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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Data-Lab/USER-bge-m3-embedder_distill-tgd")
# Run inference
sentences = [
    'сладкая закуска без сахара для пикника без орехов с низким содержанием углеводов',
    'Заливное из индейки мясной деликатес, низкокалорийное блюдо, закуска, прозрачное желе, натуральные ингредиенты, специальный рецепт, домашнее приготовление, питательное дополнение Заливное — по-настоящему деликатесное блюдо с насыщенным мясным вкусом. Оно долго готовится, но моментально съедается. Поэтому мы готовим его по рецепту, приближенному к домашнему, чтобы сэкономить ваши усилия.\nПрозрачное желе, волокна нежной индейки и немножко специй, чтобы подчеркнуть натуральный вкус свежих ингредиентов.\nЗаливное станет питательным дополнением к обеду или ужину, а также полезной закуской на каждый день. Кстати, оно одно из самых низкокалорийных блюд нашей мясной гастрономии, и поэтому прекрасно подойдёт для строгого рациона.',
    'Корм влажный диетический для взрослых кошек "Профилактика болезни почек", 340 гр диетический корм, профилактика болезней, взрослые кошки, корм для кошек, ветеринарный корм, корм с мясом, витаминно-минеральный комплекс, особый рацион, добавки для здоровья, свинина, картофель, рис Влажный диетический корм, разработанный ветеринарами для профилактики болезней почек. Подходит для&nbsp;нуждающихся в особом рационе&nbsp;взрослых кошек всех пород. Изготовлен на основе свинины с картофелем и рисом и обогащён витаминно-минеральным премиксом, содержащим необходимые добавки для здоровья любимца.',
]
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	0.9162
dot_accuracy	0.0838
manhattan_accuracy	0.9162
euclidean_accuracy	0.9162
max_accuracy	0.9162

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: 8.03 tokens max: 28 tokens	min: 23 tokens mean: 102.81 tokens max: 445 tokens	min: 27 tokens mean: 108.13 tokens max: 512 tokens	size: 3 elements

Samples:

sentence_0	sentence_1	sentence_2	label
`вода кокос`	`Вода кокосовая Chaokoh 350 мл None, кокосовая вода, очистка, гидратация, здоровье, натуральный напиток Кокосовая вода почти идентична плазме крови человека, поэтому моментально усваивается организмом. Отлично выводит токсины из организма.`	`Кофе черный в банке, кафе кофе, черный, банка, крепкий, натуральный, эфиопская арабика, напиток, холодный, доставка, кафе Крепкий напиток с насыщенным вкусом классического фильтр-кофе. 100% эфиопская арабика и вода.`	[[0.007306413259357214, 0.03650311380624771, -0.031073134392499924, -0.01659717597067356, -0.027407638728618622, -0.017671670764684677, -0.012859059497714043, 0.006015981547534466, -0.006974314339458942, 0.012663076631724834, 0.04563990980386734, -0.03247430920600891, 0.0019892901182174683, -0.017782066017389297, 0.06775614619255066, 0.01856316812336445, 0.0013995417393743992, 0.019535798579454422, -0.01987701840698719, -0.04669417068362236, -0.05016664043068886, -0.00875155907124281, 0.003561326302587986, -0.005834360606968403, 0.022958679124712944, 0.020601950585842133, 0.012450993061065674, -0.02793978899717331, -0.004538948647677898, -0.004268542863428593, -0.002337561920285225, 0.03167107328772545, 0.012892541475594044, 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-0.007218600250780582, 0.017656465992331505]]
`оладьи`	`Оладьи куриные, 320 г куриные оладьи, мясные продукты, гарнир, соус, высокое содержание белка, закуска, жареное, порция, золотистая корочка, готовое блюдо Сочные мясные оладьи из куриной грудки, поджаренные до золотистой корочки`	`Драники картофельные, 200 г картофель, драники, закуска, быстрая еда, сытное блюдо, традиционное блюдо, вегетарианское, перекус, корочка Классические картофельные драники, поджаренные до золотистой корочки. Вкусный и сытный перекус.`	[[-0.0042711771093308926, 0.045571353286504745, -0.06789152324199677, 0.012361668981611729, -0.04350458085536957, 0.03321952000260353, -0.008508319035172462, 0.015193330124020576, 0.058261070400476456, -0.03507335111498833, 0.007218105718493462, -0.014197193086147308, 0.022147243842482567, -0.008178137242794037, 0.07527805119752884, -0.046086978167295456, -0.00697836559265852, 0.013670945540070534, 0.0007886571693234146, -0.027624383568763733, -0.029053131118416786, 0.004261803813278675, -0.016580140218138695, 0.019388677552342415, 0.0032622029539197683, 0.013230957090854645, 0.023774392902851105, -0.040591102093458176, 0.000570006319321692, -0.013367976993322372, 0.021395517513155937, 0.05118681862950325, 0.027038926258683205, -0.04602302238345146, 0.0024630373809486628, 0.05148208886384964, 0.0060687498189508915, -0.012286891229450703, -0.0869002491235733, 0.01750846765935421, 0.028147634118795395, 0.03998392075300217, -0.002919994993135333, 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`индей`	`Филе грудки индейки 1000 г None, диетическое мясо, птица, филе, салаты, супы, запекание, белок, None Диетическое мясо, подходит для салатов, супов, запекания`	Ветчина из грудки индейки с паприкой 1 кг None, ветчина, индейка, паприка, диетическое питание, мясные продукты, закуски, плита, кухня, салаты, роллы, омлеты, None Диетическая ветчина из индейки с паприкой и перцем. Плотная текстура, умеренно пряный вкус. Для роллов, омлетов, салатов.	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Loss: [object Object].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	0.1208	-
0.7855	1000	0.0818	0.9179
1.0	1273	-	0.9117
1.1783	1500	0.0669	-
1.5711	2000	0.0514	0.9223
1.9639	2500	0.0361	-
2.0	2546	-	0.9188
2.3566	3000	0.0299	0.9365
2.7494	3500	0.0225	-
3.0	3819	-	0.9162

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

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",
}