all-MiniLM-L6-v8-pair_score

This is a sentence-transformers model finetuned from Remonatef/pairs_with_scores_sampled_category_v25.1 on the pairs_three_scores_v8 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: Remonatef/pairs_with_scores_sampled_category_v25.1
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • pairs_three_scores_v8
• 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 = [
    'pasabah',
    'thermos mug',
    'sports tracksuit',
]
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, 0.8039, 0.5806],
#         [0.8039, 1.0000, 0.5902],
#         [0.5806, 0.5902, 1.0000]])

Training Details

Training Dataset

pairs_three_scores_v8

• Dataset: pairs_three_scores_v8 at 7e8a1e6
• Size: 9,164,180 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: 5.6 tokens max: 20 tokens	min: 3 tokens mean: 5.79 tokens max: 24 tokens	min: 0.15 mean: 0.43 max: 1.0

• Samples:

  sentence1	sentence2	score
  booster face cleanser	pizza cutter	0.24
  line sinker	accessories	1.0
  tricovel	cot bed mattress protector	0.28

• Loss: CoSENTLoss with these parameters:

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

Evaluation Dataset

pairs_three_scores_v8

• Dataset: pairs_three_scores_v8 at 7e8a1e6
• Size: 46,052 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: 5.66 tokens max: 21 tokens	min: 3 tokens mean: 5.6 tokens max: 41 tokens	min: 0.15 mean: 0.42 max: 1.0

• Samples:

  sentence1	sentence2	score
  printed set	crushed outfit	1.0
  value	eva cosmetics serum	0.23
  zino shakes	candy	0.27

• 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: 2
• 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: 2
• 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
0.0014	100	8.8538
0.0028	200	8.8627
0.0042	300	8.6961
0.0056	400	8.5595
0.0070	500	8.5157
0.0084	600	8.4187
0.0098	700	8.3768
0.0112	800	8.3611
0.0126	900	8.2891
0.0140	1000	8.2687
0.0154	1100	8.2398
0.0168	1200	8.2112
0.0182	1300	8.1916
0.0196	1400	8.1942
0.0210	1500	8.172
0.0223	1600	8.1808
0.0237	1700	8.153
0.0251	1800	8.1209
0.0265	1900	8.1309
0.0279	2000	8.1563
0.0293	2100	8.1438
0.0307	2200	8.1089
0.0321	2300	8.1216
0.0335	2400	8.1216
0.0349	2500	8.0719
0.0363	2600	8.1142
0.0377	2700	8.0906
0.0391	2800	8.062
0.0405	2900	8.0963
0.0419	3000	8.0635
0.0433	3100	8.065
0.0447	3200	8.0464
0.0461	3300	8.0614
0.0475	3400	8.0602
0.0489	3500	8.0672
0.0503	3600	8.0667
0.0517	3700	8.0249
0.0531	3800	8.0615
0.0545	3900	8.0482
0.0559	4000	8.0344
0.0573	4100	8.0474
0.0587	4200	8.0254
0.0601	4300	8.0358
0.0615	4400	8.0259
0.0629	4500	8.0242
0.0642	4600	7.9949
0.0656	4700	8.0489
0.0670	4800	8.0133
0.0684	4900	7.983
0.0698	5000	7.9952
0.0712	5100	7.9972
0.0726	5200	7.9932
0.0740	5300	7.9921
0.0754	5400	8.0143
0.0768	5500	7.9816
0.0782	5600	7.9691
0.0796	5700	8.0094
0.0810	5800	7.9749
0.0824	5900	7.9938
0.0838	6000	7.9718
0.0852	6100	7.9842
0.0866	6200	7.9649
0.0880	6300	7.9719
0.0894	6400	7.9676
0.0908	6500	7.9628
0.0922	6600	7.9626
0.0936	6700	7.9601
0.0950	6800	7.974
0.0964	6900	7.9646
0.0978	7000	7.9379
0.0992	7100	7.9565
0.1006	7200	7.9388
0.1020	7300	7.9471
0.1034	7400	7.9171
0.1048	7500	7.915
0.1062	7600	7.919
0.1075	7700	7.9579
0.1089	7800	7.9275
0.1103	7900	7.9273
0.1117	8000	7.9294
0.1131	8100	7.9233
0.1145	8200	7.9247
0.1159	8300	7.9166
0.1173	8400	7.928
0.1187	8500	7.9068
0.1201	8600	7.919
0.1215	8700	7.8929
0.1229	8800	7.9122
0.1243	8900	7.9036
0.1257	9000	7.8954
0.1271	9100	7.8803
0.1285	9200	7.9096
0.1299	9300	7.9059
0.1313	9400	7.8716
0.1327	9500	7.8965
0.1341	9600	7.9248
0.1355	9700	7.8804
0.1369	9800	7.8841
0.1383	9900	7.8787
0.1397	10000	7.8671
0.1411	10100	7.8988
0.1425	10200	7.8662
0.1439	10300	7.8631
0.1453	10400	7.8759
0.1467	10500	7.8634
0.1481	10600	7.8621
0.1494	10700	7.8509
0.1508	10800	7.8437
0.1522	10900	7.8361
0.1536	11000	7.868
0.1550	11100	7.8887
0.1564	11200	7.8747
0.1578	11300	7.8719
0.1592	11400	7.828
0.1606	11500	7.8268
0.1620	11600	7.8638
0.1634	11700	7.8466
0.1648	11800	7.8856
0.1662	11900	7.8746
0.1676	12000	7.8293
0.1690	12100	7.8357
0.1704	12200	7.8192
0.1718	12300	7.8348
0.1732	12400	7.8417
0.1746	12500	7.8415
0.1760	12600	7.8095
0.1774	12700	7.8149
0.1788	12800	7.8309
0.1802	12900	7.8356
0.1816	13000	7.8243
0.1830	13100	7.8378
0.1844	13200	7.8265
0.1858	13300	7.8258
0.1872	13400	7.808
0.1886	13500	7.827
0.1900	13600	7.8264
0.1914	13700	7.8158
0.1927	13800	7.8034
0.1941	13900	7.8207
0.1955	14000	7.7986
0.1969	14100	7.8126
0.1983	14200	7.8103
0.1997	14300	7.7929
0.2011	14400	7.8229
0.2025	14500	7.8112
0.2039	14600	7.8117
0.2053	14700	7.8409
0.2067	14800	7.7997
0.2081	14900	7.8059
0.2095	15000	7.7917
0.2109	15100	7.8273
0.2123	15200	7.8068
0.2137	15300	7.8066
0.2151	15400	7.7925
0.2165	15500	7.7894
0.2179	15600	7.7946
0.2193	15700	7.7737
0.2207	15800	7.7471
0.2221	15900	7.7875
0.2235	16000	7.7844
0.2249	16100	7.7909
0.2263	16200	7.7729
0.2277	16300	7.7359
0.2291	16400	7.7713
0.2305	16500	7.7568
0.2319	16600	7.7483
0.2333	16700	7.8248
0.2346	16800	7.76
0.2360	16900	7.7332
0.2374	17000	7.7894
0.2388	17100	7.7706
0.2402	17200	7.7997
0.2416	17300	7.7674
0.2430	17400	7.7531
0.2444	17500	7.7372
0.2458	17600	7.7449
0.2472	17700	7.7369
0.2486	17800	7.7559
0.2500	17900	7.7437
0.2514	18000	7.7738
0.2528	18100	7.731
0.2542	18200	7.7466
0.2556	18300	7.7231
0.2570	18400	7.7509
0.2584	18500	7.712
0.2598	18600	7.7415
0.2612	18700	7.7097
0.2626	18800	7.741
0.2640	18900	7.7323
0.2654	19000	7.7421
0.2668	19100	7.7221
0.2682	19200	7.7138
0.2696	19300	7.7496
0.2710	19400	7.7311
0.2724	19500	7.7119
0.2738	19600	7.6982
0.2752	19700	7.7307
0.2766	19800	7.7392
0.2779	19900	7.7192
0.2793	20000	7.711
0.2807	20100	7.7051
0.2821	20200	7.7276
0.2835	20300	7.7433
0.2849	20400	7.6985
0.2863	20500	7.7243
0.2877	20600	7.7004
0.2891	20700	7.702
0.2905	20800	7.7282
0.2919	20900	7.7184
0.2933	21000	7.7244
0.2947	21100	7.7026
0.2961	21200	7.7052
0.2975	21300	7.7139
0.2989	21400	7.7409
0.3003	21500	7.7089
0.3017	21600	7.7075
0.3031	21700	7.7087
0.3045	21800	7.6938
0.3059	21900	7.7021
0.3073	22000	7.7086
0.3087	22100	7.7108
0.3101	22200	7.7107
0.3115	22300	7.6803
0.3129	22400	7.7361
0.3143	22500	7.7141
0.3157	22600	7.7032
0.3171	22700	7.6982
0.3185	22800	7.704
0.3199	22900	7.7382
0.3212	23000	7.6877
0.3226	23100	7.6841
0.3240	23200	7.6967
0.3254	23300	7.6743
0.3268	23400	7.6883
0.3282	23500	7.6817
0.3296	23600	7.6974
0.3310	23700	7.6739
0.3324	23800	7.6901
0.3338	23900	7.6787
0.3352	24000	7.6588
0.3366	24100	7.6395
0.3380	24200	7.6823
0.3394	24300	7.6869
0.3408	24400	7.6955
0.3422	24500	7.6883
0.3436	24600	7.6559
0.3450	24700	7.6749
0.3464	24800	7.7027
0.3478	24900	7.66
0.3492	25000	7.6944
0.3506	25100	7.6579
0.3520	25200	7.6785
0.3534	25300	7.6256
0.3548	25400	7.6397
0.3562	25500	7.6876
0.3576	25600	7.6048
0.3590	25700	7.6552
0.3604	25800	7.6586
0.3618	25900	7.684
0.3631	26000	7.6663
0.3645	26100	7.6365
0.3659	26200	7.6428
0.3673	26300	7.664
0.3687	26400	7.667
0.3701	26500	7.6688
0.3715	26600	7.6851
0.3729	26700	7.6363
0.3743	26800	7.6653
0.3757	26900	7.6045
0.3771	27000	7.6808
0.3785	27100	7.6702
0.3799	27200	7.6865
0.3813	27300	7.6526
0.3827	27400	7.6366
0.3841	27500	7.6531
0.3855	27600	7.6101
0.3869	27700	7.7053
0.3883	27800	7.6464
0.3897	27900	7.6573
0.3911	28000	7.6346
0.3925	28100	7.6761
0.3939	28200	7.5953
0.3953	28300	7.6597
0.3967	28400	7.7015
0.3981	28500	7.6681
0.3995	28600	7.6132
0.4009	28700	7.6378
0.4023	28800	7.6311
0.4037	28900	7.6349
0.4051	29000	7.6295
0.4064	29100	7.6114
0.4078	29200	7.6839
0.4092	29300	7.5965
0.4106	29400	7.6351
0.4120	29500	7.6114
0.4134	29600	7.6111
0.4148	29700	7.6095
0.4162	29800	7.5959
0.4176	29900	7.6009
0.4190	30000	7.6542
0.4204	30100	7.6446
0.4218	30200	7.6177
0.4232	30300	7.6361
0.4246	30400	7.6426
0.4260	30500	7.6226
0.4274	30600	7.6074
0.4288	30700	7.6257
0.4302	30800	7.6683
0.4316	30900	7.6385
0.4330	31000	7.5898
0.4344	31100	7.6374
0.4358	31200	7.6052
0.4372	31300	7.6044
0.4386	31400	7.6226
0.4400	31500	7.6415
0.4414	31600	7.6226
0.4428	31700	7.6336
0.4442	31800	7.6106
0.4456	31900	7.6771
0.4470	32000	7.6026
0.4483	32100	7.6129
0.4497	32200	7.602
0.4511	32300	7.6007
0.4525	32400	7.6113
0.4539	32500	7.6293
0.4553	32600	7.5768
0.4567	32700	7.6144
0.4581	32800	7.6178
0.4595	32900	7.6418
0.4609	33000	7.5932
0.4623	33100	7.6414
0.4637	33200	7.5972
0.4651	33300	7.6166
0.4665	33400	7.5887
0.4679	33500	7.64
0.4693	33600	7.5756
0.4707	33700	7.6185
0.4721	33800	7.5707
0.4735	33900	7.617
0.4749	34000	7.6021
0.4763	34100	7.5814
0.4777	34200	7.5848
0.4791	34300	7.6053
0.4805	34400	7.6331
0.4819	34500	7.5848
0.4833	34600	7.5706
0.4847	34700	7.5536
0.4861	34800	7.5782
0.4875	34900	7.6322
0.4889	35000	7.6031
0.4903	35100	7.5994
0.4916	35200	7.5684
0.4930	35300	7.5978
0.4944	35400	7.5918
0.4958	35500	7.575
0.4972	35600	7.5607
0.4986	35700	7.5845
0.5000	35800	7.5853
0.5014	35900	7.571
0.5028	36000	7.6007
0.5042	36100	7.5946
0.5056	36200	7.5754
0.5070	36300	7.5845
0.5084	36400	7.5302
0.5098	36500	7.6311
0.5112	36600	7.5893
0.5126	36700	7.6081
0.5140	36800	7.586
0.5154	36900	7.6083
0.5168	37000	7.5997
0.5182	37100	7.5887
0.5196	37200	7.5844
0.5210	37300	7.5787
0.5224	37400	7.5669
0.5238	37500	7.5913
0.5252	37600	7.5495
0.5266	37700	7.6103
0.5280	37800	7.5815
0.5294	37900	7.6002
0.5308	38000	7.5576
0.5322	38100	7.5679
0.5335	38200	7.5552
0.5349	38300	7.548
0.5363	38400	7.6028
0.5377	38500	7.5662
0.5391	38600	7.6073
0.5405	38700	7.5565
0.5419	38800	7.5448
0.5433	38900	7.5782
0.5447	39000	7.5911
0.5461	39100	7.5661
0.5475	39200	7.5355
0.5489	39300	7.5251
0.5503	39400	7.5508
0.5517	39500	7.5893
0.5531	39600	7.5748
0.5545	39700	7.5789
0.5559	39800	7.5622
0.5573	39900	7.5162
0.5587	40000	7.5782
0.5601	40100	7.5872
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1.3716	98200	7.3648
1.3730	98300	7.371
1.3744	98400	7.3481
1.3758	98500	7.3622
1.3772	98600	7.3946
1.3786	98700	7.3902
1.3800	98800	7.4024
1.3814	98900	7.3414
1.3828	99000	7.32
1.3842	99100	7.3545
1.3856	99200	7.3425
1.3869	99300	7.3701
1.3883	99400	7.3459
1.3897	99500	7.3837
1.3911	99600	7.3928
1.3925	99700	7.3882
1.3939	99800	7.3833
1.3953	99900	7.3217
1.3967	100000	7.3858
1.3981	100100	7.3856
1.3995	100200	7.3692
1.4009	100300	7.3858
1.4023	100400	7.387
1.4037	100500	7.3794
1.4051	100600	7.3653
1.4065	100700	7.3718
1.4079	100800	7.3826
1.4093	100900	7.3233
1.4107	101000	7.3859
1.4121	101100	7.3866
1.4135	101200	7.3367
1.4149	101300	7.3274
1.4163	101400	7.3774
1.4177	101500	7.3804
1.4191	101600	7.3555
1.4205	101700	7.3781
1.4219	101800	7.3523
1.4233	101900	7.3183
1.4247	102000	7.3597
1.4261	102100	7.431
1.4275	102200	7.3519
1.4289	102300	7.3591
1.4302	102400	7.3533
1.4316	102500	7.3955
1.4330	102600	7.3829
1.4344	102700	7.3542
1.4358	102800	7.3404
1.4372	102900	7.3746
1.4386	103000	7.3924
1.4400	103100	7.3267
1.4414	103200	7.3522
1.4428	103300	7.3496
1.4442	103400	7.3668
1.4456	103500	7.3394
1.4470	103600	7.3758
1.4484	103700	7.3537
1.4498	103800	7.3593
1.4512	103900	7.3289
1.4526	104000	7.3565
1.4540	104100	7.3765
1.4554	104200	7.3392
1.4568	104300	7.3714
1.4582	104400	7.3845
1.4596	104500	7.3639
1.4610	104600	7.3707
1.4624	104700	7.3687
1.4638	104800	7.3566
1.4652	104900	7.4302
1.4666	105000	7.3969
1.4680	105100	7.4001
1.4694	105200	7.3543
1.4708	105300	7.4355
1.4721	105400	7.3844
1.4735	105500	7.3793
1.4749	105600	7.3478
1.4763	105700	7.307
1.4777	105800	7.3224
1.4791	105900	7.3461
1.4805	106000	7.3312
1.4819	106100	7.3633
1.4833	106200	7.3941
1.4847	106300	7.3192
1.4861	106400	7.3662
1.4875	106500	7.3193
1.4889	106600	7.4143
1.4903	106700	7.3118
1.4917	106800	7.3539
1.4931	106900	7.3503
1.4945	107000	7.4115
1.4959	107100	7.3226
1.4973	107200	7.3466
1.4987	107300	7.3552
1.5001	107400	7.3934
1.5015	107500	7.3568
1.5029	107600	7.3349
1.5043	107700	7.3725
1.5057	107800	7.366
1.5071	107900	7.4261
1.5085	108000	7.3676
1.5099	108100	7.3846
1.5113	108200	7.3198
1.5127	108300	7.4015
1.5141	108400	7.3463
1.5154	108500	7.3471
1.5168	108600	7.3487
1.5182	108700	7.3852
1.5196	108800	7.4031
1.5210	108900	7.3399
1.5224	109000	7.4266
1.5238	109100	7.3765
1.5252	109200	7.3603
1.5266	109300	7.3121
1.5280	109400	7.3581
1.5294	109500	7.3258
1.5308	109600	7.3347
1.5322	109700	7.3783
1.5336	109800	7.3532
1.5350	109900	7.3507
1.5364	110000	7.3322
1.5378	110100	7.3451
1.5392	110200	7.3739
1.5406	110300	7.3358
1.5420	110400	7.338
1.5434	110500	7.314
1.5448	110600	7.3417
1.5462	110700	7.322
1.5476	110800	7.3636
1.5490	110900	7.3447
1.5504	111000	7.3611
1.5518	111100	7.3661
1.5532	111200	7.3952
1.5546	111300	7.3853
1.5560	111400	7.3561
1.5573	111500	7.3609
1.5587	111600	7.3485
1.5601	111700	7.3367
1.5615	111800	7.3127
1.5629	111900	7.3783
1.5643	112000	7.3195
1.5657	112100	7.399
1.5671	112200	7.3744
1.5685	112300	7.351
1.5699	112400	7.3392
1.5713	112500	7.3751
1.5727	112600	7.3669
1.5741	112700	7.3052
1.5755	112800	7.2865
1.5769	112900	7.2755
1.5783	113000	7.3691
1.5797	113100	7.3375
1.5811	113200	7.3688
1.5825	113300	7.2989
1.5839	113400	7.4042
1.5853	113500	7.3364
1.5867	113600	7.3106
1.5881	113700	7.3385
1.5895	113800	7.4001
1.5909	113900	7.3243
1.5923	114000	7.3427
1.5937	114100	7.3646
1.5951	114200	7.3357
1.5965	114300	7.3302
1.5979	114400	7.3074
1.5993	114500	7.3873
1.6006	114600	7.3812
1.6020	114700	7.3475
1.6034	114800	7.3356
1.6048	114900	7.3408
1.6062	115000	7.3533
1.6076	115100	7.3956
1.6090	115200	7.3647
1.6104	115300	7.3375
1.6118	115400	7.2829
1.6132	115500	7.3433
1.6146	115600	7.4173
1.6160	115700	7.3286
1.6174	115800	7.3041
1.6188	115900	7.3752
1.6202	116000	7.3558
1.6216	116100	7.3218
1.6230	116200	7.3603
1.6244	116300	7.3036
1.6258	116400	7.3836
1.6272	116500	7.3017
1.6286	116600	7.3106
1.6300	116700	7.3752
1.6314	116800	7.3414
1.6328	116900	7.391
1.6342	117000	7.3658
1.6356	117100	7.3149
1.6370	117200	7.3572
1.6384	117300	7.325
1.6398	117400	7.3126
1.6412	117500	7.3453
1.6425	117600	7.3882
1.6439	117700	7.3486
1.6453	117800	7.3454
1.6467	117900	7.3751
1.6481	118000	7.3227
1.6495	118100	7.3157
1.6509	118200	7.3357
1.6523	118300	7.3274
1.6537	118400	7.3359
1.6551	118500	7.3727
1.6565	118600	7.2998
1.6579	118700	7.3407
1.6593	118800	7.3188
1.6607	118900	7.3848
1.6621	119000	7.3266
1.6635	119100	7.3251
1.6649	119200	7.3311
1.6663	119300	7.3371
1.6677	119400	7.3379
1.6691	119500	7.3376
1.6705	119600	7.3188
1.6719	119700	7.3207
1.6733	119800	7.3887
1.6747	119900	7.3701
1.6761	120000	7.3286
1.6775	120100	7.342
1.6789	120200	7.3573
1.6803	120300	7.3197
1.6817	120400	7.2984
1.6831	120500	7.2911
1.6845	120600	7.3144
1.6858	120700	7.3535
1.6872	120800	7.339
1.6886	120900	7.358
1.6900	121000	7.3328
1.6914	121100	7.3226
1.6928	121200	7.3113
1.6942	121300	7.326
1.6956	121400	7.3151
1.6970	121500	7.3797
1.6984	121600	7.3192
1.6998	121700	7.3442
1.7012	121800	7.3632
1.7026	121900	7.2886
1.7040	122000	7.3824
1.7054	122100	7.3122
1.7068	122200	7.3378
1.7082	122300	7.3721
1.7096	122400	7.2905
1.7110	122500	7.3409
1.7124	122600	7.3362
1.7138	122700	7.3346
1.7152	122800	7.323
1.7166	122900	7.3928
1.7180	123000	7.2963
1.7194	123100	7.3591
1.7208	123200	7.3338
1.7222	123300	7.3551
1.7236	123400	7.2956
1.7250	123500	7.3236
1.7264	123600	7.2921
1.7278	123700	7.3118
1.7291	123800	7.4146
1.7305	123900	7.3321
1.7319	124000	7.3529
1.7333	124100	7.3409
1.7347	124200	7.3837
1.7361	124300	7.3289
1.7375	124400	7.3522
1.7389	124500	7.3246
1.7403	124600	7.3455
1.7417	124700	7.3232
1.7431	124800	7.3793
1.7445	124900	7.2792
1.7459	125000	7.3081
1.7473	125100	7.3441
1.7487	125200	7.3586
1.7501	125300	7.3084
1.7515	125400	7.3783
1.7529	125500	7.303
1.7543	125600	7.3268
1.7557	125700	7.3372
1.7571	125800	7.3209
1.7585	125900	7.2818
1.7599	126000	7.3103
1.7613	126100	7.3891
1.7627	126200	7.3761
1.7641	126300	7.3608
1.7655	126400	7.3872
1.7669	126500	7.3302
1.7683	126600	7.3309
1.7697	126700	7.3192
1.7710	126800	7.3174
1.7724	126900	7.3535
1.7738	127000	7.2949
1.7752	127100	7.2946
1.7766	127200	7.3564
1.7780	127300	7.3119
1.7794	127400	7.3449
1.7808	127500	7.3312
1.7822	127600	7.2877
1.7836	127700	7.3446
1.7850	127800	7.3816
1.7864	127900	7.2985
1.7878	128000	7.333
1.7892	128100	7.3868
1.7906	128200	7.3086
1.7920	128300	7.3502
1.7934	128400	7.321
1.7948	128500	7.2932
1.7962	128600	7.3494
1.7976	128700	7.3729
1.7990	128800	7.3541
1.8004	128900	7.3401
1.8018	129000	7.3247
1.8032	129100	7.3194
1.8046	129200	7.3274
1.8060	129300	7.3815
1.8074	129400	7.3108
1.8088	129500	7.375
1.8102	129600	7.3132
1.8116	129700	7.3787
1.8130	129800	7.2769
1.8143	129900	7.2828
1.8157	130000	7.2993
1.8171	130100	7.3093
1.8185	130200	7.3386
1.8199	130300	7.2818
1.8213	130400	7.3224
1.8227	130500	7.286
1.8241	130600	7.2744
1.8255	130700	7.3759
1.8269	130800	7.3489
1.8283	130900	7.3509
1.8297	131000	7.2824
1.8311	131100	7.3319
1.8325	131200	7.3786
1.8339	131300	7.3119
1.8353	131400	7.3332
1.8367	131500	7.3027
1.8381	131600	7.4188
1.8395	131700	7.3888
1.8409	131800	7.3368
1.8423	131900	7.3144
1.8437	132000	7.3694
1.8451	132100	7.347
1.8465	132200	7.3107
1.8479	132300	7.3205
1.8493	132400	7.3379
1.8507	132500	7.311
1.8521	132600	7.3608
1.8535	132700	7.3318
1.8549	132800	7.3338
1.8562	132900	7.3536
1.8576	133000	7.3381
1.8590	133100	7.2922
1.8604	133200	7.3138
1.8618	133300	7.3381
1.8632	133400	7.3565
1.8646	133500	7.3038
1.8660	133600	7.2952
1.8674	133700	7.3158
1.8688	133800	7.3419
1.8702	133900	7.3849
1.8716	134000	7.3149
1.8730	134100	7.2974
1.8744	134200	7.3267
1.8758	134300	7.3147
1.8772	134400	7.3384
1.8786	134500	7.3188
1.8800	134600	7.3351
1.8814	134700	7.3219
1.8828	134800	7.3039
1.8842	134900	7.3666
1.8856	135000	7.3352
1.8870	135100	7.4204
1.8884	135200	7.3394
1.8898	135300	7.3418
1.8912	135400	7.3613
1.8926	135500	7.3244
1.8940	135600	7.358
1.8954	135700	7.2659
1.8968	135800	7.3035
1.8982	135900	7.3833
1.8995	136000	7.3343
1.9009	136100	7.2412
1.9023	136200	7.3243
1.9037	136300	7.2633
1.9051	136400	7.354
1.9065	136500	7.3549
1.9079	136600	7.3155
1.9093	136700	7.329
1.9107	136800	7.3524
1.9121	136900	7.2965
1.9135	137000	7.3498
1.9149	137100	7.3346
1.9163	137200	7.3336
1.9177	137300	7.3202
1.9191	137400	7.3205
1.9205	137500	7.3243
1.9219	137600	7.2771
1.9233	137700	7.3517
1.9247	137800	7.3368
1.9261	137900	7.3492
1.9275	138000	7.346
1.9289	138100	7.2797
1.9303	138200	7.3126
1.9317	138300	7.3935
1.9331	138400	7.3713
1.9345	138500	7.3447
1.9359	138600	7.305
1.9373	138700	7.3616
1.9387	138800	7.3254
1.9401	138900	7.3129
1.9414	139000	7.3376
1.9428	139100	7.2986
1.9442	139200	7.2991
1.9456	139300	7.3564
1.9470	139400	7.3366
1.9484	139500	7.367
1.9498	139600	7.3233
1.9512	139700	7.3219
1.9526	139800	7.3252
1.9540	139900	7.2752
1.9554	140000	7.3207
1.9568	140100	7.3204
1.9582	140200	7.2825
1.9596	140300	7.2894
1.9610	140400	7.3105
1.9624	140500	7.3736
1.9638	140600	7.3018
1.9652	140700	7.3203
1.9666	140800	7.3091
1.9680	140900	7.407
1.9694	141000	7.3363
1.9708	141100	7.4118
1.9722	141200	7.35
1.9736	141300	7.3229
1.9750	141400	7.3778
1.9764	141500	7.3308
1.9778	141600	7.3342
1.9792	141700	7.3161
1.9806	141800	7.3345
1.9820	141900	7.3556
1.9834	142000	7.3832
1.9847	142100	7.3374
1.9861	142200	7.3003
1.9875	142300	7.3323
1.9889	142400	7.3312
1.9903	142500	7.3292
1.9917	142600	7.3439
1.9931	142700	7.3177
1.9945	142800	7.3478
1.9959	142900	7.3035
1.9973	143000	7.3
1.9987	143100	7.3019

Framework Versions

• Python: 3.12.3
• Sentence Transformers: 5.1.0
• Transformers: 4.55.4
• PyTorch: 2.5.1+cu121
• 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},
}