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all-MiniLM-L6-v80-pair_score

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
dense
Generated from Trainer
dataset_size:69786324
loss:CoSENTLoss
text-embeddings-inference
Model card Files
xet
 Community

all-MiniLM-L6-v80-pair_score

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2 on the pairs_with_scores_v65 dataset. It maps sentences & paragraphs to a 384-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 Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, '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})
  (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("sentence_transformers_model_id")
# Run inference
sentences = [
    'off body shirt',
    'arki soup bowl arki bowl bowl soup bowl arki bowl bowl soup bowl',
    'jbl partybox 100 speaker jbl speaker partybox partybox',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000, -0.0935, -0.0699],
#         [-0.0935,  1.0000,  0.0134],
#         [-0.0699,  0.0134,  1.0000]])

Training Details

Training Dataset

pairs_with_scores_v65

  • Dataset: pairs_with_scores_v65 at e93c4eb
  • Size: 69,786,324 training samples
  • Columns: sentence1, sentence2, and score
  • Approximate statistics based on the first 1000 samples:
    sentence1 sentence2 score
    type string string float
    details
    • min: 3 tokens
    • mean: 6.71 tokens
    • max: 24 tokens
    • min: 5 tokens
    • mean: 45.08 tokens
    • max: 256 tokens
    • min: 0.0
    • mean: 0.04
    • max: 1.0
  • Samples:
    sentence1 sentence2 score
    winter duvet microfiber 350 gsm terracotta clay pizza stone plate - 35cm terracotta pizza platter clay pizza platter seving pizza platter kitchen pizza platter kitchen dining clay pizza plate pizza plate plate terracotta pizza plate clay pizza plate pizza plate plate terracotta pizza plate 0.25
    pepper sauce steak teppanyaki salmon hot teppanyaki salmon teppanyaki teppanyaki salmon teppanyaki teppanyaki salmon 0.25
    oval shaped table 28 cm oval dutch oven grif dutch oven cast iron dutch oven brass lid knob dutch oven dutch oven oval dutch oven 28 cm oval dutch stove dutch stove oval dutch stove dutch oven oval dutch oven 28 cm oval dutch stove dutch stove oval dutch stove 0.25
  • Loss: CoSENTLoss with these parameters:
    {
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

Evaluation Dataset

pairs_with_scores_v65

  • Dataset: pairs_with_scores_v65 at e93c4eb
  • Size: 350,686 evaluation samples
  • Columns: sentence1, sentence2, and score
  • Approximate statistics based on the first 1000 samples:
    sentence1 sentence2 score
    type string string float
    details
    • min: 3 tokens
    • mean: 6.79 tokens
    • max: 25 tokens
    • min: 5 tokens
    • mean: 45.07 tokens
    • max: 256 tokens
    • min: 0.0
    • mean: 0.05
    • max: 1.0
  • Samples:
    sentence1 sentence2 score
    ceramic coating hair volumizer prostanorm - supplement with zinc gluconate saw palmetto berry stinging nettle extracts - 30 capsules prostanorm capsules prostanorm prostanorm supplement saw palmetto berry supplement stinging nettle extracts supplement zinc gluconate supplement prostanorm prostanorm supplement saw palmetto berry supplement stinging nettle extracts supplement zinc gluconate supplement 0.0
    summer blue beverage octagam 5 2.5gm 50ml 1/vial octagam octagam 0.0
    abert rinascimento spoon macrame boho stool rustic stool bohemian stool cotton stool rustic stool bohemian stool macrame stool boho stool macrame stool stool boho stool macrame stool stool 0.25
  • Loss: CoSENTLoss with these parameters:
    {
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 128
  • per_device_eval_batch_size: 128
  • learning_rate: 2e-05
  • num_train_epochs: 1
  • warmup_ratio: 0.1
  • fp16: 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: 128
  • per_device_eval_batch_size: 128
  • 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: 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: 1
  • 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: 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: 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: None
  • hub_always_push: False
  • hub_revision: None
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • include_inputs_for_metrics: False
  • include_for_metrics: []
  • 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
  • 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
  • use_liger_kernel: False
  • liger_kernel_config: None
  • eval_use_gather_object: False
  • average_tokens_across_devices: False
  • prompts: None
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: proportional
  • router_mapping: {}
  • learning_rate_mapping: {}

Training Logs

Click to expand
Epoch Step Training Loss
0.8824 481100 2.7175
0.8826 481200 3.0206
0.8828 481300 2.4544
0.8830 481400 2.8656
0.8832 481500 2.502
0.8833 481600 2.1285
0.8835 481700 2.7855
0.8837 481800 2.5049
0.8839 481900 2.4426
0.8841 482000 2.8925
0.8843 482100 2.4625
0.8844 482200 2.8099
0.8846 482300 2.5985
0.8848 482400 2.4596
0.8850 482500 2.3988
0.8852 482600 2.3042
0.8854 482700 3.0555
0.8855 482800 2.7325
0.8857 482900 2.6789
0.8859 483000 2.6981
0.8861 483100 2.423
0.8863 483200 2.5424
0.8865 483300 2.5843
0.8866 483400 2.6384
0.8868 483500 3.0053
0.8870 483600 3.1156
0.8872 483700 2.6144
0.8874 483800 1.9269
0.8876 483900 2.389
0.8877 484000 2.9943
0.8879 484100 2.6215
0.8881 484200 2.666
0.8883 484300 2.8212
0.8885 484400 2.8562
0.8887 484500 2.1903
0.8888 484600 2.6254
0.8890 484700 2.7315
0.8892 484800 3.129
0.8894 484900 2.7131
0.8896 485000 2.5708
0.8898 485100 3.0444
0.8899 485200 2.6965
0.8901 485300 2.4506
0.8903 485400 3.2936
0.8905 485500 2.6389
0.8907 485600 2.5108
0.8909 485700 2.7035
0.8910 485800 2.5258
0.8912 485900 2.7173
0.8914 486000 2.6274
0.8916 486100 2.6129
0.8918 486200 3.0652
0.8920 486300 2.873
0.8921 486400 2.6355
0.8923 486500 2.7651
0.8925 486600 3.0451
0.8927 486700 2.6154
0.8929 486800 2.6131
0.8931 486900 2.6832
0.8932 487000 2.9103
0.8934 487100 3.0292
0.8936 487200 2.5606
0.8938 487300 2.862
0.8940 487400 2.5555
0.8942 487500 2.6
0.8943 487600 2.5065
0.8945 487700 2.2685
0.8947 487800 2.734
0.8949 487900 3.4866
0.8951 488000 3.1436
0.8953 488100 2.6947
0.8954 488200 2.2818
0.8956 488300 2.2655
0.8958 488400 2.7376
0.8960 488500 2.6812
0.8962 488600 2.2931
0.8964 488700 2.5238
0.8965 488800 2.7745
0.8967 488900 2.9461
0.8969 489000 2.2439
0.8971 489100 3.2127
0.8973 489200 3.2656
0.8975 489300 3.0369
0.8976 489400 2.7061
0.8978 489500 2.6893
0.8980 489600 2.7266
0.8982 489700 2.9083
0.8984 489800 2.7386
0.8986 489900 2.7845
0.8987 490000 2.7029
0.8989 490100 2.5855
0.8991 490200 2.5816
0.8993 490300 2.6107
0.8995 490400 2.8255
0.8997 490500 3.0417
0.8998 490600 2.2608
0.9000 490700 2.7114
0.9002 490800 2.9746
0.9004 490900 2.8017
0.9006 491000 2.2731
0.9008 491100 2.9285
0.9009 491200 2.4464
0.9011 491300 2.8356
0.9013 491400 2.8536
0.9015 491500 2.9707
0.9017 491600 2.3912
0.9019 491700 2.796
0.9020 491800 2.7005
0.9022 491900 2.9101
0.9024 492000 2.7494
0.9026 492100 2.6984
0.9028 492200 2.4517
0.9030 492300 2.5462
0.9031 492400 2.5805
0.9033 492500 2.6618
0.9035 492600 3.2062
0.9037 492700 2.8984
0.9039 492800 2.2725
0.9041 492900 2.5872
0.9042 493000 2.2847
0.9044 493100 2.5741
0.9046 493200 2.6361
0.9048 493300 3.0988
0.9050 493400 2.5975
0.9052 493500 2.531
0.9053 493600 2.9442
0.9055 493700 2.772
0.9057 493800 2.4302
0.9059 493900 2.688
0.9061 494000 2.4199
0.9063 494100 2.9418
0.9064 494200 2.7193
0.9066 494300 2.2152
0.9068 494400 2.7079
0.9070 494500 2.7225
0.9072 494600 2.6579
0.9074 494700 2.7604
0.9075 494800 3.1503
0.9077 494900 2.6814
0.9079 495000 2.6373
0.9081 495100 2.5807
0.9083 495200 2.8289
0.9085 495300 2.5931
0.9086 495400 2.73
0.9088 495500 2.8232
0.9090 495600 2.6581
0.9092 495700 2.4447
0.9094 495800 2.3251
0.9096 495900 2.6718
0.9097 496000 2.7798
0.9099 496100 2.2619
0.9101 496200 2.5887
0.9103 496300 2.6294
0.9105 496400 2.8825
0.9107 496500 2.2055
0.9108 496600 2.8461
0.9110 496700 2.5142
0.9112 496800 2.5468
0.9114 496900 2.8284
0.9116 497000 2.9724
0.9118 497100 2.6067
0.9119 497200 2.3329
0.9121 497300 2.4474
0.9123 497400 2.2847
0.9125 497500 2.3007
0.9127 497600 2.9864
0.9129 497700 2.7702
0.9130 497800 2.848
0.9132 497900 2.706
0.9134 498000 3.1531
0.9136 498100 2.7927
0.9138 498200 2.4347
0.9140 498300 2.907
0.9141 498400 2.825
0.9143 498500 2.5025
0.9145 498600 2.6039
0.9147 498700 2.5945
0.9149 498800 2.841
0.9151 498900 2.7025
0.9153 499000 3.0019
0.9154 499100 2.5123
0.9156 499200 2.531
0.9158 499300 2.7774
0.9160 499400 2.7843
0.9162 499500 2.494
0.9164 499600 3.1061
0.9165 499700 2.7599
0.9167 499800 2.7056
0.9169 499900 2.5469
0.9171 500000 2.8049
0.9173 500100 2.558
0.9175 500200 2.5159
0.9176 500300 2.2319
0.9178 500400 2.9698
0.9180 500500 2.7258
0.9182 500600 2.4285
0.9184 500700 2.6223
0.9186 500800 2.9628
0.9187 500900 2.6234
0.9189 501000 2.668
0.9191 501100 2.5698
0.9193 501200 2.615
0.9195 501300 2.3538
0.9197 501400 2.5107
0.9198 501500 2.873
0.9200 501600 3.0617
0.9202 501700 2.4884
0.9204 501800 2.4277
0.9206 501900 2.5718
0.9208 502000 1.9326
0.9209 502100 2.1168
0.9211 502200 2.9307
0.9213 502300 2.7976
0.9215 502400 2.8701
0.9217 502500 2.867
0.9219 502600 2.4628
0.9220 502700 2.6038
0.9222 502800 2.4485
0.9224 502900 2.6823
0.9226 503000 2.3025
0.9228 503100 2.9928
0.9230 503200 2.4961
0.9231 503300 2.7091
0.9233 503400 2.7095
0.9235 503500 2.7122
0.9237 503600 2.4499
0.9239 503700 2.9713
0.9241 503800 2.5272
0.9242 503900 2.4948
0.9244 504000 2.4422
0.9246 504100 2.908
0.9248 504200 2.361
0.9250 504300 2.7943
0.9252 504400 2.6627
0.9253 504500 2.822
0.9255 504600 2.8372
0.9257 504700 2.8837
0.9259 504800 3.4485
0.9261 504900 2.4555
0.9263 505000 2.7592
0.9264 505100 2.9302
0.9266 505200 2.5758
0.9268 505300 2.4115
0.9270 505400 2.9652
0.9272 505500 2.7985
0.9274 505600 2.5273
0.9275 505700 2.3329
0.9277 505800 2.6292
0.9279 505900 2.261
0.9281 506000 2.7456
0.9283 506100 2.4508
0.9285 506200 2.7179
0.9286 506300 2.6759
0.9288 506400 2.7633
0.9290 506500 2.4994
0.9292 506600 2.3571
0.9294 506700 3.0846
0.9296 506800 2.2836
0.9297 506900 2.2771
0.9299 507000 2.478
0.9301 507100 2.6802
0.9303 507200 2.4984
0.9305 507300 2.6198
0.9307 507400 2.67
0.9308 507500 2.5631
0.9310 507600 2.4428
0.9312 507700 2.8799
0.9314 507800 2.0982
0.9316 507900 2.6469
0.9318 508000 2.6268
0.9319 508100 2.7433
0.9321 508200 2.6937
0.9323 508300 2.0262
0.9325 508400 2.6359
0.9327 508500 2.4837
0.9329 508600 2.9732
0.9330 508700 2.6259
0.9332 508800 2.7937
0.9334 508900 2.8028
0.9336 509000 2.6663
0.9338 509100 2.6286
0.9340 509200 2.1497
0.9341 509300 2.6403
0.9343 509400 3.0298
0.9345 509500 2.4472
0.9347 509600 2.1545
0.9349 509700 2.6822
0.9351 509800 2.8796
0.9352 509900 2.594
0.9354 510000 2.1621
0.9356 510100 2.6737
0.9358 510200 2.3829
0.9360 510300 2.917
0.9362 510400 2.6453
0.9363 510500 2.8578
0.9365 510600 2.5375
0.9367 510700 2.35
0.9369 510800 2.8708
0.9371 510900 2.7636
0.9373 511000 2.4695
0.9374 511100 2.4443
0.9376 511200 2.8824
0.9378 511300 2.8062
0.9380 511400 2.7533
0.9382 511500 2.3288
0.9384 511600 2.5772
0.9385 511700 2.8035
0.9387 511800 2.7099
0.9389 511900 2.4881
0.9391 512000 2.5668
0.9393 512100 2.7885
0.9395 512200 2.5767
0.9396 512300 2.4067
0.9398 512400 2.6582
0.9400 512500 2.4359
0.9402 512600 2.7211
0.9404 512700 2.284
0.9406 512800 2.8223
0.9407 512900 2.4584
0.9409 513000 2.4361
0.9411 513100 2.535
0.9413 513200 2.9227
0.9415 513300 2.5147
0.9417 513400 2.3569
0.9418 513500 2.5097
0.9420 513600 2.5543
0.9422 513700 2.7033
0.9424 513800 2.3489
0.9426 513900 2.9729
0.9428 514000 2.3941
0.9429 514100 2.5347
0.9431 514200 2.5137
0.9433 514300 2.4098
0.9435 514400 2.7528
0.9437 514500 2.499
0.9439 514600 2.327
0.9440 514700 2.7531
0.9442 514800 2.4671
0.9444 514900 2.5637
0.9446 515000 2.4988
0.9448 515100 2.5431
0.9450 515200 2.2775
0.9451 515300 2.7865
0.9453 515400 2.607
0.9455 515500 2.1919
0.9457 515600 2.3163
0.9459 515700 3.1294
0.9461 515800 2.8154
0.9462 515900 2.7673
0.9464 516000 2.2644
0.9466 516100 2.4852
0.9468 516200 3.1153
0.9470 516300 2.7156
0.9472 516400 2.3643
0.9473 516500 2.5582
0.9475 516600 2.6206
0.9477 516700 3.1965
0.9479 516800 2.7894
0.9481 516900 2.4275
0.9483 517000 2.1462
0.9484 517100 2.4344
0.9486 517200 2.4454
0.9488 517300 3.3861
0.9490 517400 2.5493
0.9492 517500 2.8199
0.9494 517600 2.8264
0.9495 517700 2.8281
0.9497 517800 2.8133
0.9499 517900 2.5629
0.9501 518000 2.4386
0.9503 518100 2.4635
0.9505 518200 2.3512
0.9506 518300 2.4502
0.9508 518400 2.3878
0.9510 518500 2.7957
0.9512 518600 2.8431
0.9514 518700 2.3782
0.9516 518800 2.3721
0.9518 518900 2.8117
0.9519 519000 2.8333
0.9521 519100 2.7403
0.9523 519200 2.954
0.9525 519300 2.4465
0.9527 519400 2.6299
0.9529 519500 2.5377
0.9530 519600 2.8414
0.9532 519700 2.6443
0.9534 519800 2.7145
0.9536 519900 2.4658
0.9538 520000 2.7972
0.9540 520100 2.9655
0.9541 520200 2.5363
0.9543 520300 2.9176
0.9545 520400 2.5823
0.9547 520500 2.7837
0.9549 520600 2.5973
0.9551 520700 3.2266
0.9552 520800 2.377
0.9554 520900 2.5821
0.9556 521000 2.7435
0.9558 521100 2.671
0.9560 521200 2.5401
0.9562 521300 2.4665
0.9563 521400 2.1342
0.9565 521500 3.1076
0.9567 521600 2.0961
0.9569 521700 2.4745
0.9571 521800 2.5018
0.9573 521900 2.7143
0.9574 522000 3.1001
0.9576 522100 2.6586
0.9578 522200 2.8494
0.9580 522300 2.8891
0.9582 522400 2.4793
0.9584 522500 2.4656
0.9585 522600 2.5658
0.9587 522700 2.7384
0.9589 522800 2.6812
0.9591 522900 3.2136
0.9593 523000 2.4959
0.9595 523100 2.9371
0.9596 523200 2.2753
0.9598 523300 2.6551
0.9600 523400 3.129
0.9602 523500 2.5581
0.9604 523600 2.7486
0.9606 523700 2.2617
0.9607 523800 2.5669
0.9609 523900 2.6578
0.9611 524000 2.3879
0.9613 524100 2.6888
0.9615 524200 2.5566
0.9617 524300 2.9152
0.9618 524400 3.3668
0.9620 524500 2.4619
0.9622 524600 2.8465
0.9624 524700 3.0549
0.9626 524800 2.2678
0.9628 524900 2.8254
0.9629 525000 2.4995
0.9631 525100 2.2488
0.9633 525200 2.511
0.9635 525300 2.4072
0.9637 525400 3.0351
0.9639 525500 2.5614
0.9640 525600 2.3095
0.9642 525700 3.0715
0.9644 525800 2.3529
0.9646 525900 3.0111
0.9648 526000 2.1991
0.9650 526100 2.8284
0.9651 526200 2.059
0.9653 526300 2.6111
0.9655 526400 2.4418
0.9657 526500 2.092
0.9659 526600 2.8012
0.9661 526700 2.3222
0.9662 526800 2.4338
0.9664 526900 2.8157
0.9666 527000 2.9612
0.9668 527100 2.7616
0.9670 527200 2.35
0.9672 527300 2.662
0.9673 527400 2.8691
0.9675 527500 3.2555
0.9677 527600 2.4721
0.9679 527700 2.4594
0.9681 527800 2.5609
0.9683 527900 2.2371
0.9684 528000 2.5911
0.9686 528100 2.4426
0.9688 528200 3.0184
0.9690 528300 2.0636
0.9692 528400 3.1231
0.9694 528500 2.2432
0.9695 528600 2.5129
0.9697 528700 2.8665
0.9699 528800 2.4842
0.9701 528900 2.7873
0.9703 529000 2.9359
0.9705 529100 2.5761
0.9706 529200 2.3897
0.9708 529300 2.7054
0.9710 529400 2.7971
0.9712 529500 2.9567
0.9714 529600 2.4403
0.9716 529700 2.565
0.9717 529800 2.2638
0.9719 529900 2.2746
0.9721 530000 3.0484
0.9723 530100 2.6834
0.9725 530200 2.5561
0.9727 530300 3.2475
0.9728 530400 2.7121
0.9730 530500 2.2849
0.9732 530600 2.4814
0.9734 530700 2.7966
0.9736 530800 3.1766
0.9738 530900 2.4936
0.9739 531000 2.7798
0.9741 531100 2.4917
0.9743 531200 2.7994
0.9745 531300 3.0519
0.9747 531400 2.4151
0.9749 531500 2.7532
0.9750 531600 2.5991
0.9752 531700 2.2851
0.9754 531800 2.7491
0.9756 531900 2.0752
0.9758 532000 2.6968
0.9760 532100 2.3118
0.9761 532200 2.5491
0.9763 532300 2.459
0.9765 532400 2.3761
0.9767 532500 2.3386
0.9769 532600 2.5433
0.9771 532700 3.264
0.9772 532800 2.3645
0.9774 532900 2.6076
0.9776 533000 2.3515
0.9778 533100 2.959
0.9780 533200 2.7799
0.9782 533300 2.2707
0.9783 533400 2.8511
0.9785 533500 2.9233
0.9787 533600 2.2763
0.9789 533700 2.5388
0.9791 533800 2.8377
0.9793 533900 2.3588
0.9794 534000 2.4468
0.9796 534100 3.2721
0.9798 534200 2.5564
0.9800 534300 2.7249
0.9802 534400 2.3458
0.9804 534500 3.0502
0.9805 534600 2.6946
0.9807 534700 2.3383
0.9809 534800 2.9641
0.9811 534900 2.1198
0.9813 535000 2.4989
0.9815 535100 2.059
0.9816 535200 2.8551
0.9818 535300 2.2654
0.9820 535400 2.1299
0.9822 535500 2.3774
0.9824 535600 2.4031
0.9826 535700 2.5041
0.9827 535800 2.5269
0.9829 535900 2.4162
0.9831 536000 2.8897
0.9833 536100 2.4228
0.9835 536200 2.5051
0.9837 536300 2.3459
0.9838 536400 2.3676
0.9840 536500 2.268
0.9842 536600 2.5853
0.9844 536700 2.3745
0.9846 536800 2.9038
0.9848 536900 2.7493
0.9849 537000 2.6291
0.9851 537100 2.1424
0.9853 537200 2.5444
0.9855 537300 2.7014
0.9857 537400 2.2867
0.9859 537500 2.4698
0.9860 537600 2.5611
0.9862 537700 2.2673
0.9864 537800 2.6773
0.9866 537900 2.4377
0.9868 538000 2.6352
0.9870 538100 2.3998
0.9871 538200 2.3645
0.9873 538300 2.8563
0.9875 538400 2.307
0.9877 538500 2.1448
0.9879 538600 2.8496
0.9881 538700 2.6116
0.9883 538800 2.6577
0.9884 538900 2.1195
0.9886 539000 2.4262
0.9888 539100 2.6337
0.9890 539200 3.0134
0.9892 539300 2.2623
0.9894 539400 2.5705
0.9895 539500 2.0086
0.9897 539600 2.5156
0.9899 539700 2.6428
0.9901 539800 3.043
0.9903 539900 2.9831
0.9905 540000 3.3943
0.9906 540100 2.7163
0.9908 540200 2.911
0.9910 540300 2.2939
0.9912 540400 2.9568
0.9914 540500 2.8866
0.9916 540600 2.6739
0.9917 540700 1.9967
0.9919 540800 2.9113
0.9921 540900 2.4797
0.9923 541000 2.1842
0.9925 541100 3.0816
0.9927 541200 2.6031
0.9928 541300 3.0125
0.9930 541400 2.6022
0.9932 541500 2.3722
0.9934 541600 2.2204
0.9936 541700 2.3747
0.9938 541800 2.2796
0.9939 541900 2.6716
0.9941 542000 2.4109
0.9943 542100 2.7716
0.9945 542200 2.7135
0.9947 542300 2.4205
0.9949 542400 2.4264
0.9950 542500 2.4517
0.9952 542600 2.5607
0.9954 542700 2.6655
0.9956 542800 2.1333
0.9958 542900 2.6963
0.9960 543000 2.628
0.9961 543100 2.6329
0.9963 543200 2.3033
0.9965 543300 2.6975
0.9967 543400 2.8086
0.9969 543500 2.463
0.9971 543600 2.9066
0.9972 543700 2.2997
0.9974 543800 2.075
0.9976 543900 2.1685
0.9978 544000 2.9859
0.9980 544100 2.4574
0.9982 544200 3.1355
0.9983 544300 2.6326
0.9985 544400 2.8282
0.9987 544500 2.9575
0.9989 544600 3.0306
0.9991 544700 2.6779
0.9993 544800 2.4214
0.9994 544900 3.0846
0.9996 545000 2.7292
0.9998 545100 2.6677
1.0000 545200 2.5696

Framework Versions

  • Python: 3.12.3
  • Sentence Transformers: 5.1.0
  • Transformers: 4.55.4
  • PyTorch: 2.6.0+cu124
  • Accelerate: 1.10.1
  • Datasets: 4.0.0
  • Tokenizers: 0.21.4

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},
}
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