all-MiniLM-L6-v79-pair_score

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2 on the pairs_with_scores_v64 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_v64
• 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 = [
    'extra lightening face cream',
    'elle nail care aloe-panthenol 14m elle elle nail care aloe panthenol nail care aloe panthenol elle elle nail care aloe panthenol nail care aloe panthenol',
    'the american american cheese burger crisp iceberg burger dill pickles burger burger spread burger american burger burger american hamburger hamburger american burger burger american hamburger hamburger',
]
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,  0.4973, -0.0478],
#         [ 0.4973,  1.0000, -0.1329],
#         [-0.0478, -0.1329,  1.0000]])

Training Details

Training Dataset

pairs_with_scores_v64

• Dataset: pairs_with_scores_v64 at 9fbc4fa
• Size: 23,620,160 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: 20 tokens	min: 4 tokens mean: 44.58 tokens max: 256 tokens	min: 0.0 mean: 0.06 max: 1.0

• Samples:

  sentence1	sentence2	score
  belt for or or	deer animal toy kids toy boys toy girls toy realistic animal toy plastic toy subtle body toy brown toy imaginative toy decorative toy durable toy animal toy deer toy toy animal toy deer toy toy	0.0
  wooden birds swing	baguette baguette bread bread baguette baguette	0.0
  seacube khalta seafood	kebab hala vegetables cut kebab kebob kebob hala kebab kebob kebob hala	0.5

• Loss: CoSENTLoss with these parameters:

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

Evaluation Dataset

pairs_with_scores_v64

• Dataset: pairs_with_scores_v64 at 9fbc4fa
• Size: 118,695 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: 6.61 tokens max: 24 tokens	min: 5 tokens mean: 45.05 tokens max: 256 tokens	min: 0.0 mean: 0.07 max: 1.0

• Samples:

  sentence1	sentence2	score
  knorr	bobai sunscreen cream spf 80 high protection 50ml bobai sunscreen bobai high protection sunscreen spf 80 sunscreen sunscreen sunscreen cream bobai high protection sunscreen spf 80 sunscreen sunscreen sunscreen cream	0.0
  soft knit jumpsuit	second skin hip hugger - lilac women hip hugger fabrics hip hugger waistband hip hugger breathable hip hugger workouts hip hugger stretchy hip hugger lilac hip hip hugger hip hugger second skin second skin hip hugger hip hugger second skin second skin hip hugger	0.25
  lavender scented cleaner	oneida donizetti soup ladle dining ladle cutlery ladle oneida oneida donizetti oneida ladle soup ladle ladle oneida oneida donizetti oneida ladle soup ladle	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
0.0005	100	9.6979
0.0011	200	9.6824
0.0016	300	9.4592
0.0022	400	9.5213
0.0027	500	9.1837
0.0033	600	9.2447
0.0038	700	9.2297
0.0043	800	8.9061
0.0049	900	9.0371
0.0054	1000	8.7584
0.0060	1100	8.7397
0.0065	1200	8.5183
0.0070	1300	8.3807
0.0076	1400	8.349
0.0081	1500	8.2846
0.0087	1600	8.0636
0.0092	1700	8.0581
0.0098	1800	7.9073
0.0103	1900	7.901
0.0108	2000	7.8361
0.0114	2100	7.6439
0.0119	2200	7.6329
0.0125	2300	7.5367
0.0130	2400	7.5781
0.0135	2500	7.4571
0.0141	2600	7.4961
0.0146	2700	7.4642
0.0152	2800	7.4527
0.0157	2900	7.3439
0.0163	3000	7.3525
0.0168	3100	7.3534
0.0173	3200	7.2308
0.0179	3300	7.302
0.0184	3400	7.1934
0.0190	3500	7.1783
0.0195	3600	7.1874
0.0201	3700	7.1227
0.0206	3800	7.1919
0.0211	3900	7.0342
0.0217	4000	7.0101
0.0222	4100	7.0279
0.0228	4200	7.0197
0.0233	4300	6.9749
0.0238	4400	6.924
0.0244	4500	6.9803
0.0249	4600	6.9122
0.0255	4700	6.9589
0.0260	4800	6.8731
0.0266	4900	6.8296
0.0271	5000	6.8771
0.0276	5100	6.8016
0.0282	5200	6.795
0.0287	5300	6.8132
0.0293	5400	6.7469
0.0298	5500	6.7852
0.0303	5600	6.7143
0.0309	5700	6.7156
0.0314	5800	6.661
0.0320	5900	6.7397
0.0325	6000	6.757
0.0331	6100	6.7163
0.0336	6200	6.6351
0.0341	6300	6.6479
0.0347	6400	6.6491
0.0352	6500	6.6246
0.0358	6600	6.6039
0.0363	6700	6.5783
0.0368	6800	6.6002
0.0374	6900	6.5415
0.0379	7000	6.5898
0.0385	7100	6.5771
0.0390	7200	6.5356
0.0396	7300	6.4821
0.0401	7400	6.5374
0.0406	7500	6.4747
0.0412	7600	6.4715
0.0417	7700	6.4949
0.0423	7800	6.4504
0.0428	7900	6.4637
0.0434	8000	6.2713
0.0439	8100	6.4088
0.0444	8200	6.4108
0.0450	8300	6.3839
0.0455	8400	6.342
0.0461	8500	6.2999
0.0466	8600	6.266
0.0471	8700	6.3183
0.0477	8800	6.2826
0.0482	8900	6.2686
0.0488	9000	6.333
0.0493	9100	6.2927
0.0499	9200	6.2532
0.0504	9300	6.282
0.0509	9400	6.2651
0.0515	9500	6.2387
0.0520	9600	6.1849
0.0526	9700	6.2569
0.0531	9800	6.2291
0.0536	9900	6.1957
0.0542	10000	6.1895
0.0547	10100	6.1915
0.0553	10200	6.1646
0.0558	10300	6.1066
0.0564	10400	6.1377
0.0569	10500	6.1538
0.0574	10600	6.1508
0.0580	10700	6.0729
0.0585	10800	6.1199
0.0591	10900	6.1047
0.0596	11000	6.1012
0.0602	11100	6.0918
0.0607	11200	6.119
0.0612	11300	6.0149
0.0618	11400	5.995
0.0623	11500	5.9762
0.0629	11600	5.9603
0.0634	11700	5.9511
0.0639	11800	5.9406
0.0645	11900	5.8796
0.0650	12000	6.0006
0.0656	12100	5.8714
0.0661	12200	5.9809
0.0667	12300	5.973
0.0672	12400	5.9409
0.0677	12500	5.9839
0.0683	12600	5.8713
0.0688	12700	5.8183
0.0694	12800	5.8051
0.0699	12900	5.8351
0.0704	13000	5.8759
0.0710	13100	5.8221
0.0715	13200	5.7703
0.0721	13300	5.8113
0.0726	13400	5.7681
0.0732	13500	5.7115
0.0737	13600	5.7755
0.0742	13700	5.8603
0.0748	13800	5.83
0.0753	13900	5.6995
0.0759	14000	5.6956
0.0764	14100	5.6984
0.0770	14200	5.7685
0.0775	14300	5.6245
0.0780	14400	5.6358
0.0786	14500	5.7959
0.0791	14600	5.7351
0.0797	14700	5.6231
0.0802	14800	5.6764
0.0807	14900	5.6566
0.0813	15000	5.5398
0.0818	15100	5.6209
0.0824	15200	5.5917
0.0829	15300	5.5978
0.0835	15400	5.5284
0.0840	15500	5.6096
0.0845	15600	5.4752
0.0851	15700	5.5933
0.0856	15800	5.4932
0.0862	15900	5.6393
0.0867	16000	5.4259
0.0872	16100	5.5573
0.0878	16200	5.4365
0.0883	16300	5.424
0.0889	16400	5.4403
0.0894	16500	5.4879
0.0900	16600	5.5908
0.0905	16700	5.5562
0.0910	16800	5.3587
0.0916	16900	5.324
0.0921	17000	5.3884
0.0927	17100	5.4101
0.0932	17200	5.3859
0.0938	17300	5.3781
0.0943	17400	5.4082
0.0948	17500	5.3308
0.0954	17600	5.4234
0.0959	17700	5.279
0.0965	17800	5.3187
0.0970	17900	5.3205
0.0975	18000	5.3045
0.0981	18100	5.2508
0.0986	18200	5.2714
0.0992	18300	5.3635
0.0997	18400	5.2542
0.1003	18500	5.4054
0.1008	18600	5.3346
0.1013	18700	5.3189
0.1019	18800	5.3829
0.1024	18900	5.2248
0.1030	19000	5.3606
0.1035	19100	5.3103
0.1040	19200	5.3054
0.1046	19300	5.194
0.1051	19400	5.1271
0.1057	19500	5.2321
0.1062	19600	5.1943
0.1068	19700	5.2571
0.1073	19800	5.1253
0.1078	19900	5.2736
0.1084	20000	5.3111
0.1089	20100	5.177
0.1095	20200	5.1249
0.1100	20300	4.9389
0.1105	20400	5.0981
0.1111	20500	5.1181
0.1116	20600	5.0342
0.1122	20700	5.0969
0.1127	20800	5.1257
0.1133	20900	5.2854
0.1138	21000	5.052
0.1143	21100	5.1192
0.1149	21200	5.0037
0.1154	21300	5.02
0.1160	21400	5.1483
0.1165	21500	5.1793
0.1171	21600	5.0368
0.1176	21700	5.0671
0.1181	21800	5.0695
0.1187	21900	5.1195
0.1192	22000	5.0493
0.1198	22100	5.0715
0.1203	22200	4.9734
0.1208	22300	5.0741
0.1214	22400	4.9168
0.1219	22500	4.9332
0.1225	22600	4.9433
0.1230	22700	4.8703
0.1236	22800	4.9418
0.1241	22900	4.998
0.1246	23000	4.9634
0.1252	23100	4.8902
0.1257	23200	4.9853
0.1263	23300	4.9543
0.1268	23400	4.8415
0.1273	23500	4.8764
0.1279	23600	4.7974
0.1284	23700	4.8883
0.1290	23800	4.9661
0.1295	23900	4.9261
0.1301	24000	4.7792
0.1306	24100	4.9415
0.1311	24200	4.8752
0.1317	24300	4.9023
0.1322	24400	4.7727
0.1328	24500	4.899
0.1333	24600	5.0975
0.1339	24700	4.7563
0.1344	24800	4.7705
0.1349	24900	4.7625
0.1355	25000	4.9134
0.1360	25100	4.8195
0.1366	25200	4.8466
0.1371	25300	4.7968
0.1376	25400	4.9099
0.1382	25500	4.739
0.1387	25600	4.7515
0.1393	25700	4.8079
0.1398	25800	4.9914
0.1404	25900	4.8348
0.1409	26000	4.8692
0.1414	26100	4.9753
0.1420	26200	4.7826
0.1425	26300	4.7571
0.1431	26400	4.7397
0.1436	26500	4.8283
0.1441	26600	4.7858
0.1447	26700	4.8941
0.1452	26800	4.6881
0.1458	26900	4.7692
0.1463	27000	4.685
0.1469	27100	4.6134
0.1474	27200	4.7942
0.1479	27300	4.6796
0.1485	27400	4.8311
0.1490	27500	4.7586
0.1496	27600	4.9638
0.1501	27700	4.5925
0.1507	27800	4.7721
0.1512	27900	4.9558
0.1517	28000	4.6271
0.1523	28100	4.7441
0.1528	28200	4.5941
0.1534	28300	4.7856
0.1539	28400	4.7607
0.1544	28500	4.9136
0.1550	28600	4.644
0.1555	28700	4.756
0.1561	28800	4.7792
0.1566	28900	4.6434
0.1572	29000	4.6416
0.1577	29100	4.6127
0.1582	29200	4.654
0.1588	29300	4.6169
0.1593	29400	4.7013
0.1599	29500	4.7292
0.1604	29600	4.5975
0.1609	29700	4.608
0.1615	29800	4.907
0.1620	29900	4.5744
0.1626	30000	4.862
0.1631	30100	4.6931
0.1637	30200	4.7527
0.1642	30300	4.5149
0.1647	30400	4.8814
0.1653	30500	4.7016
0.1658	30600	4.8302
0.1664	30700	4.5751
0.1669	30800	4.6344
0.1674	30900	4.5181
0.1680	31000	4.5935
0.1685	31100	4.6026
0.1691	31200	4.5826
0.1696	31300	4.5265
0.1702	31400	4.5226
0.1707	31500	4.6017
0.1712	31600	4.5966
0.1718	31700	4.5258
0.1723	31800	4.7223
0.1729	31900	4.5954
0.1734	32000	4.5924
0.1740	32100	4.5067
0.1745	32200	4.6081
0.1750	32300	4.5534
0.1756	32400	4.7593
0.1761	32500	4.6389
0.1767	32600	4.6156
0.1772	32700	4.546
0.1777	32800	4.7077
0.1783	32900	4.5972
0.1788	33000	4.4062
0.1794	33100	4.5526
0.1799	33200	4.6959
0.1805	33300	4.5721
0.1810	33400	4.5149
0.1815	33500	4.4069
0.1821	33600	4.4224
0.1826	33700	4.4751
0.1832	33800	4.5812
0.1837	33900	4.5551
0.1842	34000	4.5594
0.1848	34100	4.3867
0.1853	34200	4.5305
0.1859	34300	4.6335
0.1864	34400	4.6725
0.1870	34500	4.5943
0.1875	34600	4.4808
0.1880	34700	4.4224
0.1886	34800	4.5517
0.1891	34900	4.5108
0.1897	35000	4.4405
0.1902	35100	4.3576
0.1908	35200	4.672
0.1913	35300	4.5722
0.1918	35400	4.335
0.1924	35500	4.53
0.1929	35600	4.4868
0.1935	35700	4.5739
0.1940	35800	4.3491
0.1945	35900	4.5416
0.1951	36000	4.4393
0.1956	36100	4.3805
0.1962	36200	4.5126
0.1967	36300	4.647
0.1973	36400	4.5609
0.1978	36500	4.3491
0.1983	36600	4.4862
0.1989	36700	4.4898
0.1994	36800	4.5216
0.2000	36900	4.5497
0.2005	37000	4.5052
0.2010	37100	4.4712
0.2016	37200	4.5119
0.2021	37300	4.256
0.2027	37400	4.4665
0.2032	37500	4.6035
0.2038	37600	4.4488
0.2043	37700	4.5442
0.2048	37800	4.1885
0.2054	37900	4.5342
0.2059	38000	4.2897
0.2065	38100	4.4099
0.2070	38200	4.437
0.2076	38300	4.3529
0.2081	38400	4.3274
0.2086	38500	4.4055
0.2092	38600	4.4752
0.2097	38700	4.4522
0.2103	38800	4.491
0.2108	38900	4.2017
0.2113	39000	4.5312
0.2119	39100	4.2611
0.2124	39200	4.6368
0.2130	39300	4.3943
0.2135	39400	4.5556
0.2141	39500	4.3695
0.2146	39600	4.4407
0.2151	39700	4.4207
0.2157	39800	4.4075
0.2162	39900	4.3496
0.2168	40000	4.4074
0.2173	40100	4.3763
0.2178	40200	4.307
0.2184	40300	4.3535
0.2189	40400	4.4065
0.2195	40500	4.1822
0.2200	40600	4.2142
0.2206	40700	4.2875
0.2211	40800	4.21
0.2216	40900	4.4331
0.2222	41000	4.2681
0.2227	41100	4.3316
0.2233	41200	4.2891
0.2238	41300	4.2667
0.2244	41400	4.3882
0.2249	41500	4.2353
0.2254	41600	4.1668
0.2260	41700	4.4514
0.2265	41800	4.4001
0.2271	41900	4.366
0.2276	42000	4.3564
0.2281	42100	4.2089
0.2287	42200	4.4556
0.2292	42300	4.0671
0.2298	42400	4.6156
0.2303	42500	4.2189
0.2309	42600	4.2691
0.2314	42700	4.3431
0.2319	42800	4.4983
0.2325	42900	4.3626
0.2330	43000	4.2148
0.2336	43100	4.3264
0.2341	43200	4.3556
0.2346	43300	4.4749
0.2352	43400	4.5028
0.2357	43500	4.2421
0.2363	43600	4.3222
0.2368	43700	4.3856
0.2374	43800	4.0533
0.2379	43900	4.3533
0.2384	44000	4.3472
0.2390	44100	4.2341
0.2395	44200	4.2316
0.2401	44300	4.0937
0.2406	44400	4.0205
0.2411	44500	4.4145
0.2417	44600	4.1027
0.2422	44700	4.2126
0.2428	44800	4.3493
0.2433	44900	4.194
0.2439	45000	4.2145
0.2444	45100	4.1618
0.2449	45200	4.2429
0.2455	45300	4.1239
0.2460	45400	4.4705
0.2466	45500	4.3518
0.2471	45600	4.1217
0.2477	45700	4.1586
0.2482	45800	4.2618
0.2487	45900	4.1665
0.2493	46000	4.4498
0.2498	46100	4.1232
0.2504	46200	4.4095
0.2509	46300	4.2085
0.2514	46400	4.2656
0.2520	46500	4.0762
0.2525	46600	4.2263
0.2531	46700	4.0126
0.2536	46800	4.2503
0.2542	46900	4.1824
0.2547	47000	4.144
0.2552	47100	4.2093
0.2558	47200	4.1827
0.2563	47300	4.135
0.2569	47400	4.2215
0.2574	47500	4.3045
0.2579	47600	3.9579
0.2585	47700	4.2356
0.2590	47800	4.172
0.2596	47900	4.1998
0.2601	48000	4.0832
0.2607	48100	4.1143
0.2612	48200	4.5477
0.2617	48300	3.8689
0.2623	48400	4.2451
0.2628	48500	4.2721
0.2634	48600	4.2047
0.2639	48700	4.2534
0.2645	48800	4.1614
0.2650	48900	4.1215
0.2655	49000	4.4269
0.2661	49100	4.227
0.2666	49200	4.2768
0.2672	49300	4.2141
0.2677	49400	4.2993
0.2682	49500	4.1888
0.2688	49600	4.2367
0.2693	49700	3.8832
0.2699	49800	4.1989
0.2704	49900	3.9861
0.2710	50000	3.964
0.2715	50100	4.1715
0.2720	50200	3.974
0.2726	50300	4.0482
0.2731	50400	4.0952
0.2737	50500	4.1843
0.2742	50600	3.9632
0.2747	50700	4.1134
0.2753	50800	4.1365
0.2758	50900	4.243
0.2764	51000	4.2184
0.2769	51100	3.8697
0.2775	51200	3.9467
0.2780	51300	4.4998
0.2785	51400	4.1766
0.2791	51500	4.279
0.2796	51600	4.1397
0.2802	51700	4.2293
0.2807	51800	4.0916
0.2813	51900	4.2072
0.2818	52000	4.2114
0.2823	52100	4.2985
0.2829	52200	4.1841
0.2834	52300	4.1447
0.2840	52400	3.88
0.2845	52500	3.9899
0.2850	52600	4.2149
0.2856	52700	4.0015
0.2861	52800	3.8886
0.2867	52900	4.1215
0.2872	53000	3.9688
0.2878	53100	3.87
0.2883	53200	4.0775
0.2888	53300	4.1072
0.2894	53400	3.8488
0.2899	53500	4.0763
0.2905	53600	3.8947
0.2910	53700	4.0659
0.2915	53800	4.0005
0.2921	53900	3.9634
0.2926	54000	3.9544
0.2932	54100	4.0872
0.2937	54200	4.0976
0.2943	54300	4.1318
0.2948	54400	4.0355
0.2953	54500	3.9018
0.2959	54600	4.0719
0.2964	54700	4.0077
0.2970	54800	4.1213
0.2975	54900	3.8835
0.2980	55000	4.1213
0.2986	55100	4.0585
0.2991	55200	3.8848
0.2997	55300	3.9473
0.3002	55400	4.0747
0.3008	55500	4.0825
0.3013	55600	3.8704
0.3018	55700	3.9491
0.3024	55800	4.1454
0.3029	55900	3.9713
0.3035	56000	4.1613
0.3040	56100	3.9115
0.3046	56200	3.8252
0.3051	56300	3.9187
0.3056	56400	3.7428
0.3062	56500	4.027
0.3067	56600	4.2293
0.3073	56700	4.0512
0.3078	56800	4.1175
0.3083	56900	4.128
0.3089	57000	3.8778
0.3094	57100	4.0784
0.3100	57200	3.9406
0.3105	57300	3.9108
0.3111	57400	3.7358
0.3116	57500	4.1269
0.3121	57600	4.1604
0.3127	57700	3.7766
0.3132	57800	3.8638
0.3138	57900	4.1431
0.3143	58000	3.9454
0.3148	58100	3.9922
0.3154	58200	3.9221
0.3159	58300	3.8958
0.3165	58400	3.6278
0.3170	58500	3.8623
0.3176	58600	3.9504
0.3181	58700	3.9584
0.3186	58800	3.9199
0.3192	58900	3.9008
0.3197	59000	4.2515
0.3203	59100	4.1092
0.3208	59200	3.7406
0.3214	59300	4.155
0.3219	59400	3.8264
0.3224	59500	4.1618
0.3230	59600	4.0314
0.3235	59700	4.0193
0.3241	59800	3.7075
0.3246	59900	3.7117
0.3251	60000	3.9537
0.3257	60100	3.8738
0.3262	60200	3.9009
0.3268	60300	3.9819
0.3273	60400	3.9933
0.3279	60500	4.2019
0.3284	60600	3.9783
0.3289	60700	4.0668
0.3295	60800	3.7713
0.3300	60900	3.9294
0.3306	61000	3.9645
0.3311	61100	3.8059
0.3316	61200	4.0283
0.3322	61300	4.0794
0.3327	61400	3.8805
0.3333	61500	4.1811
0.3338	61600	4.033
0.3344	61700	3.9291
0.3349	61800	4.1059
0.3354	61900	4.0783
0.3360	62000	3.938
0.3365	62100	3.9934
0.3371	62200	4.0869
0.3376	62300	3.9529
0.3382	62400	3.868
0.3387	62500	3.9279
0.3392	62600	3.9504
0.3398	62700	4.2048
0.3403	62800	3.6506
0.3409	62900	3.9268
0.3414	63000	3.8572
0.3419	63100	3.7435
0.3425	63200	3.7565
0.3430	63300	3.8751
0.3436	63400	3.9196
0.3441	63500	3.4174
0.3447	63600	4.0529
0.3452	63700	3.9406
0.3457	63800	3.9339
0.3463	63900	3.8174
0.3468	64000	4.1419
0.3474	64100	4.0298
0.3479	64200	4.0503
0.3484	64300	3.8883
0.3490	64400	3.9311
0.3495	64500	3.8993
0.3501	64600	3.95
0.3506	64700	3.7599
0.3512	64800	4.0584
0.3517	64900	4.0861
0.3522	65000	3.7593
0.3528	65100	3.8035
0.3533	65200	4.0944
0.3539	65300	3.6439
0.3544	65400	4.187
0.3550	65500	3.9495
0.3555	65600	3.7794
0.3560	65700	3.855
0.3566	65800	3.5674
0.3571	65900	3.867
0.3577	66000	3.8955
0.3582	66100	3.8159
0.3587	66200	3.5382
0.3593	66300	3.8997
0.3598	66400	3.8334
0.3604	66500	3.7886
0.3609	66600	3.7693
0.3615	66700	3.7185
0.3620	66800	3.8964
0.3625	66900	3.874
0.3631	67000	3.7719
0.3636	67100	3.938
0.3642	67200	3.647
0.3647	67300	3.9874
0.3652	67400	3.6396
0.3658	67500	4.0213
0.3663	67600	3.8092
0.3669	67700	3.8865
0.3674	67800	3.5994
0.3680	67900	3.6779
0.3685	68000	3.7327
0.3690	68100	3.6071
0.3696	68200	3.9824
0.3701	68300	3.7766
0.3707	68400	3.6986
0.3712	68500	3.6431
0.3717	68600	3.9835
0.3723	68700	3.994
0.3728	68800	3.7414
0.3734	68900	3.8827
0.3739	69000	3.9262
0.3745	69100	3.7487
0.3750	69200	3.8314
0.3755	69300	3.6784
0.3761	69400	3.9096
0.3766	69500	3.7404
0.3772	69600	3.7935
0.3777	69700	3.647
0.3783	69800	3.9394
0.3788	69900	3.9349
0.3793	70000	3.8927
0.3799	70100	3.8635
0.3804	70200	3.8344
0.3810	70300	3.6043
0.3815	70400	3.7813
0.3820	70500	3.5654
0.3826	70600	3.7684
0.3831	70700	4.0351
0.3837	70800	3.7507
0.3842	70900	3.7312
0.3848	71000	4.0047
0.3853	71100	3.808
0.3858	71200	3.9395
0.3864	71300	3.8327
0.3869	71400	3.8233
0.3875	71500	3.7683
0.3880	71600	4.1107
0.3885	71700	3.5669
0.3891	71800	3.9346
0.3896	71900	3.8633
0.3902	72000	3.9581
0.3907	72100	3.8785
0.3913	72200	3.5878
0.3918	72300	3.8683
0.3923	72400	3.7864
0.3929	72500	3.7947
0.3934	72600	3.9279
0.3940	72700	3.833
0.3945	72800	3.9093
0.3951	72900	3.7577
0.3956	73000	3.9086
0.3961	73100	3.7477
0.3967	73200	3.5604
0.3972	73300	3.9048
0.3978	73400	3.6019
0.3983	73500	3.7298
0.3988	73600	3.7256
0.3994	73700	3.9396
0.3999	73800	3.7127
0.4005	73900	3.7631
0.4010	74000	3.9838
0.4016	74100	3.7319
0.4021	74200	3.638
0.4026	74300	3.8242
0.4032	74400	3.6188
0.4037	74500	3.5749
0.4043	74600	3.6405
0.4048	74700	3.8385
0.4053	74800	3.7064
0.4059	74900	3.801
0.4064	75000	4.1149
0.4070	75100	3.6136
0.4075	75200	3.8403
0.4081	75300	3.5386
0.4086	75400	3.6097
0.4091	75500	3.7311
0.4097	75600	3.8751
0.4102	75700	3.7026
0.4108	75800	3.6574
0.4113	75900	3.8145
0.4119	76000	4.1887
0.4124	76100	3.9118
0.4129	76200	3.9398
0.4135	76300	3.8548
0.4140	76400	3.5251
0.4146	76500	3.6026
0.4151	76600	4.2684
0.4156	76700	3.8207
0.4162	76800	3.9843
0.4167	76900	3.7712
0.4173	77000	3.6977
0.4178	77100	3.8029
0.4184	77200	3.8483
0.4189	77300	3.7878
0.4194	77400	3.833
0.4200	77500	3.4124
0.4205	77600	3.7645
0.4211	77700	3.9529
0.4216	77800	3.6303
0.4221	77900	3.8839
0.4227	78000	3.9729
0.4232	78100	3.8058
0.4238	78200	3.7984
0.4243	78300	3.7482
0.4249	78400	3.6709
0.4254	78500	3.7474
0.4259	78600	3.7411
0.4265	78700	3.9017
0.4270	78800	3.4957
0.4276	78900	4.061
0.4281	79000	3.7937
0.4286	79100	3.4857
0.4292	79200	3.4752
0.4297	79300	3.8802
0.4303	79400	4.057
0.4308	79500	3.5736
0.4314	79600	3.7759
0.4319	79700	3.7019
0.4324	79800	3.6912
0.4330	79900	3.5726
0.4335	80000	4.1672
0.4341	80100	3.6858
0.4346	80200	3.8556
0.4352	80300	3.6682
0.4357	80400	3.6421
0.4362	80500	3.8704
0.4368	80600	3.9408
0.4373	80700	3.7634
0.4379	80800	3.5259
0.4384	80900	3.7831
0.4389	81000	3.8622
0.4395	81100	3.9976
0.4400	81200	3.7958
0.4406	81300	3.6273
0.4411	81400	3.6277
0.4417	81500	3.7028
0.4422	81600	3.5579
0.4427	81700	3.9124
0.4433	81800	3.6946
0.4438	81900	3.4179
0.4444	82000	3.5546
0.4449	82100	3.808
0.4454	82200	3.812
0.4460	82300	3.5333
0.4465	82400	3.7745
0.4471	82500	3.748
0.4476	82600	3.7629
0.4482	82700	4.1335
0.4487	82800	3.7111
0.4492	82900	3.5956
0.4498	83000	3.5956
0.4503	83100	3.8415
0.4509	83200	3.8866
0.4514	83300	3.6914
0.4520	83400	3.5686
0.4525	83500	3.7117
0.4530	83600	3.6248
0.4536	83700	3.5357
0.4541	83800	3.7497
0.4547	83900	3.8515
0.4552	84000	3.7725
0.4557	84100	3.7023
0.4563	84200	3.5548
0.4568	84300	3.4968
0.4574	84400	3.3889
0.4579	84500	3.7158
0.4585	84600	3.742
0.4590	84700	3.9103
0.4595	84800	4.0112
0.4601	84900	3.7243
0.4606	85000	3.6239
0.4612	85100	3.7292
0.4617	85200	3.6057
0.4622	85300	3.4619
0.4628	85400	3.7547
0.4633	85500	3.7993
0.4639	85600	3.5164
0.4644	85700	3.8076
0.4650	85800	3.7261
0.4655	85900	3.4982
0.4660	86000	3.7568
0.4666	86100	3.5992
0.4671	86200	3.676
0.4677	86300	3.5013
0.4682	86400	3.7011
0.4688	86500	3.5917
0.4693	86600	3.6112
0.4698	86700	3.7364
0.4704	86800	3.8004
0.4709	86900	3.7949
0.4715	87000	3.3335
0.4720	87100	3.626
0.4725	87200	3.3769
0.4731	87300	3.2926
0.4736	87400	3.3715
0.4742	87500	3.8211
0.4747	87600	3.6673
0.4753	87700	3.3253
0.4758	87800	3.5153
0.4763	87900	3.3325
0.4769	88000	3.6715
0.4774	88100	3.6278
0.4780	88200	3.1883
0.4785	88300	3.8718
0.4790	88400	3.684
0.4796	88500	3.6352
0.4801	88600	3.525
0.4807	88700	3.9136
0.4812	88800	3.5173
0.4818	88900	3.4746
0.4823	89000	3.4377
0.4828	89100	3.9287
0.4834	89200	3.4878
0.4839	89300	3.4823
0.4845	89400	3.6692
0.4850	89500	3.7159
0.4856	89600	3.6456
0.4861	89700	3.5509
0.4866	89800	3.5092
0.4872	89900	3.7936
0.4877	90000	3.766
0.4883	90100	3.4384
0.4888	90200	3.353
0.4893	90300	3.7899
0.4899	90400	3.7811
0.4904	90500	3.313
0.4910	90600	3.8289
0.4915	90700	3.4727
0.4921	90800	3.5541
0.4926	90900	3.8332
0.4931	91000	3.5365
0.4937	91100	3.9425
0.4942	91200	3.601
0.4948	91300	3.3586
0.4953	91400	3.7267
0.4958	91500	3.6832
0.4964	91600	3.3702
0.4969	91700	3.7673
0.4975	91800	3.4024
0.4980	91900	3.5711
0.4986	92000	3.3207
0.4991	92100	3.6366
0.4996	92200	3.5514
0.5002	92300	3.5135
0.5007	92400	3.4673
0.5013	92500	3.7402
0.5018	92600	3.5007
0.5023	92700	3.7005
0.5029	92800	3.6721
0.5034	92900	3.5318
0.5040	93000	3.6189
0.5045	93100	3.6637
0.5051	93200	3.5362
0.5056	93300	3.4409
0.5061	93400	3.6638
0.5067	93500	3.5023
0.5072	93600	3.6503
0.5078	93700	3.5034
0.5083	93800	3.5689
0.5089	93900	3.6738
0.5094	94000	3.6803
0.5099	94100	3.8896
0.5105	94200	3.6473
0.5110	94300	3.5797
0.5116	94400	3.7409
0.5121	94500	3.4352
0.5126	94600	3.3672
0.5132	94700	3.4048
0.5137	94800	3.6485
0.5143	94900	3.6079
0.5148	95000	3.641
0.5154	95100	3.3746
0.5159	95200	3.4608
0.5164	95300	3.8232
0.5170	95400	3.3498
0.5175	95500	3.504
0.5181	95600	3.377
0.5186	95700	3.4517
0.5191	95800	3.533
0.5197	95900	3.5383
0.5202	96000	3.7814
0.5208	96100	3.614
0.5213	96200	3.7379
0.5219	96300	3.2724
0.5224	96400	3.6996
0.5229	96500	3.5416
0.5235	96600	3.4712
0.5240	96700	3.676
0.5246	96800	3.4654
0.5251	96900	3.3988
0.5257	97000	3.6054
0.5262	97100	3.7426
0.5267	97200	3.5022
0.5273	97300	3.5461
0.5278	97400	3.5596
0.5284	97500	3.432
0.5289	97600	3.1959
0.5294	97700	3.5279
0.5300	97800	3.4319
0.5305	97900	3.5623
0.5311	98000	3.7217
0.5316	98100	3.8073
0.5322	98200	3.3587
0.5327	98300	3.8093
0.5332	98400	3.5461
0.5338	98500	3.4502
0.5343	98600	3.4375
0.5349	98700	3.3169
0.5354	98800	3.4848
0.5359	98900	3.4333
0.5365	99000	3.3915
0.5370	99100	3.1441
0.5376	99200	3.7653
0.5381	99300	3.1983
0.5387	99400	3.7053
0.5392	99500	3.4417
0.5397	99600	3.4356
0.5403	99700	3.4632
0.5408	99800	3.4517
0.5414	99900	3.7073
0.5419	100000	3.6432
0.5425	100100	3.3564
0.5430	100200	3.5516
0.5435	100300	3.3361
0.5441	100400	3.312
0.5446	100500	3.5125
0.5452	100600	3.8457
0.5457	100700	3.3135
0.5462	100800	3.2858
0.5468	100900	3.5653
0.5473	101000	3.48
0.5479	101100	3.517
0.5484	101200	3.8528
0.5490	101300	3.5192
0.5495	101400	3.4581
0.5500	101500	3.3502
0.5506	101600	3.3653
0.5511	101700	3.6701
0.5517	101800	3.3262
0.5522	101900	3.4193
0.5527	102000	3.1965
0.5533	102100	3.0327
0.5538	102200	3.3827
0.5544	102300	3.3986
0.5549	102400	3.3362
0.5555	102500	3.4104
0.5560	102600	3.54
0.5565	102700	3.5323
0.5571	102800	3.3703
0.5576	102900	3.6362
0.5582	103000	3.4736
0.5587	103100	3.4162
0.5592	103200	3.5245
0.5598	103300	4.0331
0.5603	103400	3.4354
0.5609	103500	3.5132
0.5614	103600	3.7283
0.5620	103700	3.2441
0.5625	103800	3.3444
0.5630	103900	3.4487
0.5636	104000	3.5337
0.5641	104100	3.8498
0.5647	104200	3.5034
0.5652	104300	3.9938
0.5658	104400	3.5252
0.5663	104500	3.4066
0.5668	104600	3.803
0.5674	104700	3.686
0.5679	104800	3.6128
0.5685	104900	3.629
0.5690	105000	3.2731
0.5695	105100	3.6489
0.5701	105200	3.4153
0.5706	105300	3.6269
0.5712	105400	3.2187
0.5717	105500	3.4157
0.5723	105600	3.581
0.5728	105700	3.3042
0.5733	105800	3.1916
0.5739	105900	3.3971
0.5744	106000	3.5545
0.5750	106100	3.3008
0.5755	106200	3.4298
0.5760	106300	3.4735
0.5766	106400	3.5293
0.5771	106500	3.3695
0.5777	106600	3.6707
0.5782	106700	3.1681
0.5788	106800	3.6077
0.5793	106900	3.4091
0.5798	107000	3.3806
0.5804	107100	3.8152
0.5809	107200	3.2496
0.5815	107300	3.5104
0.5820	107400	3.4681
0.5826	107500	3.1207
0.5831	107600	3.4634
0.5836	107700	3.4864
0.5842	107800	3.4996
0.5847	107900	3.4038
0.5853	108000	2.9383
0.5858	108100	3.4695
0.5863	108200	3.2892
0.5869	108300	3.891
0.5874	108400	3.2495
0.5880	108500	3.7173
0.5885	108600	3.4233
0.5891	108700	3.7372
0.5896	108800	3.559
0.5901	108900	3.4231
0.5907	109000	3.4556
0.5912	109100	3.5478
0.5918	109200	3.4085
0.5923	109300	3.547
0.5928	109400	3.5033
0.5934	109500	3.5203
0.5939	109600	3.2135
0.5945	109700	3.7923
0.5950	109800	3.3681
0.5956	109900	3.3823
0.5961	110000	3.6529
0.5966	110100	3.529
0.5972	110200	3.3286
0.5977	110300	3.3619
0.5983	110400	3.3833
0.5988	110500	3.3424
0.5994	110600	3.5349
0.5999	110700	3.5949
0.6004	110800	3.2625
0.6010	110900	3.2704
0.6015	111000	3.7331
0.6021	111100	3.606
0.6026	111200	3.4453
0.6031	111300	3.6765
0.6037	111400	3.1413
0.6042	111500	3.3197
0.6048	111600	3.3339
0.6053	111700	3.2653
0.6059	111800	3.7277
0.6064	111900	3.1621
0.6069	112000	3.0778
0.6075	112100	3.6232
0.6080	112200	3.2105
0.6086	112300	3.5367
0.6091	112400	3.463
0.6096	112500	3.4323
0.6102	112600	3.5596
0.6107	112700	3.2125
0.6113	112800	3.5481
0.6118	112900	3.6179
0.6124	113000	3.0323
0.6129	113100	3.4819
0.6134	113200	3.5065
0.6140	113300	3.4604
0.6145	113400	3.4271
0.6151	113500	3.4801
0.6156	113600	3.638
0.6161	113700	3.7946
0.6167	113800	3.2251
0.6172	113900	3.2575
0.6178	114000	3.7379
0.6183	114100	3.0849
0.6189	114200	3.3312
0.6194	114300	3.2815
0.6199	114400	3.7191
0.6205	114500	3.4547
0.6210	114600	3.4939
0.6216	114700	3.0638
0.6221	114800	3.4406
0.6227	114900	3.3695
0.6232	115000	3.4457
0.6237	115100	3.4527
0.6243	115200	3.2819
0.6248	115300	3.5656
0.6254	115400	3.5925
0.6259	115500	3.4133
0.6264	115600	3.3515
0.6270	115700	3.485
0.6275	115800	3.3285
0.6281	115900	3.3581
0.6286	116000	3.5828
0.6292	116100	3.1674
0.6297	116200	3.3948
0.6302	116300	3.5006
0.6308	116400	3.1391
0.6313	116500	3.2747
0.6319	116600	3.5493
0.6324	116700	3.3325
0.6329	116800	3.7461
0.6335	116900	3.2733
0.6340	117000	3.404
0.6346	117100	3.4604
0.6351	117200	3.3714
0.6357	117300	3.651
0.6362	117400	3.4724
0.6367	117500	3.4354
0.6373	117600	3.4101
0.6378	117700	3.4841
0.6384	117800	3.0827
0.6389	117900	3.2835
0.6395	118000	3.6356
0.6400	118100	3.0957
0.6405	118200	3.6617
0.6411	118300	3.5059
0.6416	118400	3.6604
0.6422	118500	3.3308
0.6427	118600	3.3809
0.6432	118700	3.3166
0.6438	118800	3.3976
0.6443	118900	2.9875
0.6449	119000	3.0276
0.6454	119100	3.2352
0.6460	119200	3.2801
0.6465	119300	3.5469
0.6470	119400	3.6728
0.6476	119500	3.4313
0.6481	119600	3.0557
0.6487	119700	3.4277
0.6492	119800	3.3998
0.6497	119900	3.1745
0.6503	120000	3.4574
0.6508	120100	3.5468
0.6514	120200	3.3714
0.6519	120300	3.6838
0.6525	120400	3.2443
0.6530	120500	3.6654
0.6535	120600	3.4297
0.6541	120700	3.1541
0.6546	120800	3.4896
0.6552	120900	3.2221
0.6557	121000	3.1198
0.6563	121100	3.2539
0.6568	121200	3.2768
0.6573	121300	3.1839
0.6579	121400	3.3451
0.6584	121500	2.9836
0.6590	121600	3.4351
0.6595	121700	3.345
0.6600	121800	3.5986
0.6606	121900	3.1687
0.6611	122000	3.479
0.6617	122100	3.174
0.6622	122200	3.103
0.6628	122300	3.2047
0.6633	122400	3.2446
0.6638	122500	3.6554
0.6644	122600	3.5421
0.6649	122700	3.5521
0.6655	122800	3.6626
0.6660	122900	3.5789
0.6665	123000	3.1722
0.6671	123100	3.3325
0.6676	123200	3.93
0.6682	123300	3.1377
0.6687	123400	3.307
0.6693	123500	3.5206
0.6698	123600	3.6733
0.6703	123700	3.2924
0.6709	123800	3.3679
0.6714	123900	3.6824
0.6720	124000	3.9605
0.6725	124100	3.1717
0.6731	124200	3.0251
0.6736	124300	3.7617
0.6741	124400	3.3756
0.6747	124500	3.124
0.6752	124600	3.4108
0.6758	124700	3.1123
0.6763	124800	3.2929
0.6768	124900	3.2095
0.6774	125000	3.1804
0.6779	125100	3.0729
0.6785	125200	3.3301
0.6790	125300	3.6008
0.6796	125400	3.5819
0.6801	125500	3.4775
0.6806	125600	3.1594
0.6812	125700	3.1797
0.6817	125800	3.4262
0.6823	125900	3.2147
0.6828	126000	3.5528
0.6833	126100	3.0052
0.6839	126200	3.4204
0.6844	126300	3.8715
0.6850	126400	3.2574
0.6855	126500	3.6036
0.6861	126600	3.2093
0.6866	126700	3.566
0.6871	126800	3.2278
0.6877	126900	3.0335
0.6882	127000	3.4272
0.6888	127100	3.1594
0.6893	127200	3.2463
0.6898	127300	3.4236
0.6904	127400	3.4258
0.6909	127500	3.5301
0.6915	127600	3.1195
0.6920	127700	3.2412
0.6926	127800	3.1337
0.6931	127900	3.3331
0.6936	128000	2.891
0.6942	128100	2.8244
0.6947	128200	3.534
0.6953	128300	3.2359
0.6958	128400	3.4355
0.6964	128500	3.3455
0.6969	128600	3.2261
0.6974	128700	3.143
0.6980	128800	3.6865
0.6985	128900	3.5394
0.6991	129000	3.2577
0.6996	129100	3.4539
0.7001	129200	3.5311
0.7007	129300	3.3394
0.7012	129400	3.4756
0.7018	129500	2.9628
0.7023	129600	3.1284
0.7029	129700	3.4909
0.7034	129800	3.6164
0.7039	129900	3.495
0.7045	130000	3.5738
0.7050	130100	3.4743
0.7056	130200	3.1248
0.7061	130300	3.1564
0.7066	130400	3.1595
0.7072	130500	3.2934
0.7077	130600	3.2583
0.7083	130700	3.4107
0.7088	130800	3.5654
0.7094	130900	3.4632
0.7099	131000	3.4652
0.7104	131100	3.5543
0.7110	131200	3.1132
0.7115	131300	3.5088
0.7121	131400	3.358
0.7126	131500	3.2021
0.7132	131600	3.1255
0.7137	131700	3.0459
0.7142	131800	3.7834
0.7148	131900	3.0052
0.7153	132000	3.2767
0.7159	132100	3.2148
0.7164	132200	3.1788
0.7169	132300	3.1347
0.7175	132400	3.4635
0.7180	132500	3.3869
0.7186	132600	3.4435
0.7191	132700	3.0172
0.7197	132800	3.2019
0.7202	132900	3.1783
0.7207	133000	3.0891
0.7213	133100	2.9984
0.7218	133200	3.3646
0.7224	133300	3.2097
0.7229	133400	3.0814
0.7234	133500	3.1455
0.7240	133600	3.3513
0.7245	133700	3.0279
0.7251	133800	3.0552
0.7256	133900	3.4412
0.7262	134000	3.1844
0.7267	134100	3.1574
0.7272	134200	3.3107
0.7278	134300	3.3579
0.7283	134400	3.5498
0.7289	134500	3.5803
0.7294	134600	3.0485
0.7300	134700	3.5337
0.7305	134800	3.4798
0.7310	134900	3.1844
0.7316	135000	3.0767
0.7321	135100	3.3411
0.7327	135200	3.2132
0.7332	135300	3.4714
0.7337	135400	3.4099
0.7343	135500	3.6163
0.7348	135600	3.268
0.7354	135700	3.8397
0.7359	135800	3.2165
0.7365	135900	3.1147
0.7370	136000	3.0786
0.7375	136100	3.5466
0.7381	136200	3.1344
0.7386	136300	3.3048
0.7392	136400	3.2942
0.7397	136500	3.0843
0.7402	136600	3.2621
0.7408	136700	3.2517
0.7413	136800	3.5453
0.7419	136900	3.36
0.7424	137000	3.1883
0.7430	137100	3.2586
0.7435	137200	3.552
0.7440	137300	3.7281
0.7446	137400	3.0788
0.7451	137500	3.5501
0.7457	137600	3.3055
0.7462	137700	3.1211
0.7467	137800	2.9643
0.7473	137900	3.4225
0.7478	138000	3.4318
0.7484	138100	2.7126
0.7489	138200	3.0497
0.7495	138300	3.1348
0.7500	138400	3.472
0.7505	138500	3.2579
0.7511	138600	3.1062
0.7516	138700	3.4513
0.7522	138800	3.3853
0.7527	138900	3.5489
0.7533	139000	3.494
0.7538	139100	3.4054
0.7543	139200	3.2216
0.7549	139300	2.9779
0.7554	139400	3.2457
0.7560	139500	3.1762
0.7565	139600	3.485
0.7570	139700	3.6004
0.7576	139800	3.2562
0.7581	139900	3.0896
0.7587	140000	3.1
0.7592	140100	3.1869
0.7598	140200	2.947
0.7603	140300	2.9815
0.7608	140400	3.0865
0.7614	140500	3.6242
0.7619	140600	3.0755
0.7625	140700	3.1267
0.7630	140800	3.4261
0.7635	140900	3.7278
0.7641	141000	3.0543
0.7646	141100	3.3274
0.7652	141200	3.2724
0.7657	141300	3.0682
0.7663	141400	3.3453
0.7668	141500	3.2054
0.7673	141600	3.2597
0.7679	141700	3.6984
0.7684	141800	3.9943
0.7690	141900	3.1533
0.7695	142000	2.8898
0.7701	142100	3.4417
0.7706	142200	3.2123
0.7711	142300	3.2252
0.7717	142400	3.2441
0.7722	142500	2.8276
0.7728	142600	3.1527
0.7733	142700	3.4542
0.7738	142800	3.0155
0.7744	142900	3.3503
0.7749	143000	3.2277
0.7755	143100	2.8427
0.7760	143200	3.2711
0.7766	143300	3.0526
0.7771	143400	3.2197
0.7776	143500	3.1206
0.7782	143600	3.3727
0.7787	143700	3.5835
0.7793	143800	2.8325
0.7798	143900	3.1727
0.7803	144000	3.198
0.7809	144100	3.2053
0.7814	144200	3.0351
0.7820	144300	3.0664
0.7825	144400	3.2668
0.7831	144500	3.3261
0.7836	144600	3.3455
0.7841	144700	3.0796
0.7847	144800	3.3018
0.7852	144900	3.3409
0.7858	145000	3.2944
0.7863	145100	3.2288
0.7869	145200	3.1612
0.7874	145300	3.406
0.7879	145400	3.0187
0.7885	145500	3.3711
0.7890	145600	3.0728
0.7896	145700	3.3726
0.7901	145800	3.1705
0.7906	145900	3.6521
0.7912	146000	3.2295
0.7917	146100	3.2753
0.7923	146200	3.1428
0.7928	146300	3.7985
0.7934	146400	3.4198
0.7939	146500	3.1451
0.7944	146600	3.0505
0.7950	146700	3.0804
0.7955	146800	3.3127
0.7961	146900	3.4452
0.7966	147000	3.4518
0.7971	147100	3.1878
0.7977	147200	3.3758
0.7982	147300	3.4727
0.7988	147400	2.8236
0.7993	147500	3.2246
0.7999	147600	3.0481
0.8004	147700	3.385
0.8009	147800	3.3194
0.8015	147900	3.2403
0.8020	148000	3.0962
0.8026	148100	3.236
0.8031	148200	2.9968
0.8037	148300	3.2009
0.8042	148400	3.0386
0.8047	148500	3.0525
0.8053	148600	2.9943
0.8058	148700	3.2521
0.8064	148800	3.4048
0.8069	148900	3.5249
0.8074	149000	3.5672
0.8080	149100	3.3375
0.8085	149200	3.5308
0.8091	149300	3.1285
0.8096	149400	3.1743
0.8102	149500	3.0593
0.8107	149600	3.2635
0.8112	149700	3.3098
0.8118	149800	3.3456
0.8123	149900	3.5673
0.8129	150000	3.1959
0.8134	150100	3.4005
0.8139	150200	3.2055
0.8145	150300	3.034
0.8150	150400	3.4356
0.8156	150500	3.1474
0.8161	150600	3.146
0.8167	150700	3.3901
0.8172	150800	3.1172
0.8177	150900	3.3141
0.8183	151000	3.0396
0.8188	151100	3.4759
0.8194	151200	2.9189
0.8199	151300	3.3257
0.8204	151400	3.4571
0.8210	151500	3.0005
0.8215	151600	3.6512
0.8221	151700	3.3594
0.8226	151800	3.0909
0.8232	151900	3.355
0.8237	152000	3.4065
0.8242	152100	3.4222
0.8248	152200	3.2925
0.8253	152300	3.0262
0.8259	152400	3.0378
0.8264	152500	3.5207
0.8270	152600	3.7913
0.8275	152700	3.308
0.8280	152800	3.1095
0.8286	152900	3.47
0.8291	153000	3.2978
0.8297	153100	3.1286
0.8302	153200	3.2735
0.8307	153300	3.0771
0.8313	153400	3.2284
0.8318	153500	2.9624
0.8324	153600	3.3703
0.8329	153700	3.1799
0.8335	153800	3.2191
0.8340	153900	3.3137
0.8345	154000	3.1934
0.8351	154100	2.9709
0.8356	154200	3.1411
0.8362	154300	3.2359
0.8367	154400	3.055
0.8372	154500	3.2281
0.8378	154600	3.0409
0.8383	154700	3.1096
0.8389	154800	3.3166
0.8394	154900	3.3937
0.8400	155000	3.0906
0.8405	155100	3.1442
0.8410	155200	3.2257
0.8416	155300	2.9401
0.8421	155400	3.0654
0.8427	155500	3.5238
0.8432	155600	3.22
0.8438	155700	3.322
0.8443	155800	2.9993
0.8448	155900	2.895
0.8454	156000	3.2968
0.8459	156100	3.2749
0.8465	156200	2.9879
0.8470	156300	3.0704
0.8475	156400	2.969
0.8481	156500	3.3459
0.8486	156600	2.9822
0.8492	156700	2.6717
0.8497	156800	3.2806
0.8503	156900	3.5237
0.8508	157000	3.145
0.8513	157100	2.9657
0.8519	157200	3.2746
0.8524	157300	3.0232
0.8530	157400	3.3251
0.8535	157500	3.1005
0.8540	157600	3.0998
0.8546	157700	3.7319
0.8551	157800	3.2986
0.8557	157900	3.0439
0.8562	158000	3.283
0.8568	158100	2.853
0.8573	158200	3.4784
0.8578	158300	3.0626
0.8584	158400	3.1513
0.8589	158500	3.0697
0.8595	158600	3.1105
0.8600	158700	3.1415
0.8606	158800	3.3807
0.8611	158900	3.5387
0.8616	159000	3.2331
0.8622	159100	3.2941
0.8627	159200	3.1606
0.8633	159300	3.2185
0.8638	159400	3.3263
0.8643	159500	3.5841
0.8649	159600	3.3709
0.8654	159700	3.0362
0.8660	159800	3.0162
0.8665	159900	3.2266
0.8671	160000	3.1824
0.8676	160100	3.0351
0.8681	160200	2.9043
0.8687	160300	3.4886
0.8692	160400	3.0111
0.8698	160500	3.3646
0.8703	160600	3.3872
0.8708	160700	3.0493
0.8714	160800	3.2207
0.8719	160900	3.1572
0.8725	161000	3.1965
0.8730	161100	3.3083
0.8736	161200	3.14
0.8741	161300	3.2157
0.8746	161400	3.4561
0.8752	161500	3.3445
0.8757	161600	3.1781
0.8763	161700	3.5122
0.8768	161800	3.0163
0.8773	161900	3.2686
0.8779	162000	3.3872
0.8784	162100	3.4208
0.8790	162200	3.1076
0.8795	162300	3.2695
0.8801	162400	2.9025
0.8806	162500	3.4799
0.8811	162600	3.2383
0.8817	162700	2.7267
0.8822	162800	2.8283
0.8828	162900	3.2342
0.8833	163000	2.9388
0.8839	163100	3.4665
0.8844	163200	3.5253
0.8849	163300	3.0918
0.8855	163400	3.4876
0.8860	163500	3.4672
0.8866	163600	3.4052
0.8871	163700	3.0693
0.8876	163800	3.2803
0.8882	163900	2.9534
0.8887	164000	3.2656
0.8893	164100	3.1105
0.8898	164200	3.2322
0.8904	164300	3.1508
0.8909	164400	3.589
0.8914	164500	3.1528
0.8920	164600	3.2731
0.8925	164700	3.3316
0.8931	164800	2.9479
0.8936	164900	3.2115
0.8941	165000	3.3269
0.8947	165100	3.4516
0.8952	165200	3.3265
0.8958	165300	3.3281
0.8963	165400	3.2163
0.8969	165500	3.581
0.8974	165600	2.883
0.8979	165700	3.0067
0.8985	165800	3.4344
0.8990	165900	3.2981
0.8996	166000	3.2013
0.9001	166100	3.4961
0.9007	166200	3.0626
0.9012	166300	3.0138
0.9017	166400	3.3943
0.9023	166500	2.8706
0.9028	166600	3.2617
0.9034	166700	3.1115
0.9039	166800	2.9659
0.9044	166900	3.2144
0.9050	167000	3.0437
0.9055	167100	3.3251
0.9061	167200	3.1546
0.9066	167300	3.389
0.9072	167400	2.7039
0.9077	167500	3.108
0.9082	167600	3.6636
0.9088	167700	3.391
0.9093	167800	3.2681
0.9099	167900	2.9661
0.9104	168000	2.8874
0.9109	168100	3.3192
0.9115	168200	3.4738
0.9120	168300	2.9315
0.9126	168400	3.2757
0.9131	168500	3.2578
0.9137	168600	3.2513
0.9142	168700	3.0851
0.9147	168800	3.4173
0.9153	168900	3.4197
0.9158	169000	3.6811
0.9164	169100	3.1855
0.9169	169200	2.8815
0.9175	169300	3.2274
0.9180	169400	3.2403
0.9185	169500	3.3268
0.9191	169600	2.9281
0.9196	169700	2.881
0.9202	169800	3.0508
0.9207	169900	3.2189
0.9212	170000	3.0684
0.9218	170100	2.815
0.9223	170200	3.1584
0.9229	170300	3.2579
0.9234	170400	2.9662
0.9240	170500	3.1022
0.9245	170600	3.3529
0.9250	170700	2.9866
0.9256	170800	2.9475
0.9261	170900	3.1559
0.9267	171000	3.3216
0.9272	171100	3.0198
0.9277	171200	3.2216
0.9283	171300	3.1575
0.9288	171400	3.1467
0.9294	171500	3.5797
0.9299	171600	3.031
0.9305	171700	2.824
0.9310	171800	2.9834
0.9315	171900	3.1226
0.9321	172000	3.309
0.9326	172100	2.8803
0.9332	172200	3.3146
0.9337	172300	3.2103
0.9343	172400	3.0241
0.9348	172500	2.9831
0.9353	172600	3.3433
0.9359	172700	3.3195
0.9364	172800	3.2588
0.9370	172900	2.955
0.9375	173000	3.2502
0.9380	173100	3.0857
0.9386	173200	2.8721
0.9391	173300	3.2819
0.9397	173400	3.0992
0.9402	173500	3.0957
0.9408	173600	3.4065
0.9413	173700	2.8785
0.9418	173800	3.2812
0.9424	173900	3.3455
0.9429	174000	3.5245
0.9435	174100	3.5042
0.9440	174200	3.556
0.9445	174300	3.2157
0.9451	174400	3.6199
0.9456	174500	3.1146
0.9462	174600	3.1254
0.9467	174700	3.2354
0.9473	174800	3.0019
0.9478	174900	2.8214
0.9483	175000	3.0281
0.9489	175100	3.4673
0.9494	175200	2.802
0.9500	175300	3.0901
0.9505	175400	2.9828
0.9510	175500	3.0826
0.9516	175600	3.0931
0.9521	175700	3.3265
0.9527	175800	3.2094
0.9532	175900	3.1358
0.9538	176000	3.0766
0.9543	176100	2.9093
0.9548	176200	3.2949
0.9554	176300	3.026
0.9559	176400	3.1993
0.9565	176500	2.95
0.9570	176600	3.3536
0.9576	176700	2.9881
0.9581	176800	2.9867
0.9586	176900	2.9683
0.9592	177000	3.399
0.9597	177100	3.2681
0.9603	177200	3.2867
0.9608	177300	3.3481
0.9613	177400	2.8614
0.9619	177500	2.9165
0.9624	177600	3.1196
0.9630	177700	3.405
0.9635	177800	3.5663
0.9641	177900	3.614
0.9646	178000	3.2619
0.9651	178100	3.0277
0.9657	178200	3.2065
0.9662	178300	2.9517
0.9668	178400	3.5181
0.9673	178500	3.155
0.9678	178600	3.469
0.9684	178700	2.7691
0.9689	178800	3.4813
0.9695	178900	2.7654
0.9700	179000	2.8796
0.9706	179100	3.2869
0.9711	179200	3.01
0.9716	179300	3.083
0.9722	179400	3.0221
0.9727	179500	3.031
0.9733	179600	2.8592
0.9738	179700	3.4517
0.9744	179800	3.488
0.9749	179900	2.8486
0.9754	180000	3.3052
0.9760	180100	3.1744
0.9765	180200	3.2268
0.9771	180300	3.3369
0.9776	180400	2.9355
0.9781	180500	2.8209
0.9787	180600	3.2439
0.9792	180700	3.1028
0.9798	180800	3.1529
0.9803	180900	3.2019
0.9809	181000	3.3342
0.9814	181100	3.1716
0.9819	181200	3.3653
0.9825	181300	3.0301
0.9830	181400	2.8226
0.9836	181500	2.9854
0.9841	181600	3.1437
0.9846	181700	2.9811
0.9852	181800	3.0661
0.9857	181900	2.9654
0.9863	182000	3.4336
0.9868	182100	3.145
0.9874	182200	3.2862
0.9879	182300	2.9259
0.9884	182400	2.9176
0.9890	182500	3.14
0.9895	182600	3.3093
0.9901	182700	2.9282
0.9906	182800	2.8749
0.9912	182900	3.3875
0.9917	183000	3.1913
0.9922	183100	3.7078
0.9928	183200	3.221
0.9933	183300	3.0353
0.9939	183400	3.1991
0.9944	183500	3.191
0.9949	183600	3.0857
0.9955	183700	2.6309
0.9960	183800	3.111
0.9966	183900	2.7691
0.9971	184000	3.0386
0.9977	184100	3.1654
0.9982	184200	3.3117
0.9987	184300	2.9471
0.9993	184400	3.0435
0.9998	184500	2.8474

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},
}