all-MiniLM-L6-v53-pair_score

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2 on the pairs_with_scores_v45 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 Type: Sentence Transformer
• Base model: sentence-transformers/all-MiniLM-L6-v2
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • pairs_with_scores_v45
• Language: en
• License: apache-2.0

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': 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 = [
    'ps 4 game',
    'fastpaced combat game',
    'quarter-',
]
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, 0.8694, 0.2623],
#         [0.8694, 1.0000, 0.3497],
#         [0.2623, 0.3497, 1.0000]])

Training Details

Training Dataset

pairs_with_scores_v45

• Dataset: pairs_with_scores_v45 at c801d89
• Size: 21,107,979 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: 6.52 tokens max: 22 tokens	min: 3 tokens mean: 6.61 tokens max: 20 tokens	min: 0.0 mean: 0.55 max: 1.0

• Samples:

  sentence1	sentence2	score
  fruit	cucumber	1.0
  stabka omy agmal malika raatha ayni phone case	airtag loop	1.0
  the show stopper	rare overshirt	1.0

• Loss: CoSENTLoss with these parameters:

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

Evaluation Dataset

pairs_with_scores_v45

• Dataset: pairs_with_scores_v45 at c801d89
• Size: 106,071 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: 6.71 tokens max: 22 tokens	min: 3 tokens mean: 6.83 tokens max: 23 tokens	min: 0.0 mean: 0.57 max: 1.0

• Samples:

  sentence1	sentence2	score
  avocado oil for salads	sekem sweetener	1.0
  all seasons blanket	rafa pillow	1.0
  green peas risotto	vanilla muffins	0.0

• 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
0.0006	100	9.9694
0.0012	200	9.8525
0.0018	300	9.7908
0.0024	400	9.7765
0.0030	500	9.7536
0.0036	600	9.6105
0.0042	700	9.4904
0.0049	800	9.6377
0.0055	900	9.0903
0.0061	1000	9.3471
0.0067	1100	9.2062
0.0073	1200	9.1969
0.0079	1300	8.8959
0.0085	1400	8.7812
0.0091	1500	8.7048
0.0097	1600	8.7063
0.0103	1700	8.5907
0.0109	1800	8.4849
0.0115	1900	8.3739
0.0121	2000	8.3007
0.0127	2100	8.2461
0.0133	2200	8.1247
0.0139	2300	8.0001
0.0146	2400	7.9689
0.0152	2500	7.8287
0.0158	2600	7.7954
0.0164	2700	7.7125
0.0170	2800	7.7183
0.0176	2900	7.6346
0.0182	3000	7.6131
0.0188	3100	7.6499
0.0194	3200	7.5826
0.0200	3300	7.5304
0.0206	3400	7.5263
0.0212	3500	7.5138
0.0218	3600	7.4931
0.0224	3700	7.4878
0.0230	3800	7.5298
0.0236	3900	7.4467
0.0243	4000	7.4488
0.0249	4100	7.3946
0.0255	4200	7.3763
0.0261	4300	7.376
0.0267	4400	7.3699
0.0273	4500	7.3772
0.0279	4600	7.3605
0.0285	4700	7.3347
0.0291	4800	7.3572
0.0297	4900	7.3272
0.0303	5000	7.3032
0.0309	5100	7.278
0.0315	5200	7.2551
0.0321	5300	7.2378
0.0327	5400	7.2528
0.0334	5500	7.2681
0.0340	5600	7.2268
0.0346	5700	7.2578
0.0352	5800	7.2443
0.0358	5900	7.1121
0.0364	6000	7.2542
0.0370	6100	7.1736
0.0376	6200	7.1576
0.0382	6300	7.1763
0.0388	6400	7.1732
0.0394	6500	7.1261
0.0400	6600	7.1782
0.0406	6700	7.1771
0.0412	6800	7.0896
0.0418	6900	7.0998
0.0424	7000	7.0524
0.0431	7100	7.0575
0.0437	7200	7.0963
0.0443	7300	7.0819
0.0449	7400	7.0653
0.0455	7500	7.0364
0.0461	7600	7.0013
0.0467	7700	7.0642
0.0473	7800	6.9864
0.0479	7900	7.0556
0.0485	8000	7.0506
0.0491	8100	7.0206
0.0497	8200	6.9464
0.0503	8300	6.9456
0.0509	8400	7.0159
0.0515	8500	6.9641
0.0522	8600	6.9161
0.0528	8700	6.9253
0.0534	8800	6.9232
0.0540	8900	6.947
0.0546	9000	6.899
0.0552	9100	6.9191
0.0558	9200	6.9247
0.0564	9300	6.9269
0.0570	9400	6.8868
0.0576	9500	6.8617
0.0582	9600	6.9229
0.0588	9700	6.8671
0.0594	9800	6.813
0.0600	9900	6.8493
0.0606	10000	6.858
0.0612	10100	6.8108
0.0619	10200	6.8904
0.0625	10300	6.8492
0.0631	10400	6.8362
0.0637	10500	6.8243
0.0643	10600	6.7972
0.0649	10700	6.7753
0.0655	10800	6.7843
0.0661	10900	6.7709
0.0667	11000	6.7463
0.0673	11100	6.7177
0.0679	11200	6.834
0.0685	11300	6.7038
0.0691	11400	6.7283
0.0697	11500	6.7315
0.0703	11600	6.769
0.0709	11700	6.7525
0.0716	11800	6.7094
0.0722	11900	6.7247
0.0728	12000	6.7573
0.0734	12100	6.649
0.0740	12200	6.6617
0.0746	12300	6.6825
0.0752	12400	6.656
0.0758	12500	6.7018
0.0764	12600	6.7048
0.0770	12700	6.6499
0.0776	12800	6.6623
0.0782	12900	6.6071
0.0788	13000	6.6716
0.0794	13100	6.5685
0.0800	13200	6.5819
0.0807	13300	6.5602
0.0813	13400	6.5714
0.0819	13500	6.5917
0.0825	13600	6.6433
0.0831	13700	6.5848
0.0837	13800	6.6148
0.0843	13900	6.5483
0.0849	14000	6.6228
0.0855	14100	6.5952
0.0861	14200	6.5255
0.0867	14300	6.5495
0.0873	14400	6.5945
0.0879	14500	6.5184
0.0885	14600	6.5324
0.0891	14700	6.5098
0.0897	14800	6.4833
0.0904	14900	6.4551
0.0910	15000	6.4438
0.0916	15100	6.4858
0.0922	15200	6.5468
0.0928	15300	6.5321
0.0934	15400	6.4521
0.0940	15500	6.4694
0.0946	15600	6.4573
0.0952	15700	6.4844
0.0958	15800	6.4922
0.0964	15900	6.415
0.0970	16000	6.413
0.0976	16100	6.4177
0.0982	16200	6.4126
0.0988	16300	6.3557
0.0994	16400	6.4019
0.1001	16500	6.4019
0.1007	16600	6.3974
0.1013	16700	6.327
0.1019	16800	6.4035
0.1025	16900	6.3814
0.1031	17000	6.414
0.1037	17100	6.373
0.1043	17200	6.3063
0.1049	17300	6.3298
0.1055	17400	6.3458
0.1061	17500	6.4197
0.1067	17600	6.3201
0.1073	17700	6.3351
0.1079	17800	6.3769
0.1085	17900	6.3507
0.1092	18000	6.3119
0.1098	18100	6.3208
0.1104	18200	6.1896
0.1110	18300	6.2452
0.1116	18400	6.1759
0.1122	18500	6.2445
0.1128	18600	6.374
0.1134	18700	6.3409
0.1140	18800	6.2632
0.1146	18900	6.272
0.1152	19000	6.3636
0.1158	19100	6.3359
0.1164	19200	6.2001
0.1170	19300	6.2687
0.1176	19400	6.2276
0.1182	19500	6.2404
0.1189	19600	6.2169
0.1195	19700	6.1648
0.1201	19800	6.1966
0.1207	19900	6.1548
0.1213	20000	6.2205
0.1219	20100	6.2611
0.1225	20200	6.168
0.1231	20300	6.2143
0.1237	20400	6.1228
0.1243	20500	6.1075
0.1249	20600	6.1695
0.1255	20700	6.2326
0.1261	20800	6.0787
0.1267	20900	6.1372
0.1273	21000	6.12
0.1280	21100	6.1253
0.1286	21200	6.0742
0.1292	21300	6.0835
0.1298	21400	6.1145
0.1304	21500	6.2047
0.1310	21600	6.0695
0.1316	21700	6.0345
0.1322	21800	6.1103
0.1328	21900	6.0436
0.1334	22000	6.1128
0.1340	22100	6.0689
0.1346	22200	6.1196
0.1352	22300	6.1169
0.1358	22400	6.1043
0.1364	22500	6.0464
0.1370	22600	6.015
0.1377	22700	6.1172
0.1383	22800	6.0253
0.1389	22900	6.1925
0.1395	23000	6.0761
0.1401	23100	6.095
0.1407	23200	6.0575
0.1413	23300	6.1107
0.1419	23400	6.1012
0.1425	23500	5.9724
0.1431	23600	5.9796
0.1437	23700	5.9446
0.1443	23800	5.9982
0.1449	23900	5.9876
0.1455	24000	5.9478
0.1461	24100	6.0473
0.1467	24200	6.0419
0.1474	24300	6.0067
0.1480	24400	6.0248
0.1486	24500	5.9279
0.1492	24600	6.0054
0.1498	24700	6.0188
0.1504	24800	6.005
0.1510	24900	5.9774
0.1516	25000	5.9072
0.1522	25100	6.0316
0.1528	25200	5.8466
0.1534	25300	6.0188
0.1540	25400	5.9541
0.1546	25500	5.9941
0.1552	25600	5.9526
0.1558	25700	5.9341
0.1565	25800	5.9929
0.1571	25900	5.8888
0.1577	26000	5.965
0.1583	26100	6.0115
0.1589	26200	5.9024
0.1595	26300	5.8522
0.1601	26400	5.9077
0.1607	26500	5.8118
0.1613	26600	5.9724
0.1619	26700	5.9707
0.1625	26800	5.8386
0.1631	26900	5.9958
0.1637	27000	5.8511
0.1643	27100	5.8915
0.1649	27200	5.9727
0.1655	27300	5.8901
0.1662	27400	5.8777
0.1668	27500	5.945
0.1674	27600	5.8435
0.1680	27700	5.94
0.1686	27800	5.841
0.1692	27900	5.9099
0.1698	28000	5.9085
0.1704	28100	5.7861
0.1710	28200	5.8951
0.1716	28300	5.8123
0.1722	28400	5.8516
0.1728	28500	5.9108
0.1734	28600	5.794
0.1740	28700	5.8368
0.1746	28800	5.8856
0.1753	28900	5.8418
0.1759	29000	5.9231
0.1765	29100	5.7597
0.1771	29200	5.8114
0.1777	29300	5.9185
0.1783	29400	5.9468
0.1789	29500	5.8788
0.1795	29600	5.855
0.1801	29700	5.7742
0.1807	29800	5.8111
0.1813	29900	5.8272
0.1819	30000	5.7809
0.1825	30100	5.7201
0.1831	30200	5.8174
0.1837	30300	5.9181
0.1843	30400	5.7936
0.1850	30500	5.7937
0.1856	30600	5.8338
0.1862	30700	5.726
0.1868	30800	5.8903
0.1874	30900	5.8044
0.1880	31000	5.6971
0.1886	31100	5.8554
0.1892	31200	5.7149
0.1898	31300	5.7365
0.1904	31400	5.8029
0.1910	31500	5.8321
0.1916	31600	5.8855
0.1922	31700	5.8347
0.1928	31800	5.7913
0.1934	31900	5.6303
0.1940	32000	5.7349
0.1947	32100	5.6921
0.1953	32200	5.7368
0.1959	32300	5.7483
0.1965	32400	5.6905
0.1971	32500	5.8462
0.1977	32600	5.6739
0.1983	32700	5.7499
0.1989	32800	5.7281
0.1995	32900	5.7073
0.2001	33000	5.7979
0.2007	33100	5.6029
0.2013	33200	5.7725
0.2019	33300	5.7232
0.2025	33400	5.7109
0.2031	33500	5.7437
0.2038	33600	5.7765
0.2044	33700	5.5663
0.2050	33800	5.6631
0.2056	33900	5.692
0.2062	34000	5.665
0.2068	34100	5.7504
0.2074	34200	5.752
0.2080	34300	5.6464
0.2086	34400	5.789
0.2092	34500	5.7072
0.2098	34600	5.6425
0.2104	34700	5.6151
0.2110	34800	5.6946
0.2116	34900	5.6679
0.2122	35000	5.6449
0.2128	35100	5.6938
0.2135	35200	5.6047
0.2141	35300	5.6502
0.2147	35400	5.6622
0.2153	35500	5.6538
0.2159	35600	5.6035
0.2165	35700	5.7227
0.2171	35800	5.7686
0.2177	35900	5.6349
0.2183	36000	5.6662
0.2189	36100	5.5894
0.2195	36200	5.552
0.2201	36300	5.657
0.2207	36400	5.5798
0.2213	36500	5.6771
0.2219	36600	5.6195
0.2225	36700	5.6092
0.2232	36800	5.4014
0.2238	36900	5.5888
0.2244	37000	5.576
0.2250	37100	5.633
0.2256	37200	5.4905
0.2262	37300	5.6129
0.2268	37400	5.6275
0.2274	37500	5.5646
0.2280	37600	5.6031
0.2286	37700	5.6325
0.2292	37800	5.5454
0.2298	37900	5.6804
0.2304	38000	5.6005
0.2310	38100	5.7016
0.2316	38200	5.6443
0.2323	38300	5.6179
0.2329	38400	5.6503
0.2335	38500	5.6462
0.2341	38600	5.5654
0.2347	38700	5.3804
0.2353	38800	5.6199
0.2359	38900	5.4598
0.2365	39000	5.4747
0.2371	39100	5.536
0.2377	39200	5.6128
0.2383	39300	5.6343
0.2389	39400	5.4743
0.2395	39500	5.5948
0.2401	39600	5.5131
0.2407	39700	5.6108
0.2413	39800	5.5046
0.2420	39900	5.5645
0.2426	40000	5.4975
0.2432	40100	5.5156
0.2438	40200	5.7261
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0.2462	40600	5.5637
0.2468	40700	5.569
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0.2480	40900	5.4744
0.2486	41000	5.5171
0.2492	41100	5.3838
0.2498	41200	5.4662
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0.2595	42800	5.542
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0.2990	49300	5.2964
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0.3250	53600	5.3078
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0.3566	58800	5.2102
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0.3978	65600	4.8762
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0.4002	66000	4.9285
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0.4669	77000	5.0796
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0.5555	91600	4.9411
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0.5603	92400	4.6877
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0.5670	93500	4.6423
0.5676	93600	4.6185
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0.5694	93900	4.6704
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0.5821	96000	4.7011
0.5828	96100	4.6692
0.5834	96200	4.8625
0.5840	96300	4.6349
0.5846	96400	4.4006
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0.5858	96600	4.5002
0.5864	96700	4.6984
0.5870	96800	4.5878
0.5876	96900	4.5581
0.5882	97000	4.8285
0.5888	97100	4.4414
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0.5900	97300	4.6497
0.5906	97400	4.766
0.5912	97500	4.6901
0.5918	97600	4.708
0.5925	97700	4.414
0.5931	97800	4.5001
0.5937	97900	4.8657
0.5943	98000	4.3353
0.5949	98100	4.5866
0.5955	98200	4.6144
0.5961	98300	4.7795
0.5967	98400	4.4291
0.5973	98500	4.7005
0.5979	98600	4.5855
0.5985	98700	4.612
0.5991	98800	4.7681
0.5997	98900	4.5703
0.6003	99000	4.4931
0.6009	99100	4.6424
0.6016	99200	4.585
0.6022	99300	4.5104
0.6028	99400	4.7568
0.6034	99500	4.6958
0.6040	99600	4.6228
0.6046	99700	4.5492
0.6052	99800	4.5596
0.6058	99900	4.6873
0.6064	100000	4.6385
0.6070	100100	4.609
0.6076	100200	4.737
0.6082	100300	4.6223
0.6088	100400	4.6385
0.6094	100500	4.668
0.6100	100600	4.5483
0.6106	100700	4.7831
0.6113	100800	4.4546
0.6119	100900	4.6124
0.6125	101000	4.5493
0.6131	101100	4.6376
0.6137	101200	4.6439
0.6143	101300	4.7058
0.6149	101400	4.7845
0.6155	101500	4.6478
0.6161	101600	4.6431
0.6167	101700	4.5728
0.6173	101800	4.6415
0.6179	101900	4.6274
0.6185	102000	4.555
0.6191	102100	4.4831
0.6197	102200	4.5514
0.6203	102300	4.6916
0.6210	102400	4.5851
0.6216	102500	4.5758
0.6222	102600	4.5602
0.6228	102700	4.5508
0.6234	102800	4.533
0.6240	102900	4.7757
0.6246	103000	4.5115
0.6252	103100	4.7049
0.6258	103200	4.5527
0.6264	103300	4.4922
0.6270	103400	4.4487
0.6276	103500	4.8519
0.6282	103600	4.619
0.6288	103700	4.5081
0.6294	103800	4.608
0.6301	103900	4.4759
0.6307	104000	4.5708
0.6313	104100	4.5577
0.6319	104200	4.6723
0.6325	104300	4.6662
0.6331	104400	4.3562
0.6337	104500	4.6322
0.6343	104600	4.4264
0.6349	104700	4.5858
0.6355	104800	4.5557
0.6361	104900	4.7515
0.6367	105000	4.5284
0.6373	105100	4.4926
0.6379	105200	4.6206
0.6385	105300	4.5602
0.6391	105400	4.4166
0.6398	105500	4.6078
0.6404	105600	4.5358
0.6410	105700	4.5726
0.6416	105800	4.4552
0.6422	105900	4.7259
0.6428	106000	4.4716
0.6434	106100	4.6456
0.6440	106200	4.5639
0.6446	106300	4.4803
0.6452	106400	4.5244
0.6458	106500	4.8784
0.6464	106600	4.3818
0.6470	106700	4.4796
0.6476	106800	4.4784
0.6482	106900	4.549
0.6489	107000	4.524
0.6495	107100	4.5633
0.6501	107200	4.7363
0.6507	107300	4.325
0.6513	107400	4.6172
0.6519	107500	4.5833
0.6525	107600	4.4348
0.6531	107700	4.6088
0.6537	107800	4.3046
0.6543	107900	4.432
0.6549	108000	4.6121
0.6555	108100	4.6414
0.6561	108200	4.5031
0.6567	108300	4.4812
0.6573	108400	4.5324
0.6579	108500	4.5254
0.6586	108600	4.4424
0.6592	108700	4.3514
0.6598	108800	4.6931
0.6604	108900	4.4734
0.6610	109000	4.4468
0.6616	109100	4.7261
0.6622	109200	4.7163
0.6628	109300	4.5782
0.6634	109400	4.3994
0.6640	109500	4.598
0.6646	109600	4.4069
0.6652	109700	4.588
0.6658	109800	4.7045
0.6664	109900	4.587
0.6670	110000	4.6478
0.6676	110100	4.3905
0.6683	110200	4.7988
0.6689	110300	4.5663
0.6695	110400	4.5674
0.6701	110500	4.5327
0.6707	110600	4.5415
0.6713	110700	4.5668
0.6719	110800	4.5854
0.6725	110900	4.2915
0.6731	111000	4.5936
0.6737	111100	4.5558
0.6743	111200	4.631
0.6749	111300	4.5762
0.6755	111400	4.4573
0.6761	111500	4.213
0.6767	111600	4.5654
0.6774	111700	4.3623
0.6780	111800	4.6679
0.6786	111900	4.5009
0.6792	112000	4.5281
0.6798	112100	4.5608
0.6804	112200	4.2676
0.6810	112300	4.4416
0.6816	112400	4.8381
0.6822	112500	4.525
0.6828	112600	4.4362
0.6834	112700	4.4504
0.6840	112800	4.6051
0.6846	112900	4.5452
0.6852	113000	4.4777
0.6858	113100	4.5996
0.6864	113200	4.3522
0.6871	113300	4.7316
0.6877	113400	4.4086
0.6883	113500	4.5301
0.6889	113600	4.5073
0.6895	113700	4.5297
0.6901	113800	4.4981
0.6907	113900	4.5696
0.6913	114000	4.6214
0.6919	114100	4.5371
0.6925	114200	4.5091
0.6931	114300	4.4049
0.6937	114400	4.5612
0.6943	114500	4.5102
0.6949	114600	4.4283
0.6955	114700	4.3766
0.6961	114800	4.5961
0.6968	114900	4.4587
0.6974	115000	4.4413
0.6980	115100	4.4274
0.6986	115200	4.5663
0.6992	115300	4.5927
0.6998	115400	4.5118
0.7004	115500	4.4805
0.7010	115600	4.5321
0.7016	115700	4.6393
0.7022	115800	4.2259
0.7028	115900	4.5678
0.7034	116000	4.3301
0.7040	116100	4.4418
0.7046	116200	4.3144
0.7052	116300	4.4416
0.7059	116400	4.335
0.7065	116500	4.3776
0.7071	116600	4.7413
0.7077	116700	4.6511
0.7083	116800	4.5408
0.7089	116900	4.5989
0.7095	117000	4.3883
0.7101	117100	4.5499
0.7107	117200	4.5052
0.7113	117300	4.438
0.7119	117400	4.3803
0.7125	117500	4.4616
0.7131	117600	4.6112
0.7137	117700	4.3351
0.7143	117800	4.3146
0.7149	117900	4.512
0.7156	118000	4.4221
0.7162	118100	4.3042
0.7168	118200	4.3024
0.7174	118300	4.4121
0.7180	118400	4.5289
0.7186	118500	4.3812
0.7192	118600	4.5534
0.7198	118700	4.2703
0.7204	118800	4.6607
0.7210	118900	4.4461
0.7216	119000	4.529
0.7222	119100	4.3521
0.7228	119200	4.4891
0.7234	119300	4.2621
0.7240	119400	4.6005
0.7247	119500	4.0705
0.7253	119600	4.5724
0.7259	119700	4.4532
0.7265	119800	4.4289
0.7271	119900	4.5062
0.7277	120000	4.4298
0.7283	120100	4.4502
0.7289	120200	4.3561
0.7295	120300	4.1556
0.7301	120400	4.4302
0.7307	120500	4.5401
0.7313	120600	4.3323
0.7319	120700	4.3367
0.7325	120800	4.5841
0.7331	120900	4.4938
0.7337	121000	4.4991
0.7344	121100	4.3548
0.7350	121200	4.4023
0.7356	121300	4.3881
0.7362	121400	4.4357
0.7368	121500	4.4092
0.7374	121600	4.4798
0.7380	121700	4.4597
0.7386	121800	4.4792
0.7392	121900	4.5093
0.7398	122000	4.3677
0.7404	122100	4.4589
0.7410	122200	4.6772
0.7416	122300	4.5738
0.7422	122400	4.5814
0.7428	122500	4.4781
0.7434	122600	4.4471
0.7441	122700	4.5851
0.7447	122800	4.1824
0.7453	122900	4.3533
0.7459	123000	4.5358
0.7465	123100	4.4729
0.7471	123200	4.4713
0.7477	123300	4.3501
0.7483	123400	4.663
0.7489	123500	4.2405
0.7495	123600	4.5558
0.7501	123700	4.4982
0.7507	123800	4.365
0.7513	123900	4.5733
0.7519	124000	4.5893
0.7525	124100	4.4376
0.7532	124200	4.2894
0.7538	124300	4.1408
0.7544	124400	4.4159
0.7550	124500	4.595
0.7556	124600	4.4055
0.7562	124700	4.2842
0.7568	124800	4.4571
0.7574	124900	4.2383
0.7580	125000	4.5209
0.7586	125100	4.3834
0.7592	125200	4.3629
0.7598	125300	4.2782
0.7604	125400	4.3424
0.7610	125500	4.4002
0.7616	125600	4.3758
0.7622	125700	4.328
0.7629	125800	4.2879
0.7635	125900	4.2352
0.7641	126000	4.5347
0.7647	126100	4.3912
0.7653	126200	4.358
0.7659	126300	4.2972
0.7665	126400	4.3086
0.7671	126500	4.5269
0.7677	126600	4.3627
0.7683	126700	4.6415
0.7689	126800	4.4505
0.7695	126900	4.5089
0.7701	127000	4.3137
0.7707	127100	4.5995
0.7713	127200	4.296
0.7720	127300	4.4182
0.7726	127400	4.3541
0.7732	127500	4.5014
0.7738	127600	4.2764
0.7744	127700	4.8672
0.7750	127800	4.5138
0.7756	127900	4.3082
0.7762	128000	4.316
0.7768	128100	4.3581
0.7774	128200	4.2895
0.7780	128300	4.4606
0.7786	128400	4.2124
0.7792	128500	4.5035
0.7798	128600	4.259
0.7804	128700	4.2764
0.7810	128800	4.507
0.7817	128900	4.193
0.7823	129000	4.3368
0.7829	129100	4.4481
0.7835	129200	4.4882
0.7841	129300	4.5093
0.7847	129400	4.1789
0.7853	129500	4.2953
0.7859	129600	4.3375
0.7865	129700	4.3818
0.7871	129800	4.5312
0.7877	129900	4.2168
0.7883	130000	3.983
0.7889	130100	4.3901
0.7895	130200	4.1916
0.7901	130300	4.3033
0.7907	130400	4.3564
0.7914	130500	4.3997
0.7920	130600	4.6598
0.7926	130700	4.0341
0.7932	130800	4.4986
0.7938	130900	4.2054
0.7944	131000	4.3304
0.7950	131100	4.2814
0.7956	131200	4.2901
0.7962	131300	4.2643
0.7968	131400	4.1531
0.7974	131500	4.2281
0.7980	131600	4.393
0.7986	131700	4.5534
0.7992	131800	4.3015
0.7998	131900	4.382
0.8005	132000	4.5641
0.8011	132100	4.3731
0.8017	132200	4.6238
0.8023	132300	4.364
0.8029	132400	4.3393
0.8035	132500	4.5283
0.8041	132600	4.1182
0.8047	132700	4.3989
0.8053	132800	4.4597
0.8059	132900	4.513
0.8065	133000	4.1654
0.8071	133100	4.0422
0.8077	133200	4.0027
0.8083	133300	4.2419
0.8089	133400	4.3198
0.8095	133500	4.3637
0.8102	133600	4.3528
0.8108	133700	4.252
0.8114	133800	4.3709
0.8120	133900	4.1163
0.8126	134000	4.2497
0.8132	134100	4.2678
0.8138	134200	4.1707
0.8144	134300	4.2864
0.8150	134400	4.2609
0.8156	134500	4.3054
0.8162	134600	4.4579
0.8168	134700	4.477
0.8174	134800	4.4727
0.8180	134900	4.4359
0.8186	135000	4.3781
0.8192	135100	4.2214
0.8199	135200	4.2
0.8205	135300	4.1844
0.8211	135400	4.3572
0.8217	135500	4.56
0.8223	135600	4.2917
0.8229	135700	4.388
0.8235	135800	4.3608
0.8241	135900	4.4154
0.8247	136000	4.3562
0.8253	136100	4.4834
0.8259	136200	4.391
0.8265	136300	4.422
0.8271	136400	4.3156
0.8277	136500	4.2935
0.8283	136600	4.3524
0.8290	136700	4.4625
0.8296	136800	4.0025
0.8302	136900	4.2084
0.8308	137000	4.219
0.8314	137100	4.421
0.8320	137200	4.1694
0.8326	137300	4.4633
0.8332	137400	4.1632
0.8338	137500	4.2663
0.8344	137600	4.5342
0.8350	137700	4.2378
0.8356	137800	4.3492
0.8362	137900	4.2569
0.8368	138000	4.4481
0.8374	138100	4.2243
0.8380	138200	3.9944
0.8387	138300	4.1747
0.8393	138400	4.3079
0.8399	138500	4.2712
0.8405	138600	4.2347
0.8411	138700	4.2023
0.8417	138800	4.4428
0.8423	138900	4.1149
0.8429	139000	4.2472
0.8435	139100	4.4891
0.8441	139200	4.1863
0.8447	139300	4.3287
0.8453	139400	4.0947
0.8459	139500	4.4671
0.8465	139600	4.3897
0.8471	139700	4.2775
0.8478	139800	4.5518
0.8484	139900	4.338
0.8490	140000	4.1555
0.8496	140100	4.2534
0.8502	140200	4.16
0.8508	140300	4.3708
0.8514	140400	4.3306
0.8520	140500	4.3557
0.8526	140600	4.1756
0.8532	140700	4.3765
0.8538	140800	4.3583
0.8544	140900	4.3734
0.8550	141000	4.2479
0.8556	141100	4.5533
0.8562	141200	4.2861
0.8568	141300	4.3661
0.8575	141400	4.1912
0.8581	141500	4.2828
0.8587	141600	4.2702
0.8593	141700	4.6074
0.8599	141800	4.3628
0.8605	141900	4.3174
0.8611	142000	4.1844
0.8617	142100	4.36
0.8623	142200	4.3026
0.8629	142300	4.3128
0.8635	142400	4.2783
0.8641	142500	4.4546
0.8647	142600	4.2625
0.8653	142700	4.1008
0.8659	142800	4.4998
0.8665	142900	4.4295
0.8672	143000	4.2683
0.8678	143100	4.1234
0.8684	143200	4.0392
0.8690	143300	4.4175
0.8696	143400	3.9595
0.8702	143500	4.3774
0.8708	143600	4.3766
0.8714	143700	4.3013
0.8720	143800	4.184
0.8726	143900	4.4274
0.8732	144000	4.2914
0.8738	144100	4.4975
0.8744	144200	4.1944
0.8750	144300	4.1136
0.8756	144400	4.2679
0.8763	144500	4.3741
0.8769	144600	4.216
0.8775	144700	4.3126
0.8781	144800	4.3417
0.8787	144900	4.1411
0.8793	145000	4.3529
0.8799	145100	4.104
0.8805	145200	4.2357
0.8811	145300	4.1372
0.8817	145400	4.2307
0.8823	145500	4.1544
0.8829	145600	4.1549
0.8835	145700	4.1945
0.8841	145800	4.0684
0.8847	145900	4.3692
0.8853	146000	4.2259
0.8860	146100	4.312
0.8866	146200	4.081
0.8872	146300	4.1731
0.8878	146400	4.5725
0.8884	146500	4.0481
0.8890	146600	4.0452
0.8896	146700	4.1756
0.8902	146800	4.1397
0.8908	146900	4.132
0.8914	147000	4.4196
0.8920	147100	4.0694
0.8926	147200	4.6359
0.8932	147300	4.364
0.8938	147400	4.3927
0.8944	147500	4.2892
0.8950	147600	4.2986
0.8957	147700	4.3245
0.8963	147800	4.3229
0.8969	147900	4.3088
0.8975	148000	4.3297
0.8981	148100	4.3816
0.8987	148200	4.3691
0.8993	148300	4.6032
0.8999	148400	4.1676
0.9005	148500	4.4795
0.9011	148600	4.0769
0.9017	148700	4.2542
0.9023	148800	4.1192
0.9029	148900	4.3191
0.9035	149000	4.1975
0.9041	149100	4.3191
0.9048	149200	4.1526
0.9054	149300	4.5133
0.9060	149400	4.121
0.9066	149500	4.4219
0.9072	149600	4.3246
0.9078	149700	4.2211
0.9084	149800	4.227
0.9090	149900	4.4876
0.9096	150000	4.3863
0.9102	150100	4.2643
0.9108	150200	4.4331
0.9114	150300	4.3231
0.9120	150400	4.1862
0.9126	150500	4.0878
0.9132	150600	4.0267
0.9138	150700	4.2513
0.9145	150800	3.9525
0.9151	150900	4.2979
0.9157	151000	4.2695
0.9163	151100	4.3267
0.9169	151200	4.2704
0.9175	151300	4.2378
0.9181	151400	3.9035
0.9187	151500	4.4287
0.9193	151600	4.3832
0.9199	151700	4.0307
0.9205	151800	4.4091
0.9211	151900	4.3408
0.9217	152000	4.3059
0.9223	152100	4.1454
0.9229	152200	4.1986
0.9236	152300	4.071
0.9242	152400	4.3221
0.9248	152500	4.3937
0.9254	152600	4.3379
0.9260	152700	4.3898
0.9266	152800	4.2743
0.9272	152900	4.2987
0.9278	153000	4.2513
0.9284	153100	4.2496
0.9290	153200	4.4346
0.9296	153300	4.248
0.9302	153400	4.0932
0.9308	153500	3.9598
0.9314	153600	4.195
0.9320	153700	4.3803
0.9326	153800	4.1005
0.9333	153900	4.3554
0.9339	154000	4.5425
0.9345	154100	4.3845
0.9351	154200	4.218
0.9357	154300	4.2153
0.9363	154400	4.2046
0.9369	154500	4.2343
0.9375	154600	4.3571
0.9381	154700	4.399
0.9387	154800	4.0103
0.9393	154900	3.9808
0.9399	155000	4.2255
0.9405	155100	4.2886
0.9411	155200	4.3201
0.9417	155300	4.3345
0.9423	155400	4.2533
0.9430	155500	4.2805
0.9436	155600	4.348
0.9442	155700	4.206
0.9448	155800	4.0389
0.9454	155900	4.3043
0.9460	156000	4.1347
0.9466	156100	4.2671
0.9472	156200	4.2765
0.9478	156300	4.1785
0.9484	156400	4.1235
0.9490	156500	4.2425
0.9496	156600	4.3681
0.9502	156700	4.1992
0.9508	156800	4.2898
0.9514	156900	4.2509
0.9521	157000	4.1231
0.9527	157100	4.2397
0.9533	157200	4.3076
0.9539	157300	4.1678
0.9545	157400	4.3325
0.9551	157500	4.1409
0.9557	157600	4.2995
0.9563	157700	4.3295
0.9569	157800	4.1019
0.9575	157900	4.46
0.9581	158000	4.3155
0.9587	158100	4.277
0.9593	158200	4.3716
0.9599	158300	4.3133
0.9605	158400	4.1291
0.9611	158500	4.2122
0.9618	158600	4.3181
0.9624	158700	4.1592
0.9630	158800	4.204
0.9636	158900	4.3641
0.9642	159000	4.1527
0.9648	159100	4.3066
0.9654	159200	4.1223
0.9660	159300	4.3266
0.9666	159400	4.4113
0.9672	159500	4.1107
0.9678	159600	3.9951
0.9684	159700	4.5283
0.9690	159800	4.2251
0.9696	159900	4.0923
0.9702	160000	4.3321
0.9709	160100	4.0938
0.9715	160200	4.2049
0.9721	160300	4.2099
0.9727	160400	4.018
0.9733	160500	4.2467
0.9739	160600	4.0586
0.9745	160700	4.203
0.9751	160800	4.2017
0.9757	160900	4.3017
0.9763	161000	4.1493
0.9769	161100	4.3611
0.9775	161200	4.1692
0.9781	161300	4.2762
0.9787	161400	4.1154
0.9793	161500	4.3654
0.9799	161600	4.32
0.9806	161700	4.2806
0.9812	161800	4.2892
0.9818	161900	4.1483
0.9824	162000	4.4005
0.9830	162100	4.3623
0.9836	162200	4.5348
0.9842	162300	4.0473
0.9848	162400	4.3454
0.9854	162500	4.1729
0.9860	162600	4.1652
0.9866	162700	4.0703
0.9872	162800	4.1311
0.9878	162900	4.1795
0.9884	163000	4.329
0.9890	163100	4.0239
0.9896	163200	4.2576
0.9903	163300	3.9919
0.9909	163400	4.0253
0.9915	163500	4.403
0.9921	163600	4.1884
0.9927	163700	4.3683
0.9933	163800	4.0041
0.9939	163900	4.0692
0.9945	164000	4.1415
0.9951	164100	4.2978
0.9957	164200	4.4093
0.9963	164300	4.2828
0.9969	164400	4.1365
0.9975	164500	4.108
0.9981	164600	4.0841
0.9987	164700	4.3987
0.9994	164800	4.4249
1.0000	164900	4.1651

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},
}