README.md

metadata

base_model: BAAI/bge-m3
datasets: []
language: []
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:4532
  - loss:CoSENTLoss
widget:
  - source_sentence: портативный проектор umiio a 008
    sentences:
      - портативный проектор philips a 008
      - logitech c270i iptv
      - детский электромобиль sundays land rover jj012
  - source_sentence: запчасти для швейных машин bernette
    sentences:
      - мфу samsung m428fdw
      - запасные части для швейной машины bernette
      - steelseries apex pro mini wireless
  - source_sentence: сушильная машина maunfeld mfdm1410wh06
    sentences:
      - кухонные уголки
      - сушильная машина simens mfdm1410wh06
      - сетевой удлинитель евро eu-4 multi-protection 4usb qy-923 2500w
  - source_sentence: монитор acer k242hql
    sentences:
      - multiflashlight armytek zippy green
      - роутер mi router 4c r4cm dvb4231gl
      - монитор acer k224hql
  - source_sentence: набор моя первая кухня
    sentences:
      - кухонные наборы
      - ea sports fc 23 ps4
      - da vinci белая
model-index:
  - name: SentenceTransformer based on BAAI/bge-m3
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev
          type: sts-dev
        metrics:
          - type: pearson_cosine
            value: 0.9701810342203735
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.9168792089469636
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.9695654298959763
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.9165761310923896
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.9696385323216731
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.9166348972420479
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.9631206697635591
            name: Pearson Dot
          - type: spearman_dot
            value: 0.9173046326579305
            name: Spearman Dot
          - type: pearson_max
            value: 0.9701810342203735
            name: Pearson Max
          - type: spearman_max
            value: 0.9173046326579305
            name: Spearman Max

SentenceTransformer based on BAAI/bge-m3

This is a sentence-transformers model finetuned from BAAI/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: BAAI/bge-m3
• 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("seregadgl101/test_bge_2_10ep")
# Run inference
sentences = [
    'набор моя первая кухня',
    'кухонные наборы',
    'ea sports fc 23 ps4',
]
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

Semantic Similarity

• Dataset: sts-dev
• Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.9702
spearman_cosine	0.9169
pearson_manhattan	0.9696
spearman_manhattan	0.9166
pearson_euclidean	0.9696
spearman_euclidean	0.9166
pearson_dot	0.9631
spearman_dot	0.9173
pearson_max	0.9702
spearman_max	0.9173

Training Details

Training Dataset

Unnamed Dataset

• Size: 4,532 training samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 4 tokens mean: 14.45 tokens max: 48 tokens	min: 3 tokens mean: 13.09 tokens max: 51 tokens	min: 0.0 mean: 0.6 max: 1.0

• Samples:

  sentence1	sentence2	score
  батут evo jump internal 12ft	батут evo jump internal 12ft	1.0
  наручные часы orient casual	наручные часы orient	1.0
  электрический духовой шкаф weissgauff eov 19 mw	электрический духовой шкаф weissgauff eov 19 mx	0.4

• Loss: CoSENTLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 504 evaluation samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 4 tokens mean: 14.93 tokens max: 48 tokens	min: 4 tokens mean: 13.1 tokens max: 40 tokens	min: 0.0 mean: 0.59 max: 1.0

• Samples:

  sentence1	sentence2	score
  потолочный светильник yeelight smart led ceiling light c2001s500	yeelight smart led ceiling light c2001s500	1.0
  канцелярские принадлежности	канцелярские принадлежности разные	0.4
  usb-магнитола acv avs-1718g	автомагнитола acv avs-1718g	1.0

• Loss: CoSENTLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• learning_rate: 2e-05
• num_train_epochs: 10
• warmup_ratio: 0.1
• save_only_model: True
• seed: 33
• fp16: True
• load_best_model_at_end: True

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: 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: 10
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• 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: True
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 33
• 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: True
• label_names: None
• load_best_model_at_end: True
• 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
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	loss	sts-dev_spearman_cosine
0.0882	50	-	2.7444	0.4991
0.1764	100	-	2.5535	0.6093
0.2646	150	-	2.3365	0.6761
0.3527	200	-	2.1920	0.7247
0.4409	250	-	2.2210	0.7446
0.5291	300	-	2.1432	0.7610
0.6173	350	-	2.2488	0.7769
0.7055	400	-	2.3736	0.7749
0.7937	450	-	2.0688	0.7946
0.8818	500	2.3647	2.5331	0.7879
0.9700	550	-	2.1087	0.7742
1.0582	600	-	2.1302	0.8068
1.1464	650	-	2.2669	0.8114
1.2346	700	-	2.0269	0.8039
1.3228	750	-	2.2095	0.8138
1.4109	800	-	2.5288	0.8190
1.4991	850	-	2.3442	0.8222
1.5873	900	-	2.3759	0.8289
1.6755	950	-	2.1893	0.8280
1.7637	1000	2.0682	2.0056	0.8426
1.8519	1050	-	2.0832	0.8527
1.9400	1100	-	2.0336	0.8515
2.0282	1150	-	2.0571	0.8591
2.1164	1200	-	2.1516	0.8565
2.2046	1250	-	2.2035	0.8602
2.2928	1300	-	2.5294	0.8513
2.3810	1350	-	2.4177	0.8647
2.4691	1400	-	2.1630	0.8709
2.5573	1450	-	2.1279	0.8661
2.6455	1500	1.678	2.1639	0.8744
2.7337	1550	-	2.2592	0.8799
2.8219	1600	-	2.2288	0.8822
2.9101	1650	-	2.2427	0.8831
2.9982	1700	-	2.4380	0.8776
3.0864	1750	-	2.1689	0.8826
3.1746	1800	-	1.8099	0.8868
3.2628	1850	-	2.0881	0.8832
3.3510	1900	-	2.0785	0.8892
3.4392	1950	-	2.2512	0.8865
3.5273	2000	1.2168	2.1249	0.8927
3.6155	2050	-	2.1179	0.8950
3.7037	2100	-	2.1932	0.8973
3.7919	2150	-	2.2628	0.8967
3.8801	2200	-	2.0764	0.8972
3.9683	2250	-	1.9575	0.9012
4.0564	2300	-	2.3302	0.8985
4.1446	2350	-	2.3008	0.8980
4.2328	2400	-	2.2886	0.8968
4.3210	2450	-	2.1694	0.8973
4.4092	2500	1.0851	2.1102	0.9010
4.4974	2550	-	2.2596	0.9021
4.5855	2600	-	2.1944	0.9019
4.6737	2650	-	2.0728	0.9029
4.7619	2700	-	2.4573	0.9031
4.8501	2750	-	2.2306	0.9057
4.9383	2800	-	2.2637	0.9068
5.0265	2850	-	2.5110	0.9068
5.1146	2900	-	2.6613	0.9042
5.2028	2950	-	2.4713	0.9070
5.2910	3000	0.8143	2.3709	0.9082
5.3792	3050	-	2.6083	0.9058
5.4674	3100	-	2.5377	0.9044
5.5556	3150	-	2.3146	0.9071
5.6437	3200	-	2.2603	0.9085
5.7319	3250	-	2.5842	0.9068
5.8201	3300	-	2.6045	0.9093
5.9083	3350	-	2.6207	0.9103
5.9965	3400	-	2.5992	0.9098
6.0847	3450	-	2.7799	0.9090
6.1728	3500	0.5704	2.7198	0.9098
6.2610	3550	-	2.9783	0.9089
6.3492	3600	-	2.4165	0.9120
6.4374	3650	-	2.4488	0.9122
6.5256	3700	-	2.6764	0.9113
6.6138	3750	-	2.5327	0.9130
6.7019	3800	-	2.5875	0.9129
6.7901	3850	-	2.7036	0.9130
6.8783	3900	-	2.7566	0.9120
6.9665	3950	-	2.5488	0.9127
7.0547	4000	0.4287	2.8512	0.9127
7.1429	4050	-	2.7361	0.9128
7.2310	4100	-	2.7434	0.9135
7.3192	4150	-	2.9410	0.9129
7.4074	4200	-	2.9452	0.9126
7.4956	4250	-	2.8665	0.9140
7.5838	4300	-	2.8215	0.9145
7.6720	4350	-	2.6978	0.9147
7.7601	4400	-	2.8445	0.9143
7.8483	4450	-	2.6041	0.9155
7.9365	4500	0.3099	2.7219	0.9155
8.0247	4550	-	2.7180	0.9160
8.1129	4600	-	2.6906	0.9160
8.2011	4650	-	2.8628	0.9156
8.2892	4700	-	2.7820	0.9158
8.3774	4750	-	2.8457	0.9157
8.4656	4800	-	2.7286	0.9160
8.5538	4850	-	2.7131	0.9164
8.6420	4900	-	2.8368	0.9165
8.7302	4950	-	2.8033	0.9167
8.8183	5000	0.2342	2.7307	0.9169
8.9065	5050	-	2.8483	0.9167
8.9947	5100	-	2.9736	0.9167
9.0829	5150	-	2.9151	0.9168
9.1711	5200	-	2.9375	0.9167
9.2593	5250	-	2.9968	0.9168
9.3474	5300	-	3.0024	0.9167
9.4356	5350	-	2.9444	0.9167
9.5238	5400	-	2.9477	0.9167
9.6120	5450	-	2.9205	0.9168
9.7002	5500	0.1639	2.9286	0.9167
9.7884	5550	-	2.9421	0.9168
9.8765	5600	-	2.9733	0.9168
9.9647	5650	-	2.9777	0.9169

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.0.1
• Transformers: 4.41.2
• PyTorch: 2.1.2+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",
}

CoSENTLoss

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}