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all-MiniLM-L6-v10-pair_score

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:7747936
loss:CoSENTLoss
text-embeddings-inference
Model card Files
xet
Community

all-MiniLM-L6-v10-pair_score

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. 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 Type: Sentence Transformer
  • Base model: sentence-transformers/all-MiniLM-L6-v2
  • Maximum Sequence Length: 256 tokens
  • Output Dimensionality: 384 tokens
  • Similarity Function: Cosine Similarity
  • Language: en
  • License: apache-2.0

Model Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: 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 = [
    'appetizer onion ring',
    'nuttella pizza',
    'high quality sports bra',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

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: 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
  • use_liger_kernel: False
  • eval_use_gather_object: False
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: proportional

Training Logs

Click to expand
Epoch Step Training Loss
0.0017 100 13.3171
0.0033 200 12.9799
0.0050 300 12.5133
0.0066 400 11.9388
0.0083 500 11.0616
0.0099 600 10.2712
0.0116 700 9.5253
0.0132 800 8.7706
0.0149 900 8.4333
0.0165 1000 8.0902
0.0182 1100 7.8862
0.0198 1200 7.7362
0.0215 1300 7.6007
0.0231 1400 7.5304
0.0248 1500 7.4249
0.0264 1600 7.3035
0.0281 1700 7.2026
0.0297 1800 7.1572
0.0314 1900 7.0523
0.0330 2000 7.1158
0.0347 2100 6.9856
0.0363 2200 7.0865
0.0380 2300 6.9496
0.0396 2400 6.9294
0.0413 2500 6.8825
0.0430 2600 6.8218
0.0446 2700 6.8416
0.0463 2800 6.7184
0.0479 2900 6.9183
0.0496 3000 6.7166
0.0512 3100 6.6821
0.0529 3200 6.6074
0.0545 3300 6.6141
0.0562 3400 6.5374
0.0578 3500 6.4776
0.0595 3600 6.5701
0.0611 3700 6.5026
0.0628 3800 6.6502
0.0644 3900 6.5023
0.0661 4000 6.5526
0.0677 4100 6.6594
0.0694 4200 6.3643
0.0710 4300 6.3783
0.0727 4400 6.3222
0.0743 4500 6.3401
0.0760 4600 6.4005
0.0776 4700 6.3605
0.0793 4800 6.348
0.0810 4900 6.3406
0.0826 5000 6.4156
0.0843 5100 6.3786
0.0859 5200 6.376
0.0876 5300 6.2363
0.0892 5400 6.2185
0.0909 5500 6.2554
0.0925 5600 6.2177
0.0942 5700 6.3924
0.0958 5800 6.2897
0.0975 5900 6.272
0.0991 6000 6.0247
0.1008 6100 6.194
0.1024 6200 6.2757
0.1041 6300 6.2408
0.1057 6400 6.253
0.1074 6500 6.0605
0.1090 6600 6.0672
0.1107 6700 6.0414
0.1123 6800 6.0823
0.1140 6900 6.1962
0.1156 7000 6.0868
0.1173 7100 6.0795
0.1189 7200 5.9656
0.1206 7300 5.9785
0.1223 7400 6.0722
0.1239 7500 5.9443
0.1256 7600 5.8786
0.1272 7700 5.8007
0.1289 7800 5.9206
0.1305 7900 5.918
0.1322 8000 5.9443
0.1338 8100 5.8764
0.1355 8200 5.867
0.1371 8300 5.8087
0.1388 8400 5.9884
0.1404 8500 5.8741
0.1421 8600 5.9699
0.1437 8700 5.8671
0.1454 8800 5.8278
0.1470 8900 5.8892
0.1487 9000 5.7437
0.1503 9100 5.8069
0.1520 9200 6.0235
0.1536 9300 5.7214
0.1553 9400 5.7893
0.1569 9500 5.7406
0.1586 9600 5.8035
0.1602 9700 5.7965
0.1619 9800 5.638
0.1636 9900 5.8263
0.1652 10000 5.7995
0.1669 10100 5.5805
0.1685 10200 5.632
0.1702 10300 5.6944
0.1718 10400 5.5818
0.1735 10500 5.8598
0.1751 10600 5.7255
0.1768 10700 5.7536
0.1784 10800 5.6536
0.1801 10900 5.6417
0.1817 11000 5.6719
0.1834 11100 5.566
0.1850 11200 5.4893
0.1867 11300 5.7412
0.1883 11400 5.6838
0.1900 11500 5.6272
0.1916 11600 5.6538
0.1933 11700 5.7176
0.1949 11800 5.4923
0.1966 11900 5.7643
0.1982 12000 5.5674
0.1999 12100 5.6896
0.2015 12200 5.4385
0.2032 12300 5.5851
0.2049 12400 5.5132
0.2065 12500 5.3329
0.2082 12600 5.4218
0.2098 12700 5.5171
0.2115 12800 5.3414
0.2131 12900 5.4921
0.2148 13000 5.7687
0.2164 13100 5.7119
0.2181 13200 5.4975
0.2197 13300 5.4514
0.2214 13400 5.497
0.2230 13500 5.558
0.2247 13600 5.4207
0.2263 13700 5.5901
0.2280 13800 5.2041
0.2296 13900 5.2999
0.2313 14000 5.3373
0.2329 14100 5.789
0.2346 14200 5.3292
0.2362 14300 5.4059
0.2379 14400 5.1849
0.2395 14500 5.1262
0.2412 14600 5.4339
0.2429 14700 5.5185
0.2445 14800 5.3286
0.2462 14900 5.4141
0.2478 15000 5.3554
0.2495 15100 5.3489
0.2511 15200 5.4849
0.2528 15300 5.3656
0.2544 15400 5.32
0.2561 15500 5.3523
0.2577 15600 5.1146
0.2594 15700 5.2816
0.2610 15800 5.2296
0.2627 15900 5.3386
0.2643 16000 5.4917
0.2660 16100 5.0524
0.2676 16200 5.1657
0.2693 16300 5.1431
0.2709 16400 5.166
0.2726 16500 5.5738
0.2742 16600 5.2088
0.2759 16700 5.2198
0.2775 16800 5.2709
0.2792 16900 5.4027
0.2808 17000 5.25
0.2825 17100 5.1519
0.2842 17200 5.1347
0.2858 17300 5.2346
0.2875 17400 5.4128
0.2891 17500 5.1954
0.2908 17600 5.3787
0.2924 17700 5.1731
0.2941 17800 5.3714
0.2957 17900 5.2113
0.2974 18000 5.0819
0.2990 18100 5.0443
0.3007 18200 5.2041
0.3023 18300 5.1385
0.3040 18400 5.2195
0.3056 18500 5.2233
0.3073 18600 5.1198
0.3089 18700 5.106
0.3106 18800 5.335
0.3122 18900 5.1231
0.3139 19000 5.1777
0.3155 19100 5.5752
0.3172 19200 5.1902
0.3188 19300 5.0777
0.3205 19400 5.211
0.3221 19500 5.1402
0.3238 19600 5.1458
0.3255 19700 5.1091
0.3271 19800 5.1471
0.3288 19900 5.1804
0.3304 20000 4.9678
0.3321 20100 5.1655
0.3337 20200 4.9735
0.3354 20300 5.0536
0.3370 20400 5.347
0.3387 20500 4.9856
0.3403 20600 5.1035
0.3420 20700 5.0428
0.3436 20800 5.0856
0.3453 20900 5.0776
0.3469 21000 5.2031
0.3486 21100 5.1491
0.3502 21200 5.3685
0.3519 21300 4.6901
0.3535 21400 4.9809
0.3552 21500 4.9273
0.3568 21600 4.7568
0.3585 21700 4.9064
0.3601 21800 5.0399
0.3618 21900 4.9202
0.3635 22000 5.3848
0.3651 22100 4.9239
0.3668 22200 4.8744
0.3684 22300 4.8597
0.3701 22400 4.9226
0.3717 22500 5.0358
0.3734 22600 4.9895
0.3750 22700 5.004
0.3767 22800 5.0441
0.3783 22900 4.8129
0.3800 23000 4.7954
0.3816 23100 4.8156
0.3833 23200 5.0714
0.3849 23300 4.8543
0.3866 23400 5.1728
0.3882 23500 5.1891
0.3899 23600 5.087
0.3915 23700 4.9069
0.3932 23800 4.9357
0.3948 23900 4.8324
0.3965 24000 4.8091
0.3981 24100 4.7944
0.3998 24200 5.0023
0.4014 24300 4.8745
0.4031 24400 5.0884
0.4048 24500 5.0468
0.4064 24600 4.8575
0.4081 24700 4.7555
0.4097 24800 4.6052
0.4114 24900 4.8935
0.4130 25000 4.8049
0.4147 25100 4.9014
0.4163 25200 4.7199
0.4180 25300 4.6999
0.4196 25400 4.6417
0.4213 25500 5.2115
0.4229 25600 4.9171
0.4246 25700 4.9448
0.4262 25800 4.6811
0.4279 25900 5.1181
0.4295 26000 4.8061
0.4312 26100 4.815
0.4328 26200 4.7731
0.4345 26300 4.7304
0.4361 26400 4.9838
0.4378 26500 4.7998
0.4394 26600 4.6946
0.4411 26700 4.7755
0.4427 26800 4.7347
0.4444 26900 4.8356
0.4461 27000 4.8642
0.4477 27100 4.9273
0.4494 27200 4.7114
0.4510 27300 4.6088
0.4527 27400 4.5046
0.4543 27500 4.4516
0.4560 27600 4.7491
0.4576 27700 4.943
0.4593 27800 4.877
0.4609 27900 4.6912
0.4626 28000 4.8373
0.4642 28100 5.0152
0.4659 28200 4.7008
0.4675 28300 4.7549
0.4692 28400 4.5287
0.4708 28500 4.8211
0.4725 28600 4.775
0.4741 28700 4.6977
0.4758 28800 4.9122
0.4774 28900 4.9067
0.4791 29000 4.8326
0.4807 29100 4.4536
0.4824 29200 5.0073
0.4840 29300 4.5887
0.4857 29400 4.7829
0.4874 29500 4.6503
0.4890 29600 4.5202
0.4907 29700 4.9086
0.4923 29800 4.743
0.4940 29900 4.7819
0.4956 30000 4.6159
0.4973 30100 5.015
0.4989 30200 4.5351
0.5006 30300 5.0421
0.5022 30400 4.5394
0.5039 30500 4.7516
0.5055 30600 4.9236
0.5072 30700 4.833
0.5088 30800 4.5406
0.5105 30900 4.7325
0.5121 31000 4.6807
0.5138 31100 4.6052
0.5154 31200 4.7922
0.5171 31300 4.5013
0.5187 31400 4.6579
0.5204 31500 4.5152
0.5220 31600 4.535
0.5237 31700 4.4473
0.5254 31800 5.0363
0.5270 31900 4.4849
0.5287 32000 4.6337
0.5303 32100 4.3874
0.5320 32200 4.6289
0.5336 32300 4.5746
0.5353 32400 4.7222
0.5369 32500 4.3974
0.5386 32600 4.8369
0.5402 32700 4.6921
0.5419 32800 4.603
0.5435 32900 4.4542
0.5452 33000 4.6976
0.5468 33100 4.5403
0.5485 33200 4.7398
0.5501 33300 4.9736
0.5518 33400 4.6373
0.5534 33500 4.7195
0.5551 33600 4.4237
0.5567 33700 4.4319
0.5584 33800 4.6785
0.5600 33900 4.6265
0.5617 34000 4.8585
0.5633 34100 4.7605
0.5650 34200 4.5328
0.5667 34300 4.4722
0.5683 34400 4.5651
0.5700 34500 4.5748
0.5716 34600 4.4733
0.5733 34700 4.5675
0.5749 34800 4.7731
0.5766 34900 4.5179
0.5782 35000 4.5138
0.5799 35100 4.4146
0.5815 35200 4.3349
0.5832 35300 4.6789
0.5848 35400 4.6405
0.5865 35500 4.6118
0.5881 35600 4.5165
0.5898 35700 4.5453
0.5914 35800 4.5286
0.5931 35900 4.4041
0.5947 36000 4.5261
0.5964 36100 4.3889
0.5980 36200 4.4186
0.5997 36300 4.7924
0.6013 36400 4.6042
0.6030 36500 4.8725
0.6046 36600 4.509
0.6063 36700 4.3407
0.6080 36800 4.5877
0.6096 36900 4.6656
0.6113 37000 4.405
0.6129 37100 4.3588
0.6146 37200 4.7821
0.6162 37300 4.4748
0.6179 37400 4.6611
0.6195 37500 4.6503
0.6212 37600 4.3817
0.6228 37700 4.3708
0.6245 37800 4.3686
0.6261 37900 4.2679
0.6278 38000 4.4258
0.6294 38100 4.1701
0.6311 38200 4.3627
0.6327 38300 4.4051
0.6344 38400 4.4693
0.6360 38500 4.3831
0.6377 38600 4.0856
0.6393 38700 4.7917
0.6410 38800 4.4803
0.6426 38900 4.7869
0.6443 39000 4.5376
0.6460 39100 4.4829
0.6476 39200 4.7344
0.6493 39300 4.4035
0.6509 39400 4.5464
0.6526 39500 4.3932
0.6542 39600 4.3088
0.6559 39700 4.3844
0.6575 39800 4.4635
0.6592 39900 4.205
0.6608 40000 4.5705
0.6625 40100 4.541
0.6641 40200 4.2803
0.6658 40300 4.4778
0.6674 40400 4.3103
0.6691 40500 4.4215
0.6707 40600 4.1347
0.6724 40700 4.4549
0.6740 40800 4.4641
0.6757 40900 4.6036
0.6773 41000 4.1967
0.6790 41100 4.4231
0.6806 41200 4.4425
0.6823 41300 4.5512
0.6839 41400 4.4586
0.6856 41500 4.4396
0.6873 41600 4.281
0.6889 41700 4.4691
0.6906 41800 4.299
0.6922 41900 4.4199
0.6939 42000 4.325
0.6955 42100 4.8069
0.6972 42200 4.4005
0.6988 42300 4.3462
0.7005 42400 4.4979
0.7021 42500 4.3421
0.7038 42600 4.383
0.7054 42700 4.2318
0.7071 42800 4.4444
0.7087 42900 4.3806
0.7104 43000 4.468
0.7120 43100 4.2501
0.7137 43200 4.3727
0.7153 43300 4.388
0.7170 43400 4.3485
0.7186 43500 4.343
0.7203 43600 4.4982
0.7219 43700 4.3745
0.7236 43800 4.4955
0.7252 43900 4.4546
0.7269 44000 4.2144
0.7286 44100 4.5755
0.7302 44200 4.1601
0.7319 44300 4.2967
0.7335 44400 4.4625
0.7352 44500 4.2364
0.7368 44600 4.5778
0.7385 44700 4.2853
0.7401 44800 4.4863
0.7418 44900 4.1957
0.7434 45000 4.2534
0.7451 45100 4.3133
0.7467 45200 4.5476
0.7484 45300 4.3681
0.7500 45400 4.3973
0.7517 45500 4.1377
0.7533 45600 4.2803
0.7550 45700 4.4228
0.7566 45800 4.0531
0.7583 45900 3.9899
0.7599 46000 4.3483
0.7616 46100 4.1261
0.7632 46200 4.5054
0.7649 46300 4.0876
0.7665 46400 4.3376
0.7682 46500 4.1925
0.7699 46600 4.2739
0.7715 46700 4.3682
0.7732 46800 4.441
0.7748 46900 4.4299
0.7765 47000 4.2043
0.7781 47100 4.3618
0.7798 47200 4.1743
0.7814 47300 4.4187
0.7831 47400 4.2229
0.7847 47500 4.3314
0.7864 47600 4.0925
0.7880 47700 4.0808
0.7897 47800 4.5237
0.7913 47900 4.1168
0.7930 48000 4.2941
0.7946 48100 4.384
0.7963 48200 4.7188
0.7979 48300 4.3229
0.7996 48400 4.2011
0.8012 48500 4.2779
0.8029 48600 4.3589
0.8045 48700 4.2659
0.8062 48800 4.5345
0.8079 48900 3.7909
0.8095 49000 4.4958
0.8112 49100 4.1165
0.8128 49200 4.1192
0.8145 49300 4.5164
0.8161 49400 4.0759
0.8178 49500 4.2756
0.8194 49600 4.6745
0.8211 49700 4.2513
0.8227 49800 4.0886
0.8244 49900 4.2688
0.8260 50000 4.2109
0.8277 50100 3.9525
0.8293 50200 4.0889
0.8310 50300 4.1099
0.8326 50400 3.9672
0.8343 50500 4.2584
0.8359 50600 3.9683
0.8376 50700 4.1123
0.8392 50800 4.0991
0.8409 50900 4.2131
0.8425 51000 3.9701
0.8442 51100 4.6632
0.8458 51200 4.5646
0.8475 51300 4.3518
0.8492 51400 4.0883
0.8508 51500 4.5185
0.8525 51600 4.3088
0.8541 51700 4.2788
0.8558 51800 4.4045
0.8574 51900 4.1641
0.8591 52000 4.4632
0.8607 52100 4.1843
0.8624 52200 4.2139
0.8640 52300 4.2557
0.8657 52400 4.0797
0.8673 52500 4.0446
0.8690 52600 4.4987
0.8706 52700 4.1227
0.8723 52800 4.097
0.8739 52900 4.2207
0.8756 53000 4.1675
0.8772 53100 3.964
0.8789 53200 4.3966
0.8805 53300 4.173
0.8822 53400 4.704
0.8838 53500 4.1042
0.8855 53600 3.9662
0.8871 53700 4.315
0.8888 53800 4.295
0.8905 53900 3.997
0.8921 54000 4.4502
0.8938 54100 4.479
0.8954 54200 4.0461
0.8971 54300 4.2015
0.8987 54400 4.3934
0.9004 54500 4.257
0.9020 54600 4.2889
0.9037 54700 4.3432
0.9053 54800 4.2438
0.9070 54900 3.9952
0.9086 55000 4.1644
0.9103 55100 4.2173
0.9119 55200 4.4476
0.9136 55300 4.3303
0.9152 55400 4.2151
0.9169 55500 4.188
0.9185 55600 4.1958
0.9202 55700 4.305
0.9218 55800 3.8768
0.9235 55900 4.2899
0.9251 56000 4.2238
0.9268 56100 4.4298
0.9284 56200 4.325
0.9301 56300 4.5084
0.9318 56400 4.1923
0.9334 56500 4.258
0.9351 56600 3.9049
0.9367 56700 4.1926
0.9384 56800 3.7358
0.9400 56900 4.1174
0.9417 57000 4.0027
0.9433 57100 3.9343
0.9450 57200 4.1863
0.9466 57300 4.0725
0.9483 57400 4.4933
0.9499 57500 3.9865
0.9516 57600 3.9649
0.9532 57700 4.2387
0.9549 57800 4.2372
0.9565 57900 3.9313
0.9582 58000 4.2078
0.9598 58100 4.3646
0.9615 58200 4.0848
0.9631 58300 4.1224
0.9648 58400 4.2916
0.9664 58500 4.0903
0.9681 58600 3.7786
0.9698 58700 4.038
0.9714 58800 4.1145
0.9731 58900 4.0726
0.9747 59000 3.9669
0.9764 59100 4.1096
0.9780 59200 4.2828
0.9797 59300 4.2423
0.9813 59400 4.0985
0.9830 59500 4.6186
0.9846 59600 4.0591
0.9863 59700 3.7101
0.9879 59800 4.1663
0.9896 59900 3.7786
0.9912 60000 4.3359
0.9929 60100 4.1746
0.9945 60200 4.4696
0.9962 60300 4.1991
0.9978 60400 4.2198
0.9995 60500 4.4005

Framework Versions

  • Python: 3.8.10
  • Sentence Transformers: 3.1.1
  • Transformers: 4.45.2
  • PyTorch: 2.4.1+cu118
  • Accelerate: 1.0.1
  • Datasets: 3.0.1
  • Tokenizers: 0.20.3

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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