Data-Lab/USER-bge-m3-embedder_distill-tgd

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-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 нуждающихся в особом рационе 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.	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  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.	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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: 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	0.1208	-
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

@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",
}