This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2 on the pairs_with_scores_v31 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_v31
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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'cinnamon apple dessert',
    'looking through the window oversized hoodie last piece, fashion casual wear top sweatshirt, women hoodie winter hoodie cotton hoodie evil eye hoodie oversized hoodie hoodie looking through the window hoodie, hoodie looking through the window hoodie oversized hoodie, gender women design kaf generic hoodie looking through the window m - l features reflective sleeve style long fit oversized cotton black evil eye season winter, model wears medium oversizedi m protected. i m safe. i m secured. evil eye. 100 cotton model wears medium oversized weight 55 height 162',
    '100 men s 1.5 mm neoprene shorty surfing wetsuit - navy blue, surfing shorty suit surfing suit for warm water surfing shorty suit for bodyboarding surfing suit water sports wetsuit, wetsuit, gender men decathlon generic wetsuit xs cut shorty neoprene navy blue activity surfing numeric 1.5 mm target group men sport surfing fashion style shorty, our team has developed a wetsuit that s particularly suited to summer surfing and bodyboarding in waters warmer than 21 c. a surfing shorty wetsuit that s particularly suited to discovering the gliding experience on a surfboard or bodyboard in water warmer than 21 c.',
]
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, -0.1591, -0.1914],
#         [-0.1591,  1.0000,  0.0767],
#         [-0.1914,  0.0767,  1.0000]])

Training Details

Training Dataset

pairs_with_scores_v31

Dataset: pairs_with_scores_v31 at 73de461
Size: 19,761,179 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: 5.33 tokens
max: 22 tokens
min: 4 tokens
mean: 82.95 tokens
max: 256 tokens
min: 0.0
mean: 0.05
max: 1.0

Samples:

sentence1	sentence2	score
`nail polish`	`strawberry yogurt, yogurt drink strawberry yogurt yogurt nan strawberry nan strawberry yoghurt yoghurt, strawberry yogurt yogurt nan strawberry nan strawberry yoghurt yoghurt`	`0.0`
`racket`	`super sexy dress, fashion designer wear dress dress, women dress red dress dress sexy dress super sexy dress, dress sexy dress super sexy dress, gender women decollete boutique generic dress super sexy s red, red dress.`	`0.0`
`duvet`	`golden chinese tweezers, golden tweezers tweezers`	`0.0`

Loss: CoSENTLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "pairwise_cos_sim" 4}

Evaluation Dataset

pairs_with_scores_v31

Dataset: pairs_with_scores_v31 at 73de461
Size: 99,303 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: 5.37 tokens
max: 21 tokens
min: 10 tokens
mean: 80.08 tokens
max: 256 tokens
min: 0.0
mean: 0.05
max: 1.0

Samples:

sentence1	sentence2	score
`uriage micellar water`	`uriage thermal micellar water oily skin 250 ml, micellar water micellar water oily skin thermal micellar water oily skin uriage uriage micellar water, units 250 millilitre`	`1.0`
`bag snorkelling mask`	`aloe eva hair oil 100 ml aloe vera, aloe eva aloe eva hair oil aloe eva hair oil aloe vera hair oil hair oil aloe vera aloe eva hair zet aloe eva hair zet aloe vera hair zet hair zet aloe vera, units 100 millilitre`	`0.0`
`red hair curler`	`complavin 75 mg/100 mg 30/f.c.tab, complavin complavin tablets, units 0.075 gram 0.1 gram`	`0.0`

Loss: CoSENTLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "pairwise_cos_sim" 4}

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	1.5823
0.0013	200	1.4636
0.0019	300	1.376
0.0026	400	1.3
0.0032	500	1.0896
0.0039	600	1.5663
0.0045	700	0.8602
0.0052	800	0.9834
0.0058	900	1.0634
0.0065	1000	0.9649
0.0071	1100	0.8329
0.0078	1200	0.6398
0.0084	1300	0.7543
0.0091	1400	0.5512
0.0097	1500	0.6766
0.0104	1600	0.8817
0.0110	1700	0.6294
0.0117	1800	0.833
0.0123	1900	0.6526
0.0130	2000	0.5499
0.0136	2100	0.6342
0.0143	2200	0.6776
0.0149	2300	0.4318
0.0155	2400	0.4701
0.0162	2500	0.549
0.0168	2600	0.5486
0.0175	2700	0.4827
0.0181	2800	0.4634
0.0188	2900	0.4971
0.0194	3000	0.5619
0.0201	3100	0.4267
0.0207	3200	0.6918
0.0214	3300	0.4517
0.0220	3400	0.3933
0.0227	3500	0.4665
0.0233	3600	0.3437
0.0240	3700	0.4848
0.0246	3800	0.3165
0.0253	3900	0.4933
0.0259	4000	0.2126
0.0266	4100	0.5308
0.0272	4200	0.3926
0.0279	4300	0.2848
0.0285	4400	0.2725
0.0291	4500	0.2844
0.0298	4600	0.2951
0.0304	4700	0.4632
0.0311	4800	0.2545
0.0317	4900	0.2843
0.0324	5000	0.3071
0.0330	5100	0.3323
0.0337	5200	0.3086
0.0343	5300	0.4073
0.0350	5400	0.3946
0.0356	5500	0.5159
0.0363	5600	0.2412
0.0369	5700	0.2137
0.0376	5800	0.1762
0.0382	5900	0.1919
0.0389	6000	0.3527
0.0395	6100	0.1943
0.0402	6200	0.2353
0.0408	6300	0.1732
0.0415	6400	0.3741
0.0421	6500	0.1358
0.0428	6600	0.1606
0.0434	6700	0.2179
0.0440	6800	0.1781
0.0447	6900	0.2368
0.0453	7000	0.2294
0.0460	7100	0.2606
0.0466	7200	0.2907
0.0473	7300	0.2124
0.0479	7400	0.1637
0.0486	7500	0.2347
0.0492	7600	0.2317
0.0499	7700	0.1694
0.0505	7800	0.1558
0.0512	7900	0.222
0.0518	8000	0.0909
0.0525	8100	0.2385
0.0531	8200	0.1299
0.0538	8300	0.1353
0.0544	8400	0.1487
0.0551	8500	0.1242
0.0557	8600	0.2009
0.0564	8700	0.1409
0.0570	8800	0.2487
0.0576	8900	0.1793
0.0583	9000	0.1498
0.0589	9100	0.0823
0.0596	9200	0.1602
0.0602	9300	0.1206
0.0609	9400	0.1595
0.0615	9500	0.1784
0.0622	9600	0.11
0.0628	9700	0.1678
0.0635	9800	0.2087
0.0641	9900	0.1403
0.0648	10000	0.1794
0.0654	10100	0.1609
0.0661	10200	0.2599
0.0667	10300	0.0528
0.0674	10400	0.1181
0.0680	10500	0.1728
0.0687	10600	0.182
0.0693	10700	0.1202
0.0700	10800	0.099
0.0706	10900	0.2058
0.0713	11000	0.0741
0.0719	11100	0.1938
0.0725	11200	0.1493
0.0732	11300	0.2292
0.0738	11400	0.0673
0.0745	11500	0.1716
0.0751	11600	0.1337
0.0758	11700	0.1632
0.0764	11800	0.1872
0.0771	11900	0.1539
0.0777	12000	0.1162
0.0784	12100	0.1431
0.0790	12200	0.0873
0.0797	12300	0.0796
0.0803	12400	0.1551
0.0810	12500	0.0928
0.0816	12600	0.0907
0.0823	12700	0.0756
0.0829	12800	0.1325
0.0836	12900	0.1563
0.0842	13000	0.1868
0.0849	13100	0.231
0.0855	13200	0.0871
0.0861	13300	0.1509
0.0868	13400	0.0833
0.0874	13500	0.1274
0.0881	13600	0.0927
0.0887	13700	0.0812
0.0894	13800	0.078
0.0900	13900	0.1386
0.0907	14000	0.0857
0.0913	14100	0.0747
0.0920	14200	0.1646
0.0926	14300	0.1615
0.0933	14400	0.0752
0.0939	14500	0.0864
0.0946	14600	0.0822
0.0952	14700	0.152
0.0959	14800	0.0663
0.0965	14900	0.1123
0.0972	15000	0.1288
0.0978	15100	0.0771
0.0985	15200	0.1117
0.0991	15300	0.1039
0.0998	15400	0.0886
0.1004	15500	0.0769
0.1010	15600	0.0551
0.1017	15700	0.1388
0.1023	15800	0.0728
0.1030	15900	0.1526
0.1036	16000	0.1342
0.1043	16100	0.0744
0.1049	16200	0.1066
0.1056	16300	0.0438
0.1062	16400	0.0375
0.1069	16500	0.1356
0.1075	16600	0.1788
0.1082	16700	0.0617
0.1088	16800	0.0572
0.1095	16900	0.0542
0.1101	17000	0.0639
0.1108	17100	0.1582
0.1114	17200	0.0278
0.1121	17300	0.157
0.1127	17400	0.0378
0.1134	17500	0.037
0.1140	17600	0.0561
0.1146	17700	0.1092
0.1153	17800	0.1111
0.1159	17900	0.0246
0.1166	18000	0.0382
0.1172	18100	0.1529
0.1179	18200	0.063
0.1185	18300	0.0261
0.1192	18400	0.0417
0.1198	18500	0.148
0.1205	18600	0.1095
0.1211	18700	0.0637
0.1218	18800	0.0496
0.1224	18900	0.0656
0.1231	19000	0.0486
0.1237	19100	0.0618
0.1244	19200	0.1072
0.1250	19300	0.0895
0.1257	19400	0.1324
0.1263	19500	0.1132
0.1270	19600	0.024
0.1276	19700	0.0804
0.1283	19800	0.1699
0.1289	19900	0.0769
0.1295	20000	0.0616
0.1302	20100	0.1174
0.1308	20200	0.0471
0.1315	20300	0.0354
0.1321	20400	0.0685
0.1328	20500	0.093
0.1334	20600	0.0523
0.1341	20700	0.0963
0.1347	20800	0.0664
0.1354	20900	0.0654
0.1360	21000	0.0513
0.1367	21100	0.1174
0.1373	21200	0.1877
0.1380	21300	0.0702
0.1386	21400	0.0394
0.1393	21500	0.0918
0.1399	21600	0.1654
0.1406	21700	0.0795
0.1412	21800	0.1238
0.1419	21900	0.1017
0.1425	22000	0.0387
0.1431	22100	0.1049
0.1438	22200	0.0386
0.1444	22300	0.0687
0.1451	22400	0.0379
0.1457	22500	0.0451
0.1464	22600	0.0403
0.1470	22700	0.0383
0.1477	22800	0.1026
0.1483	22900	0.1465
0.1490	23000	0.0958
0.1496	23100	0.0812
0.1503	23200	0.0196
0.1509	23300	0.1433
0.1516	23400	0.0785
0.1522	23500	0.0871
0.1529	23600	0.0531
0.1535	23700	0.0586
0.1542	23800	0.0683
0.1548	23900	0.0757
0.1555	24000	0.0285
0.1561	24100	0.1305
0.1568	24200	0.0341
0.1574	24300	0.1278
0.1580	24400	0.0662
0.1587	24500	0.1141
0.1593	24600	0.0744
0.1600	24700	0.0576
0.1606	24800	0.0115
0.1613	24900	0.0524
0.1619	25000	0.0551
0.1626	25100	0.1077
0.1632	25200	0.1331
0.1639	25300	0.0345
0.1645	25400	0.0653
0.1652	25500	0.093
0.1658	25600	0.0841
0.1665	25700	0.0621
0.1671	25800	0.0617
0.1678	25900	0.1153
0.1684	26000	0.0323
0.1691	26100	0.1369
0.1697	26200	0.0786
0.1704	26300	0.0884
0.1710	26400	0.0905
0.1716	26500	0.0964
0.1723	26600	0.0789
0.1729	26700	0.0317
0.1736	26800	0.0364
0.1742	26900	0.0182
0.1749	27000	0.1379
0.1755	27100	0.1186
0.1762	27200	0.061
0.1768	27300	0.0431
0.1775	27400	0.0643
0.1781	27500	0.03
0.1788	27600	0.0814
0.1794	27700	0.0915
0.1801	27800	0.0152
0.1807	27900	0.0258
0.1814	28000	0.0363
0.1820	28100	0.0634
0.1827	28200	0.0306
0.1833	28300	0.1996
0.1840	28400	0.0652
0.1846	28500	0.0388
0.1853	28600	0.0669
0.1859	28700	0.0397
0.1865	28800	0.0726
0.1872	28900	0.0593
0.1878	29000	0.0663
0.1885	29100	0.084
0.1891	29200	0.0425
0.1898	29300	0.0412
0.1904	29400	0.0731
0.1911	29500	0.0596
0.1917	29600	0.0423
0.1924	29700	0.0222
0.1930	29800	0.0761
0.1937	29900	0.0434
0.1943	30000	0.0167
0.1950	30100	0.0915
0.1956	30200	0.0785
0.1963	30300	0.0417
0.1969	30400	0.0156
0.1976	30500	0.0163
0.1982	30600	0.0465
0.1989	30700	0.0397
0.1995	30800	0.0395
0.2001	30900	0.1167
0.2008	31000	0.0672
0.2014	31100	0.0284
0.2021	31200	0.0323
0.2027	31300	0.0227
0.2034	31400	0.02
0.2040	31500	0.0142
0.2047	31600	0.056
0.2053	31700	0.0508
0.2060	31800	0.111
0.2066	31900	0.0218
0.2073	32000	0.0107
0.2079	32100	0.0391
0.2086	32200	0.0428
0.2092	32300	0.0093
0.2099	32400	0.0195
0.2105	32500	0.0533
0.2112	32600	0.0182
0.2118	32700	0.0914
0.2125	32800	0.07
0.2131	32900	0.0767
0.2138	33000	0.03
0.2144	33100	0.124
0.2150	33200	0.0608
0.2157	33300	0.1232
0.2163	33400	0.1309
0.2170	33500	0.0083
0.2176	33600	0.0311
0.2183	33700	0.0576
0.2189	33800	0.0962
0.2196	33900	0.0403
0.2202	34000	0.1385
0.2209	34100	0.0873
0.2215	34200	0.0687
0.2222	34300	0.0375
0.2228	34400	0.0238
0.2235	34500	0.0781
0.2241	34600	0.0505
0.2248	34700	0.0603
0.2254	34800	0.0454
0.2261	34900	0.0145
0.2267	35000	0.0523
0.2274	35100	0.0201
0.2280	35200	0.0936
0.2286	35300	0.0775
0.2293	35400	0.0603
0.2299	35500	0.0181
0.2306	35600	0.0645
0.2312	35700	0.0595
0.2319	35800	0.0202
0.2325	35900	0.0177
0.2332	36000	0.0208
0.2338	36100	0.0317
0.2345	36200	0.0264
0.2351	36300	0.0245
0.2358	36400	0.0333
0.2364	36500	0.0867
0.2371	36600	0.0696
0.2377	36700	0.0649
0.2384	36800	0.0653
0.2390	36900	0.0216
0.2397	37000	0.0278
0.2403	37100	0.049
0.2410	37200	0.0327
0.2416	37300	0.0383
0.2423	37400	0.0513
0.2429	37500	0.0269
0.2435	37600	0.0095
0.2442	37700	0.0301
0.2448	37800	0.038
0.2455	37900	0.023
0.2461	38000	0.0547
0.2468	38100	0.0712
0.2474	38200	0.0166
0.2481	38300	0.0629
0.2487	38400	0.0145
0.2494	38500	0.0304
0.2500	38600	0.0358
0.2507	38700	0.0216
0.2513	38800	0.0141
0.2520	38900	0.0284
0.2526	39000	0.0384
0.2533	39100	0.0214
0.2539	39200	0.0099
0.2546	39300	0.047
0.2552	39400	0.0743
0.2559	39500	0.0279
0.2565	39600	0.0264
0.2571	39700	0.1151
0.2578	39800	0.0448
0.2584	39900	0.0425
0.2591	40000	0.0171
0.2597	40100	0.0299
0.2604	40200	0.041
0.2610	40300	0.0297
0.2617	40400	0.0323
0.2623	40500	0.0156
0.2630	40600	0.0309
0.2636	40700	0.0585
0.2643	40800	0.0497
0.2649	40900	0.0254
0.2656	41000	0.0346
0.2662	41100	0.093
0.2669	41200	0.0111
0.2675	41300	0.0363
0.2682	41400	0.0192
0.2688	41500	0.0289
0.2695	41600	0.0434
0.2701	41700	0.0462
0.2708	41800	0.021
0.2714	41900	0.0279
0.2720	42000	0.016
0.2727	42100	0.0135
0.2733	42200	0.0422
0.2740	42300	0.013
0.2746	42400	0.0182
0.2753	42500	0.0723
0.2759	42600	0.0353
0.2766	42700	0.043
0.2772	42800	0.0081
0.2779	42900	0.0276
0.2785	43000	0.0184
0.2792	43100	0.0244
0.2798	43200	0.0079
0.2805	43300	0.0258
0.2811	43400	0.0174
0.2818	43500	0.0524
0.2824	43600	0.0127
0.2831	43700	0.0518
0.2837	43800	0.0459
0.2844	43900	0.0511
0.2850	44000	0.005
0.2856	44100	0.0182
0.2863	44200	0.0222
0.2869	44300	0.0534
0.2876	44400	0.044
0.2882	44500	0.0316
0.2889	44600	0.0249
0.2895	44700	0.0123
0.2902	44800	0.0111
0.2908	44900	0.0246
0.2915	45000	0.0114
0.2921	45100	0.0112
0.2928	45200	0.0487
0.2934	45300	0.0066
0.2941	45400	0.0358
0.2947	45500	0.0669
0.2954	45600	0.0221
0.2960	45700	0.0328
0.2967	45800	0.0281
0.2973	45900	0.0528
0.2980	46000	0.0413
0.2986	46100	0.0349
0.2993	46200	0.0097
0.2999	46300	0.0111
0.3005	46400	0.0254
0.3012	46500	0.0299
0.3018	46600	0.0189
0.3025	46700	0.0438
0.3031	46800	0.0302
0.3038	46900	0.0095
0.3044	47000	0.0246
0.3051	47100	0.0086
0.3057	47200	0.0617
0.3064	47300	0.0573
0.3070	47400	0.0209
0.3077	47500	0.0452
0.3083	47600	0.0626
0.3090	47700	0.0086
0.3096	47800	0.0191
0.3103	47900	0.0098
0.3109	48000	0.0589
0.3116	48100	0.0338
0.3122	48200	0.0578
0.3129	48300	0.0639
0.3135	48400	0.0171
0.3141	48500	0.0177
0.3148	48600	0.0681
0.3154	48700	0.0093
0.3161	48800	0.0264
0.3167	48900	0.007
0.3174	49000	0.0144
0.3180	49100	0.0221
0.3187	49200	0.0094
0.3193	49300	0.0191
0.3200	49400	0.0525
0.3206	49500	0.0179
0.3213	49600	0.0656
0.3219	49700	0.0202
0.3226	49800	0.0124
0.3232	49900	0.0305
0.3239	50000	0.011
0.3245	50100	0.0087
0.3252	50200	0.0038
0.3258	50300	0.0308
0.3265	50400	0.0104
0.3271	50500	0.0259
0.3278	50600	0.0144
0.3284	50700	0.0049
0.3290	50800	0.1213
0.3297	50900	0.0122
0.3303	51000	0.0282
0.3310	51100	0.0078
0.3316	51200	0.007
0.3323	51300	0.0319
0.3329	51400	0.0121
0.3336	51500	0.0405
0.3342	51600	0.0783
0.3349	51700	0.006
0.3355	51800	0.0504
0.3362	51900	0.0559
0.3368	52000	0.0733
0.3375	52100	0.0118
0.3381	52200	0.0269
0.3388	52300	0.0159
0.3394	52400	0.0632
0.3401	52500	0.0132
0.3407	52600	0.0593
0.3414	52700	0.0356
0.3420	52800	0.1713
0.3426	52900	0.0317
0.3433	53000	0.0026
0.3439	53100	0.0269
0.3446	53200	0.0211
0.3452	53300	0.0209
0.3459	53400	0.0213
0.3465	53500	0.0411
0.3472	53600	0.0065
0.3478	53700	0.0437
0.3485	53800	0.0266
0.3491	53900	0.0171
0.3498	54000	0.0063
0.3504	54100	0.0229
0.3511	54200	0.0102
0.3517	54300	0.0332
0.3524	54400	0.0108
0.3530	54500	0.0369
0.3537	54600	0.0066
0.3543	54700	0.0066
0.3550	54800	0.0202
0.3556	54900	0.0276
0.3563	55000	0.0424
0.3569	55100	0.0459
0.3575	55200	0.0076
0.3582	55300	0.0339
0.3588	55400	0.0169
0.3595	55500	0.029
0.3601	55600	0.0177
0.3608	55700	0.0294
0.3614	55800	0.0491
0.3621	55900	0.0207
0.3627	56000	0.0128
0.3634	56100	0.0315
0.3640	56200	0.0731
0.3647	56300	0.0458
0.3653	56400	0.0402
0.3660	56500	0.0678
0.3666	56600	0.0698
0.3673	56700	0.0064
0.3679	56800	0.0222
0.3686	56900	0.007
0.3692	57000	0.0272
0.3699	57100	0.0187
0.3705	57200	0.0297
0.3712	57300	0.0249
0.3718	57400	0.0363
0.3724	57500	0.0115
0.3731	57600	0.0519
0.3737	57700	0.0842
0.3744	57800	0.0139
0.3750	57900	0.015
0.3757	58000	0.0161
0.3763	58100	0.0467
0.3770	58200	0.0244
0.3776	58300	0.017
0.3783	58400	0.0706
0.3789	58500	0.0111
0.3796	58600	0.0438
0.3802	58700	0.007
0.3809	58800	0.0101
0.3815	58900	0.0223
0.3822	59000	0.0129
0.3828	59100	0.0398
0.3835	59200	0.02
0.3841	59300	0.0164
0.3848	59400	0.0658
0.3854	59500	0.0339
0.3860	59600	0.037
0.3867	59700	0.0296
0.3873	59800	0.0248
0.3880	59900	0.0038
0.3886	60000	0.0226
0.3893	60100	0.0463
0.3899	60200	0.0054
0.3906	60300	0.0354
0.3912	60400	0.0058
0.3919	60500	0.0421
0.3925	60600	0.0793
0.3932	60700	0.0511
0.3938	60800	0.0082
0.3945	60900	0.0316
0.3951	61000	0.0113
0.3958	61100	0.011
0.3964	61200	0.0053
0.3971	61300	0.0087
0.3977	61400	0.0147
0.3984	61500	0.0495
0.3990	61600	0.0148
0.3997	61700	0.0295
0.4003	61800	0.0399
0.4009	61900	0.0077
0.4016	62000	0.0074
0.4022	62100	0.0052
0.4029	62200	0.0099
0.4035	62300	0.0116
0.4042	62400	0.032
0.4048	62500	0.005
0.4055	62600	0.0035
0.4061	62700	0.026
0.4068	62800	0.0145
0.4074	62900	0.0159
0.4081	63000	0.0151
0.4087	63100	0.0881
0.4094	63200	0.0341
0.4100	63300	0.0287
0.4107	63400	0.0279
0.4113	63500	0.0113
0.4120	63600	0.0395
0.4126	63700	0.0049
0.4133	63800	0.0425
0.4139	63900	0.0132
0.4145	64000	0.02
0.4152	64100	0.0266
0.4158	64200	0.0313
0.4165	64300	0.0232
0.4171	64400	0.0683
0.4178	64500	0.0045
0.4184	64600	0.0191
0.4191	64700	0.0131
0.4197	64800	0.0115
0.4204	64900	0.0175
0.4210	65000	0.0114
0.4217	65100	0.0124
0.4223	65200	0.0221
0.4230	65300	0.0249
0.4236	65400	0.0125
0.4243	65500	0.0083
0.4249	65600	0.0118
0.4256	65700	0.0229
0.4262	65800	0.0103
0.4269	65900	0.0276
0.4275	66000	0.0525
0.4282	66100	0.026
0.4288	66200	0.0367
0.4294	66300	0.0181
0.4301	66400	0.0209
0.4307	66500	0.0421
0.4314	66600	0.0342
0.4320	66700	0.029
0.4327	66800	0.0122
0.4333	66900	0.1073
0.4340	67000	0.0093
0.4346	67100	0.0441
0.4353	67200	0.0411
0.4359	67300	0.0323
0.4366	67400	0.0125
0.4372	67500	0.0195
0.4379	67600	0.043
0.4385	67700	0.0195
0.4392	67800	0.0201
0.4398	67900	0.0081
0.4405	68000	0.0222
0.4411	68100	0.0286
0.4418	68200	0.0159
0.4424	68300	0.0478
0.4430	68400	0.0154
0.4437	68500	0.0078
0.4443	68600	0.0084
0.4450	68700	0.006
0.4456	68800	0.0271
0.4463	68900	0.016
0.4469	69000	0.0136
0.4476	69100	0.0062
0.4482	69200	0.032
0.4489	69300	0.0174
0.4495	69400	0.0317
0.4502	69500	0.0669
0.4508	69600	0.0153
0.4515	69700	0.008
0.4521	69800	0.0291
0.4528	69900	0.0128
0.4534	70000	0.0286
0.4541	70100	0.0078
0.4547	70200	0.0282
0.4554	70300	0.0063
0.4560	70400	0.0038
0.4567	70500	0.0102
0.4573	70600	0.0569
0.4579	70700	0.1021
0.4586	70800	0.0101
0.4592	70900	0.0172
0.4599	71000	0.0053
0.4605	71100	0.0116
0.4612	71200	0.0108
0.4618	71300	0.0242
0.4625	71400	0.0226
0.4631	71500	0.0427
0.4638	71600	0.0039
0.4644	71700	0.0409
0.4651	71800	0.0083
0.4657	71900	0.0559
0.4664	72000	0.0114
0.4670	72100	0.0034
0.4677	72200	0.0942
0.4683	72300	0.0554
0.4690	72400	0.0174
0.4696	72500	0.0029
0.4703	72600	0.0105
0.4709	72700	0.0071
0.4715	72800	0.0142
0.4722	72900	0.0834
0.4728	73000	0.0167
0.4735	73100	0.0045
0.4741	73200	0.0042
0.4748	73300	0.0146
0.4754	73400	0.0059
0.4761	73500	0.0424
0.4767	73600	0.0257
0.4774	73700	0.0165
0.4780	73800	0.0129
0.4787	73900	0.0118
0.4793	74000	0.0113
0.4800	74100	0.0091
0.4806	74200	0.0098
0.4813	74300	0.0188
0.4819	74400	0.0157
0.4826	74500	0.0091
0.4832	74600	0.0236
0.4839	74700	0.0468
0.4845	74800	0.0125
0.4852	74900	0.0258
0.4858	75000	0.0709
0.4864	75100	0.0154
0.4871	75200	0.0142
0.4877	75300	0.0051
0.4884	75400	0.0221
0.4890	75500	0.0155
0.4897	75600	0.0409
0.4903	75700	0.023
0.4910	75800	0.0067
0.4916	75900	0.0041
0.4923	76000	0.0213
0.4929	76100	0.0167
0.4936	76200	0.0048
0.4942	76300	0.0063
0.4949	76400	0.0402
0.4955	76500	0.0192
0.4962	76600	0.0245
0.4968	76700	0.0934
0.4975	76800	0.023
0.4981	76900	0.0475
0.4988	77000	0.003
0.4994	77100	0.018
0.5000	77200	0.0233
0.5007	77300	0.0412
0.5013	77400	0.0049
0.5020	77500	0.0073
0.5026	77600	0.1214
0.5033	77700	0.0083
0.5039	77800	0.0032
0.5046	77900	0.0023
0.5052	78000	0.0263
0.5059	78100	0.0198
0.5065	78200	0.0059
0.5072	78300	0.0238
0.5078	78400	0.0762
0.5085	78500	0.0273
0.5091	78600	0.0041
0.5098	78700	0.0137
0.5104	78800	0.0232
0.5111	78900	0.0168
0.5117	79000	0.0277
0.5124	79100	0.0137
0.5130	79200	0.0586
0.5137	79300	0.0058
0.5143	79400	0.0056
0.5149	79500	0.0115
0.5156	79600	0.0143
0.5162	79700	0.0618
0.5169	79800	0.0083
0.5175	79900	0.0423
0.5182	80000	0.0643
0.5188	80100	0.0058
0.5195	80200	0.0194
0.5201	80300	0.0087
0.5208	80400	0.0405
0.5214	80500	0.0315
0.5221	80600	0.0215
0.5227	80700	0.0102
0.5234	80800	0.0759
0.5240	80900	0.0071
0.5247	81000	0.0393
0.5253	81100	0.0084
0.5260	81200	0.0169
0.5266	81300	0.0781
0.5273	81400	0.064
0.5279	81500	0.026
0.5285	81600	0.0074
0.5292	81700	0.0055
0.5298	81800	0.0154
0.5305	81900	0.0142
0.5311	82000	0.0358
0.5318	82100	0.0182
0.5324	82200	0.0115
0.5331	82300	0.0138
0.5337	82400	0.0157
0.5344	82500	0.0113
0.5350	82600	0.0047
0.5357	82700	0.0034
0.5363	82800	0.0229
0.5370	82900	0.0319
0.5376	83000	0.018
0.5383	83100	0.0077
0.5389	83200	0.0059
0.5396	83300	0.003
0.5402	83400	0.0441
0.5409	83500	0.0036
0.5415	83600	0.0115
0.5422	83700	0.0398
0.5428	83800	0.0242
0.5434	83900	0.0227
0.5441	84000	0.0254
0.5447	84100	0.0096
0.5454	84200	0.053
0.5460	84300	0.0042
0.5467	84400	0.012
0.5473	84500	0.0129
0.5480	84600	0.0301
0.5486	84700	0.0064
0.5493	84800	0.0163
0.5499	84900	0.0066
0.5506	85000	0.0065
0.5512	85100	0.0331
0.5519	85200	0.0046
0.5525	85300	0.008
0.5532	85400	0.008
0.5538	85500	0.052
0.5545	85600	0.0043
0.5551	85700	0.0128
0.5558	85800	0.0167
0.5564	85900	0.0366
0.5570	86000	0.0385
0.5577	86100	0.0036
0.5583	86200	0.0417
0.5590	86300	0.0108
0.5596	86400	0.0066
0.5603	86500	0.015
0.5609	86600	0.0236
0.5616	86700	0.0059
0.5622	86800	0.0042
0.5629	86900	0.0035
0.5635	87000	0.0047
0.5642	87100	0.0452
0.5648	87200	0.0157
0.5655	87300	0.0036
0.5661	87400	0.0038
0.5668	87500	0.0062
0.5674	87600	0.0183
0.5681	87700	0.0164
0.5687	87800	0.0053
0.5694	87900	0.0103
0.5700	88000	0.0083
0.5707	88100	0.0092
0.5713	88200	0.0076
0.5719	88300	0.0992
0.5726	88400	0.0047
0.5732	88500	0.0086
0.5739	88600	0.0275
0.5745	88700	0.0172
0.5752	88800	0.0341
0.5758	88900	0.0102
0.5765	89000	0.0455
0.5771	89100	0.025
0.5778	89200	0.0016
0.5784	89300	0.0407
0.5791	89400	0.0073
0.5797	89500	0.0247
0.5804	89600	0.0025
0.5810	89700	0.0551
0.5817	89800	0.0069
0.5823	89900	0.0042
0.5830	90000	0.0059
0.5836	90100	0.0208
0.5843	90200	0.0193
0.5849	90300	0.0111
0.5855	90400	0.0519
0.5862	90500	0.0128
0.5868	90600	0.0127
0.5875	90700	0.0198
0.5881	90800	0.0057
0.5888	90900	0.0037
0.5894	91000	0.008
0.5901	91100	0.0029
0.5907	91200	0.0026
0.5914	91300	0.0252
0.5920	91400	0.0025
0.5927	91500	0.0338
0.5933	91600	0.0093
0.5940	91700	0.0216
0.5946	91800	0.0131
0.5953	91900	0.009
0.5959	92000	0.0041
0.5966	92100	0.0027
0.5972	92200	0.0142
0.5979	92300	0.0325
0.5985	92400	0.0085
0.5992	92500	0.0084
0.5998	92600	0.0206
0.6004	92700	0.0055
0.6011	92800	0.0039
0.6017	92900	0.009
0.6024	93000	0.0162
0.6030	93100	0.0074
0.6037	93200	0.0082
0.6043	93300	0.0306
0.6050	93400	0.0034
0.6056	93500	0.0034
0.6063	93600	0.0101
0.6069	93700	0.0022
0.6076	93800	0.0111
0.6082	93900	0.0382
0.6089	94000	0.0413
0.6095	94100	0.0037
0.6102	94200	0.0159
0.6108	94300	0.0149
0.6115	94400	0.0225
0.6121	94500	0.0057
0.6128	94600	0.0164
0.6134	94700	0.0787
0.6140	94800	0.0914
0.6147	94900	0.014
0.6153	95000	0.0189
0.6160	95100	0.1374
0.6166	95200	0.0282
0.6173	95300	0.0177
0.6179	95400	0.0437
0.6186	95500	0.0033
0.6192	95600	0.0025
0.6199	95700	0.0061
0.6205	95800	0.0233
0.6212	95900	0.0033
0.6218	96000	0.0077
0.6225	96100	0.0061
0.6231	96200	0.0079
0.6238	96300	0.0051
0.6244	96400	0.0061
0.6251	96500	0.0115
0.6257	96600	0.0192
0.6264	96700	0.0073
0.6270	96800	0.0632
0.6277	96900	0.0185
0.6283	97000	0.0042
0.6289	97100	0.0146
0.6296	97200	0.0642
0.6302	97300	0.004
0.6309	97400	0.0295
0.6315	97500	0.0054
0.6322	97600	0.0598
0.6328	97700	0.0119
0.6335	97800	0.0184
0.6341	97900	0.0161
0.6348	98000	0.0034
0.6354	98100	0.0093
0.6361	98200	0.0061
0.6367	98300	0.0053
0.6374	98400	0.0031
0.6380	98500	0.0076
0.6387	98600	0.015
0.6393	98700	0.0125
0.6400	98800	0.0107
0.6406	98900	0.0133
0.6413	99000	0.0029
0.6419	99100	0.0177
0.6425	99200	0.01
0.6432	99300	0.0073
0.6438	99400	0.0474
0.6445	99500	0.0054
0.6451	99600	0.059
0.6458	99700	0.0186
0.6464	99800	0.016
0.6471	99900	0.0096
0.6477	100000	0.0174
0.6484	100100	0.0113
0.6490	100200	0.0097
0.6497	100300	0.0142
0.6503	100400	0.0058
0.6510	100500	0.0397
0.6516	100600	0.0306
0.6523	100700	0.0156
0.6529	100800	0.0096
0.6536	100900	0.007
0.6542	101000	0.0032
0.6549	101100	0.0046
0.6555	101200	0.0057
0.6562	101300	0.006
0.6568	101400	0.0063
0.6574	101500	0.0289
0.6581	101600	0.0154
0.6587	101700	0.0044
0.6594	101800	0.007
0.6600	101900	0.0057
0.6607	102000	0.0559
0.6613	102100	0.0025
0.6620	102200	0.0088
0.6626	102300	0.0067
0.6633	102400	0.0037
0.6639	102500	0.0187
0.6646	102600	0.0032
0.6652	102700	0.0203
0.6659	102800	0.003
0.6665	102900	0.0091
0.6672	103000	0.0138
0.6678	103100	0.0031
0.6685	103200	0.0033
0.6691	103300	0.0074
0.6698	103400	0.0049
0.6704	103500	0.0025
0.6710	103600	0.0282
0.6717	103700	0.0073
0.6723	103800	0.0233
0.6730	103900	0.0162
0.6736	104000	0.0416
0.6743	104100	0.0146
0.6749	104200	0.0018
0.6756	104300	0.0056
0.6762	104400	0.0083
0.6769	104500	0.0346
0.6775	104600	0.0251
0.6782	104700	0.0068
0.6788	104800	0.0414
0.6795	104900	0.0031
0.6801	105000	0.0064
0.6808	105100	0.0048
0.6814	105200	0.0042
0.6821	105300	0.014
0.6827	105400	0.0211
0.6834	105500	0.0025
0.6840	105600	0.0212
0.6847	105700	0.0072
0.6853	105800	0.003
0.6859	105900	0.0197
0.6866	106000	0.0521
0.6872	106100	0.0067
0.6879	106200	0.0042
0.6885	106300	0.0091
0.6892	106400	0.0126
0.6898	106500	0.0094
0.6905	106600	0.0119
0.6911	106700	0.0131
0.6918	106800	0.0053
0.6924	106900	0.0049
0.6931	107000	0.0129
0.6937	107100	0.0054
0.6944	107200	0.0035
0.6950	107300	0.0193
0.6957	107400	0.0128
0.6963	107500	0.0055
0.6970	107600	0.0052
0.6976	107700	0.0048
0.6983	107800	0.0063
0.6989	107900	0.0057
0.6995	108000	0.0181
0.7002	108100	0.0023
0.7008	108200	0.0157
0.7015	108300	0.0426
0.7021	108400	0.0049
0.7028	108500	0.0077
0.7034	108600	0.018
0.7041	108700	0.0032
0.7047	108800	0.0049
0.7054	108900	0.0084
0.7060	109000	0.0023
0.7067	109100	0.0133
0.7073	109200	0.0062
0.7080	109300	0.012
0.7086	109400	0.0083
0.7093	109500	0.0411
0.7099	109600	0.0155
0.7106	109700	0.0055
0.7112	109800	0.0031
0.7119	109900	0.0079
0.7125	110000	0.0042
0.7132	110100	0.0066
0.7138	110200	0.0255
0.7144	110300	0.0083
0.7151	110400	0.0037
0.7157	110500	0.0082
0.7164	110600	0.0221
0.7170	110700	0.0098
0.7177	110800	0.0126
0.7183	110900	0.0126
0.7190	111000	0.013
0.7196	111100	0.0122
0.7203	111200	0.0098
0.7209	111300	0.0115
0.7216	111400	0.0043
0.7222	111500	0.0024
0.7229	111600	0.0033
0.7235	111700	0.0176
0.7242	111800	0.0087
0.7248	111900	0.0037
0.7255	112000	0.0044
0.7261	112100	0.0106
0.7268	112200	0.0229
0.7274	112300	0.0134
0.7281	112400	0.0024
0.7287	112500	0.0142
0.7293	112600	0.0174
0.7300	112700	0.0017
0.7306	112800	0.028
0.7313	112900	0.0083
0.7319	113000	0.0166
0.7326	113100	0.0015
0.7332	113200	0.0067
0.7339	113300	0.0188
0.7345	113400	0.0172
0.7352	113500	0.0026
0.7358	113600	0.0062
0.7365	113700	0.0059
0.7371	113800	0.0317
0.7378	113900	0.0131
0.7384	114000	0.0104
0.7391	114100	0.0044
0.7397	114200	0.0152
0.7404	114300	0.01
0.7410	114400	0.0027
0.7417	114500	0.0104
0.7423	114600	0.0075
0.7429	114700	0.0034
0.7436	114800	0.01
0.7442	114900	0.0294
0.7449	115000	0.0178
0.7455	115100	0.0211
0.7462	115200	0.0052
0.7468	115300	0.0057
0.7475	115400	0.0049
0.7481	115500	0.0355
0.7488	115600	0.0015
0.7494	115700	0.0026
0.7501	115800	0.0018
0.7507	115900	0.0454
0.7514	116000	0.004
0.7520	116100	0.0065
0.7527	116200	0.0018
0.7533	116300	0.0077
0.7540	116400	0.0099
0.7546	116500	0.0072
0.7553	116600	0.0109
0.7559	116700	0.0138
0.7566	116800	0.0172
0.7572	116900	0.0032
0.7578	117000	0.0145
0.7585	117100	0.0216
0.7591	117200	0.0033
0.7598	117300	0.0022
0.7604	117400	0.0065
0.7611	117500	0.0206
0.7617	117600	0.0039
0.7624	117700	0.0044
0.7630	117800	0.0125
0.7637	117900	0.0045
0.7643	118000	0.0386
0.7650	118100	0.0023
0.7656	118200	0.0096
0.7663	118300	0.0551
0.7669	118400	0.0099
0.7676	118500	0.0026
0.7682	118600	0.01
0.7689	118700	0.0065
0.7695	118800	0.0285
0.7702	118900	0.0053
0.7708	119000	0.0015
0.7714	119100	0.0074
0.7721	119200	0.0068
0.7727	119300	0.0054
0.7734	119400	0.0047
0.7740	119500	0.0097
0.7747	119600	0.0291
0.7753	119700	0.0081
0.7760	119800	0.0067
0.7766	119900	0.0164
0.7773	120000	0.0056
0.7779	120100	0.0244
0.7786	120200	0.0043
0.7792	120300	0.0153
0.7799	120400	0.0016
0.7805	120500	0.0149
0.7812	120600	0.011
0.7818	120700	0.0018
0.7825	120800	0.0086
0.7831	120900	0.0029
0.7838	121000	0.0911
0.7844	121100	0.0079
0.7851	121200	0.0277
0.7857	121300	0.0058
0.7863	121400	0.0024
0.7870	121500	0.0129
0.7876	121600	0.0028
0.7883	121700	0.011
0.7889	121800	0.028
0.7896	121900	0.004
0.7902	122000	0.0179
0.7909	122100	0.0016
0.7915	122200	0.0024
0.7922	122300	0.0319
0.7928	122400	0.0112
0.7935	122500	0.0074
0.7941	122600	0.0075
0.7948	122700	0.0151
0.7954	122800	0.0047
0.7961	122900	0.0018
0.7967	123000	0.0035
0.7974	123100	0.0015
0.7980	123200	0.0363
0.7987	123300	0.0124
0.7993	123400	0.0035
0.7999	123500	0.0036
0.8006	123600	0.0036
0.8012	123700	0.0108
0.8019	123800	0.0337
0.8025	123900	0.0056
0.8032	124000	0.0155
0.8038	124100	0.0118
0.8045	124200	0.0225
0.8051	124300	0.0019
0.8058	124400	0.0032
0.8064	124500	0.0024
0.8071	124600	0.0029
0.8077	124700	0.0179
0.8084	124800	0.0131
0.8090	124900	0.0051
0.8097	125000	0.0068
0.8103	125100	0.0013
0.8110	125200	0.0025
0.8116	125300	0.0164
0.8123	125400	0.0075
0.8129	125500	0.0027
0.8136	125600	0.0027
0.8142	125700	0.0373
0.8148	125800	0.0052
0.8155	125900	0.0409
0.8161	126000	0.0014
0.8168	126100	0.0012
0.8174	126200	0.025
0.8181	126300	0.0246
0.8187	126400	0.0132
0.8194	126500	0.0025
0.8200	126600	0.0425
0.8207	126700	0.0054
0.8213	126800	0.0514
0.8220	126900	0.0479
0.8226	127000	0.0053
0.8233	127100	0.0171
0.8239	127200	0.0081
0.8246	127300	0.0106
0.8252	127400	0.0039
0.8259	127500	0.0092
0.8265	127600	0.0031
0.8272	127700	0.0042
0.8278	127800	0.0142
0.8284	127900	0.1233
0.8291	128000	0.0032
0.8297	128100	0.0143
0.8304	128200	0.0032
0.8310	128300	0.0219
0.8317	128400	0.0025
0.8323	128500	0.0078
0.8330	128600	0.0121
0.8336	128700	0.0025
0.8343	128800	0.0026
0.8349	128900	0.0076
0.8356	129000	0.0215
0.8362	129100	0.004
0.8369	129200	0.0127
0.8375	129300	0.0025
0.8382	129400	0.0038
0.8388	129500	0.0226
0.8395	129600	0.0062
0.8401	129700	0.0805
0.8408	129800	0.0042
0.8414	129900	0.0068
0.8421	130000	0.0185
0.8427	130100	0.003
0.8433	130200	0.0018
0.8440	130300	0.0046
0.8446	130400	0.012
0.8453	130500	0.006
0.8459	130600	0.0101
0.8466	130700	0.0022
0.8472	130800	0.0022
0.8479	130900	0.0034
0.8485	131000	0.0014
0.8492	131100	0.0991
0.8498	131200	0.0062
0.8505	131300	0.0037
0.8511	131400	0.0041
0.8518	131500	0.0092
0.8524	131600	0.0049
0.8531	131700	0.0204
0.8537	131800	0.0211
0.8544	131900	0.0128
0.8550	132000	0.0026
0.8557	132100	0.0045
0.8563	132200	0.037
0.8569	132300	0.004
0.8576	132400	0.0151
0.8582	132500	0.0082
0.8589	132600	0.0021
0.8595	132700	0.0035
0.8602	132800	0.0053
0.8608	132900	0.006
0.8615	133000	0.0047
0.8621	133100	0.0014
0.8628	133200	0.0054
0.8634	133300	0.004
0.8641	133400	0.0506
0.8647	133500	0.0087
0.8654	133600	0.0517
0.8660	133700	0.0082
0.8667	133800	0.0014
0.8673	133900	0.0025
0.8680	134000	0.0047
0.8686	134100	0.015
0.8693	134200	0.0524
0.8699	134300	0.0041
0.8706	134400	0.0206
0.8712	134500	0.005
0.8718	134600	0.032
0.8725	134700	0.0093
0.8731	134800	0.0018
0.8738	134900	0.0009
0.8744	135000	0.0079
0.8751	135100	0.007
0.8757	135200	0.0086
0.8764	135300	0.0031
0.8770	135400	0.0021
0.8777	135500	0.009
0.8783	135600	0.044
0.8790	135700	0.0023
0.8796	135800	0.0216
0.8803	135900	0.0041
0.8809	136000	0.0086
0.8816	136100	0.0053
0.8822	136200	0.0018
0.8829	136300	0.0339
0.8835	136400	0.0043
0.8842	136500	0.0068
0.8848	136600	0.0088
0.8854	136700	0.0083
0.8861	136800	0.0048
0.8867	136900	0.0028
0.8874	137000	0.0181
0.8880	137100	0.0025
0.8887	137200	0.0013
0.8893	137300	0.0041
0.8900	137400	0.0079
0.8906	137500	0.0043
0.8913	137600	0.0066
0.8919	137700	0.0266
0.8926	137800	0.0057
0.8932	137900	0.0047
0.8939	138000	0.0078
0.8945	138100	0.0047
0.8952	138200	0.0048
0.8958	138300	0.014
0.8965	138400	0.0066
0.8971	138500	0.0032
0.8978	138600	0.0235
0.8984	138700	0.0042
0.8991	138800	0.0159
0.8997	138900	0.0048
0.9003	139000	0.0233
0.9010	139100	0.0065
0.9016	139200	0.0018
0.9023	139300	0.0376
0.9029	139400	0.009
0.9036	139500	0.0035
0.9042	139600	0.0083
0.9049	139700	0.0047
0.9055	139800	0.0106
0.9062	139900	0.0155
0.9068	140000	0.0023
0.9075	140100	0.0031
0.9081	140200	0.0094
0.9088	140300	0.0053
0.9094	140400	0.0036
0.9101	140500	0.0192
0.9107	140600	0.0045
0.9114	140700	0.0031
0.9120	140800	0.0049
0.9127	140900	0.0351
0.9133	141000	0.0194
0.9139	141100	0.0099
0.9146	141200	0.0035
0.9152	141300	0.0031
0.9159	141400	0.0058
0.9165	141500	0.0075
0.9172	141600	0.0015
0.9178	141700	0.0031
0.9185	141800	0.0247
0.9191	141900	0.0045
0.9198	142000	0.0093
0.9204	142100	0.0046
0.9211	142200	0.002
0.9217	142300	0.0165
0.9224	142400	0.0025
0.9230	142500	0.0052
0.9237	142600	0.0087
0.9243	142700	0.0147
0.9250	142800	0.0152
0.9256	142900	0.0094
0.9263	143000	0.0259
0.9269	143100	0.0023
0.9276	143200	0.0024
0.9282	143300	0.002
0.9288	143400	0.0029
0.9295	143500	0.0057
0.9301	143600	0.0037
0.9308	143700	0.0089
0.9314	143800	0.0077
0.9321	143900	0.0423
0.9327	144000	0.0133
0.9334	144100	0.0026
0.9340	144200	0.0024
0.9347	144300	0.008
0.9353	144400	0.0054
0.9360	144500	0.0037
0.9366	144600	0.0037
0.9373	144700	0.0147
0.9379	144800	0.0021
0.9386	144900	0.0172
0.9392	145000	0.0025
0.9399	145100	0.004
0.9405	145200	0.0148
0.9412	145300	0.0108
0.9418	145400	0.0025
0.9424	145500	0.019
0.9431	145600	0.0043
0.9437	145700	0.0027
0.9444	145800	0.0013
0.9450	145900	0.0101
0.9457	146000	0.0019
0.9463	146100	0.0044
0.9470	146200	0.0062
0.9476	146300	0.0058
0.9483	146400	0.0019
0.9489	146500	0.0027
0.9496	146600	0.0268
0.9502	146700	0.0029
0.9509	146800	0.006
0.9515	146900	0.0024
0.9522	147000	0.03
0.9528	147100	0.0027
0.9535	147200	0.0046
0.9541	147300	0.008
0.9548	147400	0.0121
0.9554	147500	0.0051
0.9561	147600	0.0016
0.9567	147700	0.0096
0.9573	147800	0.0018
0.9580	147900	0.0107
0.9586	148000	0.0029
0.9593	148100	0.0048
0.9599	148200	0.0029
0.9606	148300	0.0371
0.9612	148400	0.0025
0.9619	148500	0.0033
0.9625	148600	0.005
0.9632	148700	0.0285
0.9638	148800	0.0019
0.9645	148900	0.0028
0.9651	149000	0.0065
0.9658	149100	0.0021
0.9664	149200	0.0891
0.9671	149300	0.0106
0.9677	149400	0.0022
0.9684	149500	0.0066
0.9690	149600	0.0043
0.9697	149700	0.0044
0.9703	149800	0.0188
0.9709	149900	0.0142
0.9716	150000	0.002
0.9722	150100	0.0131
0.9729	150200	0.0094
0.9735	150300	0.0148
0.9742	150400	0.0225
0.9748	150500	0.0136
0.9755	150600	0.0051
0.9761	150700	0.0009
0.9768	150800	0.0015
0.9774	150900	0.0194
0.9781	151000	0.0227
0.9787	151100	0.0025
0.9794	151200	0.0041
0.9800	151300	0.0071
0.9807	151400	0.0116
0.9813	151500	0.0179
0.9820	151600	0.0021
0.9826	151700	0.0023
0.9833	151800	0.0012
0.9839	151900	0.0057
0.9846	152000	0.0027
0.9852	152100	0.0193
0.9858	152200	0.0035
0.9865	152300	0.0051
0.9871	152400	0.0012
0.9878	152500	0.004
0.9884	152600	0.0028
0.9891	152700	0.0179
0.9897	152800	0.0113
0.9904	152900	0.0026
0.9910	153000	0.0021
0.9917	153100	0.002
0.9923	153200	0.0062
0.9930	153300	0.02
0.9936	153400	0.0062
0.9943	153500	0.0018
0.9949	153600	0.0055
0.9956	153700	0.0074
0.9962	153800	0.0037
0.9969	153900	0.0038
0.9975	154000	0.0183
0.9982	154100	0.0027
0.9988	154200	0.0086
0.9994	154300	0.0127

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

bibtex

@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

bibtex

@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},
}

	sentence1	sentence2	score
type	string	string	float
details	min: 3 tokens mean: 5.33 tokens max: 22 tokens	min: 4 tokens mean: 82.95 tokens max: 256 tokens	min: 0.0 mean: 0.05 max: 1.0

KhaledReda/all-MiniLM-L6-v37-pair_score

all-MiniLM-L6-v37-pair_score

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Training Details

Training Dataset

pairs_with_scores_v31

Evaluation Dataset

pairs_with_scores_v31

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

CoSENTLoss

KhaledReda/
all-MiniLM-L6-v37-pair_score