чизкейк "Зелёный чай" чизкейк, зелёный чай, матча, десерт, нежный, воздушный, сладости, печенье, аллергены, гармоничный вкус, многогранный вкус, кофе, чай`	`Суп харчо суп, грузинская кухня, мясное блюдо, рисовый суп, овощи`	[[0.03582163155078888, 0.019288262352347374, 0.028281278908252716, -0.02735191211104393, -0.032050926238298416, 0.013213074766099453, 0.017194636166095734, 0.015436804853379726, 0.003997071646153927, -0.018121561035513878, 0.034479159861803055, -0.002112550660967827, -0.020967258140444756, -0.003420935943722725, 0.013761233538389206, -0.034191399812698364, 0.013754503801465034, -0.021977368742227554, -0.021714234724640846, -0.014674955047667027, -0.026225587353110313, 0.04479273408651352, -0.08182252198457718, -0.007007253356277943, -0.0024338087532669306, 0.025204962119460106, -0.03770007938146591, -0.011210216209292412, 0.05218064785003662, -0.020946335047483444, -0.004369739443063736, 0.016734888777136803, 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Loss: main.DistillationTripletLoss

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 4
per_device_eval_batch_size: 4
fp16: True
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 4
per_device_eval_batch_size: 4
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1
num_train_epochs: 3
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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: True
dataloader_num_workers: 0
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
eval_use_gather_object: False
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss	dev_max_accuracy
0.3928	500	0.1197	-
0.7855	1000	0.0825	0.9073
1.0	1273	-	0.9241
1.1783	1500	0.0696	-
1.5711	2000	0.0478	0.9259
1.9639	2500	0.0397	-
2.0	2546	-	0.9135
2.3566	3000	0.0313	0.9259
2.7494	3500	0.0249	-
3.0	3819	-	0.9312

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.2.0
Transformers: 4.44.0
PyTorch: 2.3.1+cu121
Accelerate: 0.31.0
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",
}

Data-Lab
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USER-bge-m3-embedder_distill-tg