Upload 16 files
Browse files- .gitattributes +3 -0
- BigBang-Proton_Test_Results/.DS_Store +0 -0
- BigBang-Proton_Test_Results/Arithmetic_Add_Subtract_Multiply_200/test_add_subtract_multiply_200.log +0 -0
- BigBang-Proton_Test_Results/Genome/.DS_Store +0 -0
- BigBang-Proton_Test_Results/Genome/Protein(DNA) to Predict Functional Fitness/run--train_prot_IF1_ECOLI_Kelsic_2016_protein_function.log +960 -0
- BigBang-Proton_Test_Results/Genome/Regulatory DNA to Predict Protein Expression/run---train-reg-promoter_gene_expression_regulatory_DNA.log +447 -0
- BigBang-Proton_Test_Results/Genome/ncRNA to Predict Functional Fitness /run--nt-kobori-2016-train_ncRNA_Function.log +861 -0
- BigBang-Proton_Test_Results/Particle_JOI_10000/.DS_Store +0 -0
- BigBang-Proton_Test_Results/Particle_JOI_10000/save5/pred_jet_11_classificaation_0.txt +0 -0
- LLMs_Test_Results/.DS_Store +0 -0
- LLMs_Test_Results/Arithmetic Operations/LLMs on Arithmetic Operations.xlsx +0 -0
- LLMs_Test_Results/Genome Modeling in Biology/.DS_Store +0 -0
- LLMs_Test_Results/Genome Modeling in Biology/Genome Sequence Prediction.xlsx +3 -0
- LLMs_Test_Results/Genome Modeling in Biology/LLMs on Genome and Functional Fitness Prediction.xlsx +3 -0
- LLMs_Test_Results/Genome Modeling in Biology/~$Genome Sequence Prediction.xlsx +0 -0
- LLMs_Test_Results/Inter-Atomic Simulation in Material Science/LLMs on Inter-Atomic Potential Simulation.xlsx +0 -0
- LLMs_Test_Results/Jet Tagging in Particle Physics/General Purpose LLMs on Jet Tagging.xlsx +3 -0
.gitattributes
CHANGED
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@@ -36,3 +36,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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| 36 |
General[[:space:]]Purpose[[:space:]]LLMs[[:space:]]on[[:space:]]Jet[[:space:]]Tagging.xlsx filter=lfs diff=lfs merge=lfs -text
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Genome[[:space:]]Sequence[[:space:]]Prediction.xlsx filter=lfs diff=lfs merge=lfs -text
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LLMs[[:space:]]on[[:space:]]Genome[[:space:]]and[[:space:]]Functional[[:space:]]Fitness[[:space:]]Prediction.xlsx filter=lfs diff=lfs merge=lfs -text
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General[[:space:]]Purpose[[:space:]]LLMs[[:space:]]on[[:space:]]Jet[[:space:]]Tagging.xlsx filter=lfs diff=lfs merge=lfs -text
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| 37 |
Genome[[:space:]]Sequence[[:space:]]Prediction.xlsx filter=lfs diff=lfs merge=lfs -text
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| 38 |
LLMs[[:space:]]on[[:space:]]Genome[[:space:]]and[[:space:]]Functional[[:space:]]Fitness[[:space:]]Prediction.xlsx filter=lfs diff=lfs merge=lfs -text
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LLMs_Test_Results/Genome[[:space:]]Modeling[[:space:]]in[[:space:]]Biology/Genome[[:space:]]Sequence[[:space:]]Prediction.xlsx filter=lfs diff=lfs merge=lfs -text
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LLMs_Test_Results/Genome[[:space:]]Modeling[[:space:]]in[[:space:]]Biology/LLMs[[:space:]]on[[:space:]]Genome[[:space:]]and[[:space:]]Functional[[:space:]]Fitness[[:space:]]Prediction.xlsx filter=lfs diff=lfs merge=lfs -text
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LLMs_Test_Results/Jet[[:space:]]Tagging[[:space:]]in[[:space:]]Particle[[:space:]]Physics/General[[:space:]]Purpose[[:space:]]LLMs[[:space:]]on[[:space:]]Jet[[:space:]]Tagging.xlsx filter=lfs diff=lfs merge=lfs -text
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BigBang-Proton_Test_Results/.DS_Store
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Binary file (10.2 kB). View file
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BigBang-Proton_Test_Results/Arithmetic_Add_Subtract_Multiply_200/test_add_subtract_multiply_200.log
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The diff for this file is too large to render.
See raw diff
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BigBang-Proton_Test_Results/Genome/.DS_Store
ADDED
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Binary file (10.2 kB). View file
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BigBang-Proton_Test_Results/Genome/Protein(DNA) to Predict Functional Fitness/run--train_prot_IF1_ECOLI_Kelsic_2016_protein_function.log
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|
| 1 |
+
processed_dms_prot_IF1_ECOLI_Kelsic_2016.tsv
|
| 2 |
+
total iter per epoch :89
|
| 3 |
+
validation data numbers per epoch :10
|
| 4 |
+
data.shape: torch.Size([19, 222])
|
| 5 |
+
tx_v.shape: torch.Size([19])
|
| 6 |
+
total iter per epoch :89
|
| 7 |
+
validation data numbers per epoch :10
|
| 8 |
+
total iter per epoch :89
|
| 9 |
+
validation data numbers per epoch :10
|
| 10 |
+
total iter per epoch :89
|
| 11 |
+
validation data numbers per epoch :10
|
| 12 |
+
data.shape: torch.Size([19, 222])
|
| 13 |
+
tx_v.shape: torch.Size([19])
|
| 14 |
+
data.shape: torch.Size([19, 222])
|
| 15 |
+
tx_v.shape: torch.Size([19])
|
| 16 |
+
data.shape: torch.Size([19, 222])
|
| 17 |
+
tx_v.shape: torch.Size([19])
|
| 18 |
+
fc_reg.weight不匹配
|
| 19 |
+
fc_reg.bias不匹配
|
| 20 |
+
fc_reg.weight不匹配
|
| 21 |
+
fc_reg.bias不匹配
|
| 22 |
+
fc_reg.weight不匹配
|
| 23 |
+
fc_reg.bias不匹配
|
| 24 |
+
fc_reg.weight不匹配
|
| 25 |
+
fc_reg.bias不匹配
|
| 26 |
+
Total number of parameters: 3.82849548
|
| 27 |
+
Total number of parameters: 3.82849548
|
| 28 |
+
Total number of parameters: 3.82849548
|
| 29 |
+
Total number of parameters: 3.82849548
|
| 30 |
+
NCCL version 2.20.5+cuda12.4
|
| 31 |
+
Resuming training from epoch 1, batch_idx 171500, total_train_iter 171500
|
| 32 |
+
Resuming training from epoch 1, batch_idx 171500, total_train_iter 171500
|
| 33 |
+
Resuming training from epoch 1, batch_idx 171500, total_train_iter 171500
|
| 34 |
+
Resuming training from epoch 1, batch_idx 171500, total_train_iter 171500
|
| 35 |
+
started training...
|
| 36 |
+
started training...
|
| 37 |
+
####################epoch:2
|
| 38 |
+
started training...
|
| 39 |
+
started training...
|
| 40 |
+
NCCL version 2.20.5+cuda12.4
|
| 41 |
+
NCCL version 2.20.5+cuda12.4
|
| 42 |
+
NCCL version 2.20.5+cuda12.4
|
| 43 |
+
epoch=1, iter=9, iter2=10, loss=0.13503, spearmanr=0.25110,p_value=0.29976555598401555 times:8.28s, 19 samples/iter
|
| 44 |
+
Validation: epoch=1, loss=0.12992
|
| 45 |
+
Pearson Correlation: 0.72738,p_value: 1.4386265572019488e-32
|
| 46 |
+
Spearman Correlation: 0.73634,p_value; 9.969971939593732e-34
|
| 47 |
+
epoch=1,iter=14,iter2=15,spearman_corr=0.7363432579254907
|
| 48 |
+
|
| 49 |
+
epoch=1, iter=19, iter2=20, loss=0.12222, spearmanr=0.19974,p_value=0.4123101580224716 times:6.11s, 19 samples/iter
|
| 50 |
+
epoch=1, iter=29, iter2=30, loss=0.13056, spearmanr=0.18294,p_value=0.4534755871901123 times:4.58s, 19 samples/iter
|
| 51 |
+
Validation: epoch=1, loss=0.14187
|
| 52 |
+
Pearson Correlation: 0.45604,p_value: 3.789089875194662e-11
|
| 53 |
+
Spearman Correlation: 0.48545,p_value; 1.2587030061352246e-12
|
| 54 |
+
epoch=1,iter=29,iter2=30,spearman_corr=0.48544886139807747
|
| 55 |
+
|
| 56 |
+
epoch=1, iter=39, iter2=40, loss=0.12795, spearmanr=0.15880,p_value=0.5161102849180297 times:6.10s, 19 samples/iter
|
| 57 |
+
Validation: epoch=1, loss=0.12267
|
| 58 |
+
Pearson Correlation: 0.63580,p_value: 6.523839927588144e-23
|
| 59 |
+
Spearman Correlation: 0.63613,p_value; 6.096827190246552e-23
|
| 60 |
+
epoch=1,iter=44,iter2=45,spearman_corr=0.6361317582458959
|
| 61 |
+
|
| 62 |
+
epoch=1, iter=49, iter2=50, loss=0.12516, spearmanr=0.29213,p_value=0.22490242904280994 times:6.01s, 19 samples/iter
|
| 63 |
+
epoch=1, iter=59, iter2=60, loss=0.12507, spearmanr=0.32953,p_value=0.16829583894361125 times:4.62s, 19 samples/iter
|
| 64 |
+
Validation: epoch=1, loss=0.12459
|
| 65 |
+
Pearson Correlation: 0.72436,p_value: 3.449014742138209e-32
|
| 66 |
+
Spearman Correlation: 0.70394,p_value; 9.563847258700025e-30
|
| 67 |
+
epoch=1,iter=59,iter2=60,spearman_corr=0.7039418986813518
|
| 68 |
+
|
| 69 |
+
epoch=1, iter=69, iter2=70, loss=0.12647, spearmanr=0.57721,p_value=0.009662178210279888 times:5.90s, 19 samples/iter
|
| 70 |
+
Validation: epoch=1, loss=0.12472
|
| 71 |
+
Pearson Correlation: 0.75497,p_value: 2.7072948218778822e-36
|
| 72 |
+
Spearman Correlation: 0.74188,p_value; 1.8139283791056497e-34
|
| 73 |
+
epoch=1,iter=74,iter2=75,spearman_corr=0.741883189777622
|
| 74 |
+
|
| 75 |
+
epoch=1, iter=79, iter2=80, loss=0.12447, spearmanr=0.45079,p_value=0.052740497426619126 times:5.90s, 19 samples/iter
|
| 76 |
+
####################epoch:3
|
| 77 |
+
epoch=2, iter=9, iter2=99, loss=0.12511, spearmanr=0.31736,p_value=0.18551052206179963 times:5.90s, 19 samples/iter
|
| 78 |
+
Validation: epoch=2, loss=0.13761
|
| 79 |
+
Pearson Correlation: 0.73710,p_value: 7.929701219654836e-34
|
| 80 |
+
Spearman Correlation: 0.72022,p_value; 1.1243271183826406e-31
|
| 81 |
+
epoch=2,iter=14,iter2=104,spearman_corr=0.7202158608910011
|
| 82 |
+
|
| 83 |
+
epoch=2, iter=19, iter2=109, loss=0.12355, spearmanr=0.64383,p_value=0.002933046604377197 times:6.71s, 19 samples/iter
|
| 84 |
+
epoch=2, iter=29, iter2=119, loss=0.12447, spearmanr=0.29692,p_value=0.21703687504793984 times:4.84s, 19 samples/iter
|
| 85 |
+
Validation: epoch=2, loss=0.12604
|
| 86 |
+
Pearson Correlation: 0.76969,p_value: 1.7198082803316836e-38
|
| 87 |
+
Spearman Correlation: 0.76182,p_value; 2.691760889265954e-37
|
| 88 |
+
epoch=2,iter=29,iter2=119,spearman_corr=0.7618216065332878
|
| 89 |
+
|
| 90 |
+
epoch=2, iter=39, iter2=129, loss=0.12391, spearmanr=-0.14166,p_value=0.562922072776757 times:6.65s, 19 samples/iter
|
| 91 |
+
Validation: epoch=2, loss=0.11834
|
| 92 |
+
Pearson Correlation: 0.74233,p_value: 1.5760914609398735e-34
|
| 93 |
+
Spearman Correlation: 0.73921,p_value; 4.14645825324962e-34
|
| 94 |
+
epoch=2,iter=44,iter2=134,spearman_corr=0.7392129877572363
|
| 95 |
+
|
| 96 |
+
epoch=2, iter=49, iter2=139, loss=0.12324, spearmanr=-0.02723,p_value=0.9118938984407896 times:6.76s, 19 samples/iter
|
| 97 |
+
epoch=2, iter=59, iter2=149, loss=0.12344, spearmanr=0.10022,p_value=0.683110581607205 times:4.54s, 19 samples/iter
|
| 98 |
+
Validation: epoch=2, loss=0.11846
|
| 99 |
+
Pearson Correlation: 0.76966,p_value: 1.7412750517663683e-38
|
| 100 |
+
Spearman Correlation: 0.76208,p_value; 2.4674453163121194e-37
|
| 101 |
+
epoch=2,iter=59,iter2=149,spearman_corr=0.7620754259622762
|
| 102 |
+
|
| 103 |
+
epoch=2, iter=69, iter2=159, loss=0.12497, spearmanr=0.09043,p_value=0.7127451357980417 times:6.73s, 19 samples/iter
|
| 104 |
+
Validation: epoch=2, loss=0.14253
|
| 105 |
+
Pearson Correlation: 0.76025,p_value: 4.598041414632021e-37
|
| 106 |
+
Spearman Correlation: 0.75339,p_value; 4.5647660912203063e-36
|
| 107 |
+
epoch=2,iter=74,iter2=164,spearman_corr=0.7533879444972418
|
| 108 |
+
|
| 109 |
+
epoch=2, iter=79, iter2=169, loss=0.12620, spearmanr=0.72932,p_value=0.00039529500111630983 times:6.61s, 19 samples/iter
|
| 110 |
+
####################epoch:4
|
| 111 |
+
epoch=3, iter=9, iter2=188, loss=0.12623, spearmanr=-0.00881,p_value=0.9714560826981299 times:6.68s, 19 samples/iter
|
| 112 |
+
Validation: epoch=3, loss=0.12023
|
| 113 |
+
Pearson Correlation: 0.76573,p_value: 6.959057808087305e-38
|
| 114 |
+
Spearman Correlation: 0.75639,p_value; 1.6859477816294617e-36
|
| 115 |
+
epoch=3,iter=14,iter2=193,spearman_corr=0.7563949063450366
|
| 116 |
+
|
| 117 |
+
epoch=3, iter=19, iter2=198, loss=0.12536, spearmanr=-0.04447,p_value=0.8565358907702433 times:7.34s, 19 samples/iter
|
| 118 |
+
epoch=3, iter=29, iter2=208, loss=0.12567, spearmanr=0.13093,p_value=0.593147077409418 times:4.66s, 19 samples/iter
|
| 119 |
+
Validation: epoch=3, loss=0.12763
|
| 120 |
+
Pearson Correlation: 0.77254,p_value: 6.204330226109058e-39
|
| 121 |
+
Spearman Correlation: 0.76978,p_value; 1.669536127337487e-38
|
| 122 |
+
epoch=3,iter=29,iter2=208,spearman_corr=0.7697775619766758
|
| 123 |
+
|
| 124 |
+
epoch=3, iter=39, iter2=218, loss=0.12531, spearmanr=0.28295,p_value=0.24047008881482873 times:7.02s, 19 samples/iter
|
| 125 |
+
Validation: epoch=3, loss=0.12868
|
| 126 |
+
Pearson Correlation: 0.76970,p_value: 1.7151960465662049e-38
|
| 127 |
+
Spearman Correlation: 0.77011,p_value; 1.4821173706439576e-38
|
| 128 |
+
epoch=3,iter=44,iter2=223,spearman_corr=0.7701113233683061
|
| 129 |
+
|
| 130 |
+
epoch=3, iter=49, iter2=228, loss=0.12523, spearmanr=0.19085,p_value=0.43382090136133955 times:7.25s, 19 samples/iter
|
| 131 |
+
epoch=3, iter=59, iter2=238, loss=0.12525, spearmanr=0.65320,p_value=0.002425368333617341 times:5.02s, 19 samples/iter
|
| 132 |
+
Validation: epoch=3, loss=0.11860
|
| 133 |
+
Pearson Correlation: 0.75466,p_value: 2.9961992482835303e-36
|
| 134 |
+
Spearman Correlation: 0.76271,p_value; 1.984781112924113e-37
|
| 135 |
+
epoch=3,iter=59,iter2=238,spearman_corr=0.7627090081010431
|
| 136 |
+
|
| 137 |
+
epoch=3, iter=69, iter2=248, loss=0.12559, spearmanr=0.82334,p_value=1.4874751009411154e-05 times:7.15s, 19 samples/iter
|
| 138 |
+
Validation: epoch=3, loss=0.12805
|
| 139 |
+
Pearson Correlation: 0.73045,p_value: 5.837281234914839e-33
|
| 140 |
+
Spearman Correlation: 0.72735,p_value; 1.447394104677892e-32
|
| 141 |
+
epoch=3,iter=74,iter2=253,spearman_corr=0.7273548240257349
|
| 142 |
+
|
| 143 |
+
epoch=3, iter=79, iter2=258, loss=0.12507, spearmanr=0.31129,p_value=0.19453321237009438 times:7.29s, 19 samples/iter
|
| 144 |
+
####################epoch:5
|
| 145 |
+
epoch=4, iter=9, iter2=277, loss=0.12514, spearmanr=0.36268,p_value=0.1269944066587078 times:6.37s, 19 samples/iter
|
| 146 |
+
Validation: epoch=4, loss=0.12524
|
| 147 |
+
Pearson Correlation: 0.77141,p_value: 9.297601297810518e-39
|
| 148 |
+
Spearman Correlation: 0.77681,p_value; 1.3024675751343071e-39
|
| 149 |
+
epoch=4,iter=14,iter2=282,spearman_corr=0.776806200285536
|
| 150 |
+
|
| 151 |
+
epoch=4, iter=19, iter2=287, loss=0.12447, spearmanr=0.44571,p_value=0.05580061145772405 times:7.01s, 19 samples/iter
|
| 152 |
+
epoch=4, iter=29, iter2=297, loss=0.12525, spearmanr=0.15866,p_value=0.516488654662819 times:4.50s, 19 samples/iter
|
| 153 |
+
Validation: epoch=4, loss=0.15233
|
| 154 |
+
Pearson Correlation: 0.78585,p_value: 4.2557434361544694e-41
|
| 155 |
+
Spearman Correlation: 0.78546,p_value; 4.942052613678324e-41
|
| 156 |
+
epoch=4,iter=29,iter2=297,spearman_corr=0.7854607977296324
|
| 157 |
+
|
| 158 |
+
epoch=4, iter=39, iter2=307, loss=0.12544, spearmanr=0.57356,p_value=0.010242413649802577 times:6.76s, 19 samples/iter
|
| 159 |
+
Validation: epoch=4, loss=0.12089
|
| 160 |
+
Pearson Correlation: 0.74097,p_value: 2.4113921217100514e-34
|
| 161 |
+
Spearman Correlation: 0.73953,p_value; 3.7622410466101558e-34
|
| 162 |
+
epoch=4,iter=44,iter2=312,spearman_corr=0.7395287521678012
|
| 163 |
+
|
| 164 |
+
epoch=4, iter=49, iter2=317, loss=0.12527, spearmanr=0.40158,p_value=0.08833925149613979 times:7.26s, 19 samples/iter
|
| 165 |
+
epoch=4, iter=59, iter2=327, loss=0.12523, spearmanr=0.52817,p_value=0.020095820417118784 times:4.59s, 19 samples/iter
|
| 166 |
+
Validation: epoch=4, loss=0.12136
|
| 167 |
+
Pearson Correlation: 0.76324,p_value: 1.650243060147821e-37
|
| 168 |
+
Spearman Correlation: 0.75586,p_value; 2.011708439188325e-36
|
| 169 |
+
epoch=4,iter=59,iter2=327,spearman_corr=0.7558647615299174
|
| 170 |
+
|
| 171 |
+
epoch=4, iter=69, iter2=337, loss=0.12557, spearmanr=0.68481,p_value=0.0012167627631060449 times:7.00s, 19 samples/iter
|
| 172 |
+
Validation: epoch=4, loss=0.13436
|
| 173 |
+
Pearson Correlation: 0.77530,p_value: 2.26372900549199e-39
|
| 174 |
+
Spearman Correlation: 0.76270,p_value; 1.9916052483497463e-37
|
| 175 |
+
epoch=4,iter=74,iter2=342,spearman_corr=0.762699033009611
|
| 176 |
+
|
| 177 |
+
epoch=4, iter=79, iter2=347, loss=0.12518, spearmanr=0.29609,p_value=0.21839105197676267 times:7.02s, 19 samples/iter
|
| 178 |
+
####################epoch:6
|
| 179 |
+
epoch=5, iter=9, iter2=366, loss=0.12524, spearmanr=0.36340,p_value=0.1261859425307603 times:6.21s, 19 samples/iter
|
| 180 |
+
Validation: epoch=5, loss=0.12377
|
| 181 |
+
Pearson Correlation: 0.75571,p_value: 2.1159983480331948e-36
|
| 182 |
+
Spearman Correlation: 0.74461,p_value; 7.716975690489039e-35
|
| 183 |
+
epoch=5,iter=14,iter2=371,spearman_corr=0.7446092749574404
|
| 184 |
+
|
| 185 |
+
epoch=5, iter=19, iter2=376, loss=0.12474, spearmanr=0.20562,p_value=0.3983761570665215 times:6.86s, 19 samples/iter
|
| 186 |
+
epoch=5, iter=29, iter2=386, loss=0.12510, spearmanr=0.44963,p_value=0.05343059504057587 times:4.45s, 19 samples/iter
|
| 187 |
+
Validation: epoch=5, loss=0.13737
|
| 188 |
+
Pearson Correlation: 0.76787,p_value: 3.2880711764211097e-38
|
| 189 |
+
Spearman Correlation: 0.75404,p_value; 3.687344016197934e-36
|
| 190 |
+
epoch=5,iter=29,iter2=386,spearman_corr=0.7540360342872052
|
| 191 |
+
|
| 192 |
+
epoch=5, iter=39, iter2=396, loss=0.12524, spearmanr=0.29516,p_value=0.21990644866845063 times:7.18s, 19 samples/iter
|
| 193 |
+
Validation: epoch=5, loss=0.12178
|
| 194 |
+
Pearson Correlation: 0.75806,p_value: 9.66025027783352e-37
|
| 195 |
+
Spearman Correlation: 0.75580,p_value; 2.0577200709231018e-36
|
| 196 |
+
epoch=5,iter=44,iter2=401,spearman_corr=0.7557967981576509
|
| 197 |
+
|
| 198 |
+
epoch=5, iter=49, iter2=406, loss=0.12515, spearmanr=0.25823,p_value=0.2857728995461941 times:7.04s, 19 samples/iter
|
| 199 |
+
epoch=5, iter=59, iter2=416, loss=0.12514, spearmanr=0.40703,p_value=0.083705299631416 times:4.51s, 19 samples/iter
|
| 200 |
+
Validation: epoch=5, loss=0.12166
|
| 201 |
+
Pearson Correlation: 0.78047,p_value: 3.323193321130947e-40
|
| 202 |
+
Spearman Correlation: 0.77141,p_value; 9.3107979939482e-39
|
| 203 |
+
epoch=5,iter=59,iter2=416,spearman_corr=0.771408998315061
|
| 204 |
+
|
| 205 |
+
epoch=5, iter=69, iter2=426, loss=0.12540, spearmanr=0.56528,p_value=0.011663855654244656 times:7.12s, 19 samples/iter
|
| 206 |
+
Validation: epoch=5, loss=0.13339
|
| 207 |
+
Pearson Correlation: 0.76356,p_value: 1.4820223362839754e-37
|
| 208 |
+
Spearman Correlation: 0.75298,p_value; 5.216221353817427e-36
|
| 209 |
+
epoch=5,iter=74,iter2=431,spearman_corr=0.7529818859962868
|
| 210 |
+
|
| 211 |
+
epoch=5, iter=79, iter2=436, loss=0.12508, spearmanr=0.15415,p_value=0.5286337877991265 times:6.97s, 19 samples/iter
|
| 212 |
+
####################epoch:7
|
| 213 |
+
epoch=6, iter=9, iter2=455, loss=0.12513, spearmanr=0.39226,p_value=0.09669258239398165 times:6.15s, 19 samples/iter
|
| 214 |
+
Validation: epoch=6, loss=0.12428
|
| 215 |
+
Pearson Correlation: 0.75354,p_value: 4.3402567554727935e-36
|
| 216 |
+
Spearman Correlation: 0.74841,p_value; 2.3013337045551163e-35
|
| 217 |
+
epoch=6,iter=14,iter2=460,spearman_corr=0.7484102369634287
|
| 218 |
+
|
| 219 |
+
epoch=6, iter=19, iter2=465, loss=0.12472, spearmanr=0.47956,p_value=0.03773620468041884 times:7.08s, 19 samples/iter
|
| 220 |
+
epoch=6, iter=29, iter2=475, loss=0.12498, spearmanr=0.15960,p_value=0.5139858162158699 times:4.85s, 19 samples/iter
|
| 221 |
+
Validation: epoch=6, loss=0.13476
|
| 222 |
+
Pearson Correlation: 0.78115,p_value: 2.5665482023341187e-40
|
| 223 |
+
Spearman Correlation: 0.77568,p_value; 1.9753068442748067e-39
|
| 224 |
+
epoch=6,iter=29,iter2=475,spearman_corr=0.7756758620249555
|
| 225 |
+
|
| 226 |
+
epoch=6, iter=39, iter2=485, loss=0.12529, spearmanr=0.14148,p_value=0.5634433822414773 times:9.02s, 19 samples/iter
|
| 227 |
+
Validation: epoch=6, loss=0.12884
|
| 228 |
+
Pearson Correlation: 0.76946,p_value: 1.8706279320992671e-38
|
| 229 |
+
Spearman Correlation: 0.76738,p_value; 3.898127526094882e-38
|
| 230 |
+
epoch=6,iter=44,iter2=490,spearman_corr=0.7673843955850939
|
| 231 |
+
|
| 232 |
+
epoch=6, iter=49, iter2=495, loss=0.12505, spearmanr=0.69543,p_value=0.0009471829067284215 times:7.07s, 19 samples/iter
|
| 233 |
+
Checkpoint saved at epoch 6, batch_idx 54, total_train_iter 499
|
| 234 |
+
iter 500 save model /home/chipan/shuffle_token_pan/checkpoint/2025_01_15_09_17_17/last_add_499.pth
|
| 235 |
+
epoch=6, iter=59, iter2=505, loss=0.12502, spearmanr=-0.03686,p_value=0.8809188815898528 times:16.69s, 19 samples/iter
|
| 236 |
+
Validation: epoch=6, loss=0.12015
|
| 237 |
+
Pearson Correlation: 0.76619,p_value: 5.93312460288179e-38
|
| 238 |
+
Spearman Correlation: 0.73110,p_value; 4.8104863574367074e-33
|
| 239 |
+
epoch=6,iter=59,iter2=505,spearman_corr=0.7310997467131011
|
| 240 |
+
|
| 241 |
+
epoch=6, iter=69, iter2=515, loss=0.12529, spearmanr=0.63269,p_value=0.003647468616422087 times:7.47s, 19 samples/iter
|
| 242 |
+
Validation: epoch=6, loss=0.12619
|
| 243 |
+
Pearson Correlation: 0.77047,p_value: 1.306044360433259e-38
|
| 244 |
+
Spearman Correlation: 0.74624,p_value; 4.609959766119631e-35
|
| 245 |
+
epoch=6,iter=74,iter2=520,spearman_corr=0.7462360506072291
|
| 246 |
+
|
| 247 |
+
epoch=6, iter=79, iter2=525, loss=0.12517, spearmanr=0.00088,p_value=0.9971362009801843 times:7.48s, 19 samples/iter
|
| 248 |
+
####################epoch:8
|
| 249 |
+
epoch=7, iter=9, iter2=544, loss=0.12471, spearmanr=0.43291,p_value=0.06411505012421273 times:6.19s, 19 samples/iter
|
| 250 |
+
Validation: epoch=7, loss=0.12369
|
| 251 |
+
Pearson Correlation: 0.72992,p_value: 6.82196677131776e-33
|
| 252 |
+
Spearman Correlation: 0.70533,p_value; 6.616327482388389e-30
|
| 253 |
+
epoch=7,iter=14,iter2=549,spearman_corr=0.7053348990623104
|
| 254 |
+
|
| 255 |
+
epoch=7, iter=19, iter2=554, loss=0.12471, spearmanr=0.44641,p_value=0.055372311542209215 times:7.16s, 19 samples/iter
|
| 256 |
+
epoch=7, iter=29, iter2=564, loss=0.12448, spearmanr=-0.16975,p_value=0.4872181494813679 times:4.51s, 19 samples/iter
|
| 257 |
+
Validation: epoch=7, loss=0.12066
|
| 258 |
+
Pearson Correlation: 0.75688,p_value: 1.4335189122786076e-36
|
| 259 |
+
Spearman Correlation: 0.74089,p_value; 2.470656975385912e-34
|
| 260 |
+
epoch=7,iter=29,iter2=564,spearman_corr=0.7408890806971561
|
| 261 |
+
|
| 262 |
+
epoch=7, iter=39, iter2=574, loss=0.12415, spearmanr=0.38160,p_value=0.10693120026706418 times:7.20s, 19 samples/iter
|
| 263 |
+
Validation: epoch=7, loss=0.12156
|
| 264 |
+
Pearson Correlation: 0.76751,p_value: 3.728944896670055e-38
|
| 265 |
+
Spearman Correlation: 0.73733,p_value; 7.3730366300427564e-34
|
| 266 |
+
epoch=7,iter=44,iter2=579,spearman_corr=0.7373345261064009
|
| 267 |
+
|
| 268 |
+
epoch=7, iter=49, iter2=584, loss=0.12407, spearmanr=0.24846,p_value=0.3050325195831854 times:7.18s, 19 samples/iter
|
| 269 |
+
epoch=7, iter=59, iter2=594, loss=0.12380, spearmanr=0.33701,p_value=0.15826246945187689 times:4.56s, 19 samples/iter
|
| 270 |
+
Validation: epoch=7, loss=0.11998
|
| 271 |
+
Pearson Correlation: 0.76499,p_value: 9.01272684884284e-38
|
| 272 |
+
Spearman Correlation: 0.75126,p_value; 9.160974659411157e-36
|
| 273 |
+
epoch=7,iter=59,iter2=594,spearman_corr=0.751258960965417
|
| 274 |
+
|
| 275 |
+
epoch=7, iter=69, iter2=604, loss=0.12392, spearmanr=-0.13964,p_value=0.5685648877239313 times:7.07s, 19 samples/iter
|
| 276 |
+
Validation: epoch=7, loss=0.12043
|
| 277 |
+
Pearson Correlation: 0.76311,p_value: 1.7271519250638242e-37
|
| 278 |
+
Spearman Correlation: 0.74691,p_value; 3.720086722894371e-35
|
| 279 |
+
epoch=7,iter=74,iter2=609,spearman_corr=0.746909621936986
|
| 280 |
+
|
| 281 |
+
epoch=7, iter=79, iter2=614, loss=0.12353, spearmanr=0.09833,p_value=0.6887931879309772 times:7.08s, 19 samples/iter
|
| 282 |
+
####################epoch:9
|
| 283 |
+
epoch=8, iter=9, iter2=633, loss=0.12316, spearmanr=0.61553,p_value=0.005024215038062236 times:6.12s, 19 samples/iter
|
| 284 |
+
Validation: epoch=8, loss=0.13064
|
| 285 |
+
Pearson Correlation: 0.76338,p_value: 1.5765878981621028e-37
|
| 286 |
+
Spearman Correlation: 0.74254,p_value; 1.4788651908561588e-34
|
| 287 |
+
epoch=8,iter=14,iter2=638,spearman_corr=0.7425377271300367
|
| 288 |
+
|
| 289 |
+
epoch=8, iter=19, iter2=643, loss=0.12317, spearmanr=0.30181,p_value=0.20920236698463887 times:6.90s, 19 samples/iter
|
| 290 |
+
epoch=8, iter=29, iter2=653, loss=0.12300, spearmanr=0.07206,p_value=0.7694118304118931 times:4.55s, 19 samples/iter
|
| 291 |
+
Validation: epoch=8, loss=0.12098
|
| 292 |
+
Pearson Correlation: 0.76472,p_value: 9.910006281519307e-38
|
| 293 |
+
Spearman Correlation: 0.74599,p_value; 4.983665455065689e-35
|
| 294 |
+
epoch=8,iter=29,iter2=653,spearman_corr=0.7459907240910606
|
| 295 |
+
|
| 296 |
+
epoch=8, iter=39, iter2=663, loss=0.12273, spearmanr=0.06708,p_value=0.7849689140859496 times:6.72s, 19 samples/iter
|
| 297 |
+
Validation: epoch=8, loss=0.12129
|
| 298 |
+
Pearson Correlation: 0.77608,p_value: 1.7030652860202378e-39
|
| 299 |
+
Spearman Correlation: 0.76065,p_value; 4.017456573385052e-37
|
| 300 |
+
epoch=8,iter=44,iter2=668,spearman_corr=0.7606493626944725
|
| 301 |
+
|
| 302 |
+
epoch=8, iter=49, iter2=673, loss=0.12266, spearmanr=0.11532,p_value=0.6382797978168254 times:6.77s, 19 samples/iter
|
| 303 |
+
epoch=8, iter=59, iter2=683, loss=0.12245, spearmanr=0.63608,p_value=0.0034160332904648203 times:4.51s, 19 samples/iter
|
| 304 |
+
Validation: epoch=8, loss=0.12290
|
| 305 |
+
Pearson Correlation: 0.74682,p_value: 3.8278805501811033e-35
|
| 306 |
+
Spearman Correlation: 0.73080,p_value; 5.252592254800676e-33
|
| 307 |
+
epoch=8,iter=59,iter2=683,spearman_corr=0.730803124314316
|
| 308 |
+
|
| 309 |
+
epoch=8, iter=69, iter2=693, loss=0.12257, spearmanr=0.07937,p_value=0.7467157320125752 times:6.98s, 19 samples/iter
|
| 310 |
+
Validation: epoch=8, loss=0.12221
|
| 311 |
+
Pearson Correlation: 0.76969,p_value: 1.7246489257468472e-38
|
| 312 |
+
Spearman Correlation: 0.74392,p_value; 9.596633057821532e-35
|
| 313 |
+
epoch=8,iter=74,iter2=698,spearman_corr=0.7439172293120416
|
| 314 |
+
|
| 315 |
+
epoch=8, iter=79, iter2=703, loss=0.12224, spearmanr=0.36923,p_value=0.11976783204529032 times:7.20s, 19 samples/iter
|
| 316 |
+
####################epoch:10
|
| 317 |
+
epoch=9, iter=9, iter2=722, loss=0.12195, spearmanr=-0.00264,p_value=0.9914504185727406 times:6.24s, 19 samples/iter
|
| 318 |
+
Validation: epoch=9, loss=0.12279
|
| 319 |
+
Pearson Correlation: 0.76661,p_value: 5.11124007226047e-38
|
| 320 |
+
Spearman Correlation: 0.74562,p_value; 5.60509758502484e-35
|
| 321 |
+
epoch=9,iter=14,iter2=727,spearman_corr=0.7456203410205838
|
| 322 |
+
|
| 323 |
+
epoch=9, iter=19, iter2=732, loss=0.12196, spearmanr=0.12159,p_value=0.6199988101678597 times:6.87s, 19 samples/iter
|
| 324 |
+
epoch=9, iter=29, iter2=742, loss=0.12185, spearmanr=0.47607,p_value=0.039358527487377074 times:4.55s, 19 samples/iter
|
| 325 |
+
Validation: epoch=9, loss=0.11954
|
| 326 |
+
Pearson Correlation: 0.76188,p_value: 2.6370091885483163e-37
|
| 327 |
+
Spearman Correlation: 0.74945,p_value; 1.6479610603869554e-35
|
| 328 |
+
epoch=9,iter=29,iter2=742,spearman_corr=0.7494474780119498
|
| 329 |
+
|
| 330 |
+
epoch=9, iter=39, iter2=752, loss=0.12163, spearmanr=0.11043,p_value=0.6526818616128638 times:7.07s, 19 samples/iter
|
| 331 |
+
Validation: epoch=9, loss=0.12704
|
| 332 |
+
Pearson Correlation: 0.74877,p_value: 2.0492009030716628e-35
|
| 333 |
+
Spearman Correlation: 0.73939,p_value; 3.923708169066917e-34
|
| 334 |
+
epoch=9,iter=44,iter2=757,spearman_corr=0.7393923500204354
|
| 335 |
+
|
| 336 |
+
epoch=9, iter=49, iter2=762, loss=0.12159, spearmanr=0.20387,p_value=0.4025083467079378 times:6.86s, 19 samples/iter
|
| 337 |
+
epoch=9, iter=59, iter2=772, loss=0.12144, spearmanr=0.45463,p_value=0.05051461057739181 times:4.52s, 19 samples/iter
|
| 338 |
+
Validation: epoch=9, loss=0.12603
|
| 339 |
+
Pearson Correlation: 0.76975,p_value: 1.6866517369777548e-38
|
| 340 |
+
Spearman Correlation: 0.75829,p_value; 8.936505489462421e-37
|
| 341 |
+
epoch=9,iter=59,iter2=772,spearman_corr=0.758288692188435
|
| 342 |
+
|
| 343 |
+
epoch=9, iter=69, iter2=782, loss=0.12156, spearmanr=0.02465,p_value=0.9202180647518026 times:6.79s, 19 samples/iter
|
| 344 |
+
Validation: epoch=9, loss=0.11995
|
| 345 |
+
Pearson Correlation: 0.71371,p_value: 6.8989965634788395e-31
|
| 346 |
+
Spearman Correlation: 0.68672,p_value; 7.676825015755849e-28
|
| 347 |
+
epoch=9,iter=74,iter2=787,spearman_corr=0.6867154069963538
|
| 348 |
+
|
| 349 |
+
epoch=9, iter=79, iter2=792, loss=0.12127, spearmanr=0.03608,p_value=0.8834301543187015 times:6.93s, 19 samples/iter
|
| 350 |
+
####################epoch:11
|
| 351 |
+
epoch=10, iter=9, iter2=811, loss=0.12102, spearmanr=0.11439,p_value=0.641010284769037 times:6.31s, 19 samples/iter
|
| 352 |
+
Validation: epoch=10, loss=0.12315
|
| 353 |
+
Pearson Correlation: 0.76162,p_value: 2.883765965104939e-37
|
| 354 |
+
Spearman Correlation: 0.74258,p_value; 1.4610312264673112e-34
|
| 355 |
+
epoch=10,iter=14,iter2=816,spearman_corr=0.7425765502938934
|
| 356 |
+
|
| 357 |
+
epoch=10, iter=19, iter2=821, loss=0.12104, spearmanr=0.49405,p_value=0.03155377115995278 times:7.11s, 19 samples/iter
|
| 358 |
+
epoch=10, iter=29, iter2=831, loss=0.12094, spearmanr=0.18541,p_value=0.4472807285885231 times:4.39s, 19 samples/iter
|
| 359 |
+
Validation: epoch=10, loss=0.11988
|
| 360 |
+
Pearson Correlation: 0.77611,p_value: 1.683264938719328e-39
|
| 361 |
+
Spearman Correlation: 0.76327,p_value; 1.635082291166561e-37
|
| 362 |
+
epoch=10,iter=29,iter2=831,spearman_corr=0.7632714814914676
|
| 363 |
+
|
| 364 |
+
epoch=10, iter=39, iter2=841, loss=0.12076, spearmanr=0.45768,p_value=0.04879543144646076 times:6.80s, 19 samples/iter
|
| 365 |
+
Validation: epoch=10, loss=0.12705
|
| 366 |
+
Pearson Correlation: 0.76706,p_value: 4.3671051856337484e-38
|
| 367 |
+
Spearman Correlation: 0.75189,p_value; 7.463460535613332e-36
|
| 368 |
+
epoch=10,iter=44,iter2=846,spearman_corr=0.7518875494446756
|
| 369 |
+
|
| 370 |
+
epoch=10, iter=49, iter2=851, loss=0.12073, spearmanr=-0.07953,p_value=0.7462231954914018 times:7.07s, 19 samples/iter
|
| 371 |
+
epoch=10, iter=59, iter2=861, loss=0.12059, spearmanr=0.40000,p_value=0.08971861060350508 times:4.49s, 19 samples/iter
|
| 372 |
+
Validation: epoch=10, loss=0.12477
|
| 373 |
+
Pearson Correlation: 0.73876,p_value: 4.761868840463546e-34
|
| 374 |
+
Spearman Correlation: 0.72263,p_value; 5.667825948642041e-32
|
| 375 |
+
epoch=10,iter=59,iter2=861,spearman_corr=0.7226261658589939
|
| 376 |
+
|
| 377 |
+
epoch=10, iter=69, iter2=871, loss=0.12071, spearmanr=0.12929,p_value=0.5978344367521193 times:6.85s, 19 samples/iter
|
| 378 |
+
Validation: epoch=10, loss=0.12212
|
| 379 |
+
Pearson Correlation: 0.72320,p_value: 4.810383843310683e-32
|
| 380 |
+
Spearman Correlation: 0.69369,p_value; 1.3507562441262418e-28
|
| 381 |
+
epoch=10,iter=74,iter2=876,spearman_corr=0.6936868099251632
|
| 382 |
+
|
| 383 |
+
epoch=10, iter=79, iter2=881, loss=0.12048, spearmanr=0.44415,p_value=0.05676859699145429 times:6.95s, 19 samples/iter
|
| 384 |
+
####################epoch:12
|
| 385 |
+
epoch=11, iter=9, iter2=900, loss=0.12027, spearmanr=0.23043,p_value=0.3425713567294796 times:5.96s, 19 samples/iter
|
| 386 |
+
Validation: epoch=11, loss=0.12545
|
| 387 |
+
Pearson Correlation: 0.75703,p_value: 1.364066973062601e-36
|
| 388 |
+
Spearman Correlation: 0.73349,p_value; 2.3588928244337173e-33
|
| 389 |
+
epoch=11,iter=14,iter2=905,spearman_corr=0.7334893529100283
|
| 390 |
+
|
| 391 |
+
epoch=11, iter=19, iter2=910, loss=0.12029, spearmanr=0.68016,p_value=0.0013535958579368422 times:7.18s, 19 samples/iter
|
| 392 |
+
epoch=11, iter=29, iter2=920, loss=0.12020, spearmanr=0.16674,p_value=0.4950659763305283 times:4.67s, 19 samples/iter
|
| 393 |
+
Validation: epoch=11, loss=0.12078
|
| 394 |
+
Pearson Correlation: 0.77604,p_value: 1.728156936322438e-39
|
| 395 |
+
Spearman Correlation: 0.75538,p_value; 2.3601004615847195e-36
|
| 396 |
+
epoch=11,iter=29,iter2=920,spearman_corr=0.7553842729816423
|
| 397 |
+
|
| 398 |
+
epoch=11, iter=39, iter2=930, loss=0.12003, spearmanr=0.44718,p_value=0.05490101180412668 times:7.20s, 19 samples/iter
|
| 399 |
+
Validation: epoch=11, loss=0.12584
|
| 400 |
+
Pearson Correlation: 0.75849,p_value: 8.348991853970325e-37
|
| 401 |
+
Spearman Correlation: 0.73067,p_value; 5.46437998369864e-33
|
| 402 |
+
epoch=11,iter=44,iter2=935,spearman_corr=0.7306696393070157
|
| 403 |
+
|
| 404 |
+
epoch=11, iter=49, iter2=940, loss=0.12003, spearmanr=0.61616,p_value=0.004966826078911069 times:7.34s, 19 samples/iter
|
| 405 |
+
epoch=11, iter=59, iter2=950, loss=0.11991, spearmanr=0.34879,p_value=0.14331971188246556 times:4.41s, 19 samples/iter
|
| 406 |
+
Validation: epoch=11, loss=0.12467
|
| 407 |
+
Pearson Correlation: 0.73723,p_value: 7.620667887420983e-34
|
| 408 |
+
Spearman Correlation: 0.73109,p_value; 4.8244163357446596e-33
|
| 409 |
+
epoch=11,iter=59,iter2=950,spearman_corr=0.7310899978689366
|
| 410 |
+
|
| 411 |
+
epoch=11, iter=69, iter2=960, loss=0.12003, spearmanr=-0.08278,p_value=0.7361720948214434 times:6.78s, 19 samples/iter
|
| 412 |
+
Validation: epoch=11, loss=0.12226
|
| 413 |
+
Pearson Correlation: 0.72682,p_value: 1.69346036121198e-32
|
| 414 |
+
Spearman Correlation: 0.68810,p_value; 5.453198360373893e-28
|
| 415 |
+
epoch=11,iter=74,iter2=965,spearman_corr=0.6881032415832955
|
| 416 |
+
|
| 417 |
+
epoch=11, iter=79, iter2=970, loss=0.11981, spearmanr=0.14857,p_value=0.5438321174229512 times:6.80s, 19 samples/iter
|
| 418 |
+
####################epoch:13
|
| 419 |
+
epoch=12, iter=9, iter2=989, loss=0.11963, spearmanr=-0.13778,p_value=0.5737821597271895 times:6.04s, 19 samples/iter
|
| 420 |
+
Validation: epoch=12, loss=0.12178
|
| 421 |
+
Pearson Correlation: 0.73603,p_value: 1.0952285659767685e-33
|
| 422 |
+
Spearman Correlation: 0.70416,p_value; 9.026169127114496e-30
|
| 423 |
+
epoch=12,iter=14,iter2=994,spearman_corr=0.7041611966787877
|
| 424 |
+
|
| 425 |
+
epoch=12, iter=19, iter2=999, loss=0.11967, spearmanr=0.52587,p_value=0.020747392993564432 times:6.92s, 19 samples/iter
|
| 426 |
+
Checkpoint saved at epoch 12, batch_idx 20, total_train_iter 999
|
| 427 |
+
iter 1000 save model /home/chipan/shuffle_token_pan/checkpoint/2025_01_15_09_17_17/last_add_999.pth
|
| 428 |
+
epoch=12, iter=29, iter2=1009, loss=0.11960, spearmanr=0.02904,p_value=0.9060680814985129 times:15.89s, 19 samples/iter
|
| 429 |
+
Validation: epoch=12, loss=0.11600
|
| 430 |
+
Pearson Correlation: 0.69000,p_value: 3.403711879637603e-28
|
| 431 |
+
Spearman Correlation: 0.68303,p_value; 1.8877977652556187e-27
|
| 432 |
+
epoch=12,iter=29,iter2=1009,spearman_corr=0.6830267098172247
|
| 433 |
+
|
| 434 |
+
epoch=12, iter=39, iter2=1019, loss=0.11947, spearmanr=0.53535,p_value=0.01817053519532404 times:7.38s, 19 samples/iter
|
| 435 |
+
Validation: epoch=12, loss=0.12067
|
| 436 |
+
Pearson Correlation: 0.65624,p_value: 8.907713803761855e-25
|
| 437 |
+
Spearman Correlation: 0.64436,p_value; 1.123426447131605e-23
|
| 438 |
+
epoch=12,iter=44,iter2=1024,spearman_corr=0.6443616925046775
|
| 439 |
+
|
| 440 |
+
epoch=12, iter=49, iter2=1029, loss=0.11946, spearmanr=0.25706,p_value=0.2880534494637485 times:7.71s, 19 samples/iter
|
| 441 |
+
epoch=12, iter=59, iter2=1039, loss=0.11936, spearmanr=0.02208,p_value=0.92852462111975 times:4.73s, 19 samples/iter
|
| 442 |
+
Validation: epoch=12, loss=0.11927
|
| 443 |
+
Pearson Correlation: 0.63080,p_value: 1.7753259136191143e-22
|
| 444 |
+
Spearman Correlation: 0.61683,p_value; 2.6513300656798113e-21
|
| 445 |
+
epoch=12,iter=59,iter2=1039,spearman_corr=0.6168331571345713
|
| 446 |
+
|
| 447 |
+
epoch=12, iter=69, iter2=1049, loss=0.11947, spearmanr=0.18750,p_value=0.44209000331332793 times:7.34s, 19 samples/iter
|
| 448 |
+
Validation: epoch=12, loss=0.11732
|
| 449 |
+
Pearson Correlation: 0.59446,p_value: 1.5389085250847322e-19
|
| 450 |
+
Spearman Correlation: 0.57815,p_value; 2.444879866937871e-18
|
| 451 |
+
epoch=12,iter=74,iter2=1054,spearman_corr=0.5781547543268516
|
| 452 |
+
|
| 453 |
+
epoch=12, iter=79, iter2=1059, loss=0.11929, spearmanr=0.20659,p_value=0.3961056376745542 times:7.65s, 19 samples/iter
|
| 454 |
+
####################epoch:14
|
| 455 |
+
epoch=13, iter=9, iter2=1078, loss=0.11941, spearmanr=0.44991,p_value=0.05326057506633919 times:6.33s, 19 samples/iter
|
| 456 |
+
Validation: epoch=13, loss=0.11712
|
| 457 |
+
Pearson Correlation: 0.69358,p_value: 1.3876094137716708e-28
|
| 458 |
+
Spearman Correlation: 0.67698,p_value; 8.016514624605661e-27
|
| 459 |
+
epoch=13,iter=14,iter2=1083,spearman_corr=0.6769824021283386
|
| 460 |
+
|
| 461 |
+
epoch=13, iter=19, iter2=1088, loss=0.11939, spearmanr=0.36604,p_value=0.12325228998882684 times:6.98s, 19 samples/iter
|
| 462 |
+
epoch=13, iter=29, iter2=1098, loss=0.11931, spearmanr=0.35796,p_value=0.13238369659470792 times:4.69s, 19 samples/iter
|
| 463 |
+
Validation: epoch=13, loss=0.11982
|
| 464 |
+
Pearson Correlation: 0.70610,p_value: 5.4044449280185115e-30
|
| 465 |
+
Spearman Correlation: 0.68201,p_value; 2.4135695588262474e-27
|
| 466 |
+
epoch=13,iter=29,iter2=1098,spearman_corr=0.6820099449165269
|
| 467 |
+
|
| 468 |
+
epoch=13, iter=39, iter2=1108, loss=0.11929, spearmanr=0.37676,p_value=0.1118298368736729 times:6.78s, 19 samples/iter
|
| 469 |
+
Validation: epoch=13, loss=0.11780
|
| 470 |
+
Pearson Correlation: 0.72387,p_value: 3.9726719084274077e-32
|
| 471 |
+
Spearman Correlation: 0.70827,p_value; 3.0224954901102418e-30
|
| 472 |
+
epoch=13,iter=44,iter2=1113,spearman_corr=0.7082701022071134
|
| 473 |
+
|
| 474 |
+
epoch=13, iter=49, iter2=1118, loss=0.11930, spearmanr=0.05719,p_value=0.8160994216066411 times:6.80s, 19 samples/iter
|
| 475 |
+
epoch=13, iter=59, iter2=1128, loss=0.11946, spearmanr=0.73190,p_value=0.000367909474654738 times:4.51s, 19 samples/iter
|
| 476 |
+
Validation: epoch=13, loss=0.11585
|
| 477 |
+
Pearson Correlation: 0.70669,p_value: 4.612925336694584e-30
|
| 478 |
+
Spearman Correlation: 0.69555,p_value; 8.427452332758538e-29
|
| 479 |
+
epoch=13,iter=59,iter2=1128,spearman_corr=0.6955459451264213
|
| 480 |
+
|
| 481 |
+
epoch=13, iter=69, iter2=1138, loss=0.11945, spearmanr=0.04726,p_value=0.8476383331103599 times:6.88s, 19 samples/iter
|
| 482 |
+
Validation: epoch=13, loss=0.11580
|
| 483 |
+
Pearson Correlation: 0.71069,p_value: 1.5746815118530568e-30
|
| 484 |
+
Spearman Correlation: 0.70148,p_value; 1.822578302080852e-29
|
| 485 |
+
epoch=13,iter=74,iter2=1143,spearman_corr=0.7014842344008854
|
| 486 |
+
|
| 487 |
+
epoch=13, iter=79, iter2=1148, loss=0.11952, spearmanr=0.71504,p_value=0.0005796556136575017 times:7.05s, 19 samples/iter
|
| 488 |
+
####################epoch:15
|
| 489 |
+
epoch=14, iter=9, iter2=1167, loss=0.11964, spearmanr=0.18453,p_value=0.4494747558980461 times:5.89s, 19 samples/iter
|
| 490 |
+
Validation: epoch=14, loss=0.11604
|
| 491 |
+
Pearson Correlation: 0.71057,p_value: 1.6236067152088572e-30
|
| 492 |
+
Spearman Correlation: 0.70611,p_value; 5.386533679277342e-30
|
| 493 |
+
epoch=14,iter=14,iter2=1172,spearman_corr=0.7061088338398674
|
| 494 |
+
|
| 495 |
+
epoch=14, iter=19, iter2=1177, loss=0.11966, spearmanr=0.72336,p_value=0.0004651095514841315 times:6.99s, 19 samples/iter
|
| 496 |
+
epoch=14, iter=29, iter2=1187, loss=0.11960, spearmanr=0.52576,p_value=0.020776519495230926 times:4.52s, 19 samples/iter
|
| 497 |
+
Validation: epoch=14, loss=0.12184
|
| 498 |
+
Pearson Correlation: 0.74007,p_value: 3.1835536977785765e-34
|
| 499 |
+
Spearman Correlation: 0.73022,p_value; 6.247782571495598e-33
|
| 500 |
+
epoch=14,iter=29,iter2=1187,spearman_corr=0.7302166227369803
|
| 501 |
+
|
| 502 |
+
epoch=14, iter=39, iter2=1197, loss=0.11958, spearmanr=0.35924,p_value=0.13090103505806733 times:6.86s, 19 samples/iter
|
| 503 |
+
Validation: epoch=14, loss=0.11856
|
| 504 |
+
Pearson Correlation: 0.72605,p_value: 2.1170907458962763e-32
|
| 505 |
+
Spearman Correlation: 0.71170,p_value; 1.1935749697056399e-30
|
| 506 |
+
epoch=14,iter=44,iter2=1202,spearman_corr=0.7117045041935296
|
| 507 |
+
|
| 508 |
+
epoch=14, iter=49, iter2=1207, loss=0.11958, spearmanr=0.46549,p_value=0.04459286130270019 times:7.01s, 19 samples/iter
|
| 509 |
+
epoch=14, iter=59, iter2=1217, loss=0.11974, spearmanr=-0.01930,p_value=0.9374978309415845 times:4.62s, 19 samples/iter
|
| 510 |
+
Validation: epoch=14, loss=0.11582
|
| 511 |
+
Pearson Correlation: 0.71583,p_value: 3.8438630007058277e-31
|
| 512 |
+
Spearman Correlation: 0.71101,p_value; 1.441069795992814e-30
|
| 513 |
+
epoch=14,iter=59,iter2=1217,spearman_corr=0.7110120255809819
|
| 514 |
+
|
| 515 |
+
epoch=14, iter=69, iter2=1227, loss=0.11972, spearmanr=0.22354,p_value=0.3575967213310497 times:6.95s, 19 samples/iter
|
| 516 |
+
Validation: epoch=14, loss=0.11595
|
| 517 |
+
Pearson Correlation: 0.74119,p_value: 2.2502277136149886e-34
|
| 518 |
+
Spearman Correlation: 0.74264,p_value; 1.4309964099135382e-34
|
| 519 |
+
epoch=14,iter=74,iter2=1232,spearman_corr=0.7426430013661703
|
| 520 |
+
|
| 521 |
+
epoch=14, iter=79, iter2=1237, loss=0.11980, spearmanr=0.27880,p_value=0.2477305090446527 times:6.94s, 19 samples/iter
|
| 522 |
+
####################epoch:16
|
| 523 |
+
epoch=15, iter=9, iter2=1256, loss=0.11991, spearmanr=0.50461,p_value=0.027570809598783046 times:5.88s, 19 samples/iter
|
| 524 |
+
Validation: epoch=15, loss=0.11611
|
| 525 |
+
Pearson Correlation: 0.74218,p_value: 1.651579435221151e-34
|
| 526 |
+
Spearman Correlation: 0.73005,p_value; 6.565584303909183e-33
|
| 527 |
+
epoch=15,iter=14,iter2=1261,spearman_corr=0.7300486253378878
|
| 528 |
+
|
| 529 |
+
epoch=15, iter=19, iter2=1266, loss=0.11991, spearmanr=0.42688,p_value=0.06833912280214188 times:6.99s, 19 samples/iter
|
| 530 |
+
epoch=15, iter=29, iter2=1276, loss=0.11986, spearmanr=0.35491,p_value=0.13595041387451717 times:4.57s, 19 samples/iter
|
| 531 |
+
Validation: epoch=15, loss=0.12136
|
| 532 |
+
Pearson Correlation: 0.73905,p_value: 4.362593817107524e-34
|
| 533 |
+
Spearman Correlation: 0.73314,p_value; 2.621865518272368e-33
|
| 534 |
+
epoch=15,iter=29,iter2=1276,spearman_corr=0.7331365456838262
|
| 535 |
+
|
| 536 |
+
epoch=15, iter=39, iter2=1286, loss=0.11983, spearmanr=0.01937,p_value=0.9372501060068235 times:6.71s, 19 samples/iter
|
| 537 |
+
Validation: epoch=15, loss=0.11889
|
| 538 |
+
Pearson Correlation: 0.73204,p_value: 3.640872611797287e-33
|
| 539 |
+
Spearman Correlation: 0.72090,p_value; 9.2530761875518e-32
|
| 540 |
+
epoch=15,iter=44,iter2=1291,spearman_corr=0.7209039714117238
|
| 541 |
+
|
| 542 |
+
epoch=15, iter=49, iter2=1296, loss=0.11983, spearmanr=0.25760,p_value=0.2870066158527269 times:6.86s, 19 samples/iter
|
| 543 |
+
epoch=15, iter=59, iter2=1306, loss=0.11998, spearmanr=0.84950,p_value=4.186452044708814e-06 times:4.42s, 19 samples/iter
|
| 544 |
+
Validation: epoch=15, loss=0.11580
|
| 545 |
+
Pearson Correlation: 0.72268,p_value: 5.588147917290723e-32
|
| 546 |
+
Spearman Correlation: 0.72205,p_value; 6.672229894815228e-32
|
| 547 |
+
epoch=15,iter=59,iter2=1306,spearman_corr=0.7220543653726168
|
| 548 |
+
|
| 549 |
+
epoch=15, iter=69, iter2=1316, loss=0.11996, spearmanr=0.39075,p_value=0.09809839013729416 times:6.84s, 19 samples/iter
|
| 550 |
+
Validation: epoch=15, loss=0.11651
|
| 551 |
+
Pearson Correlation: 0.73189,p_value: 3.80186060390423e-33
|
| 552 |
+
Spearman Correlation: 0.72272,p_value; 5.524277612049698e-32
|
| 553 |
+
epoch=15,iter=74,iter2=1321,spearman_corr=0.7227159444172344
|
| 554 |
+
|
| 555 |
+
epoch=15, iter=79, iter2=1326, loss=0.12002, spearmanr=0.58260,p_value=0.008854687545857436 times:7.14s, 19 samples/iter
|
| 556 |
+
####################epoch:17
|
| 557 |
+
epoch=16, iter=9, iter2=1345, loss=0.12011, spearmanr=0.68832,p_value=0.0011212648743033529 times:6.05s, 19 samples/iter
|
| 558 |
+
Validation: epoch=16, loss=0.11637
|
| 559 |
+
Pearson Correlation: 0.72641,p_value: 1.9054557715317887e-32
|
| 560 |
+
Spearman Correlation: 0.71879,p_value; 1.6793835094270916e-31
|
| 561 |
+
epoch=16,iter=14,iter2=1350,spearman_corr=0.7187920911218861
|
| 562 |
+
|
| 563 |
+
epoch=16, iter=19, iter2=1355, loss=0.12011, spearmanr=0.71504,p_value=0.0005796556136575017 times:6.70s, 19 samples/iter
|
| 564 |
+
epoch=16, iter=29, iter2=1365, loss=0.12006, spearmanr=0.45496,p_value=0.050324954246446375 times:4.43s, 19 samples/iter
|
| 565 |
+
Validation: epoch=16, loss=0.12050
|
| 566 |
+
Pearson Correlation: 0.66895,p_value: 5.202948740419903e-26
|
| 567 |
+
Spearman Correlation: 0.65316,p_value; 1.737326331766164e-24
|
| 568 |
+
epoch=16,iter=29,iter2=1365,spearman_corr=0.6531611173771715
|
| 569 |
+
|
| 570 |
+
epoch=16, iter=39, iter2=1375, loss=0.12004, spearmanr=0.32952,p_value=0.16830593655494056 times:6.65s, 19 samples/iter
|
| 571 |
+
Validation: epoch=16, loss=0.12106
|
| 572 |
+
Pearson Correlation: 0.69997,p_value: 2.703743835817864e-29
|
| 573 |
+
Spearman Correlation: 0.68146,p_value; 2.7528489474771276e-27
|
| 574 |
+
epoch=16,iter=44,iter2=1380,spearman_corr=0.6814639371696719
|
| 575 |
+
|
| 576 |
+
epoch=16, iter=49, iter2=1385, loss=0.12005, spearmanr=0.46093,p_value=0.047008850600647266 times:7.43s, 19 samples/iter
|
| 577 |
+
epoch=16, iter=59, iter2=1395, loss=0.12018, spearmanr=0.72568,p_value=0.00043676673710309254 times:4.39s, 19 samples/iter
|
| 578 |
+
Validation: epoch=16, loss=0.11580
|
| 579 |
+
Pearson Correlation: 0.69728,p_value: 5.414749631232283e-29
|
| 580 |
+
Spearman Correlation: 0.69204,p_value; 2.0448356718630306e-28
|
| 581 |
+
epoch=16,iter=59,iter2=1395,spearman_corr=0.6920409654851633
|
| 582 |
+
|
| 583 |
+
epoch=16, iter=69, iter2=1405, loss=0.12016, spearmanr=0.34492,p_value=0.14811444339964128 times:6.78s, 19 samples/iter
|
| 584 |
+
Validation: epoch=16, loss=0.11655
|
| 585 |
+
Pearson Correlation: 0.69178,p_value: 2.1859035238602913e-28
|
| 586 |
+
Spearman Correlation: 0.69031,p_value; 3.152871723717649e-28
|
| 587 |
+
epoch=16,iter=74,iter2=1410,spearman_corr=0.6903104628825956
|
| 588 |
+
|
| 589 |
+
epoch=16, iter=79, iter2=1415, loss=0.12022, spearmanr=0.12467,p_value=0.6110843351786199 times:6.99s, 19 samples/iter
|
| 590 |
+
####################epoch:18
|
| 591 |
+
epoch=17, iter=9, iter2=1434, loss=0.12029, spearmanr=0.33656,p_value=0.158859375273761 times:6.12s, 19 samples/iter
|
| 592 |
+
Validation: epoch=17, loss=0.11672
|
| 593 |
+
Pearson Correlation: 0.67557,p_value: 1.118137995222073e-26
|
| 594 |
+
Spearman Correlation: 0.65792,p_value; 6.16991900677492e-25
|
| 595 |
+
epoch=17,iter=14,iter2=1439,spearman_corr=0.6579185682636396
|
| 596 |
+
|
| 597 |
+
epoch=17, iter=19, iter2=1444, loss=0.12027, spearmanr=0.74758,p_value=0.00023376635809132745 times:6.86s, 19 samples/iter
|
| 598 |
+
epoch=17, iter=29, iter2=1454, loss=0.12021, spearmanr=0.25198,p_value=0.29800383645237133 times:4.41s, 19 samples/iter
|
| 599 |
+
Validation: epoch=17, loss=0.12130
|
| 600 |
+
Pearson Correlation: 0.40092,p_value: 9.916758969552575e-09
|
| 601 |
+
Spearman Correlation: 0.38432,p_value; 4.39587918635491e-08
|
| 602 |
+
epoch=17,iter=29,iter2=1454,spearman_corr=0.38431546381653947
|
| 603 |
+
|
| 604 |
+
epoch=17, iter=39, iter2=1464, loss=0.12019, spearmanr=0.11656,p_value=0.6346434609895153 times:6.69s, 19 samples/iter
|
| 605 |
+
Validation: epoch=17, loss=0.12111
|
| 606 |
+
Pearson Correlation: 0.59971,p_value: 6.098075642143221e-20
|
| 607 |
+
Spearman Correlation: 0.54489,p_value; 4.388141199160203e-16
|
| 608 |
+
epoch=17,iter=44,iter2=1469,spearman_corr=0.54488608143821
|
| 609 |
+
|
| 610 |
+
epoch=17, iter=49, iter2=1474, loss=0.12019, spearmanr=0.47662,p_value=0.03909880168540846 times:6.68s, 19 samples/iter
|
| 611 |
+
epoch=17, iter=59, iter2=1484, loss=0.12032, spearmanr=0.61599,p_value=0.0049819877142601296 times:4.66s, 19 samples/iter
|
| 612 |
+
Validation: epoch=17, loss=0.11588
|
| 613 |
+
Pearson Correlation: 0.69836,p_value: 4.1031847187203917e-29
|
| 614 |
+
Spearman Correlation: 0.67096,p_value; 3.2757044792213123e-26
|
| 615 |
+
epoch=17,iter=59,iter2=1484,spearman_corr=0.6709572094126588
|
| 616 |
+
|
| 617 |
+
epoch=17, iter=69, iter2=1494, loss=0.12030, spearmanr=0.21557,p_value=0.37542222103843614 times:6.79s, 19 samples/iter
|
| 618 |
+
Validation: epoch=17, loss=0.11621
|
| 619 |
+
Pearson Correlation: 0.67971,p_value: 4.187702396918751e-27
|
| 620 |
+
Spearman Correlation: 0.65854,p_value; 5.37891701325148e-25
|
| 621 |
+
epoch=17,iter=74,iter2=1499,spearman_corr=0.6585426639711498
|
| 622 |
+
|
| 623 |
+
Checkpoint saved at epoch 17, batch_idx 75, total_train_iter 1499
|
| 624 |
+
iter 1500 save model /home/chipan/shuffle_token_pan/checkpoint/2025_01_15_09_17_17/last_add_1499.pth
|
| 625 |
+
epoch=17, iter=79, iter2=1504, loss=0.12035, spearmanr=0.11298,p_value=0.6451570542224391 times:19.03s, 19 samples/iter
|
| 626 |
+
####################epoch:19
|
| 627 |
+
epoch=18, iter=9, iter2=1523, loss=0.12037, spearmanr=0.36516,p_value=0.12422474689592998 times:5.98s, 19 samples/iter
|
| 628 |
+
Validation: epoch=18, loss=0.11815
|
| 629 |
+
Pearson Correlation: 0.58255,p_value: 1.1770255890916378e-18
|
| 630 |
+
Spearman Correlation: 0.57875,p_value; 2.217048151749949e-18
|
| 631 |
+
epoch=18,iter=14,iter2=1528,spearman_corr=0.5787471963681959
|
| 632 |
+
|
| 633 |
+
epoch=18, iter=19, iter2=1533, loss=0.12045, spearmanr=-0.01582,p_value=0.9487563176578284 times:6.86s, 19 samples/iter
|
| 634 |
+
epoch=18, iter=29, iter2=1543, loss=0.12036, spearmanr=-0.20335,p_value=0.4037358622596873 times:4.37s, 19 samples/iter
|
| 635 |
+
Validation: epoch=18, loss=0.11796
|
| 636 |
+
Pearson Correlation: 0.64274,p_value: 1.5743988127648706e-23
|
| 637 |
+
Spearman Correlation: 0.63411,p_value; 9.156716942260326e-23
|
| 638 |
+
epoch=18,iter=29,iter2=1543,spearman_corr=0.6341149908179539
|
| 639 |
+
|
| 640 |
+
epoch=18, iter=39, iter2=1553, loss=0.12037, spearmanr=0.41934,p_value=0.07390397323726741 times:6.67s, 19 samples/iter
|
| 641 |
+
Validation: epoch=18, loss=0.11752
|
| 642 |
+
Pearson Correlation: 0.71010,p_value: 1.843431493897374e-30
|
| 643 |
+
Spearman Correlation: 0.70179,p_value; 1.6843217157231262e-29
|
| 644 |
+
epoch=18,iter=44,iter2=1558,spearman_corr=0.7017862544348401
|
| 645 |
+
|
| 646 |
+
epoch=18, iter=49, iter2=1563, loss=0.12028, spearmanr=0.60316,p_value=0.006259711216676435 times:6.89s, 19 samples/iter
|
| 647 |
+
epoch=18, iter=59, iter2=1573, loss=0.12016, spearmanr=0.15167,p_value=0.5353605108207018 times:4.68s, 19 samples/iter
|
| 648 |
+
Validation: epoch=18, loss=0.11779
|
| 649 |
+
Pearson Correlation: 0.73897,p_value: 4.469724055973148e-34
|
| 650 |
+
Spearman Correlation: 0.72940,p_value; 7.958464879367048e-33
|
| 651 |
+
epoch=18,iter=59,iter2=1573,spearman_corr=0.7293959767344018
|
| 652 |
+
|
| 653 |
+
epoch=18, iter=69, iter2=1583, loss=0.12016, spearmanr=0.18964,p_value=0.43680496059368734 times:7.12s, 19 samples/iter
|
| 654 |
+
Validation: epoch=18, loss=0.11680
|
| 655 |
+
Pearson Correlation: 0.71720,p_value: 2.624175234467766e-31
|
| 656 |
+
Spearman Correlation: 0.70685,p_value; 4.423684060339038e-30
|
| 657 |
+
epoch=18,iter=74,iter2=1588,spearman_corr=0.7068476384731135
|
| 658 |
+
|
| 659 |
+
epoch=18, iter=79, iter2=1593, loss=0.12005, spearmanr=0.44288,p_value=0.05756493680536293 times:7.08s, 19 samples/iter
|
| 660 |
+
####################epoch:20
|
| 661 |
+
epoch=19, iter=9, iter2=1612, loss=0.12004, spearmanr=0.51210,p_value=0.024990244295094718 times:6.14s, 19 samples/iter
|
| 662 |
+
Validation: epoch=19, loss=0.11860
|
| 663 |
+
Pearson Correlation: 0.73091,p_value: 5.082634800180054e-33
|
| 664 |
+
Spearman Correlation: 0.72494,p_value; 2.9140476753892667e-32
|
| 665 |
+
epoch=19,iter=14,iter2=1617,spearman_corr=0.7249430282856147
|
| 666 |
+
|
| 667 |
+
epoch=19, iter=19, iter2=1622, loss=0.12011, spearmanr=0.44793,p_value=0.05444779906107693 times:6.90s, 19 samples/iter
|
| 668 |
+
epoch=19, iter=29, iter2=1632, loss=0.12003, spearmanr=-0.03692,p_value=0.8807106566850423 times:4.55s, 19 samples/iter
|
| 669 |
+
Validation: epoch=19, loss=0.11744
|
| 670 |
+
Pearson Correlation: 0.72054,p_value: 1.0260596314740042e-31
|
| 671 |
+
Spearman Correlation: 0.71240,p_value; 9.881208625359727e-31
|
| 672 |
+
epoch=19,iter=29,iter2=1632,spearman_corr=0.7123966668388849
|
| 673 |
+
|
| 674 |
+
epoch=19, iter=39, iter2=1642, loss=0.12004, spearmanr=0.42227,p_value=0.07170157628173902 times:6.86s, 19 samples/iter
|
| 675 |
+
Validation: epoch=19, loss=0.11738
|
| 676 |
+
Pearson Correlation: 0.68817,p_value: 5.357692993664839e-28
|
| 677 |
+
Spearman Correlation: 0.67990,p_value; 4.007161057669206e-27
|
| 678 |
+
epoch=19,iter=44,iter2=1647,spearman_corr=0.6798988222590554
|
| 679 |
+
|
| 680 |
+
epoch=19, iter=49, iter2=1652, loss=0.11995, spearmanr=0.41037,p_value=0.08095947225835087 times:6.99s, 19 samples/iter
|
| 681 |
+
epoch=19, iter=59, iter2=1662, loss=0.11984, spearmanr=0.38033,p_value=0.10820937746877647 times:4.90s, 19 samples/iter
|
| 682 |
+
Validation: epoch=19, loss=0.11711
|
| 683 |
+
Pearson Correlation: 0.67746,p_value: 7.155103995822662e-27
|
| 684 |
+
Spearman Correlation: 0.66565,p_value; 1.1021372308975606e-25
|
| 685 |
+
epoch=19,iter=59,iter2=1662,spearman_corr=0.6656476766242723
|
| 686 |
+
|
| 687 |
+
epoch=19, iter=69, iter2=1672, loss=0.11984, spearmanr=0.57708,p_value=0.009683594002550333 times:6.88s, 19 samples/iter
|
| 688 |
+
Validation: epoch=19, loss=0.11693
|
| 689 |
+
Pearson Correlation: 0.69764,p_value: 4.9378147472166585e-29
|
| 690 |
+
Spearman Correlation: 0.68759,p_value; 6.18586411948474e-28
|
| 691 |
+
epoch=19,iter=74,iter2=1677,spearman_corr=0.6875925811910092
|
| 692 |
+
|
| 693 |
+
epoch=19, iter=79, iter2=1682, loss=0.11974, spearmanr=0.39614,p_value=0.09315383609197486 times:6.80s, 19 samples/iter
|
| 694 |
+
####################epoch:21
|
| 695 |
+
epoch=20, iter=9, iter2=1701, loss=0.11974, spearmanr=0.27472,p_value=0.25502349372959143 times:6.10s, 19 samples/iter
|
| 696 |
+
Validation: epoch=20, loss=0.11806
|
| 697 |
+
Pearson Correlation: 0.65614,p_value: 9.097942680675244e-25
|
| 698 |
+
Spearman Correlation: 0.64424,p_value; 1.1520826852344852e-23
|
| 699 |
+
epoch=20,iter=14,iter2=1706,spearman_corr=0.6442409537117691
|
| 700 |
+
|
| 701 |
+
epoch=20, iter=19, iter2=1711, loss=0.11981, spearmanr=0.58422,p_value=0.008623073864955175 times:7.10s, 19 samples/iter
|
| 702 |
+
epoch=20, iter=29, iter2=1721, loss=0.11974, spearmanr=0.45499,p_value=0.050309773635799734 times:4.54s, 19 samples/iter
|
| 703 |
+
Validation: epoch=20, loss=0.11746
|
| 704 |
+
Pearson Correlation: 0.62285,p_value: 8.409227967139184e-22
|
| 705 |
+
Spearman Correlation: 0.61791,p_value; 2.1640862587620714e-21
|
| 706 |
+
epoch=20,iter=29,iter2=1721,spearman_corr=0.6179068494056122
|
| 707 |
+
|
| 708 |
+
epoch=20, iter=39, iter2=1731, loss=0.11974, spearmanr=0.66579,p_value=0.001860406348643111 times:6.96s, 19 samples/iter
|
| 709 |
+
Validation: epoch=20, loss=0.11746
|
| 710 |
+
Pearson Correlation: 0.64982,p_value: 3.5529056659533175e-24
|
| 711 |
+
Spearman Correlation: 0.64252,p_value; 1.648843527478229e-23
|
| 712 |
+
epoch=20,iter=44,iter2=1736,spearman_corr=0.6425165733946422
|
| 713 |
+
|
| 714 |
+
epoch=20, iter=49, iter2=1741, loss=0.11967, spearmanr=0.23835,p_value=0.32577514573318633 times:6.75s, 19 samples/iter
|
| 715 |
+
epoch=20, iter=59, iter2=1751, loss=0.11956, spearmanr=0.51278,p_value=0.024766033586556692 times:4.55s, 19 samples/iter
|
| 716 |
+
Validation: epoch=20, loss=0.11755
|
| 717 |
+
Pearson Correlation: 0.64754,p_value: 5.765779623094944e-24
|
| 718 |
+
Spearman Correlation: 0.63259,p_value; 1.2436623667479763e-22
|
| 719 |
+
epoch=20,iter=59,iter2=1751,spearman_corr=0.6325869892990151
|
| 720 |
+
|
| 721 |
+
epoch=20, iter=69, iter2=1761, loss=0.11957, spearmanr=0.44826,p_value=0.05424737833975807 times:6.94s, 19 samples/iter
|
| 722 |
+
Validation: epoch=20, loss=0.11713
|
| 723 |
+
Pearson Correlation: 0.65487,p_value: 1.2003357823725623e-24
|
| 724 |
+
Spearman Correlation: 0.63670,p_value; 5.43091452734813e-23
|
| 725 |
+
epoch=20,iter=74,iter2=1766,spearman_corr=0.6367025641528251
|
| 726 |
+
|
| 727 |
+
epoch=20, iter=79, iter2=1771, loss=0.11947, spearmanr=0.57281,p_value=0.010364718174374921 times:7.15s, 19 samples/iter
|
| 728 |
+
####################epoch:22
|
| 729 |
+
epoch=21, iter=9, iter2=1790, loss=0.11947, spearmanr=-0.08432,p_value=0.7314415164132249 times:5.98s, 19 samples/iter
|
| 730 |
+
Validation: epoch=21, loss=0.11821
|
| 731 |
+
Pearson Correlation: 0.60147,p_value: 4.4585616627976393e-20
|
| 732 |
+
Spearman Correlation: 0.57158,p_value; 7.139680668543926e-18
|
| 733 |
+
epoch=21,iter=14,iter2=1795,spearman_corr=0.5715846077552986
|
| 734 |
+
|
| 735 |
+
epoch=21, iter=19, iter2=1800, loss=0.11954, spearmanr=0.03477,p_value=0.8876196970247823 times:7.02s, 19 samples/iter
|
| 736 |
+
epoch=21, iter=29, iter2=1810, loss=0.11949, spearmanr=0.61505,p_value=0.005068090016759205 times:4.61s, 19 samples/iter
|
| 737 |
+
Validation: epoch=21, loss=0.11959
|
| 738 |
+
Pearson Correlation: 0.55771,p_value: 6.358662438403742e-17
|
| 739 |
+
Spearman Correlation: 0.53531,p_value; 1.7625169392293315e-15
|
| 740 |
+
epoch=21,iter=29,iter2=1810,spearman_corr=0.535306963485517
|
| 741 |
+
|
| 742 |
+
epoch=21, iter=39, iter2=1820, loss=0.11950, spearmanr=0.18478,p_value=0.44886461364517516 times:6.72s, 19 samples/iter
|
| 743 |
+
Validation: epoch=21, loss=0.11770
|
| 744 |
+
Pearson Correlation: 0.50731,p_value: 8.04966445062949e-14
|
| 745 |
+
Spearman Correlation: 0.47878,p_value; 2.802572079310811e-12
|
| 746 |
+
epoch=21,iter=44,iter2=1825,spearman_corr=0.4787825488556255
|
| 747 |
+
|
| 748 |
+
epoch=21, iter=49, iter2=1830, loss=0.11943, spearmanr=0.11537,p_value=0.6381294418492818 times:6.80s, 19 samples/iter
|
| 749 |
+
epoch=21, iter=59, iter2=1840, loss=0.11933, spearmanr=0.26038,p_value=0.28165515161567395 times:4.72s, 19 samples/iter
|
| 750 |
+
Validation: epoch=21, loss=0.11765
|
| 751 |
+
Pearson Correlation: 0.51968,p_value: 1.5529400411006672e-14
|
| 752 |
+
Spearman Correlation: 0.49759,p_value; 2.8010751219406276e-13
|
| 753 |
+
epoch=21,iter=59,iter2=1840,spearman_corr=0.4975865303117048
|
| 754 |
+
|
| 755 |
+
epoch=21, iter=69, iter2=1850, loss=0.11933, spearmanr=0.07824,p_value=0.7501939020886261 times:7.17s, 19 samples/iter
|
| 756 |
+
Validation: epoch=21, loss=0.11763
|
| 757 |
+
Pearson Correlation: 0.52445,p_value: 8.090075206696867e-15
|
| 758 |
+
Spearman Correlation: 0.50891,p_value; 6.526946669000326e-14
|
| 759 |
+
epoch=21,iter=74,iter2=1855,spearman_corr=0.5089120376469407
|
| 760 |
+
|
| 761 |
+
epoch=21, iter=79, iter2=1860, loss=0.11924, spearmanr=0.42411,p_value=0.07034451814746182 times:6.78s, 19 samples/iter
|
| 762 |
+
####################epoch:23
|
| 763 |
+
epoch=22, iter=9, iter2=1879, loss=0.11924, spearmanr=0.14619,p_value=0.5503745805083213 times:5.90s, 19 samples/iter
|
| 764 |
+
Validation: epoch=22, loss=0.11817
|
| 765 |
+
Pearson Correlation: 0.44515,p_value: 1.233290830571221e-10
|
| 766 |
+
Spearman Correlation: 0.41640,p_value; 2.2946794930050105e-09
|
| 767 |
+
epoch=22,iter=14,iter2=1884,spearman_corr=0.4164017034212444
|
| 768 |
+
|
| 769 |
+
epoch=22, iter=19, iter2=1889, loss=0.11931, spearmanr=-0.15216,p_value=0.534046976776636 times:6.90s, 19 samples/iter
|
| 770 |
+
epoch=22, iter=29, iter2=1899, loss=0.11925, spearmanr=0.24262,p_value=0.31692457267966956 times:4.60s, 19 samples/iter
|
| 771 |
+
Validation: epoch=22, loss=0.11793
|
| 772 |
+
Pearson Correlation: 0.41961,p_value: 1.6776772193694e-09
|
| 773 |
+
Spearman Correlation: 0.40708,p_value; 5.5875676461610585e-09
|
| 774 |
+
epoch=22,iter=29,iter2=1899,spearman_corr=0.40708352764080485
|
| 775 |
+
|
| 776 |
+
epoch=22, iter=39, iter2=1909, loss=0.11926, spearmanr=-0.01582,p_value=0.9487337172542816 times:6.84s, 19 samples/iter
|
| 777 |
+
Validation: epoch=22, loss=0.11768
|
| 778 |
+
Pearson Correlation: 0.39867,p_value: 1.2195025433925366e-08
|
| 779 |
+
Spearman Correlation: 0.36465,p_value; 2.3103105808847929e-07
|
| 780 |
+
epoch=22,iter=44,iter2=1914,spearman_corr=0.36465424552961534
|
| 781 |
+
|
| 782 |
+
epoch=22, iter=49, iter2=1919, loss=0.11919, spearmanr=0.19701,p_value=0.4188499367186873 times:6.71s, 19 samples/iter
|
| 783 |
+
epoch=22, iter=59, iter2=1929, loss=0.11910, spearmanr=0.30349,p_value=0.20654554766000693 times:4.47s, 19 samples/iter
|
| 784 |
+
Validation: epoch=22, loss=0.11756
|
| 785 |
+
Pearson Correlation: 0.43901,p_value: 2.357951056630725e-10
|
| 786 |
+
Spearman Correlation: 0.40531,p_value; 6.598042344920672e-09
|
| 787 |
+
epoch=22,iter=59,iter2=1929,spearman_corr=0.40531093180497363
|
| 788 |
+
|
| 789 |
+
epoch=22, iter=69, iter2=1939, loss=0.11910, spearmanr=0.32572,p_value=0.1735547542530343 times:6.62s, 19 samples/iter
|
| 790 |
+
Validation: epoch=22, loss=0.11853
|
| 791 |
+
Pearson Correlation: 0.47030,p_value: 7.569821505737373e-12
|
| 792 |
+
Spearman Correlation: 0.46289,p_value; 1.7646387308380245e-11
|
| 793 |
+
epoch=22,iter=74,iter2=1944,spearman_corr=0.46288950881902147
|
| 794 |
+
|
| 795 |
+
epoch=22, iter=79, iter2=1949, loss=0.11901, spearmanr=0.66080,p_value=0.002069429077128982 times:6.74s, 19 samples/iter
|
| 796 |
+
####################epoch:24
|
| 797 |
+
epoch=23, iter=9, iter2=1968, loss=0.11902, spearmanr=0.42411,p_value=0.07034451814746182 times:6.04s, 19 samples/iter
|
| 798 |
+
Validation: epoch=23, loss=0.11787
|
| 799 |
+
Pearson Correlation: 0.39929,p_value: 1.1516365638897241e-08
|
| 800 |
+
Spearman Correlation: 0.37217,p_value; 1.2410065210298845e-07
|
| 801 |
+
epoch=23,iter=14,iter2=1973,spearman_corr=0.37217136935189654
|
| 802 |
+
|
| 803 |
+
epoch=23, iter=19, iter2=1978, loss=0.11909, spearmanr=-0.18706,p_value=0.44318290435304053 times:6.61s, 19 samples/iter
|
| 804 |
+
epoch=23, iter=29, iter2=1988, loss=0.11903, spearmanr=0.41590,p_value=0.07655185144934731 times:4.41s, 19 samples/iter
|
| 805 |
+
Validation: epoch=23, loss=0.11802
|
| 806 |
+
Pearson Correlation: 0.37041,p_value: 1.437214081079219e-07
|
| 807 |
+
Spearman Correlation: 0.36801,p_value; 1.7544204165050617e-07
|
| 808 |
+
epoch=23,iter=29,iter2=1988,spearman_corr=0.3680071900141596
|
| 809 |
+
|
| 810 |
+
epoch=23, iter=39, iter2=1998, loss=0.11903, spearmanr=-0.13451,p_value=0.5830059449397084 times:6.78s, 19 samples/iter
|
| 811 |
+
Checkpoint saved at epoch 23, batch_idx 41, total_train_iter 1999
|
| 812 |
+
iter 2000 save model /home/chipan/shuffle_token_pan/checkpoint/2025_01_15_09_17_17/last_add_1999.pth
|
| 813 |
+
Validation: epoch=23, loss=0.11909
|
| 814 |
+
Pearson Correlation: 0.43043,p_value: 5.706186079201814e-10
|
| 815 |
+
Spearman Correlation: 0.40680,p_value; 5.739784952846181e-09
|
| 816 |
+
epoch=23,iter=44,iter2=2003,spearman_corr=0.4067976160085378
|
| 817 |
+
|
| 818 |
+
epoch=23, iter=49, iter2=2008, loss=0.11897, spearmanr=0.28232,p_value=0.24156407442870276 times:17.37s, 19 samples/iter
|
| 819 |
+
epoch=23, iter=59, iter2=2018, loss=0.11888, spearmanr=-0.04837,p_value=0.8440990239322073 times:4.47s, 19 samples/iter
|
| 820 |
+
Validation: epoch=23, loss=0.12069
|
| 821 |
+
Pearson Correlation: 0.44134,p_value: 1.8462215090053746e-10
|
| 822 |
+
Spearman Correlation: 0.39912,p_value; 1.1701506170182049e-08
|
| 823 |
+
epoch=23,iter=59,iter2=2018,spearman_corr=0.39912059878188966
|
| 824 |
+
|
| 825 |
+
epoch=23, iter=69, iter2=2028, loss=0.11888, spearmanr=0.24956,p_value=0.3028312959249931 times:7.48s, 19 samples/iter
|
| 826 |
+
Validation: epoch=23, loss=0.11949
|
| 827 |
+
Pearson Correlation: 0.49679,p_value: 3.0976718586039897e-13
|
| 828 |
+
Spearman Correlation: 0.45584,p_value; 3.875582251820946e-11
|
| 829 |
+
epoch=23,iter=74,iter2=2033,spearman_corr=0.4558358069114128
|
| 830 |
+
|
| 831 |
+
epoch=23, iter=79, iter2=2038, loss=0.11879, spearmanr=0.12988,p_value=0.5961399623680771 times:7.15s, 19 samples/iter
|
| 832 |
+
####################epoch:25
|
| 833 |
+
epoch=24, iter=9, iter2=2057, loss=0.11876, spearmanr=0.44113,p_value=0.05868090824945709 times:5.63s, 19 samples/iter
|
| 834 |
+
Validation: epoch=24, loss=0.11885
|
| 835 |
+
Pearson Correlation: 0.62720,p_value: 3.6132555762410785e-22
|
| 836 |
+
Spearman Correlation: 0.57670,p_value; 3.1047819886536928e-18
|
| 837 |
+
epoch=24,iter=14,iter2=2062,spearman_corr=0.5767025123398593
|
| 838 |
+
|
| 839 |
+
epoch=24, iter=19, iter2=2067, loss=0.11875, spearmanr=0.51793,p_value=0.02311827434491012 times:6.50s, 19 samples/iter
|
| 840 |
+
epoch=24, iter=29, iter2=2077, loss=0.11876, spearmanr=0.26444,p_value=0.2739452459243888 times:4.55s, 19 samples/iter
|
| 841 |
+
Validation: epoch=24, loss=0.12097
|
| 842 |
+
Pearson Correlation: 0.72087,p_value: 9.335957160360634e-32
|
| 843 |
+
Spearman Correlation: 0.66315,p_value; 1.9323168907574448e-25
|
| 844 |
+
epoch=24,iter=29,iter2=2077,spearman_corr=0.663153265257744
|
| 845 |
+
|
| 846 |
+
epoch=24, iter=39, iter2=2087, loss=0.11876, spearmanr=0.02779,p_value=0.9100838747434039 times:6.47s, 19 samples/iter
|
| 847 |
+
Validation: epoch=24, loss=0.11915
|
| 848 |
+
Pearson Correlation: 0.73857,p_value: 5.052164517238802e-34
|
| 849 |
+
Spearman Correlation: 0.69449,p_value; 1.1026574311221714e-28
|
| 850 |
+
epoch=24,iter=44,iter2=2092,spearman_corr=0.6944883145803079
|
| 851 |
+
|
| 852 |
+
epoch=24, iter=49, iter2=2097, loss=0.11875, spearmanr=0.30925,p_value=0.19762307835498552 times:6.64s, 19 samples/iter
|
| 853 |
+
epoch=24, iter=59, iter2=2107, loss=0.11868, spearmanr=-0.12571,p_value=0.6080795544136303 times:4.47s, 19 samples/iter
|
| 854 |
+
Validation: epoch=24, loss=0.11851
|
| 855 |
+
Pearson Correlation: 0.76353,p_value: 1.4980386172118223e-37
|
| 856 |
+
Spearman Correlation: 0.72189,p_value; 6.992129495877441e-32
|
| 857 |
+
epoch=24,iter=59,iter2=2107,spearman_corr=0.7218899679855479
|
| 858 |
+
|
| 859 |
+
epoch=24, iter=69, iter2=2117, loss=0.11865, spearmanr=0.30071,p_value=0.210942218905656 times:7.06s, 19 samples/iter
|
| 860 |
+
Validation: epoch=24, loss=0.12837
|
| 861 |
+
Pearson Correlation: 0.75590,p_value: 1.986062959139064e-36
|
| 862 |
+
Spearman Correlation: 0.70942,p_value; 2.218243538304033e-30
|
| 863 |
+
epoch=24,iter=74,iter2=2122,spearman_corr=0.7094191999592525
|
| 864 |
+
|
| 865 |
+
epoch=24, iter=79, iter2=2127, loss=0.11865, spearmanr=0.34168,p_value=0.15221727495185722 times:6.59s, 19 samples/iter
|
| 866 |
+
####################epoch:26
|
| 867 |
+
epoch=25, iter=9, iter2=2146, loss=0.11862, spearmanr=0.36939,p_value=0.11959215454430437 times:5.63s, 19 samples/iter
|
| 868 |
+
Validation: epoch=25, loss=0.11784
|
| 869 |
+
Pearson Correlation: 0.71251,p_value: 9.584172196491567e-31
|
| 870 |
+
Spearman Correlation: 0.66578,p_value; 1.0696365183147781e-25
|
| 871 |
+
epoch=25,iter=14,iter2=2151,spearman_corr=0.6657799850826926
|
| 872 |
+
|
| 873 |
+
epoch=25, iter=19, iter2=2156, loss=0.11859, spearmanr=0.34301,p_value=0.15052383578474404 times:6.72s, 19 samples/iter
|
| 874 |
+
epoch=25, iter=29, iter2=2166, loss=0.11859, spearmanr=0.38137,p_value=0.1071624273015059 times:4.55s, 19 samples/iter
|
| 875 |
+
Validation: epoch=25, loss=0.12207
|
| 876 |
+
Pearson Correlation: 0.73413,p_value: 1.9467407861715982e-33
|
| 877 |
+
Spearman Correlation: 0.68373,p_value; 1.5918929752299891e-27
|
| 878 |
+
epoch=25,iter=29,iter2=2166,spearman_corr=0.6837298203000348
|
| 879 |
+
|
| 880 |
+
epoch=25, iter=39, iter2=2176, loss=0.11860, spearmanr=0.32864,p_value=0.1695097877459255 times:6.68s, 19 samples/iter
|
| 881 |
+
Validation: epoch=25, loss=0.12011
|
| 882 |
+
Pearson Correlation: 0.72351,p_value: 4.40537931355196e-32
|
| 883 |
+
Spearman Correlation: 0.67273,p_value; 2.171369077775595e-26
|
| 884 |
+
epoch=25,iter=44,iter2=2181,spearman_corr=0.6727319653188811
|
| 885 |
+
|
| 886 |
+
epoch=25, iter=49, iter2=2186, loss=0.11860, spearmanr=0.12137,p_value=0.6206211727010029 times:6.42s, 19 samples/iter
|
| 887 |
+
epoch=25, iter=59, iter2=2196, loss=0.11853, spearmanr=0.35447,p_value=0.13647343650783733 times:4.59s, 19 samples/iter
|
| 888 |
+
Validation: epoch=25, loss=0.11870
|
| 889 |
+
Pearson Correlation: 0.72275,p_value: 5.463111170940932e-32
|
| 890 |
+
Spearman Correlation: 0.68270,p_value; 2.043524503202994e-27
|
| 891 |
+
epoch=25,iter=59,iter2=2196,spearman_corr=0.6826991369958285
|
| 892 |
+
|
| 893 |
+
epoch=25, iter=69, iter2=2206, loss=0.11850, spearmanr=0.29674,p_value=0.21733114673343415 times:6.62s, 19 samples/iter
|
| 894 |
+
Validation: epoch=25, loss=0.12990
|
| 895 |
+
Pearson Correlation: 0.72081,p_value: 9.495886693273059e-32
|
| 896 |
+
Spearman Correlation: 0.67614,p_value; 9.775847212396705e-27
|
| 897 |
+
epoch=25,iter=74,iter2=2211,spearman_corr=0.676141697843294
|
| 898 |
+
|
| 899 |
+
epoch=25, iter=79, iter2=2216, loss=0.11850, spearmanr=0.27614,p_value=0.2524667257157677 times:6.59s, 19 samples/iter
|
| 900 |
+
####################epoch:27
|
| 901 |
+
epoch=26, iter=9, iter2=2235, loss=0.11847, spearmanr=0.11506,p_value=0.6390233145619958 times:5.85s, 19 samples/iter
|
| 902 |
+
Validation: epoch=26, loss=0.11792
|
| 903 |
+
Pearson Correlation: 0.70528,p_value: 6.70669894726059e-30
|
| 904 |
+
Spearman Correlation: 0.65667,p_value; 8.103263801962251e-25
|
| 905 |
+
epoch=26,iter=14,iter2=2240,spearman_corr=0.6566742258985132
|
| 906 |
+
|
| 907 |
+
epoch=26, iter=19, iter2=2245, loss=0.11845, spearmanr=0.51692,p_value=0.023432583768375564 times:6.70s, 19 samples/iter
|
| 908 |
+
epoch=26, iter=29, iter2=2255, loss=0.11845, spearmanr=0.70361,p_value=0.0007753480464528904 times:4.62s, 19 samples/iter
|
| 909 |
+
Validation: epoch=26, loss=0.12169
|
| 910 |
+
Pearson Correlation: 0.72059,p_value: 1.0126528833040059e-31
|
| 911 |
+
Spearman Correlation: 0.66257,p_value; 2.2005367735573796e-25
|
| 912 |
+
epoch=26,iter=29,iter2=2255,spearman_corr=0.662572375554387
|
| 913 |
+
|
| 914 |
+
epoch=26, iter=39, iter2=2265, loss=0.11845, spearmanr=0.32012,p_value=0.18150236621346708 times:6.71s, 19 samples/iter
|
| 915 |
+
Validation: epoch=26, loss=0.12039
|
| 916 |
+
Pearson Correlation: 0.68363,p_value: 1.6302573274650186e-27
|
| 917 |
+
Spearman Correlation: 0.63204,p_value; 1.3880387179060604e-22
|
| 918 |
+
epoch=26,iter=44,iter2=2270,spearman_corr=0.6320367488732491
|
| 919 |
+
|
| 920 |
+
epoch=26, iter=49, iter2=2275, loss=0.11845, spearmanr=0.46640,p_value=0.04412302876731472 times:6.98s, 19 samples/iter
|
| 921 |
+
epoch=26, iter=59, iter2=2285, loss=0.11838, spearmanr=0.23646,p_value=0.3297344200535479 times:4.56s, 19 samples/iter
|
| 922 |
+
Validation: epoch=26, loss=0.11869
|
| 923 |
+
Pearson Correlation: 0.65381,p_value: 1.510458895105062e-24
|
| 924 |
+
Spearman Correlation: 0.61824,p_value; 2.0309751179560775e-21
|
| 925 |
+
epoch=26,iter=59,iter2=2285,spearman_corr=0.6182416666249168
|
| 926 |
+
|
| 927 |
+
epoch=26, iter=69, iter2=2295, loss=0.11836, spearmanr=0.39419,p_value=0.09491494750001367 times:6.61s, 19 samples/iter
|
| 928 |
+
Validation: epoch=26, loss=0.13128
|
| 929 |
+
Pearson Correlation: 0.62895,p_value: 2.561197099118211e-22
|
| 930 |
+
Spearman Correlation: 0.59180,p_value; 2.4390445754226046e-19
|
| 931 |
+
epoch=26,iter=74,iter2=2300,spearman_corr=0.5918043550835741
|
| 932 |
+
|
| 933 |
+
epoch=26, iter=79, iter2=2305, loss=0.11835, spearmanr=0.07560,p_value=0.7583713895592128 times:6.50s, 19 samples/iter
|
| 934 |
+
####################epoch:28
|
| 935 |
+
epoch=27, iter=9, iter2=2324, loss=0.11832, spearmanr=0.13708,p_value=0.5757403782260488 times:5.80s, 19 samples/iter
|
| 936 |
+
Validation: epoch=27, loss=0.11793
|
| 937 |
+
Pearson Correlation: 0.58711,p_value: 5.456985981243219e-19
|
| 938 |
+
Spearman Correlation: 0.55752,p_value; 6.539504500285412e-17
|
| 939 |
+
epoch=27,iter=14,iter2=2329,spearman_corr=0.5575246823659995
|
| 940 |
+
|
| 941 |
+
epoch=27, iter=19, iter2=2334, loss=0.11829, spearmanr=0.02197,p_value=0.928869540953681 times:6.66s, 19 samples/iter
|
| 942 |
+
epoch=27, iter=29, iter2=2344, loss=0.11830, spearmanr=0.53691,p_value=0.017773853502121774 times:4.45s, 19 samples/iter
|
| 943 |
+
Validation: epoch=27, loss=0.12894
|
| 944 |
+
Pearson Correlation: 0.58019,p_value: 1.7446138677659505e-18
|
| 945 |
+
Spearman Correlation: 0.55116,p_value; 1.7226831367722814e-16
|
| 946 |
+
epoch=27,iter=29,iter2=2344,spearman_corr=0.5511605445280711
|
| 947 |
+
|
| 948 |
+
epoch=27, iter=39, iter2=2354, loss=0.11831, spearmanr=-0.02904,p_value=0.9060682987514483 times:6.82s, 19 samples/iter
|
| 949 |
+
Validation: epoch=27, loss=0.12413
|
| 950 |
+
Pearson Correlation: 0.58085,p_value: 1.5632058182051351e-18
|
| 951 |
+
Spearman Correlation: 0.53947,p_value; 9.688703878634444e-16
|
| 952 |
+
epoch=27,iter=44,iter2=2359,spearman_corr=0.5394666496852297
|
| 953 |
+
|
| 954 |
+
epoch=27, iter=49, iter2=2364, loss=0.11830, spearmanr=0.22340,p_value=0.35791481618903165 times:6.50s, 19 samples/iter
|
| 955 |
+
epoch=27, iter=59, iter2=2374, loss=0.11824, spearmanr=0.33216,p_value=0.16471415497956748 times:4.43s, 19 samples/iter
|
| 956 |
+
Validation: epoch=27, loss=0.11825
|
| 957 |
+
Pearson Correlation: 0.60580,p_value: 2.0436141652388972e-20
|
| 958 |
+
Spearman Correlation: 0.57350,p_value; 5.2334018965156734e-18
|
| 959 |
+
epoch=27,iter=59,iter2=2374,spearman_corr=0.5735040203577076
|
| 960 |
+
|
BigBang-Proton_Test_Results/Genome/Regulatory DNA to Predict Protein Expression/run---train-reg-promoter_gene_expression_regulatory_DNA.log
ADDED
|
@@ -0,0 +1,447 @@
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|
| 1 |
+
|
| 2 |
+
total iter per epoch :38
|
| 3 |
+
validation data numbers per epoch :5
|
| 4 |
+
data.shape: torch.Size([15, 82])
|
| 5 |
+
tx_v.shape: torch.Size([15])
|
| 6 |
+
total iter per epoch :38
|
| 7 |
+
validation data numbers per epoch :5
|
| 8 |
+
total iter per epoch :38
|
| 9 |
+
validation data numbers per epoch :5
|
| 10 |
+
total iter per epoch :38
|
| 11 |
+
validation data numbers per epoch :5
|
| 12 |
+
data.shape: torch.Size([15, 82])
|
| 13 |
+
tx_v.shape: torch.Size([15])
|
| 14 |
+
data.shape: torch.Size([15, 82])
|
| 15 |
+
tx_v.shape: torch.Size([15])
|
| 16 |
+
data.shape: torch.Size([15, 82])
|
| 17 |
+
tx_v.shape: torch.Size([15])
|
| 18 |
+
fc_reg.weight不匹配
|
| 19 |
+
fc_reg.bias不匹配
|
| 20 |
+
fc_reg.weight不匹配
|
| 21 |
+
fc_reg.bias不匹配
|
| 22 |
+
fc_reg.weight不匹配
|
| 23 |
+
fc_reg.bias不匹配
|
| 24 |
+
fc_reg.weight不匹配
|
| 25 |
+
fc_reg.bias不匹配
|
| 26 |
+
Total number of parameters: 3.82849548
|
| 27 |
+
Total number of parameters: 3.82849548
|
| 28 |
+
Total number of parameters: 3.82849548
|
| 29 |
+
Total number of parameters: 3.82849548
|
| 30 |
+
NCCL version 2.20.5+cuda12.4
|
| 31 |
+
Resuming training from epoch 1, batch_idx 171500, total_train_iter 171500
|
| 32 |
+
Resuming training from epoch 1, batch_idx 171500, total_train_iter 171500
|
| 33 |
+
Resuming training from epoch 1, batch_idx 171500, total_train_iter 171500
|
| 34 |
+
Resuming training from epoch 1, batch_idx 171500, total_train_iter 171500
|
| 35 |
+
started training...
|
| 36 |
+
started training...
|
| 37 |
+
started training...
|
| 38 |
+
started training...
|
| 39 |
+
####################epoch:2
|
| 40 |
+
NCCL version 2.20.5+cuda12.4
|
| 41 |
+
NCCL version 2.20.5+cuda12.4
|
| 42 |
+
NCCL version 2.20.5+cuda12.4
|
| 43 |
+
epoch=1, iter=9, iter2=10, loss=0.65521, spearmanr=-0.17707,p_value=0.5278415033492152 times:7.66s, 15 samples/iter
|
| 44 |
+
Validation: epoch=1, loss=0.79722
|
| 45 |
+
Pearson Correlation: 0.10720,p_value: 0.3878999948501587
|
| 46 |
+
Spearman Correlation: 0.07990,p_value; 0.5204178624709455
|
| 47 |
+
epoch=1,iter=14,iter2=15,spearman_corr=0.07989637121567897
|
| 48 |
+
|
| 49 |
+
epoch=1, iter=19, iter2=20, loss=0.76733, spearmanr=0.24417,p_value=0.38048383826914656 times:4.64s, 15 samples/iter
|
| 50 |
+
epoch=1, iter=29, iter2=30, loss=0.77983, spearmanr=0.49416,p_value=0.0611570290655702 times:3.50s, 15 samples/iter
|
| 51 |
+
Validation: epoch=1, loss=0.79545
|
| 52 |
+
Pearson Correlation: 0.67738,p_value: 3.081389865489115e-10
|
| 53 |
+
Spearman Correlation: 0.56036,p_value; 8.178618055551742e-07
|
| 54 |
+
epoch=1,iter=29,iter2=30,spearman_corr=0.5603576767735299
|
| 55 |
+
|
| 56 |
+
####################epoch:3
|
| 57 |
+
epoch=2, iter=9, iter2=48, loss=0.74222, spearmanr=0.50229,p_value=0.05638012275481917 times:5.51s, 15 samples/iter
|
| 58 |
+
Validation: epoch=2, loss=0.79476
|
| 59 |
+
Pearson Correlation: 0.59484,p_value: 1.1075703554297434e-07
|
| 60 |
+
Spearman Correlation: 0.54804,p_value; 1.5833475430447243e-06
|
| 61 |
+
epoch=2,iter=14,iter2=53,spearman_corr=0.5480441628243016
|
| 62 |
+
|
| 63 |
+
epoch=2, iter=19, iter2=58, loss=0.75652, spearmanr=0.55357,p_value=0.03228803778924151 times:5.44s, 15 samples/iter
|
| 64 |
+
epoch=2, iter=29, iter2=68, loss=0.76593, spearmanr=0.75293,p_value=0.0011954848948147 times:3.87s, 15 samples/iter
|
| 65 |
+
Validation: epoch=2, loss=0.83262
|
| 66 |
+
Pearson Correlation: 0.73661,p_value: 1.2047004019355434e-12
|
| 67 |
+
Spearman Correlation: 0.63941,p_value; 5.748230503028602e-09
|
| 68 |
+
epoch=2,iter=29,iter2=68,spearman_corr=0.6394056095076123
|
| 69 |
+
|
| 70 |
+
####################epoch:4
|
| 71 |
+
epoch=3, iter=9, iter2=86, loss=0.74562, spearmanr=0.35433,p_value=0.19505218103976127 times:5.12s, 15 samples/iter
|
| 72 |
+
Validation: epoch=3, loss=0.79370
|
| 73 |
+
Pearson Correlation: 0.64022,p_value: 5.419692694630385e-09
|
| 74 |
+
Spearman Correlation: 0.56704,p_value; 5.650868418174646e-07
|
| 75 |
+
epoch=3,iter=14,iter2=91,spearman_corr=0.5670389660914624
|
| 76 |
+
|
| 77 |
+
epoch=3, iter=19, iter2=96, loss=0.75979, spearmanr=0.85384,p_value=5.115105215604128e-05 times:5.67s, 15 samples/iter
|
| 78 |
+
epoch=3, iter=29, iter2=106, loss=0.76395, spearmanr=0.74866,p_value=0.0013213659004125608 times:3.68s, 15 samples/iter
|
| 79 |
+
Validation: epoch=3, loss=0.80388
|
| 80 |
+
Pearson Correlation: 0.77636,p_value: 1.174166050033152e-14
|
| 81 |
+
Spearman Correlation: 0.72107,p_value; 5.914409882447463e-12
|
| 82 |
+
epoch=3,iter=29,iter2=106,spearman_corr=0.721074050325767
|
| 83 |
+
|
| 84 |
+
####################epoch:5
|
| 85 |
+
epoch=4, iter=9, iter2=124, loss=0.74978, spearmanr=0.41443,p_value=0.1245697155941261 times:5.15s, 15 samples/iter
|
| 86 |
+
Validation: epoch=4, loss=0.79379
|
| 87 |
+
Pearson Correlation: 0.59595,p_value: 1.0346872159061604e-07
|
| 88 |
+
Spearman Correlation: 0.58364,p_value; 2.1748470001818976e-07
|
| 89 |
+
epoch=4,iter=14,iter2=129,spearman_corr=0.583641771030316
|
| 90 |
+
|
| 91 |
+
epoch=4, iter=19, iter2=134, loss=0.75730, spearmanr=0.71615,p_value=0.0026720563860855497 times:5.41s, 15 samples/iter
|
| 92 |
+
epoch=4, iter=29, iter2=144, loss=0.76199, spearmanr=0.75653,p_value=0.0010972215056006822 times:3.59s, 15 samples/iter
|
| 93 |
+
Validation: epoch=4, loss=0.85701
|
| 94 |
+
Pearson Correlation: 0.72780,p_value: 3.0106498891124822e-12
|
| 95 |
+
Spearman Correlation: 0.68411,p_value; 1.7522713235240854e-10
|
| 96 |
+
epoch=4,iter=29,iter2=144,spearman_corr=0.6841087435626793
|
| 97 |
+
|
| 98 |
+
####################epoch:6
|
| 99 |
+
epoch=5, iter=9, iter2=162, loss=0.75215, spearmanr=0.53497,p_value=0.03989554777716561 times:5.53s, 15 samples/iter
|
| 100 |
+
Validation: epoch=5, loss=0.80922
|
| 101 |
+
Pearson Correlation: 0.51916,p_value: 6.754656169505324e-06
|
| 102 |
+
Spearman Correlation: 0.48227,p_value; 3.587729600613044e-05
|
| 103 |
+
epoch=5,iter=14,iter2=167,spearman_corr=0.48226508473451746
|
| 104 |
+
|
| 105 |
+
epoch=5, iter=19, iter2=172, loss=0.75459, spearmanr=0.49418,p_value=0.06114480745732903 times:5.94s, 15 samples/iter
|
| 106 |
+
epoch=5, iter=29, iter2=182, loss=0.75691, spearmanr=0.66910,p_value=0.006374435857583901 times:3.67s, 15 samples/iter
|
| 107 |
+
Validation: epoch=5, loss=0.79850
|
| 108 |
+
Pearson Correlation: 0.71768,p_value: 8.25682317656673e-12
|
| 109 |
+
Spearman Correlation: 0.64471,p_value; 3.914993818301277e-09
|
| 110 |
+
epoch=5,iter=29,iter2=182,spearman_corr=0.6447061273383298
|
| 111 |
+
|
| 112 |
+
####################epoch:7
|
| 113 |
+
epoch=6, iter=9, iter2=200, loss=0.74999, spearmanr=0.30632,p_value=0.26682333514992385 times:5.03s, 15 samples/iter
|
| 114 |
+
Validation: epoch=6, loss=0.80708
|
| 115 |
+
Pearson Correlation: 0.52081,p_value: 6.237779416551348e-06
|
| 116 |
+
Spearman Correlation: 0.49789,p_value; 1.810608423196146e-05
|
| 117 |
+
epoch=6,iter=14,iter2=205,spearman_corr=0.49789239836546817
|
| 118 |
+
|
| 119 |
+
epoch=6, iter=19, iter2=210, loss=0.75165, spearmanr=0.28113,p_value=0.31009050577478603 times:5.38s, 15 samples/iter
|
| 120 |
+
epoch=6, iter=29, iter2=220, loss=0.75392, spearmanr=0.67445,p_value=0.0058188300383247345 times:3.39s, 15 samples/iter
|
| 121 |
+
Validation: epoch=6, loss=0.79639
|
| 122 |
+
Pearson Correlation: 0.67135,p_value: 5.045499573697043e-10
|
| 123 |
+
Spearman Correlation: 0.61163,p_value; 3.835382328566407e-08
|
| 124 |
+
epoch=6,iter=29,iter2=220,spearman_corr=0.6116252802424584
|
| 125 |
+
|
| 126 |
+
####################epoch:8
|
| 127 |
+
epoch=7, iter=9, iter2=238, loss=0.74741, spearmanr=0.60903,p_value=0.01596012092014902 times:5.56s, 15 samples/iter
|
| 128 |
+
Validation: epoch=7, loss=0.79346
|
| 129 |
+
Pearson Correlation: 0.55700,p_value: 9.816266128837015e-07
|
| 130 |
+
Spearman Correlation: 0.50012,p_value; 1.6378313380666833e-05
|
| 131 |
+
epoch=7,iter=14,iter2=243,spearman_corr=0.5001213459283772
|
| 132 |
+
|
| 133 |
+
epoch=7, iter=19, iter2=248, loss=0.74970, spearmanr=0.39893,p_value=0.1407560883087637 times:5.43s, 15 samples/iter
|
| 134 |
+
epoch=7, iter=29, iter2=258, loss=0.75105, spearmanr=0.49104,p_value=0.0630665231902101 times:3.57s, 15 samples/iter
|
| 135 |
+
Validation: epoch=7, loss=0.80003
|
| 136 |
+
Pearson Correlation: 0.55765,p_value: 9.477612934460922e-07
|
| 137 |
+
Spearman Correlation: 0.51622,p_value; 7.770903391638252e-06
|
| 138 |
+
epoch=7,iter=29,iter2=258,spearman_corr=0.5162197368048972
|
| 139 |
+
|
| 140 |
+
####################epoch:9
|
| 141 |
+
epoch=8, iter=9, iter2=276, loss=0.74529, spearmanr=0.37504,p_value=0.16838445469997695 times:5.47s, 15 samples/iter
|
| 142 |
+
Validation: epoch=8, loss=0.79365
|
| 143 |
+
Pearson Correlation: 0.37771,p_value: 0.0016262788558378816
|
| 144 |
+
Spearman Correlation: 0.32388,p_value; 0.0075031405483868075
|
| 145 |
+
epoch=8,iter=14,iter2=281,spearman_corr=0.32387802773604923
|
| 146 |
+
|
| 147 |
+
epoch=8, iter=19, iter2=286, loss=0.74818, spearmanr=0.41490,p_value=0.12410242173109891 times:5.62s, 15 samples/iter
|
| 148 |
+
epoch=8, iter=29, iter2=296, loss=0.74971, spearmanr=0.53274,p_value=0.04088885401571942 times:3.94s, 15 samples/iter
|
| 149 |
+
Validation: epoch=8, loss=0.82639
|
| 150 |
+
Pearson Correlation: 0.41261,p_value: 0.0005209079245105386
|
| 151 |
+
Spearman Correlation: 0.39328,p_value; 0.0009935659317967364
|
| 152 |
+
epoch=8,iter=29,iter2=296,spearman_corr=0.3932843679049363
|
| 153 |
+
|
| 154 |
+
####################epoch:10
|
| 155 |
+
epoch=9, iter=9, iter2=314, loss=0.74498, spearmanr=0.54155,p_value=0.037066369519369716 times:5.21s, 15 samples/iter
|
| 156 |
+
Validation: epoch=9, loss=0.80255
|
| 157 |
+
Pearson Correlation: 0.31100,p_value: 0.010420049540698528
|
| 158 |
+
Spearman Correlation: 0.33277,p_value; 0.005932185959364782
|
| 159 |
+
epoch=9,iter=14,iter2=319,spearman_corr=0.3327742055527074
|
| 160 |
+
|
| 161 |
+
epoch=9, iter=19, iter2=324, loss=0.74639, spearmanr=0.38490,p_value=0.15657784453744794 times:5.24s, 15 samples/iter
|
| 162 |
+
epoch=9, iter=29, iter2=334, loss=0.74730, spearmanr=0.60699,p_value=0.01641681189272149 times:3.69s, 15 samples/iter
|
| 163 |
+
Validation: epoch=9, loss=0.79552
|
| 164 |
+
Pearson Correlation: 0.60446,p_value: 6.07566761345879e-08
|
| 165 |
+
Spearman Correlation: 0.52680,p_value; 4.662554275620706e-06
|
| 166 |
+
epoch=9,iter=29,iter2=334,spearman_corr=0.5267950919522189
|
| 167 |
+
|
| 168 |
+
####################epoch:11
|
| 169 |
+
epoch=10, iter=9, iter2=352, loss=0.74303, spearmanr=0.66069,p_value=0.007333274358109857 times:5.44s, 15 samples/iter
|
| 170 |
+
Validation: epoch=10, loss=0.80742
|
| 171 |
+
Pearson Correlation: 0.20347,p_value: 0.09865019470453262
|
| 172 |
+
Spearman Correlation: 0.27246,p_value; 0.025707131654091896
|
| 173 |
+
epoch=10,iter=14,iter2=357,spearman_corr=0.27246139632151867
|
| 174 |
+
|
| 175 |
+
epoch=10, iter=19, iter2=362, loss=0.74410, spearmanr=0.17774,p_value=0.5262482172790564 times:5.50s, 15 samples/iter
|
| 176 |
+
epoch=10, iter=29, iter2=372, loss=0.74469, spearmanr=0.58782,p_value=0.02119396530663213 times:3.57s, 15 samples/iter
|
| 177 |
+
Validation: epoch=10, loss=0.79631
|
| 178 |
+
Pearson Correlation: 0.60183,p_value: 7.174721616820534e-08
|
| 179 |
+
Spearman Correlation: 0.55326,p_value; 1.2007751567654305e-06
|
| 180 |
+
epoch=10,iter=29,iter2=372,spearman_corr=0.5532595648957107
|
| 181 |
+
|
| 182 |
+
####################epoch:12
|
| 183 |
+
epoch=11, iter=9, iter2=390, loss=0.74072, spearmanr=0.45587,p_value=0.08766813978723544 times:5.07s, 15 samples/iter
|
| 184 |
+
Validation: epoch=11, loss=0.80115
|
| 185 |
+
Pearson Correlation: 0.36283,p_value: 0.0025482408236712217
|
| 186 |
+
Spearman Correlation: 0.37075,p_value; 0.002011593530363076
|
| 187 |
+
epoch=11,iter=14,iter2=395,spearman_corr=0.37075367971715417
|
| 188 |
+
|
| 189 |
+
epoch=11, iter=19, iter2=400, loss=0.74200, spearmanr=0.27594,p_value=0.3194964649689256 times:5.52s, 15 samples/iter
|
| 190 |
+
epoch=11, iter=29, iter2=410, loss=0.74263, spearmanr=0.47227,p_value=0.0754742534674208 times:3.78s, 15 samples/iter
|
| 191 |
+
Validation: epoch=11, loss=0.82263
|
| 192 |
+
Pearson Correlation: 0.36613,p_value: 0.002310579875484109
|
| 193 |
+
Spearman Correlation: 0.35386,p_value; 0.0033070572649205776
|
| 194 |
+
epoch=11,iter=29,iter2=410,spearman_corr=0.35386080098254985
|
| 195 |
+
|
| 196 |
+
####################epoch:13
|
| 197 |
+
epoch=12, iter=9, iter2=428, loss=0.73909, spearmanr=0.35559,p_value=0.19334674303549867 times:5.26s, 15 samples/iter
|
| 198 |
+
Validation: epoch=12, loss=0.81355
|
| 199 |
+
Pearson Correlation: 0.19993,p_value: 0.10477811843156815
|
| 200 |
+
Spearman Correlation: 0.26839,p_value; 0.028091842474696673
|
| 201 |
+
epoch=12,iter=14,iter2=433,spearman_corr=0.26839184222673657
|
| 202 |
+
|
| 203 |
+
epoch=12, iter=19, iter2=438, loss=0.73999, spearmanr=0.03578,p_value=0.8992677954879353 times:5.37s, 15 samples/iter
|
| 204 |
+
epoch=12, iter=29, iter2=448, loss=0.74073, spearmanr=0.68347,p_value=0.004967678042305151 times:3.67s, 15 samples/iter
|
| 205 |
+
Validation: epoch=12, loss=0.80543
|
| 206 |
+
Pearson Correlation: 0.63006,p_value: 1.1119378307000716e-08
|
| 207 |
+
Spearman Correlation: 0.54732,p_value; 1.6445731151010378e-06
|
| 208 |
+
epoch=12,iter=29,iter2=448,spearman_corr=0.5473218094928496
|
| 209 |
+
|
| 210 |
+
####################epoch:14
|
| 211 |
+
epoch=13, iter=9, iter2=466, loss=0.73749, spearmanr=0.24461,p_value=0.3795949959644799 times:5.18s, 15 samples/iter
|
| 212 |
+
Validation: epoch=13, loss=0.80994
|
| 213 |
+
Pearson Correlation: 0.08504,p_value: 0.4938548803329468
|
| 214 |
+
Spearman Correlation: 0.11216,p_value; 0.3661797076693436
|
| 215 |
+
epoch=13,iter=14,iter2=471,spearman_corr=0.11216057211020679
|
| 216 |
+
|
| 217 |
+
epoch=13, iter=19, iter2=476, loss=0.73838, spearmanr=-0.15398,p_value=0.5837422920648789 times:5.37s, 15 samples/iter
|
| 218 |
+
epoch=13, iter=29, iter2=486, loss=0.73860, spearmanr=0.44763,p_value=0.09429904221766619 times:3.69s, 15 samples/iter
|
| 219 |
+
Validation: epoch=13, loss=0.81137
|
| 220 |
+
Pearson Correlation: 0.44625,p_value: 0.0001537586358608678
|
| 221 |
+
Spearman Correlation: 0.48407,p_value; 3.321497497826073e-05
|
| 222 |
+
epoch=13,iter=29,iter2=486,spearman_corr=0.4840656202769509
|
| 223 |
+
|
| 224 |
+
####################epoch:15
|
| 225 |
+
epoch=14, iter=9, iter2=504, loss=0.73564, spearmanr=0.05264,p_value=0.852201867451684 times:5.36s, 15 samples/iter
|
| 226 |
+
Validation: epoch=14, loss=0.80537
|
| 227 |
+
Pearson Correlation: 0.13226,p_value: 0.28600364923477173
|
| 228 |
+
Spearman Correlation: 0.15153,p_value; 0.2209275377924623
|
| 229 |
+
epoch=14,iter=14,iter2=509,spearman_corr=0.15152972323660732
|
| 230 |
+
|
| 231 |
+
epoch=14, iter=19, iter2=514, loss=0.73668, spearmanr=0.16265,p_value=0.5624860699025238 times:5.43s, 15 samples/iter
|
| 232 |
+
epoch=14, iter=29, iter2=524, loss=0.73700, spearmanr=0.29875,p_value=0.27942444062671423 times:3.92s, 15 samples/iter
|
| 233 |
+
Validation: epoch=14, loss=0.80852
|
| 234 |
+
Pearson Correlation: 0.37186,p_value: 0.0019453298300504684
|
| 235 |
+
Spearman Correlation: 0.33545,p_value; 0.005519722485612274
|
| 236 |
+
epoch=14,iter=29,iter2=524,spearman_corr=0.3354528738451696
|
| 237 |
+
|
| 238 |
+
####################epoch:16
|
| 239 |
+
epoch=15, iter=9, iter2=542, loss=0.73428, spearmanr=0.24753,p_value=0.3737459680059879 times:5.50s, 15 samples/iter
|
| 240 |
+
Validation: epoch=15, loss=0.80091
|
| 241 |
+
Pearson Correlation: 0.20810,p_value: 0.09104523062705994
|
| 242 |
+
Spearman Correlation: 0.21053,p_value; 0.0872611432938775
|
| 243 |
+
epoch=15,iter=14,iter2=547,spearman_corr=0.21052633702576842
|
| 244 |
+
|
| 245 |
+
epoch=15, iter=19, iter2=552, loss=0.73531, spearmanr=0.23648,p_value=0.39613496571592133 times:5.44s, 15 samples/iter
|
| 246 |
+
epoch=15, iter=29, iter2=562, loss=0.73543, spearmanr=0.37355,p_value=0.17021949847976173 times:3.49s, 15 samples/iter
|
| 247 |
+
Validation: epoch=15, loss=0.82669
|
| 248 |
+
Pearson Correlation: 0.25216,p_value: 0.03953263536095619
|
| 249 |
+
Spearman Correlation: 0.23076,p_value; 0.060281401611787085
|
| 250 |
+
epoch=15,iter=29,iter2=562,spearman_corr=0.23075774098586146
|
| 251 |
+
|
| 252 |
+
####################epoch:17
|
| 253 |
+
epoch=16, iter=9, iter2=580, loss=0.73276, spearmanr=-0.12051,p_value=0.668800054731205 times:5.34s, 15 samples/iter
|
| 254 |
+
Validation: epoch=16, loss=0.80790
|
| 255 |
+
Pearson Correlation: 0.11132,p_value: 0.36982211470603943
|
| 256 |
+
Spearman Correlation: 0.10694,p_value; 0.3890526716287488
|
| 257 |
+
epoch=16,iter=14,iter2=585,spearman_corr=0.10693929377981223
|
| 258 |
+
|
| 259 |
+
epoch=16, iter=19, iter2=590, loss=0.73382, spearmanr=-0.09148,p_value=0.7457512615510298 times:5.40s, 15 samples/iter
|
| 260 |
+
epoch=16, iter=29, iter2=600, loss=0.73397, spearmanr=0.42346,p_value=0.11575555078082327 times:3.58s, 15 samples/iter
|
| 261 |
+
Validation: epoch=16, loss=0.81221
|
| 262 |
+
Pearson Correlation: 0.20556,p_value: 0.09516486525535583
|
| 263 |
+
Spearman Correlation: 0.20091,p_value; 0.103053294686241
|
| 264 |
+
epoch=16,iter=29,iter2=600,spearman_corr=0.2009084428056533
|
| 265 |
+
|
| 266 |
+
####################epoch:18
|
| 267 |
+
epoch=17, iter=9, iter2=618, loss=0.73156, spearmanr=0.00000,p_value=1.0 times:5.44s, 15 samples/iter
|
| 268 |
+
Validation: epoch=17, loss=0.80910
|
| 269 |
+
Pearson Correlation: 0.14725,p_value: 0.23441030085086823
|
| 270 |
+
Spearman Correlation: 0.13170,p_value; 0.2880866994804675
|
| 271 |
+
epoch=17,iter=14,iter2=623,spearman_corr=0.13169614182793604
|
| 272 |
+
|
| 273 |
+
epoch=17, iter=19, iter2=628, loss=0.73229, spearmanr=-0.09490,p_value=0.7365609070130484 times:5.40s, 15 samples/iter
|
| 274 |
+
epoch=17, iter=29, iter2=638, loss=0.73239, spearmanr=0.25962,p_value=0.3500703175353669 times:3.55s, 15 samples/iter
|
| 275 |
+
Validation: epoch=17, loss=0.82879
|
| 276 |
+
Pearson Correlation: 0.29134,p_value: 0.016756096854805946
|
| 277 |
+
Spearman Correlation: 0.29286,p_value; 0.016172151546242176
|
| 278 |
+
epoch=17,iter=29,iter2=638,spearman_corr=0.29285633971931596
|
| 279 |
+
|
| 280 |
+
####################epoch:19
|
| 281 |
+
epoch=18, iter=9, iter2=656, loss=0.73013, spearmanr=-0.04152,p_value=0.8832007194537921 times:5.46s, 15 samples/iter
|
| 282 |
+
Validation: epoch=18, loss=0.80437
|
| 283 |
+
Pearson Correlation: 0.23982,p_value: 0.05061575770378113
|
| 284 |
+
Spearman Correlation: 0.19730,p_value; 0.109520053741386
|
| 285 |
+
epoch=18,iter=14,iter2=661,spearman_corr=0.19730016678672646
|
| 286 |
+
|
| 287 |
+
epoch=18, iter=19, iter2=666, loss=0.73095, spearmanr=0.20560,p_value=0.4622683263968642 times:5.32s, 15 samples/iter
|
| 288 |
+
epoch=18, iter=29, iter2=676, loss=0.73096, spearmanr=0.42807,p_value=0.11142563133750279 times:3.57s, 15 samples/iter
|
| 289 |
+
Validation: epoch=18, loss=0.81196
|
| 290 |
+
Pearson Correlation: 0.25251,p_value: 0.03925613686442375
|
| 291 |
+
Spearman Correlation: 0.23830,p_value; 0.05214309329249534
|
| 292 |
+
epoch=18,iter=29,iter2=676,spearman_corr=0.2383015525452377
|
| 293 |
+
|
| 294 |
+
####################epoch:20
|
| 295 |
+
epoch=19, iter=9, iter2=694, loss=0.72881, spearmanr=-0.05909,p_value=0.8343143629677875 times:5.52s, 15 samples/iter
|
| 296 |
+
Validation: epoch=19, loss=0.80068
|
| 297 |
+
Pearson Correlation: 0.17582,p_value: 0.1546914130449295
|
| 298 |
+
Spearman Correlation: 0.16894,p_value; 0.17172117657353436
|
| 299 |
+
epoch=19,iter=14,iter2=699,spearman_corr=0.16894466035545427
|
| 300 |
+
|
| 301 |
+
epoch=19, iter=19, iter2=704, loss=0.72974, spearmanr=-0.03414,p_value=0.9038526237837423 times:5.46s, 15 samples/iter
|
| 302 |
+
epoch=19, iter=29, iter2=714, loss=0.72980, spearmanr=0.34347,p_value=0.21005406977367103 times:3.45s, 15 samples/iter
|
| 303 |
+
Validation: epoch=19, loss=0.81369
|
| 304 |
+
Pearson Correlation: 0.25588,p_value: 0.036619819700717926
|
| 305 |
+
Spearman Correlation: 0.22949,p_value; 0.06174927274724146
|
| 306 |
+
epoch=19,iter=29,iter2=714,spearman_corr=0.2294862920839474
|
| 307 |
+
|
| 308 |
+
####################epoch:21
|
| 309 |
+
epoch=20, iter=9, iter2=732, loss=0.72775, spearmanr=-0.20628,p_value=0.4607577065784959 times:5.37s, 15 samples/iter
|
| 310 |
+
Validation: epoch=20, loss=0.80063
|
| 311 |
+
Pearson Correlation: 0.19131,p_value: 0.12094786763191223
|
| 312 |
+
Spearman Correlation: 0.18320,p_value; 0.13782181114973632
|
| 313 |
+
epoch=20,iter=14,iter2=737,spearman_corr=0.1832029276792868
|
| 314 |
+
|
| 315 |
+
epoch=20, iter=19, iter2=742, loss=0.72842, spearmanr=-0.03063,p_value=0.9137031632285362 times:5.39s, 15 samples/iter
|
| 316 |
+
epoch=20, iter=29, iter2=752, loss=0.72861, spearmanr=0.42755,p_value=0.11190723439287621 times:3.62s, 15 samples/iter
|
| 317 |
+
Validation: epoch=20, loss=0.81869
|
| 318 |
+
Pearson Correlation: 0.33211,p_value: 0.006038063671439886
|
| 319 |
+
Spearman Correlation: 0.26986,p_value; 0.027210136285768356
|
| 320 |
+
epoch=20,iter=29,iter2=752,spearman_corr=0.2698612875904159
|
| 321 |
+
|
| 322 |
+
####################epoch:22
|
| 323 |
+
epoch=21, iter=9, iter2=770, loss=0.72669, spearmanr=-0.02334,p_value=0.9341995394452439 times:5.43s, 15 samples/iter
|
| 324 |
+
Validation: epoch=21, loss=0.81551
|
| 325 |
+
Pearson Correlation: 0.09862,p_value: 0.42718327045440674
|
| 326 |
+
Spearman Correlation: 0.04819,p_value; 0.6985802278098615
|
| 327 |
+
epoch=21,iter=14,iter2=775,spearman_corr=0.04818796502927159
|
| 328 |
+
|
| 329 |
+
epoch=21, iter=19, iter2=780, loss=0.72728, spearmanr=-0.27932,p_value=0.31335532231763535 times:5.56s, 15 samples/iter
|
| 330 |
+
epoch=21, iter=29, iter2=790, loss=0.72733, spearmanr=0.33484,p_value=0.2224855021847703 times:3.64s, 15 samples/iter
|
| 331 |
+
Validation: epoch=21, loss=0.80941
|
| 332 |
+
Pearson Correlation: 0.24477,p_value: 0.04590359330177307
|
| 333 |
+
Spearman Correlation: 0.25939,p_value; 0.03403043568955055
|
| 334 |
+
epoch=21,iter=29,iter2=790,spearman_corr=0.2593949576439252
|
| 335 |
+
|
| 336 |
+
####################epoch:23
|
| 337 |
+
epoch=22, iter=9, iter2=808, loss=0.72545, spearmanr=-0.06661,p_value=0.8135449037029504 times:5.10s, 15 samples/iter
|
| 338 |
+
Validation: epoch=22, loss=0.80611
|
| 339 |
+
Pearson Correlation: 0.10220,p_value: 0.41051700711250305
|
| 340 |
+
Spearman Correlation: 0.10447,p_value; 0.4001645398147524
|
| 341 |
+
epoch=22,iter=14,iter2=813,spearman_corr=0.1044688345513464
|
| 342 |
+
|
| 343 |
+
epoch=22, iter=19, iter2=818, loss=0.72605, spearmanr=-0.00717,p_value=0.9797536566251921 times:5.57s, 15 samples/iter
|
| 344 |
+
epoch=22, iter=29, iter2=828, loss=0.72606, spearmanr=0.19911,p_value=0.47683353583183863 times:3.58s, 15 samples/iter
|
| 345 |
+
Validation: epoch=22, loss=0.81462
|
| 346 |
+
Pearson Correlation: 0.18469,p_value: 0.13460183143615723
|
| 347 |
+
Spearman Correlation: 0.16867,p_value; 0.17243936360610168
|
| 348 |
+
epoch=22,iter=29,iter2=828,spearman_corr=0.16866605094875484
|
| 349 |
+
|
| 350 |
+
####################epoch:24
|
| 351 |
+
epoch=23, iter=9, iter2=846, loss=0.72428, spearmanr=-0.12724,p_value=0.651345358804303 times:5.08s, 15 samples/iter
|
| 352 |
+
Validation: epoch=23, loss=0.80217
|
| 353 |
+
Pearson Correlation: 0.07913,p_value: 0.5244140028953552
|
| 354 |
+
Spearman Correlation: 0.12209,p_value; 0.3250199626563639
|
| 355 |
+
epoch=23,iter=14,iter2=851,spearman_corr=0.12208546606643882
|
| 356 |
+
|
| 357 |
+
epoch=23, iter=19, iter2=856, loss=0.72493, spearmanr=0.06685,p_value=0.8128754959553938 times:5.47s, 15 samples/iter
|
| 358 |
+
epoch=23, iter=29, iter2=866, loss=0.72498, spearmanr=0.07335,p_value=0.7950407004548103 times:3.60s, 15 samples/iter
|
| 359 |
+
Validation: epoch=23, loss=0.81371
|
| 360 |
+
Pearson Correlation: 0.16681,p_value: 0.17729002237319946
|
| 361 |
+
Spearman Correlation: 0.14881,p_value; 0.22941475955182328
|
| 362 |
+
epoch=23,iter=29,iter2=866,spearman_corr=0.14881280258642213
|
| 363 |
+
|
| 364 |
+
####################epoch:25
|
| 365 |
+
epoch=24, iter=9, iter2=884, loss=0.72353, spearmanr=0.39713,p_value=0.1427199533120391 times:5.30s, 15 samples/iter
|
| 366 |
+
Validation: epoch=24, loss=0.80858
|
| 367 |
+
Pearson Correlation: 0.16079,p_value: 0.19367118179798126
|
| 368 |
+
Spearman Correlation: 0.18929,p_value; 0.12498840601053375
|
| 369 |
+
epoch=24,iter=14,iter2=889,spearman_corr=0.18929172499237193
|
| 370 |
+
|
| 371 |
+
epoch=24, iter=19, iter2=894, loss=0.72408, spearmanr=0.29803,p_value=0.2806390138032251 times:5.66s, 15 samples/iter
|
| 372 |
+
epoch=24, iter=29, iter2=904, loss=0.72412, spearmanr=0.08431,p_value=0.7651535837961108 times:3.60s, 15 samples/iter
|
| 373 |
+
Validation: epoch=24, loss=0.81208
|
| 374 |
+
Pearson Correlation: 0.18751,p_value: 0.12865054607391357
|
| 375 |
+
Spearman Correlation: 0.20616,p_value; 0.09417705562963909
|
| 376 |
+
epoch=24,iter=29,iter2=904,spearman_corr=0.2061594428016823
|
| 377 |
+
|
| 378 |
+
####################epoch:26
|
| 379 |
+
epoch=25, iter=9, iter2=922, loss=0.72268, spearmanr=0.39462,p_value=0.1454937819728262 times:5.50s, 15 samples/iter
|
| 380 |
+
Validation: epoch=25, loss=0.80539
|
| 381 |
+
Pearson Correlation: 0.14182,p_value: 0.2522796392440796
|
| 382 |
+
Spearman Correlation: 0.16791,p_value; 0.17440570099862393
|
| 383 |
+
epoch=25,iter=14,iter2=927,spearman_corr=0.16790755273147048
|
| 384 |
+
|
| 385 |
+
epoch=25, iter=19, iter2=932, loss=0.72320, spearmanr=-0.07907,p_value=0.7794027219194971 times:5.26s, 15 samples/iter
|
| 386 |
+
epoch=25, iter=29, iter2=942, loss=0.72328, spearmanr=0.39677,p_value=0.1431140387744784 times:3.63s, 15 samples/iter
|
| 387 |
+
Validation: epoch=25, loss=0.81026
|
| 388 |
+
Pearson Correlation: 0.12638,p_value: 0.30815234780311584
|
| 389 |
+
Spearman Correlation: 0.14977,p_value; 0.22639616611473892
|
| 390 |
+
epoch=25,iter=29,iter2=942,spearman_corr=0.14977095342802485
|
| 391 |
+
|
| 392 |
+
####################epoch:27
|
| 393 |
+
epoch=26, iter=9, iter2=960, loss=0.72192, spearmanr=0.25494,p_value=0.3591401649905601 times:5.19s, 15 samples/iter
|
| 394 |
+
Validation: epoch=26, loss=0.81199
|
| 395 |
+
Pearson Correlation: 0.20422,p_value: 0.09737545996904373
|
| 396 |
+
Spearman Correlation: 0.18825,p_value; 0.12711282711168934
|
| 397 |
+
epoch=26,iter=14,iter2=965,spearman_corr=0.188252033043758
|
| 398 |
+
|
| 399 |
+
epoch=26, iter=19, iter2=970, loss=0.72239, spearmanr=0.22863,p_value=0.41244236490441066 times:5.27s, 15 samples/iter
|
| 400 |
+
epoch=26, iter=29, iter2=980, loss=0.72247, spearmanr=0.35875,p_value=0.18914377614431344 times:3.76s, 15 samples/iter
|
| 401 |
+
Validation: epoch=26, loss=0.80781
|
| 402 |
+
Pearson Correlation: 0.07971,p_value: 0.5214157700538635
|
| 403 |
+
Spearman Correlation: 0.12672,p_value; 0.3068331954259532
|
| 404 |
+
epoch=26,iter=29,iter2=980,spearman_corr=0.1267243061730589
|
| 405 |
+
|
| 406 |
+
####################epoch:28
|
| 407 |
+
epoch=27, iter=9, iter2=998, loss=0.72106, spearmanr=0.23160,p_value=0.4062266757293307 times:5.06s, 15 samples/iter
|
| 408 |
+
Validation: epoch=27, loss=0.81358
|
| 409 |
+
Pearson Correlation: 0.13812,p_value: 0.2649901807308197
|
| 410 |
+
Spearman Correlation: 0.15824,p_value; 0.20091805472154844
|
| 411 |
+
epoch=27,iter=14,iter2=1003,spearman_corr=0.15823878191317706
|
| 412 |
+
|
| 413 |
+
epoch=27, iter=19, iter2=1008, loss=0.72153, spearmanr=0.06475,p_value=0.8186677739111919 times:5.59s, 15 samples/iter
|
| 414 |
+
epoch=27, iter=29, iter2=1018, loss=0.72158, spearmanr=0.11638,p_value=0.6795657963975599 times:3.69s, 15 samples/iter
|
| 415 |
+
Validation: epoch=27, loss=0.80869
|
| 416 |
+
Pearson Correlation: 0.10996,p_value: 0.3757292628288269
|
| 417 |
+
Spearman Correlation: 0.13637,p_value; 0.2711841682107695
|
| 418 |
+
epoch=27,iter=29,iter2=1018,spearman_corr=0.13636501121022415
|
| 419 |
+
|
| 420 |
+
####################epoch:29
|
| 421 |
+
epoch=28, iter=9, iter2=1036, loss=0.72021, spearmanr=0.30992,p_value=0.26094773037562125 times:5.21s, 15 samples/iter
|
| 422 |
+
Validation: epoch=28, loss=0.81681
|
| 423 |
+
Pearson Correlation: 0.15600,p_value: 0.20743300020694733
|
| 424 |
+
Spearman Correlation: 0.17203,p_value; 0.16390311817283063
|
| 425 |
+
epoch=28,iter=14,iter2=1041,spearman_corr=0.1720342194685309
|
| 426 |
+
|
| 427 |
+
epoch=28, iter=19, iter2=1046, loss=0.72068, spearmanr=0.06741,p_value=0.8113425786842124 times:5.51s, 15 samples/iter
|
| 428 |
+
epoch=28, iter=29, iter2=1056, loss=0.72070, spearmanr=0.24596,p_value=0.3768799119783869 times:3.63s, 15 samples/iter
|
| 429 |
+
Validation: epoch=28, loss=0.80889
|
| 430 |
+
Pearson Correlation: 0.13163,p_value: 0.2883281409740448
|
| 431 |
+
Spearman Correlation: 0.16505,p_value; 0.18195682441443187
|
| 432 |
+
epoch=28,iter=29,iter2=1056,spearman_corr=0.16505137061720587
|
| 433 |
+
|
| 434 |
+
####################epoch:30
|
| 435 |
+
epoch=29, iter=9, iter2=1074, loss=0.71936, spearmanr=0.19749,p_value=0.4804904792675754 times:5.02s, 15 samples/iter
|
| 436 |
+
Validation: epoch=29, loss=0.82500
|
| 437 |
+
Pearson Correlation: 0.13782,p_value: 0.26605233550071716
|
| 438 |
+
Spearman Correlation: 0.15254,p_value; 0.21783405030856073
|
| 439 |
+
epoch=29,iter=14,iter2=1079,spearman_corr=0.1525381478643904
|
| 440 |
+
|
| 441 |
+
epoch=29, iter=19, iter2=1084, loss=0.71986, spearmanr=-0.42832,p_value=0.11119448204789599 times:5.23s, 15 samples/iter
|
| 442 |
+
epoch=29, iter=29, iter2=1094, loss=0.71989, spearmanr=0.16488,p_value=0.5570660043236273 times:3.59s, 15 samples/iter
|
| 443 |
+
Validation: epoch=29, loss=0.80957
|
| 444 |
+
Pearson Correlation: 0.11918,p_value: 0.33675307035446167
|
| 445 |
+
Spearman Correlation: 0.13926,p_value; 0.261040317074018
|
| 446 |
+
epoch=29,iter=29,iter2=1094,spearman_corr=0.1392609892664239
|
| 447 |
+
|
BigBang-Proton_Test_Results/Genome/ncRNA to Predict Functional Fitness /run--nt-kobori-2016-train_ncRNA_Function.log
ADDED
|
@@ -0,0 +1,861 @@
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|
| 1 |
+
|
| 2 |
+
total iter per epoch :73
|
| 3 |
+
validation data numbers per epoch :9
|
| 4 |
+
data.shape: torch.Size([22, 57])
|
| 5 |
+
tx_v.shape: torch.Size([22])
|
| 6 |
+
total iter per epoch :73
|
| 7 |
+
validation data numbers per epoch :9
|
| 8 |
+
total iter per epoch :73
|
| 9 |
+
validation data numbers per epoch :9
|
| 10 |
+
total iter per epoch :73
|
| 11 |
+
validation data numbers per epoch :9
|
| 12 |
+
data.shape: torch.Size([22, 57])
|
| 13 |
+
tx_v.shape: torch.Size([22])
|
| 14 |
+
data.shape: torch.Size([22, 57])
|
| 15 |
+
tx_v.shape: torch.Size([22])
|
| 16 |
+
data.shape: torch.Size([22, 57])
|
| 17 |
+
tx_v.shape: torch.Size([22])
|
| 18 |
+
fc_reg.weight不匹配
|
| 19 |
+
fc_reg.bias不匹配
|
| 20 |
+
fc_reg.weight不匹配
|
| 21 |
+
fc_reg.bias不匹配
|
| 22 |
+
fc_reg.weight不匹配
|
| 23 |
+
fc_reg.bias不匹配
|
| 24 |
+
fc_reg.weight不匹配
|
| 25 |
+
fc_reg.bias不匹配
|
| 26 |
+
Total number of parameters: 3.82849548
|
| 27 |
+
Total number of parameters: 3.82849548
|
| 28 |
+
Total number of parameters: 3.82849548
|
| 29 |
+
Total number of parameters: 3.82849548
|
| 30 |
+
NCCL version 2.20.5+cuda12.4
|
| 31 |
+
Resuming training from epoch 1, batch_idx 171500, total_train_iter 171500
|
| 32 |
+
Resuming training from epoch 1, batch_idx 171500, total_train_iter 171500
|
| 33 |
+
Resuming training from epoch 1, batch_idx 171500, total_train_iter 171500
|
| 34 |
+
Resuming training from epoch 1, batch_idx 171500, total_train_iter 171500
|
| 35 |
+
started training...
|
| 36 |
+
started training...
|
| 37 |
+
started training...
|
| 38 |
+
started training...
|
| 39 |
+
####################epoch:2
|
| 40 |
+
NCCL version 2.20.5+cuda12.4
|
| 41 |
+
NCCL version 2.20.5+cuda12.4
|
| 42 |
+
NCCL version 2.20.5+cuda12.4
|
| 43 |
+
epoch=1, iter=9, iter2=10, loss=0.13633, spearmanr=0.05236,p_value=0.8169819303862604 times:6.92s, 22 samples/iter
|
| 44 |
+
Validation: epoch=1, loss=0.13320
|
| 45 |
+
Pearson Correlation: -0.13612,p_value: 0.07002847641706467
|
| 46 |
+
Spearman Correlation: -0.09832,p_value; 0.1916570932680401
|
| 47 |
+
epoch=1,iter=14,iter2=15,spearman_corr=-0.09832120264092892
|
| 48 |
+
|
| 49 |
+
epoch=1, iter=19, iter2=20, loss=0.11822, spearmanr=0.08027,p_value=0.7225128671925349 times:5.15s, 22 samples/iter
|
| 50 |
+
epoch=1, iter=29, iter2=30, loss=0.10580, spearmanr=0.30613,p_value=0.16587501762159335 times:4.02s, 22 samples/iter
|
| 51 |
+
Validation: epoch=1, loss=0.08944
|
| 52 |
+
Pearson Correlation: 0.24415,p_value: 0.0010224333964288235
|
| 53 |
+
Spearman Correlation: 0.20779,p_value; 0.00538363316866725
|
| 54 |
+
epoch=1,iter=29,iter2=30,spearman_corr=0.2077869767817004
|
| 55 |
+
|
| 56 |
+
epoch=1, iter=39, iter2=40, loss=0.10584, spearmanr=0.24151,p_value=0.2789091190936819 times:4.92s, 22 samples/iter
|
| 57 |
+
Validation: epoch=1, loss=0.08797
|
| 58 |
+
Pearson Correlation: 0.42864,p_value: 2.3841624230414027e-09
|
| 59 |
+
Spearman Correlation: 0.39855,p_value; 3.592083775877914e-08
|
| 60 |
+
epoch=1,iter=44,iter2=45,spearman_corr=0.3985466209919692
|
| 61 |
+
|
| 62 |
+
epoch=1, iter=49, iter2=50, loss=0.10553, spearmanr=0.40221,p_value=0.06350149645505129 times:5.16s, 22 samples/iter
|
| 63 |
+
epoch=1, iter=59, iter2=60, loss=0.10491, spearmanr=0.35324,p_value=0.10682662276112041 times:3.96s, 22 samples/iter
|
| 64 |
+
Validation: epoch=1, loss=0.08723
|
| 65 |
+
Pearson Correlation: 0.36772,p_value: 4.4295018142292975e-07
|
| 66 |
+
Spearman Correlation: 0.38327,p_value; 1.28894804966402e-07
|
| 67 |
+
epoch=1,iter=59,iter2=60,spearman_corr=0.3832651637583041
|
| 68 |
+
|
| 69 |
+
epoch=1, iter=69, iter2=70, loss=0.10521, spearmanr=0.33646,p_value=0.12575797760410865 times:5.11s, 22 samples/iter
|
| 70 |
+
####################epoch:3
|
| 71 |
+
epoch=2, iter=9, iter2=83, loss=0.10646, spearmanr=0.46716,p_value=0.028376296629712527 times:5.23s, 22 samples/iter
|
| 72 |
+
Validation: epoch=2, loss=0.08837
|
| 73 |
+
Pearson Correlation: 0.40778,p_value: 1.6080793585615538e-08
|
| 74 |
+
Spearman Correlation: 0.39014,p_value; 7.313629343675326e-08
|
| 75 |
+
epoch=2,iter=14,iter2=88,spearman_corr=0.3901385158432747
|
| 76 |
+
|
| 77 |
+
epoch=2, iter=19, iter2=93, loss=0.10499, spearmanr=-0.02208,p_value=0.9223134781283803 times:5.60s, 22 samples/iter
|
| 78 |
+
epoch=2, iter=29, iter2=103, loss=0.10256, spearmanr=0.48718,p_value=0.021470609895939692 times:3.65s, 22 samples/iter
|
| 79 |
+
Validation: epoch=2, loss=0.09335
|
| 80 |
+
Pearson Correlation: 0.40099,p_value: 2.910091190244657e-08
|
| 81 |
+
Spearman Correlation: 0.38050,p_value; 1.6126484455559736e-07
|
| 82 |
+
epoch=2,iter=29,iter2=103,spearman_corr=0.3805035567154321
|
| 83 |
+
|
| 84 |
+
epoch=2, iter=39, iter2=113, loss=0.10267, spearmanr=-0.16752,p_value=0.45618383347146785 times:6.03s, 22 samples/iter
|
| 85 |
+
Validation: epoch=2, loss=0.08687
|
| 86 |
+
Pearson Correlation: 0.57546,p_value: 4.3890610274925025e-17
|
| 87 |
+
Spearman Correlation: 0.54785,p_value; 2.4845433929753283e-15
|
| 88 |
+
epoch=2,iter=44,iter2=118,spearman_corr=0.547852414926711
|
| 89 |
+
|
| 90 |
+
epoch=2, iter=49, iter2=123, loss=0.10216, spearmanr=0.22644,p_value=0.31090373247536573 times:5.62s, 22 samples/iter
|
| 91 |
+
epoch=2, iter=59, iter2=133, loss=0.10227, spearmanr=0.28766,p_value=0.1942481666116485 times:3.70s, 22 samples/iter
|
| 92 |
+
Validation: epoch=2, loss=0.08894
|
| 93 |
+
Pearson Correlation: 0.59330,p_value: 2.623975514900661e-18
|
| 94 |
+
Spearman Correlation: 0.57914,p_value; 2.490153125549347e-17
|
| 95 |
+
epoch=2,iter=59,iter2=133,spearman_corr=0.5791396720762559
|
| 96 |
+
|
| 97 |
+
epoch=2, iter=69, iter2=143, loss=0.10290, spearmanr=0.28653,p_value=0.19608149909711736 times:5.61s, 22 samples/iter
|
| 98 |
+
####################epoch:4
|
| 99 |
+
epoch=3, iter=9, iter2=156, loss=0.10439, spearmanr=-0.12344,p_value=0.5841711516865553 times:5.40s, 22 samples/iter
|
| 100 |
+
Validation: epoch=3, loss=0.08759
|
| 101 |
+
Pearson Correlation: 0.57321,p_value: 6.18937893342803e-17
|
| 102 |
+
Spearman Correlation: 0.55941,p_value; 4.798308027503376e-16
|
| 103 |
+
epoch=3,iter=14,iter2=161,spearman_corr=0.5594116110789542
|
| 104 |
+
|
| 105 |
+
epoch=3, iter=19, iter2=166, loss=0.10363, spearmanr=0.24908,p_value=0.26363720780055994 times:5.73s, 22 samples/iter
|
| 106 |
+
epoch=3, iter=29, iter2=176, loss=0.10219, spearmanr=-0.20041,p_value=0.37118218851297236 times:3.70s, 22 samples/iter
|
| 107 |
+
Validation: epoch=3, loss=0.09422
|
| 108 |
+
Pearson Correlation: 0.55610,p_value: 7.735405571022546e-16
|
| 109 |
+
Spearman Correlation: 0.54040,p_value; 6.935112201796155e-15
|
| 110 |
+
epoch=3,iter=29,iter2=176,spearman_corr=0.5404044067257721
|
| 111 |
+
|
| 112 |
+
epoch=3, iter=39, iter2=186, loss=0.10205, spearmanr=0.39468,p_value=0.06910170103753309 times:5.67s, 22 samples/iter
|
| 113 |
+
Validation: epoch=3, loss=0.08694
|
| 114 |
+
Pearson Correlation: 0.61978,p_value: 2.869659181595512e-20
|
| 115 |
+
Spearman Correlation: 0.61573,p_value; 5.88930876160101e-20
|
| 116 |
+
epoch=3,iter=44,iter2=191,spearman_corr=0.6157256478748149
|
| 117 |
+
|
| 118 |
+
epoch=3, iter=49, iter2=196, loss=0.10174, spearmanr=0.10614,p_value=0.638267299742959 times:5.64s, 22 samples/iter
|
| 119 |
+
epoch=3, iter=59, iter2=206, loss=0.10183, spearmanr=-0.10006,p_value=0.6577454843092314 times:4.10s, 22 samples/iter
|
| 120 |
+
Validation: epoch=3, loss=0.08757
|
| 121 |
+
Pearson Correlation: 0.59441,p_value: 2.1894587798974742e-18
|
| 122 |
+
Spearman Correlation: 0.58468,p_value; 1.0466742731199728e-17
|
| 123 |
+
epoch=3,iter=59,iter2=206,spearman_corr=0.5846772034935218
|
| 124 |
+
|
| 125 |
+
epoch=3, iter=69, iter2=216, loss=0.10227, spearmanr=0.26356,p_value=0.23595436577657153 times:6.20s, 22 samples/iter
|
| 126 |
+
####################epoch:5
|
| 127 |
+
epoch=4, iter=9, iter2=229, loss=0.10301, spearmanr=-0.09410,p_value=0.6770074581151568 times:6.09s, 22 samples/iter
|
| 128 |
+
Validation: epoch=4, loss=0.08686
|
| 129 |
+
Pearson Correlation: 0.58470,p_value: 1.0429923652403576e-17
|
| 130 |
+
Spearman Correlation: 0.58003,p_value; 2.1702131488190512e-17
|
| 131 |
+
epoch=4,iter=14,iter2=234,spearman_corr=0.5800253161508492
|
| 132 |
+
|
| 133 |
+
epoch=4, iter=19, iter2=239, loss=0.10251, spearmanr=0.14580,p_value=0.5173501008108605 times:5.86s, 22 samples/iter
|
| 134 |
+
epoch=4, iter=29, iter2=249, loss=0.10152, spearmanr=0.23067,p_value=0.3016945852056532 times:3.78s, 22 samples/iter
|
| 135 |
+
Validation: epoch=4, loss=0.09002
|
| 136 |
+
Pearson Correlation: 0.61800,p_value: 3.9418632931663906e-20
|
| 137 |
+
Spearman Correlation: 0.61679,p_value; 4.8797066562450713e-20
|
| 138 |
+
epoch=4,iter=29,iter2=249,spearman_corr=0.6167914016589686
|
| 139 |
+
|
| 140 |
+
epoch=4, iter=39, iter2=259, loss=0.10181, spearmanr=0.40147,p_value=0.06403284150707708 times:5.93s, 22 samples/iter
|
| 141 |
+
Validation: epoch=4, loss=0.08755
|
| 142 |
+
Pearson Correlation: 0.63069,p_value: 3.92676328879882e-21
|
| 143 |
+
Spearman Correlation: 0.62577,p_value; 9.731208743945308e-21
|
| 144 |
+
epoch=4,iter=44,iter2=264,spearman_corr=0.6257658495922207
|
| 145 |
+
|
| 146 |
+
epoch=4, iter=49, iter2=269, loss=0.10146, spearmanr=0.51587,p_value=0.013988590376714337 times:5.80s, 22 samples/iter
|
| 147 |
+
epoch=4, iter=59, iter2=279, loss=0.10146, spearmanr=0.13056,p_value=0.5625118053525051 times:3.78s, 22 samples/iter
|
| 148 |
+
Validation: epoch=4, loss=0.08690
|
| 149 |
+
Pearson Correlation: 0.61508,p_value: 6.592153206960559e-20
|
| 150 |
+
Spearman Correlation: 0.61693,p_value; 4.759839104652869e-20
|
| 151 |
+
epoch=4,iter=59,iter2=279,spearman_corr=0.6169320514824045
|
| 152 |
+
|
| 153 |
+
epoch=4, iter=69, iter2=289, loss=0.10214, spearmanr=-0.09460,p_value=0.6753866191975614 times:5.96s, 22 samples/iter
|
| 154 |
+
####################epoch:6
|
| 155 |
+
epoch=5, iter=9, iter2=302, loss=0.10274, spearmanr=0.41016,p_value=0.05796957052785619 times:5.27s, 22 samples/iter
|
| 156 |
+
Validation: epoch=5, loss=0.08698
|
| 157 |
+
Pearson Correlation: 0.62122,p_value: 2.21665841068043e-20
|
| 158 |
+
Spearman Correlation: 0.63112,p_value; 3.6297068610111845e-21
|
| 159 |
+
epoch=5,iter=14,iter2=307,spearman_corr=0.6311153221702088
|
| 160 |
+
|
| 161 |
+
epoch=5, iter=19, iter2=312, loss=0.10236, spearmanr=-0.00057,p_value=0.9980079489967122 times:5.71s, 22 samples/iter
|
| 162 |
+
epoch=5, iter=29, iter2=322, loss=0.10162, spearmanr=0.08694,p_value=0.7004675189092643 times:3.80s, 22 samples/iter
|
| 163 |
+
Validation: epoch=5, loss=0.09465
|
| 164 |
+
Pearson Correlation: 0.59266,p_value: 2.9146871072066567e-18
|
| 165 |
+
Spearman Correlation: 0.58326,p_value; 1.3085126894533494e-17
|
| 166 |
+
epoch=5,iter=29,iter2=322,spearman_corr=0.583260719411292
|
| 167 |
+
|
| 168 |
+
epoch=5, iter=39, iter2=332, loss=0.10156, spearmanr=0.17497,p_value=0.43608418456658515 times:5.82s, 22 samples/iter
|
| 169 |
+
Validation: epoch=5, loss=0.08687
|
| 170 |
+
Pearson Correlation: 0.63035,p_value: 4.184711969563952e-21
|
| 171 |
+
Spearman Correlation: 0.62602,p_value; 9.287989097962027e-21
|
| 172 |
+
epoch=5,iter=44,iter2=337,spearman_corr=0.6260210607468348
|
| 173 |
+
|
| 174 |
+
epoch=5, iter=49, iter2=342, loss=0.10153, spearmanr=0.20731,p_value=0.35459713501047263 times:5.74s, 22 samples/iter
|
| 175 |
+
epoch=5, iter=59, iter2=352, loss=0.10153, spearmanr=0.28053,p_value=0.20601879590875746 times:3.99s, 22 samples/iter
|
| 176 |
+
Validation: epoch=5, loss=0.08687
|
| 177 |
+
Pearson Correlation: 0.64808,p_value: 1.3963200267339586e-22
|
| 178 |
+
Spearman Correlation: 0.66557,p_value; 3.8771841879163096e-24
|
| 179 |
+
epoch=5,iter=59,iter2=352,spearman_corr=0.6655725541414532
|
| 180 |
+
|
| 181 |
+
epoch=5, iter=69, iter2=362, loss=0.10198, spearmanr=0.12188,p_value=0.588989746930626 times:5.85s, 22 samples/iter
|
| 182 |
+
####################epoch:7
|
| 183 |
+
epoch=6, iter=9, iter2=375, loss=0.10247, spearmanr=0.37638,p_value=0.08426120533546168 times:5.21s, 22 samples/iter
|
| 184 |
+
Validation: epoch=6, loss=0.08708
|
| 185 |
+
Pearson Correlation: 0.59102,p_value: 3.802390043638352e-18
|
| 186 |
+
Spearman Correlation: 0.59137,p_value; 3.592923390739205e-18
|
| 187 |
+
epoch=6,iter=14,iter2=380,spearman_corr=0.5913665635495875
|
| 188 |
+
|
| 189 |
+
epoch=6, iter=19, iter2=385, loss=0.10217, spearmanr=0.10264,p_value=0.6494580962866128 times:5.94s, 22 samples/iter
|
| 190 |
+
epoch=6, iter=29, iter2=395, loss=0.10160, spearmanr=0.16691,p_value=0.45783211988298456 times:3.64s, 22 samples/iter
|
| 191 |
+
Validation: epoch=6, loss=0.09504
|
| 192 |
+
Pearson Correlation: 0.61591,p_value: 5.700583692303667e-20
|
| 193 |
+
Spearman Correlation: 0.62935,p_value; 5.037061402479144e-21
|
| 194 |
+
epoch=6,iter=29,iter2=395,spearman_corr=0.6293493903766912
|
| 195 |
+
|
| 196 |
+
epoch=6, iter=39, iter2=405, loss=0.10156, spearmanr=0.41818,p_value=0.05277596332688222 times:5.80s, 22 samples/iter
|
| 197 |
+
Validation: epoch=6, loss=0.08692
|
| 198 |
+
Pearson Correlation: 0.63610,p_value: 1.4231725237815453e-21
|
| 199 |
+
Spearman Correlation: 0.64895,p_value; 1.175286850779026e-22
|
| 200 |
+
epoch=6,iter=44,iter2=410,spearman_corr=0.6489459296321137
|
| 201 |
+
|
| 202 |
+
epoch=6, iter=49, iter2=415, loss=0.10145, spearmanr=-0.19796,p_value=0.3771788352293882 times:5.91s, 22 samples/iter
|
| 203 |
+
epoch=6, iter=59, iter2=425, loss=0.10147, spearmanr=0.34061,p_value=0.12086909821826437 times:3.79s, 22 samples/iter
|
| 204 |
+
Validation: epoch=6, loss=0.08717
|
| 205 |
+
Pearson Correlation: 0.64095,p_value: 5.6303039960025255e-22
|
| 206 |
+
Spearman Correlation: 0.65970,p_value; 1.324916374681015e-23
|
| 207 |
+
epoch=6,iter=59,iter2=425,spearman_corr=0.6597041851434925
|
| 208 |
+
|
| 209 |
+
epoch=6, iter=69, iter2=435, loss=0.10179, spearmanr=-0.09458,p_value=0.6754736351600239 times:5.77s, 22 samples/iter
|
| 210 |
+
####################epoch:8
|
| 211 |
+
epoch=7, iter=9, iter2=448, loss=0.10221, spearmanr=0.33560,p_value=0.12678875144269083 times:5.27s, 22 samples/iter
|
| 212 |
+
Validation: epoch=7, loss=0.08700
|
| 213 |
+
Pearson Correlation: 0.59961,p_value: 9.294044198329434e-19
|
| 214 |
+
Spearman Correlation: 0.60813,p_value; 2.2025217302090744e-19
|
| 215 |
+
epoch=7,iter=14,iter2=453,spearman_corr=0.6081346421743482
|
| 216 |
+
|
| 217 |
+
epoch=7, iter=19, iter2=458, loss=0.10198, spearmanr=0.30204,p_value=0.17189485208030966 times:5.90s, 22 samples/iter
|
| 218 |
+
epoch=7, iter=29, iter2=468, loss=0.10148, spearmanr=0.18728,p_value=0.403965108937244 times:4.07s, 22 samples/iter
|
| 219 |
+
Validation: epoch=7, loss=0.09359
|
| 220 |
+
Pearson Correlation: 0.61341,p_value: 8.841606905548016e-20
|
| 221 |
+
Spearman Correlation: 0.61278,p_value; 9.865285834598117e-20
|
| 222 |
+
epoch=7,iter=29,iter2=468,spearman_corr=0.6127810625925576
|
| 223 |
+
|
| 224 |
+
epoch=7, iter=39, iter2=478, loss=0.10144, spearmanr=0.68812,p_value=0.0004002937025143224 times:5.81s, 22 samples/iter
|
| 225 |
+
Validation: epoch=7, loss=0.08687
|
| 226 |
+
Pearson Correlation: 0.64909,p_value: 1.141844479653461e-22
|
| 227 |
+
Spearman Correlation: 0.66252,p_value; 7.366849369660751e-24
|
| 228 |
+
epoch=7,iter=44,iter2=483,spearman_corr=0.6625236699897691
|
| 229 |
+
|
| 230 |
+
epoch=7, iter=49, iter2=488, loss=0.10138, spearmanr=0.34834,p_value=0.11212136219734001 times:5.72s, 22 samples/iter
|
| 231 |
+
epoch=7, iter=59, iter2=498, loss=0.10140, spearmanr=0.21196,p_value=0.3436588644818901 times:3.84s, 22 samples/iter
|
| 232 |
+
Validation: epoch=7, loss=0.08699
|
| 233 |
+
Pearson Correlation: 0.66948,p_value: 1.6840081956460714e-24
|
| 234 |
+
Spearman Correlation: 0.68272,p_value; 9.073131852722099e-26
|
| 235 |
+
epoch=7,iter=59,iter2=498,spearman_corr=0.6827181298405948
|
| 236 |
+
|
| 237 |
+
Checkpoint saved at epoch 7, batch_idx 61, total_train_iter 499
|
| 238 |
+
iter 500 save model /home/chipan/shuffle_token_pan/checkpoint/2025_01_14_09_47_45/last_add_499.pth
|
| 239 |
+
epoch=7, iter=69, iter2=508, loss=0.10166, spearmanr=-0.01504,p_value=0.9470213386794533 times:17.44s, 22 samples/iter
|
| 240 |
+
####################epoch:9
|
| 241 |
+
epoch=8, iter=9, iter2=521, loss=0.10185, spearmanr=0.09337,p_value=0.6794013008005696 times:5.02s, 22 samples/iter
|
| 242 |
+
Validation: epoch=8, loss=0.08848
|
| 243 |
+
Pearson Correlation: 0.53016,p_value: 2.731209796356677e-14
|
| 244 |
+
Spearman Correlation: 0.52839,p_value; 3.449134335475391e-14
|
| 245 |
+
epoch=8,iter=14,iter2=526,spearman_corr=0.5283851975823551
|
| 246 |
+
|
| 247 |
+
epoch=8, iter=19, iter2=531, loss=0.10178, spearmanr=0.38144,p_value=0.07984419134812414 times:5.58s, 22 samples/iter
|
| 248 |
+
epoch=8, iter=29, iter2=541, loss=0.10281, spearmanr=0.23933,p_value=0.28339106201517633 times:3.74s, 22 samples/iter
|
| 249 |
+
Validation: epoch=8, loss=0.08856
|
| 250 |
+
Pearson Correlation: 0.57992,p_value: 2.2067871797546945e-17
|
| 251 |
+
Spearman Correlation: 0.54108,p_value; 6.3227934301797235e-15
|
| 252 |
+
epoch=8,iter=29,iter2=541,spearman_corr=0.5410826805827963
|
| 253 |
+
|
| 254 |
+
epoch=8, iter=39, iter2=551, loss=0.10241, spearmanr=0.16638,p_value=0.45927846751929857 times:5.69s, 22 samples/iter
|
| 255 |
+
Validation: epoch=8, loss=0.09124
|
| 256 |
+
Pearson Correlation: 0.60136,p_value: 6.944765955678166e-19
|
| 257 |
+
Spearman Correlation: 0.56000,p_value; 4.4076350303504984e-16
|
| 258 |
+
epoch=8,iter=44,iter2=556,spearman_corr=0.5599966121643787
|
| 259 |
+
|
| 260 |
+
epoch=8, iter=49, iter2=561, loss=0.10244, spearmanr=0.13448,p_value=0.5507144701841916 times:5.57s, 22 samples/iter
|
| 261 |
+
epoch=8, iter=59, iter2=571, loss=0.10227, spearmanr=0.30798,p_value=0.16320372858558338 times:3.89s, 22 samples/iter
|
| 262 |
+
Validation: epoch=8, loss=0.08642
|
| 263 |
+
Pearson Correlation: 0.58088,p_value: 1.9005740159743017e-17
|
| 264 |
+
Spearman Correlation: 0.55221,p_value; 1.3461947092693429e-15
|
| 265 |
+
epoch=8,iter=59,iter2=571,spearman_corr=0.552212397235527
|
| 266 |
+
|
| 267 |
+
epoch=8, iter=69, iter2=581, loss=0.10217, spearmanr=-0.19536,p_value=0.38361337896321734 times:5.93s, 22 samples/iter
|
| 268 |
+
####################epoch:10
|
| 269 |
+
epoch=9, iter=9, iter2=594, loss=0.10235, spearmanr=-0.25640,p_value=0.24939756110416242 times:5.31s, 22 samples/iter
|
| 270 |
+
Validation: epoch=9, loss=0.08672
|
| 271 |
+
Pearson Correlation: 0.57043,p_value: 9.426421882223235e-17
|
| 272 |
+
Spearman Correlation: 0.56227,p_value; 3.163690438596798e-16
|
| 273 |
+
epoch=9,iter=14,iter2=599,spearman_corr=0.5622698687342536
|
| 274 |
+
|
| 275 |
+
epoch=9, iter=19, iter2=604, loss=0.10235, spearmanr=0.45110,p_value=0.03510124558015449 times:5.70s, 22 samples/iter
|
| 276 |
+
epoch=9, iter=29, iter2=614, loss=0.10298, spearmanr=0.18156,p_value=0.418716235611457 times:3.79s, 22 samples/iter
|
| 277 |
+
Validation: epoch=9, loss=0.08671
|
| 278 |
+
Pearson Correlation: 0.58879,p_value: 5.440672531997601e-18
|
| 279 |
+
Spearman Correlation: 0.54907,p_value; 2.096932497599605e-15
|
| 280 |
+
epoch=9,iter=29,iter2=614,spearman_corr=0.5490655263563364
|
| 281 |
+
|
| 282 |
+
epoch=9, iter=39, iter2=624, loss=0.10261, spearmanr=0.25710,p_value=0.24805980739615632 times:5.66s, 22 samples/iter
|
| 283 |
+
Validation: epoch=9, loss=0.08859
|
| 284 |
+
Pearson Correlation: 0.61599,p_value: 5.616346332907652e-20
|
| 285 |
+
Spearman Correlation: 0.58255,p_value; 1.4631544459857015e-17
|
| 286 |
+
epoch=9,iter=44,iter2=629,spearman_corr=0.5825494623388804
|
| 287 |
+
|
| 288 |
+
epoch=9, iter=49, iter2=634, loss=0.10264, spearmanr=0.44699,p_value=0.03701112741305171 times:5.64s, 22 samples/iter
|
| 289 |
+
epoch=9, iter=59, iter2=644, loss=0.10249, spearmanr=0.00226,p_value=0.9920206352075737 times:3.78s, 22 samples/iter
|
| 290 |
+
Validation: epoch=9, loss=0.08645
|
| 291 |
+
Pearson Correlation: 0.60413,p_value: 4.353560131321982e-19
|
| 292 |
+
Spearman Correlation: 0.58588,p_value; 8.647346316458383e-18
|
| 293 |
+
epoch=9,iter=59,iter2=644,spearman_corr=0.5858831838942391
|
| 294 |
+
|
| 295 |
+
epoch=9, iter=69, iter2=654, loss=0.10240, spearmanr=-0.10300,p_value=0.648298791609897 times:5.59s, 22 samples/iter
|
| 296 |
+
####################epoch:11
|
| 297 |
+
epoch=10, iter=9, iter2=667, loss=0.10256, spearmanr=0.09045,p_value=0.6889459137582798 times:5.20s, 22 samples/iter
|
| 298 |
+
Validation: epoch=10, loss=0.08710
|
| 299 |
+
Pearson Correlation: 0.61809,p_value: 3.87578093262878e-20
|
| 300 |
+
Spearman Correlation: 0.58612,p_value; 8.32302335590956e-18
|
| 301 |
+
epoch=10,iter=14,iter2=672,spearman_corr=0.5861240133213119
|
| 302 |
+
|
| 303 |
+
epoch=10, iter=19, iter2=677, loss=0.10255, spearmanr=0.36903,p_value=0.09100334740665067 times:5.94s, 22 samples/iter
|
| 304 |
+
epoch=10, iter=29, iter2=687, loss=0.10309, spearmanr=-0.05986,p_value=0.7912921314487111 times:3.70s, 22 samples/iter
|
| 305 |
+
Validation: epoch=10, loss=0.08708
|
| 306 |
+
Pearson Correlation: 0.61073,p_value: 1.4078661847031694e-19
|
| 307 |
+
Spearman Correlation: 0.58483,p_value; 1.021302755828981e-17
|
| 308 |
+
epoch=10,iter=29,iter2=687,spearman_corr=0.5848324623314488
|
| 309 |
+
|
| 310 |
+
epoch=10, iter=39, iter2=697, loss=0.10276, spearmanr=0.00000,p_value=1.0 times:5.89s, 22 samples/iter
|
| 311 |
+
Validation: epoch=10, loss=0.08749
|
| 312 |
+
Pearson Correlation: 0.63288,p_value: 2.6131031369397948e-21
|
| 313 |
+
Spearman Correlation: 0.59781,p_value; 1.253771570108596e-18
|
| 314 |
+
epoch=10,iter=44,iter2=702,spearman_corr=0.5978053458754906
|
| 315 |
+
|
| 316 |
+
epoch=10, iter=49, iter2=707, loss=0.10277, spearmanr=0.31043,p_value=0.15969829685185985 times:5.79s, 22 samples/iter
|
| 317 |
+
epoch=10, iter=59, iter2=717, loss=0.10263, spearmanr=-0.19049,p_value=0.39580505744833117 times:3.71s, 22 samples/iter
|
| 318 |
+
Validation: epoch=10, loss=0.08647
|
| 319 |
+
Pearson Correlation: 0.63652,p_value: 1.3147524907099281e-21
|
| 320 |
+
Spearman Correlation: 0.61874,p_value; 3.4541394520943546e-20
|
| 321 |
+
epoch=10,iter=59,iter2=717,spearman_corr=0.6187390160948368
|
| 322 |
+
|
| 323 |
+
epoch=10, iter=69, iter2=727, loss=0.10254, spearmanr=0.27880,p_value=0.20895280931960492 times:5.47s, 22 samples/iter
|
| 324 |
+
####################epoch:12
|
| 325 |
+
epoch=11, iter=9, iter2=740, loss=0.10268, spearmanr=-0.12436,p_value=0.5813544106482844 times:5.29s, 22 samples/iter
|
| 326 |
+
Validation: epoch=11, loss=0.08691
|
| 327 |
+
Pearson Correlation: 0.61998,p_value: 2.7686381337003393e-20
|
| 328 |
+
Spearman Correlation: 0.59634,p_value; 1.5969286740588522e-18
|
| 329 |
+
epoch=11,iter=14,iter2=745,spearman_corr=0.5963384627733056
|
| 330 |
+
|
| 331 |
+
epoch=11, iter=19, iter2=750, loss=0.10266, spearmanr=0.07971,p_value=0.7243931445528782 times:5.68s, 22 samples/iter
|
| 332 |
+
epoch=11, iter=29, iter2=760, loss=0.10315, spearmanr=0.60212,p_value=0.003026260378964401 times:3.78s, 22 samples/iter
|
| 333 |
+
Validation: epoch=11, loss=0.08722
|
| 334 |
+
Pearson Correlation: 0.61656,p_value: 5.087264811012543e-20
|
| 335 |
+
Spearman Correlation: 0.58768,p_value; 6.495053977488337e-18
|
| 336 |
+
epoch=11,iter=29,iter2=760,spearman_corr=0.5876814882241151
|
| 337 |
+
|
| 338 |
+
epoch=11, iter=39, iter2=770, loss=0.10286, spearmanr=-0.00795,p_value=0.9719976910298593 times:5.96s, 22 samples/iter
|
| 339 |
+
Validation: epoch=11, loss=0.09266
|
| 340 |
+
Pearson Correlation: 0.63696,p_value: 1.2089913101639148e-21
|
| 341 |
+
Spearman Correlation: 0.60966,p_value; 1.6938120629599856e-19
|
| 342 |
+
epoch=11,iter=44,iter2=775,spearman_corr=0.6096623563118744
|
| 343 |
+
|
| 344 |
+
epoch=11, iter=49, iter2=780, loss=0.10283, spearmanr=0.35068,p_value=0.10957393946291796 times:5.75s, 22 samples/iter
|
| 345 |
+
epoch=11, iter=59, iter2=790, loss=0.10271, spearmanr=0.28082,p_value=0.2055416459108249 times:3.70s, 22 samples/iter
|
| 346 |
+
Validation: epoch=11, loss=0.08716
|
| 347 |
+
Pearson Correlation: 0.65916,p_value: 1.4823022997517573e-23
|
| 348 |
+
Spearman Correlation: 0.63217,p_value; 2.9794915329185535e-21
|
| 349 |
+
epoch=11,iter=59,iter2=790,spearman_corr=0.6321737242071304
|
| 350 |
+
|
| 351 |
+
epoch=11, iter=69, iter2=800, loss=0.10265, spearmanr=0.05458,p_value=0.8093649716781448 times:6.18s, 22 samples/iter
|
| 352 |
+
####################epoch:13
|
| 353 |
+
epoch=12, iter=9, iter2=813, loss=0.10278, spearmanr=0.13481,p_value=0.549727554281799 times:5.56s, 22 samples/iter
|
| 354 |
+
Validation: epoch=12, loss=0.08715
|
| 355 |
+
Pearson Correlation: 0.65689,p_value: 2.3676861742972586e-23
|
| 356 |
+
Spearman Correlation: 0.62255,p_value; 1.7458620903122826e-20
|
| 357 |
+
epoch=12,iter=14,iter2=818,spearman_corr=0.6225457879712941
|
| 358 |
+
|
| 359 |
+
epoch=12, iter=19, iter2=823, loss=0.10274, spearmanr=0.33804,p_value=0.12387229975053489 times:5.52s, 22 samples/iter
|
| 360 |
+
epoch=12, iter=29, iter2=833, loss=0.10318, spearmanr=0.67958,p_value=0.0005037618068545962 times:3.72s, 22 samples/iter
|
| 361 |
+
Validation: epoch=12, loss=0.08740
|
| 362 |
+
Pearson Correlation: 0.63262,p_value: 2.7387461366522744e-21
|
| 363 |
+
Spearman Correlation: 0.58525,p_value; 9.555400949806913e-18
|
| 364 |
+
epoch=12,iter=29,iter2=833,spearman_corr=0.5852531615884828
|
| 365 |
+
|
| 366 |
+
epoch=12, iter=39, iter2=843, loss=0.10294, spearmanr=0.56486,p_value=0.0061618851662422185 times:5.74s, 22 samples/iter
|
| 367 |
+
Validation: epoch=12, loss=0.09409
|
| 368 |
+
Pearson Correlation: 0.64704,p_value: 1.7129194429514422e-22
|
| 369 |
+
Spearman Correlation: 0.61200,p_value; 1.131100405432939e-19
|
| 370 |
+
epoch=12,iter=44,iter2=848,spearman_corr=0.6119953012462036
|
| 371 |
+
|
| 372 |
+
epoch=12, iter=49, iter2=853, loss=0.10288, spearmanr=0.42383,p_value=0.04933369901988782 times:5.67s, 22 samples/iter
|
| 373 |
+
epoch=12, iter=59, iter2=863, loss=0.10277, spearmanr=0.65940,p_value=0.000843197801519634 times:3.95s, 22 samples/iter
|
| 374 |
+
Validation: epoch=12, loss=0.08752
|
| 375 |
+
Pearson Correlation: 0.62828,p_value: 6.130886197348663e-21
|
| 376 |
+
Spearman Correlation: 0.60135,p_value; 6.949147167135574e-19
|
| 377 |
+
epoch=12,iter=59,iter2=863,spearman_corr=0.60135231628054
|
| 378 |
+
|
| 379 |
+
epoch=12, iter=69, iter2=873, loss=0.10271, spearmanr=0.01611,p_value=0.9432706815342202 times:5.73s, 22 samples/iter
|
| 380 |
+
####################epoch:14
|
| 381 |
+
epoch=13, iter=9, iter2=886, loss=0.10283, spearmanr=0.24396,p_value=0.2738973190509651 times:5.39s, 22 samples/iter
|
| 382 |
+
Validation: epoch=13, loss=0.08675
|
| 383 |
+
Pearson Correlation: 0.62760,p_value: 6.959753078972691e-21
|
| 384 |
+
Spearman Correlation: 0.58997,p_value; 4.5023759569581245e-18
|
| 385 |
+
epoch=13,iter=14,iter2=891,spearman_corr=0.5899677075808528
|
| 386 |
+
|
| 387 |
+
epoch=13, iter=19, iter2=896, loss=0.10279, spearmanr=0.16011,p_value=0.476605476767181 times:5.65s, 22 samples/iter
|
| 388 |
+
epoch=13, iter=29, iter2=906, loss=0.10320, spearmanr=0.02731,p_value=0.9039687460285148 times:3.98s, 22 samples/iter
|
| 389 |
+
Validation: epoch=13, loss=0.08722
|
| 390 |
+
Pearson Correlation: 0.64888,p_value: 1.190608705370603e-22
|
| 391 |
+
Spearman Correlation: 0.60772,p_value; 2.365418573768053e-19
|
| 392 |
+
epoch=13,iter=29,iter2=906,spearman_corr=0.607718148248339
|
| 393 |
+
|
| 394 |
+
epoch=13, iter=39, iter2=916, loss=0.10296, spearmanr=0.13159,p_value=0.5593860170643308 times:5.56s, 22 samples/iter
|
| 395 |
+
Validation: epoch=13, loss=0.09249
|
| 396 |
+
Pearson Correlation: 0.66777,p_value: 2.4316751788268946e-24
|
| 397 |
+
Spearman Correlation: 0.62641,p_value; 8.648507826554603e-21
|
| 398 |
+
epoch=13,iter=44,iter2=921,spearman_corr=0.6264111484724583
|
| 399 |
+
|
| 400 |
+
epoch=13, iter=49, iter2=926, loss=0.10293, spearmanr=0.34201,p_value=0.11924877891757386 times:5.73s, 22 samples/iter
|
| 401 |
+
epoch=13, iter=59, iter2=936, loss=0.10283, spearmanr=-0.02209,p_value=0.9222697284010513 times:3.91s, 22 samples/iter
|
| 402 |
+
Validation: epoch=13, loss=0.08730
|
| 403 |
+
Pearson Correlation: 0.62540,p_value: 1.0400872087013736e-20
|
| 404 |
+
Spearman Correlation: 0.57729,p_value; 3.314209816610204e-17
|
| 405 |
+
epoch=13,iter=59,iter2=936,spearman_corr=0.577289976261413
|
| 406 |
+
|
| 407 |
+
epoch=13, iter=69, iter2=946, loss=0.10279, spearmanr=0.19045,p_value=0.39590711838202186 times:5.65s, 22 samples/iter
|
| 408 |
+
####################epoch:15
|
| 409 |
+
epoch=14, iter=9, iter2=959, loss=0.10288, spearmanr=-0.05777,p_value=0.7984444174290112 times:5.80s, 22 samples/iter
|
| 410 |
+
Validation: epoch=14, loss=0.08666
|
| 411 |
+
Pearson Correlation: 0.66918,p_value: 1.7977443860238e-24
|
| 412 |
+
Spearman Correlation: 0.63400,p_value; 2.115471795362743e-21
|
| 413 |
+
epoch=14,iter=14,iter2=964,spearman_corr=0.6340003468743579
|
| 414 |
+
|
| 415 |
+
epoch=14, iter=19, iter2=969, loss=0.10286, spearmanr=0.22696,p_value=0.30974757848570245 times:5.71s, 22 samples/iter
|
| 416 |
+
epoch=14, iter=29, iter2=979, loss=0.10323, spearmanr=0.40491,p_value=0.061580504097370196 times:3.82s, 22 samples/iter
|
| 417 |
+
Validation: epoch=14, loss=0.08723
|
| 418 |
+
Pearson Correlation: 0.65569,p_value: 3.0207064938166605e-23
|
| 419 |
+
Spearman Correlation: 0.61223,p_value; 1.0865933163919034e-19
|
| 420 |
+
epoch=14,iter=29,iter2=979,spearman_corr=0.612226185364758
|
| 421 |
+
|
| 422 |
+
epoch=14, iter=39, iter2=989, loss=0.10301, spearmanr=0.29744,p_value=0.17883100269909136 times:5.80s, 22 samples/iter
|
| 423 |
+
Validation: epoch=14, loss=0.09198
|
| 424 |
+
Pearson Correlation: 0.66464,p_value: 4.718707977516085e-24
|
| 425 |
+
Spearman Correlation: 0.63181,p_value; 3.187049386865631e-21
|
| 426 |
+
epoch=14,iter=44,iter2=994,spearman_corr=0.6318131080689817
|
| 427 |
+
|
| 428 |
+
epoch=14, iter=49, iter2=999, loss=0.10297, spearmanr=0.34559,p_value=0.1151821523131585 times:5.66s, 22 samples/iter
|
| 429 |
+
Checkpoint saved at epoch 14, batch_idx 50, total_train_iter 999
|
| 430 |
+
iter 1000 save model /home/chipan/shuffle_token_pan/checkpoint/2025_01_14_09_47_45/last_add_999.pth
|
| 431 |
+
epoch=14, iter=59, iter2=1009, loss=0.10287, spearmanr=0.61190,p_value=0.0024748380843317317 times:15.97s, 22 samples/iter
|
| 432 |
+
Validation: epoch=14, loss=0.09159
|
| 433 |
+
Pearson Correlation: 0.63015,p_value: 4.344651056851488e-21
|
| 434 |
+
Spearman Correlation: 0.61387,p_value; 8.158553689444621e-20
|
| 435 |
+
epoch=14,iter=59,iter2=1009,spearman_corr=0.6138688858074891
|
| 436 |
+
|
| 437 |
+
epoch=14, iter=69, iter2=1019, loss=0.10283, spearmanr=0.23678,p_value=0.288723009101003 times:6.22s, 22 samples/iter
|
| 438 |
+
####################epoch:16
|
| 439 |
+
epoch=15, iter=9, iter2=1032, loss=0.10261, spearmanr=0.19045,p_value=0.39590711838202186 times:4.96s, 22 samples/iter
|
| 440 |
+
Validation: epoch=15, loss=0.09118
|
| 441 |
+
Pearson Correlation: 0.56899,p_value: 1.1685595071915403e-16
|
| 442 |
+
Spearman Correlation: 0.56009,p_value; 4.350818179936393e-16
|
| 443 |
+
epoch=15,iter=14,iter2=1037,spearman_corr=0.5600858841300039
|
| 444 |
+
|
| 445 |
+
epoch=15, iter=19, iter2=1042, loss=0.10266, spearmanr=0.05314,p_value=0.8143098961434112 times:5.90s, 22 samples/iter
|
| 446 |
+
epoch=15, iter=29, iter2=1052, loss=0.10239, spearmanr=0.28656,p_value=0.19602198149002867 times:3.72s, 22 samples/iter
|
| 447 |
+
Validation: epoch=15, loss=0.09113
|
| 448 |
+
Pearson Correlation: 0.62441,p_value: 1.2461534897041749e-20
|
| 449 |
+
Spearman Correlation: 0.63477,p_value; 1.828435465271598e-21
|
| 450 |
+
epoch=15,iter=29,iter2=1052,spearman_corr=0.6347743924642137
|
| 451 |
+
|
| 452 |
+
epoch=15, iter=39, iter2=1062, loss=0.10232, spearmanr=0.30510,p_value=0.1673729872087984 times:5.49s, 22 samples/iter
|
| 453 |
+
Validation: epoch=15, loss=0.09112
|
| 454 |
+
Pearson Correlation: 0.61243,p_value: 1.0489547787383982e-19
|
| 455 |
+
Spearman Correlation: 0.60853,p_value; 2.0579419292453595e-19
|
| 456 |
+
epoch=15,iter=44,iter2=1067,spearman_corr=0.6085303984004504
|
| 457 |
+
|
| 458 |
+
epoch=15, iter=49, iter2=1072, loss=0.10224, spearmanr=0.29430,p_value=0.18368650636338876 times:5.44s, 22 samples/iter
|
| 459 |
+
epoch=15, iter=59, iter2=1082, loss=0.10222, spearmanr=0.61616,p_value=0.002262616834705637 times:3.71s, 22 samples/iter
|
| 460 |
+
Validation: epoch=15, loss=0.09319
|
| 461 |
+
Pearson Correlation: 0.62668,p_value: 8.237431278082443e-21
|
| 462 |
+
Spearman Correlation: 0.61228,p_value; 1.0757108512236783e-19
|
| 463 |
+
epoch=15,iter=59,iter2=1082,spearman_corr=0.612284048243367
|
| 464 |
+
|
| 465 |
+
epoch=15, iter=69, iter2=1092, loss=0.10233, spearmanr=0.25681,p_value=0.24861522107664363 times:5.55s, 22 samples/iter
|
| 466 |
+
####################epoch:17
|
| 467 |
+
epoch=16, iter=9, iter2=1105, loss=0.10216, spearmanr=0.12779,p_value=0.5708898271578675 times:4.95s, 22 samples/iter
|
| 468 |
+
Validation: epoch=16, loss=0.09168
|
| 469 |
+
Pearson Correlation: 0.63016,p_value: 4.3374742149059535e-21
|
| 470 |
+
Spearman Correlation: 0.60950,p_value; 1.7422407915676013e-19
|
| 471 |
+
epoch=16,iter=14,iter2=1110,spearman_corr=0.6094987617967066
|
| 472 |
+
|
| 473 |
+
epoch=16, iter=19, iter2=1115, loss=0.10224, spearmanr=0.04524,p_value=0.8415674454379428 times:5.52s, 22 samples/iter
|
| 474 |
+
epoch=16, iter=29, iter2=1125, loss=0.10199, spearmanr=-0.02976,p_value=0.895398890914618 times:3.68s, 22 samples/iter
|
| 475 |
+
Validation: epoch=16, loss=0.09127
|
| 476 |
+
Pearson Correlation: 0.61412,p_value: 7.808233430740246e-20
|
| 477 |
+
Spearman Correlation: 0.60657,p_value; 2.8782284141839905e-19
|
| 478 |
+
epoch=16,iter=29,iter2=1125,spearman_corr=0.606569617264542
|
| 479 |
+
|
| 480 |
+
epoch=16, iter=39, iter2=1135, loss=0.10192, spearmanr=-0.28640,p_value=0.19628609806673378 times:5.66s, 22 samples/iter
|
| 481 |
+
Validation: epoch=16, loss=0.09190
|
| 482 |
+
Pearson Correlation: 0.62766,p_value: 6.873172148880251e-21
|
| 483 |
+
Spearman Correlation: 0.61041,p_value; 1.489577848142574e-19
|
| 484 |
+
epoch=16,iter=44,iter2=1140,spearman_corr=0.6104068059306588
|
| 485 |
+
|
| 486 |
+
epoch=16, iter=49, iter2=1145, loss=0.10184, spearmanr=0.34816,p_value=0.11232396597411932 times:5.82s, 22 samples/iter
|
| 487 |
+
epoch=16, iter=59, iter2=1155, loss=0.10183, spearmanr=0.24730,p_value=0.26717390697765625 times:3.78s, 22 samples/iter
|
| 488 |
+
Validation: epoch=16, loss=0.09299
|
| 489 |
+
Pearson Correlation: 0.58282,p_value: 1.4023968820026053e-17
|
| 490 |
+
Spearman Correlation: 0.57662,p_value; 3.675700015350976e-17
|
| 491 |
+
epoch=16,iter=59,iter2=1155,spearman_corr=0.5766172552725667
|
| 492 |
+
|
| 493 |
+
epoch=16, iter=69, iter2=1165, loss=0.10193, spearmanr=0.23610,p_value=0.2901485682251552 times:5.98s, 22 samples/iter
|
| 494 |
+
####################epoch:18
|
| 495 |
+
epoch=17, iter=9, iter2=1178, loss=0.10177, spearmanr=-0.03763,p_value=0.867939977889499 times:5.04s, 22 samples/iter
|
| 496 |
+
Validation: epoch=17, loss=0.09167
|
| 497 |
+
Pearson Correlation: 0.60030,p_value: 8.281830003590771e-19
|
| 498 |
+
Spearman Correlation: 0.58308,p_value; 1.3456329951411184e-17
|
| 499 |
+
epoch=17,iter=14,iter2=1183,spearman_corr=0.5830827653478802
|
| 500 |
+
|
| 501 |
+
epoch=17, iter=19, iter2=1188, loss=0.10186, spearmanr=0.30160,p_value=0.17255079214757058 times:5.53s, 22 samples/iter
|
| 502 |
+
epoch=17, iter=29, iter2=1198, loss=0.10162, spearmanr=-0.11083,p_value=0.6234252341821973 times:3.78s, 22 samples/iter
|
| 503 |
+
Validation: epoch=17, loss=0.09119
|
| 504 |
+
Pearson Correlation: 0.59056,p_value: 4.09070715486616e-18
|
| 505 |
+
Spearman Correlation: 0.57013,p_value; 9.850919090154902e-17
|
| 506 |
+
epoch=17,iter=29,iter2=1198,spearman_corr=0.5701329097907295
|
| 507 |
+
|
| 508 |
+
epoch=17, iter=39, iter2=1208, loss=0.10156, spearmanr=0.26451,p_value=0.23420501938344468 times:5.40s, 22 samples/iter
|
| 509 |
+
Validation: epoch=17, loss=0.09143
|
| 510 |
+
Pearson Correlation: 0.60640,p_value: 2.962572696264928e-19
|
| 511 |
+
Spearman Correlation: 0.59631,p_value; 1.603619431065542e-18
|
| 512 |
+
epoch=17,iter=44,iter2=1213,spearman_corr=0.5963130458783127
|
| 513 |
+
|
| 514 |
+
epoch=17, iter=49, iter2=1218, loss=0.10149, spearmanr=0.31600,p_value=0.15195348760810184 times:5.56s, 22 samples/iter
|
| 515 |
+
epoch=17, iter=59, iter2=1228, loss=0.10147, spearmanr=0.29437,p_value=0.18357573260910004 times:3.66s, 22 samples/iter
|
| 516 |
+
Validation: epoch=17, loss=0.09266
|
| 517 |
+
Pearson Correlation: 0.58187,p_value: 1.628601166469929e-17
|
| 518 |
+
Spearman Correlation: 0.56889,p_value; 1.1870941686532604e-16
|
| 519 |
+
epoch=17,iter=59,iter2=1228,spearman_corr=0.5688897898721618
|
| 520 |
+
|
| 521 |
+
epoch=17, iter=69, iter2=1238, loss=0.10158, spearmanr=0.18763,p_value=0.4030798508569462 times:5.54s, 22 samples/iter
|
| 522 |
+
####################epoch:19
|
| 523 |
+
epoch=18, iter=9, iter2=1251, loss=0.10143, spearmanr=0.15241,p_value=0.4983398369728198 times:4.82s, 22 samples/iter
|
| 524 |
+
Validation: epoch=18, loss=0.09154
|
| 525 |
+
Pearson Correlation: 0.61175,p_value: 1.1811866436149034e-19
|
| 526 |
+
Spearman Correlation: 0.58590,p_value; 8.629968041721688e-18
|
| 527 |
+
epoch=18,iter=14,iter2=1256,spearman_corr=0.5858958624912483
|
| 528 |
+
|
| 529 |
+
epoch=18, iter=19, iter2=1261, loss=0.10151, spearmanr=0.42955,p_value=0.046030490397948085 times:5.54s, 22 samples/iter
|
| 530 |
+
epoch=18, iter=29, iter2=1271, loss=0.10129, spearmanr=0.28555,p_value=0.19767605096780663 times:3.71s, 22 samples/iter
|
| 531 |
+
Validation: epoch=18, loss=0.09115
|
| 532 |
+
Pearson Correlation: 0.64015,p_value: 6.570982129066407e-22
|
| 533 |
+
Spearman Correlation: 0.61802,p_value; 3.927014375751241e-20
|
| 534 |
+
epoch=18,iter=29,iter2=1271,spearman_corr=0.6180173482164302
|
| 535 |
+
|
| 536 |
+
epoch=18, iter=39, iter2=1281, loss=0.10124, spearmanr=0.06942,p_value=0.758847636656594 times:6.24s, 22 samples/iter
|
| 537 |
+
Validation: epoch=18, loss=0.09126
|
| 538 |
+
Pearson Correlation: 0.60641,p_value: 2.9570481637488395e-19
|
| 539 |
+
Spearman Correlation: 0.58621,p_value; 8.20604248056406e-18
|
| 540 |
+
epoch=18,iter=44,iter2=1286,spearman_corr=0.5862131379484469
|
| 541 |
+
|
| 542 |
+
epoch=18, iter=49, iter2=1291, loss=0.10118, spearmanr=0.01643,p_value=0.9421625855246065 times:5.63s, 22 samples/iter
|
| 543 |
+
epoch=18, iter=59, iter2=1301, loss=0.10116, spearmanr=0.55658,p_value=0.007139662168929662 times:3.78s, 22 samples/iter
|
| 544 |
+
Validation: epoch=18, loss=0.09330
|
| 545 |
+
Pearson Correlation: 0.57193,p_value: 7.512879824913695e-17
|
| 546 |
+
Spearman Correlation: 0.55730,p_value; 6.50968162147969e-16
|
| 547 |
+
epoch=18,iter=59,iter2=1301,spearman_corr=0.5573009344184584
|
| 548 |
+
|
| 549 |
+
epoch=18, iter=69, iter2=1311, loss=0.10126, spearmanr=0.00085,p_value=0.9970000446368241 times:5.53s, 22 samples/iter
|
| 550 |
+
####################epoch:20
|
| 551 |
+
epoch=19, iter=9, iter2=1324, loss=0.10112, spearmanr=0.13549,p_value=0.5477173627468073 times:5.43s, 22 samples/iter
|
| 552 |
+
Validation: epoch=19, loss=0.09160
|
| 553 |
+
Pearson Correlation: 0.55567,p_value: 8.228462920223682e-16
|
| 554 |
+
Spearman Correlation: 0.55421,p_value; 1.0137474411258164e-15
|
| 555 |
+
epoch=19,iter=14,iter2=1329,spearman_corr=0.5542090827611159
|
| 556 |
+
|
| 557 |
+
epoch=19, iter=19, iter2=1334, loss=0.10120, spearmanr=0.33107,p_value=0.13232178774173373 times:5.79s, 22 samples/iter
|
| 558 |
+
epoch=19, iter=29, iter2=1344, loss=0.10100, spearmanr=-0.07513,p_value=0.7396536964035301 times:3.78s, 22 samples/iter
|
| 559 |
+
Validation: epoch=19, loss=0.09113
|
| 560 |
+
Pearson Correlation: 0.61107,p_value: 1.3290136418479035e-19
|
| 561 |
+
Spearman Correlation: 0.59774,p_value; 1.2682991349168125e-18
|
| 562 |
+
epoch=19,iter=29,iter2=1344,spearman_corr=0.5977356627303593
|
| 563 |
+
|
| 564 |
+
epoch=19, iter=39, iter2=1354, loss=0.10095, spearmanr=0.25871,p_value=0.24500413450885836 times:5.74s, 22 samples/iter
|
| 565 |
+
Validation: epoch=19, loss=0.09125
|
| 566 |
+
Pearson Correlation: 0.62331,p_value: 1.5203930241132703e-20
|
| 567 |
+
Spearman Correlation: 0.60940,p_value; 1.7728147176343923e-19
|
| 568 |
+
epoch=19,iter=44,iter2=1359,spearman_corr=0.6093977598577721
|
| 569 |
+
|
| 570 |
+
epoch=19, iter=49, iter2=1364, loss=0.10090, spearmanr=0.32870,p_value=0.13527610416262065 times:5.70s, 22 samples/iter
|
| 571 |
+
epoch=19, iter=59, iter2=1374, loss=0.10088, spearmanr=0.19403,p_value=0.386921354314398 times:4.06s, 22 samples/iter
|
| 572 |
+
Validation: epoch=19, loss=0.09316
|
| 573 |
+
Pearson Correlation: 0.59347,p_value: 2.552270536320889e-18
|
| 574 |
+
Spearman Correlation: 0.56330,p_value; 2.7210417403001344e-16
|
| 575 |
+
epoch=19,iter=59,iter2=1374,spearman_corr=0.5632974348397483
|
| 576 |
+
|
| 577 |
+
epoch=19, iter=69, iter2=1384, loss=0.10098, spearmanr=0.33656,p_value=0.1256387242685956 times:5.63s, 22 samples/iter
|
| 578 |
+
####################epoch:21
|
| 579 |
+
epoch=20, iter=9, iter2=1397, loss=0.10085, spearmanr=0.24002,p_value=0.2819633177627852 times:5.14s, 22 samples/iter
|
| 580 |
+
Validation: epoch=20, loss=0.09166
|
| 581 |
+
Pearson Correlation: 0.55399,p_value: 1.0454184396548613e-15
|
| 582 |
+
Spearman Correlation: 0.53175,p_value; 2.2162742077980875e-14
|
| 583 |
+
epoch=20,iter=14,iter2=1402,spearman_corr=0.5317470456558314
|
| 584 |
+
|
| 585 |
+
epoch=20, iter=19, iter2=1407, loss=0.10093, spearmanr=0.25249,p_value=0.25694134749897535 times:5.39s, 22 samples/iter
|
| 586 |
+
epoch=20, iter=29, iter2=1417, loss=0.10074, spearmanr=0.33948,p_value=0.12217888327991301 times:3.83s, 22 samples/iter
|
| 587 |
+
Validation: epoch=20, loss=0.09120
|
| 588 |
+
Pearson Correlation: 0.59019,p_value: 4.344284334727966e-18
|
| 589 |
+
Spearman Correlation: 0.57584,p_value; 4.138550864351496e-17
|
| 590 |
+
epoch=20,iter=29,iter2=1417,spearman_corr=0.5758446569226329
|
| 591 |
+
|
| 592 |
+
epoch=20, iter=39, iter2=1427, loss=0.10069, spearmanr=0.63156,p_value=0.0016185766341366018 times:5.49s, 22 samples/iter
|
| 593 |
+
Validation: epoch=20, loss=0.09155
|
| 594 |
+
Pearson Correlation: 0.57960,p_value: 2.31774138209174e-17
|
| 595 |
+
Spearman Correlation: 0.56521,p_value; 2.0536435277439906e-16
|
| 596 |
+
epoch=20,iter=44,iter2=1432,spearman_corr=0.5652064160363204
|
| 597 |
+
|
| 598 |
+
epoch=20, iter=49, iter2=1437, loss=0.10064, spearmanr=0.61728,p_value=0.002209315613695095 times:5.70s, 22 samples/iter
|
| 599 |
+
epoch=20, iter=59, iter2=1447, loss=0.10063, spearmanr=0.05662,p_value=0.8023952353166044 times:3.89s, 22 samples/iter
|
| 600 |
+
Validation: epoch=20, loss=0.09334
|
| 601 |
+
Pearson Correlation: 0.54706,p_value: 2.773582704246368e-15
|
| 602 |
+
Spearman Correlation: 0.51908,p_value; 1.1443459447632054e-13
|
| 603 |
+
epoch=20,iter=59,iter2=1447,spearman_corr=0.5190781594151441
|
| 604 |
+
|
| 605 |
+
epoch=20, iter=69, iter2=1457, loss=0.10073, spearmanr=0.28649,p_value=0.196149408482356 times:5.63s, 22 samples/iter
|
| 606 |
+
####################epoch:22
|
| 607 |
+
epoch=21, iter=9, iter2=1470, loss=0.10061, spearmanr=0.27275,p_value=0.21940984249790005 times:4.85s, 22 samples/iter
|
| 608 |
+
Validation: epoch=21, loss=0.09170
|
| 609 |
+
Pearson Correlation: 0.55775,p_value: 6.098209997988191e-16
|
| 610 |
+
Spearman Correlation: 0.53583,p_value; 1.2870202322776208e-14
|
| 611 |
+
epoch=21,iter=14,iter2=1475,spearman_corr=0.5358277838234152
|
| 612 |
+
|
| 613 |
+
epoch=21, iter=19, iter2=1480, loss=0.10069, spearmanr=0.16032,p_value=0.4760261696584053 times:5.21s, 22 samples/iter
|
| 614 |
+
epoch=21, iter=29, iter2=1490, loss=0.10051, spearmanr=0.30360,p_value=0.16958065223053934 times:3.68s, 22 samples/iter
|
| 615 |
+
Validation: epoch=21, loss=0.09115
|
| 616 |
+
Pearson Correlation: 0.58306,p_value: 1.3507516123036732e-17
|
| 617 |
+
Spearman Correlation: 0.56314,p_value; 2.784534642850711e-16
|
| 618 |
+
epoch=21,iter=29,iter2=1490,spearman_corr=0.5631404123215602
|
| 619 |
+
|
| 620 |
+
Checkpoint saved at epoch 21, batch_idx 39, total_train_iter 1499
|
| 621 |
+
iter 1500 save model /home/chipan/shuffle_token_pan/checkpoint/2025_01_14_09_47_45/last_add_1499.pth
|
| 622 |
+
epoch=21, iter=39, iter2=1500, loss=0.10047, spearmanr=0.09459,p_value=0.6754254906376911 times:17.22s, 22 samples/iter
|
| 623 |
+
Validation: epoch=21, loss=0.09082
|
| 624 |
+
Pearson Correlation: 0.57822,p_value: 2.8728793310966953e-17
|
| 625 |
+
Spearman Correlation: 0.60119,p_value; 7.144237761981507e-19
|
| 626 |
+
epoch=21,iter=44,iter2=1505,spearman_corr=0.601186879355686
|
| 627 |
+
|
| 628 |
+
epoch=21, iter=49, iter2=1510, loss=0.10042, spearmanr=0.50213,p_value=0.01725110256192266 times:6.33s, 22 samples/iter
|
| 629 |
+
epoch=21, iter=59, iter2=1520, loss=0.10041, spearmanr=0.22229,p_value=0.32008656273134983 times:3.72s, 22 samples/iter
|
| 630 |
+
Validation: epoch=21, loss=0.09223
|
| 631 |
+
Pearson Correlation: 0.53050,p_value: 2.611240284872314e-14
|
| 632 |
+
Spearman Correlation: 0.58201,p_value; 1.593285593027033e-17
|
| 633 |
+
epoch=21,iter=59,iter2=1520,spearman_corr=0.5820057722559109
|
| 634 |
+
|
| 635 |
+
epoch=21, iter=69, iter2=1530, loss=0.10050, spearmanr=0.14497,p_value=0.5197885467189387 times:6.00s, 22 samples/iter
|
| 636 |
+
####################epoch:23
|
| 637 |
+
epoch=22, iter=9, iter2=1543, loss=0.10053, spearmanr=0.34774,p_value=0.11278599984321386 times:5.25s, 22 samples/iter
|
| 638 |
+
Validation: epoch=22, loss=0.09057
|
| 639 |
+
Pearson Correlation: 0.56125,p_value: 3.6742857236815097e-16
|
| 640 |
+
Spearman Correlation: 0.59501,p_value; 1.987117183567175e-18
|
| 641 |
+
epoch=22,iter=14,iter2=1548,spearman_corr=0.5950065303655826
|
| 642 |
+
|
| 643 |
+
epoch=22, iter=19, iter2=1553, loss=0.10059, spearmanr=0.04613,p_value=0.8384771424221206 times:5.62s, 22 samples/iter
|
| 644 |
+
epoch=22, iter=29, iter2=1563, loss=0.10052, spearmanr=0.26009,p_value=0.24241188613820586 times:3.70s, 22 samples/iter
|
| 645 |
+
Validation: epoch=22, loss=0.09249
|
| 646 |
+
Pearson Correlation: 0.57114,p_value: 8.460971081964823e-17
|
| 647 |
+
Spearman Correlation: 0.59156,p_value; 3.482568145700491e-18
|
| 648 |
+
epoch=22,iter=29,iter2=1563,spearman_corr=0.5915594341304464
|
| 649 |
+
|
| 650 |
+
epoch=22, iter=39, iter2=1573, loss=0.10046, spearmanr=0.16228,p_value=0.4705886782434152 times:5.69s, 22 samples/iter
|
| 651 |
+
Validation: epoch=22, loss=0.09086
|
| 652 |
+
Pearson Correlation: 0.61338,p_value: 8.881352933981189e-20
|
| 653 |
+
Spearman Correlation: 0.64471,p_value; 2.7121732240635087e-22
|
| 654 |
+
epoch=22,iter=44,iter2=1578,spearman_corr=0.6447062724297133
|
| 655 |
+
|
| 656 |
+
epoch=22, iter=49, iter2=1583, loss=0.10042, spearmanr=0.43188,p_value=0.044734661446974806 times:5.56s, 22 samples/iter
|
| 657 |
+
epoch=22, iter=59, iter2=1593, loss=0.10043, spearmanr=0.18025,p_value=0.4221483684465501 times:3.85s, 22 samples/iter
|
| 658 |
+
Validation: epoch=22, loss=0.09103
|
| 659 |
+
Pearson Correlation: 0.57639,p_value: 3.80889441294904e-17
|
| 660 |
+
Spearman Correlation: 0.58798,p_value; 6.19664046264471e-18
|
| 661 |
+
epoch=22,iter=59,iter2=1593,spearman_corr=0.5879759447780243
|
| 662 |
+
|
| 663 |
+
epoch=22, iter=69, iter2=1603, loss=0.10050, spearmanr=0.48973,p_value=0.020697815843147935 times:5.74s, 22 samples/iter
|
| 664 |
+
####################epoch:24
|
| 665 |
+
epoch=23, iter=9, iter2=1616, loss=0.10051, spearmanr=0.10758,p_value=0.6337022414266318 times:4.97s, 22 samples/iter
|
| 666 |
+
Validation: epoch=23, loss=0.09096
|
| 667 |
+
Pearson Correlation: 0.64694,p_value: 1.7484929045913302e-22
|
| 668 |
+
Spearman Correlation: 0.66532,p_value; 4.093908166369237e-24
|
| 669 |
+
epoch=23,iter=14,iter2=1621,spearman_corr=0.6653155841071912
|
| 670 |
+
|
| 671 |
+
epoch=23, iter=19, iter2=1626, loss=0.10056, spearmanr=0.50827,p_value=0.01572341784072689 times:5.81s, 22 samples/iter
|
| 672 |
+
epoch=23, iter=29, iter2=1636, loss=0.10047, spearmanr=0.67454,p_value=0.0005750395995303845 times:3.79s, 22 samples/iter
|
| 673 |
+
Validation: epoch=23, loss=0.09063
|
| 674 |
+
Pearson Correlation: 0.60999,p_value: 1.6018050124177916e-19
|
| 675 |
+
Spearman Correlation: 0.62973,p_value; 4.698131958999513e-21
|
| 676 |
+
epoch=23,iter=29,iter2=1636,spearman_corr=0.6297257503110394
|
| 677 |
+
|
| 678 |
+
epoch=23, iter=39, iter2=1646, loss=0.10039, spearmanr=0.31891,p_value=0.14800415507441003 times:5.59s, 22 samples/iter
|
| 679 |
+
Validation: epoch=23, loss=0.09068
|
| 680 |
+
Pearson Correlation: 0.58885,p_value: 5.3875741541165974e-18
|
| 681 |
+
Spearman Correlation: 0.58804,p_value; 6.136454144782806e-18
|
| 682 |
+
epoch=23,iter=44,iter2=1651,spearman_corr=0.5880370116252339
|
| 683 |
+
|
| 684 |
+
epoch=23, iter=49, iter2=1656, loss=0.10036, spearmanr=-0.11934,p_value=0.5968093306514979 times:5.51s, 22 samples/iter
|
| 685 |
+
epoch=23, iter=59, iter2=1666, loss=0.10038, spearmanr=0.39029,p_value=0.07252981425559035 times:3.78s, 22 samples/iter
|
| 686 |
+
Validation: epoch=23, loss=0.09078
|
| 687 |
+
Pearson Correlation: 0.64678,p_value: 1.8051218842069085e-22
|
| 688 |
+
Spearman Correlation: 0.66159,p_value; 8.958174790771026e-24
|
| 689 |
+
epoch=23,iter=59,iter2=1666,spearman_corr=0.6615875600973438
|
| 690 |
+
|
| 691 |
+
epoch=23, iter=69, iter2=1676, loss=0.10043, spearmanr=0.01310,p_value=0.9538530477410274 times:5.64s, 22 samples/iter
|
| 692 |
+
####################epoch:25
|
| 693 |
+
epoch=24, iter=9, iter2=1689, loss=0.10045, spearmanr=0.01646,p_value=0.9420480635681019 times:5.06s, 22 samples/iter
|
| 694 |
+
Validation: epoch=24, loss=0.09101
|
| 695 |
+
Pearson Correlation: 0.60937,p_value: 1.7809264504221749e-19
|
| 696 |
+
Spearman Correlation: 0.60911,p_value; 1.8641576138100473e-19
|
| 697 |
+
epoch=24,iter=14,iter2=1694,spearman_corr=0.6091058645083637
|
| 698 |
+
|
| 699 |
+
epoch=24, iter=19, iter2=1699, loss=0.10049, spearmanr=0.26432,p_value=0.2345642127957578 times:5.43s, 22 samples/iter
|
| 700 |
+
epoch=24, iter=29, iter2=1709, loss=0.10040, spearmanr=-0.00482,p_value=0.9830014273959038 times:3.77s, 22 samples/iter
|
| 701 |
+
Validation: epoch=24, loss=0.09082
|
| 702 |
+
Pearson Correlation: 0.62307,p_value: 1.5873733279798982e-20
|
| 703 |
+
Spearman Correlation: 0.62852,p_value; 5.869451835496648e-21
|
| 704 |
+
epoch=24,iter=29,iter2=1709,spearman_corr=0.6285212600561116
|
| 705 |
+
|
| 706 |
+
epoch=24, iter=39, iter2=1719, loss=0.10033, spearmanr=0.34653,p_value=0.1141240943998098 times:5.62s, 22 samples/iter
|
| 707 |
+
Validation: epoch=24, loss=0.09053
|
| 708 |
+
Pearson Correlation: 0.63522,p_value: 1.6814349313732405e-21
|
| 709 |
+
Spearman Correlation: 0.65181,p_value; 6.624093386516908e-23
|
| 710 |
+
epoch=24,iter=44,iter2=1724,spearman_corr=0.6518145618377181
|
| 711 |
+
|
| 712 |
+
epoch=24, iter=49, iter2=1729, loss=0.10030, spearmanr=0.23975,p_value=0.2825253816319706 times:5.82s, 22 samples/iter
|
| 713 |
+
epoch=24, iter=59, iter2=1739, loss=0.10031, spearmanr=0.55666,p_value=0.007129382940320665 times:4.10s, 22 samples/iter
|
| 714 |
+
Validation: epoch=24, loss=0.09090
|
| 715 |
+
Pearson Correlation: 0.62026,p_value: 2.6314343963545075e-20
|
| 716 |
+
Spearman Correlation: 0.62587,p_value; 9.549042069681099e-21
|
| 717 |
+
epoch=24,iter=59,iter2=1739,spearman_corr=0.6258693357804855
|
| 718 |
+
|
| 719 |
+
epoch=24, iter=69, iter2=1749, loss=0.10037, spearmanr=0.40804,p_value=0.05940737715244447 times:5.69s, 22 samples/iter
|
| 720 |
+
####################epoch:26
|
| 721 |
+
epoch=25, iter=9, iter2=1762, loss=0.10039, spearmanr=0.19626,p_value=0.381384146126049 times:4.98s, 22 samples/iter
|
| 722 |
+
Validation: epoch=25, loss=0.09094
|
| 723 |
+
Pearson Correlation: 0.66193,p_value: 8.347259882253761e-24
|
| 724 |
+
Spearman Correlation: 0.66519,p_value; 4.2006009889652024e-24
|
| 725 |
+
epoch=25,iter=14,iter2=1767,spearman_corr=0.6651939454449592
|
| 726 |
+
|
| 727 |
+
epoch=25, iter=19, iter2=1772, loss=0.10043, spearmanr=0.35318,p_value=0.10688751525021066 times:5.54s, 22 samples/iter
|
| 728 |
+
epoch=25, iter=29, iter2=1782, loss=0.10035, spearmanr=0.51350,p_value=0.014513120642730233 times:3.69s, 22 samples/iter
|
| 729 |
+
Validation: epoch=25, loss=0.09061
|
| 730 |
+
Pearson Correlation: 0.60236,p_value: 5.872510856177341e-19
|
| 731 |
+
Spearman Correlation: 0.62013,p_value; 2.6943721574982373e-20
|
| 732 |
+
epoch=25,iter=29,iter2=1782,spearman_corr=0.6201309350498304
|
| 733 |
+
|
| 734 |
+
epoch=25, iter=39, iter2=1792, loss=0.10028, spearmanr=0.50184,p_value=0.017325057893682366 times:5.61s, 22 samples/iter
|
| 735 |
+
Validation: epoch=25, loss=0.09063
|
| 736 |
+
Pearson Correlation: 0.65286,p_value: 5.36245302828408e-23
|
| 737 |
+
Spearman Correlation: 0.66135,p_value; 9.420058766117359e-24
|
| 738 |
+
epoch=25,iter=44,iter2=1797,spearman_corr=0.6613463807876991
|
| 739 |
+
|
| 740 |
+
epoch=25, iter=49, iter2=1802, loss=0.10025, spearmanr=0.14767,p_value=0.5119550007822593 times:5.42s, 22 samples/iter
|
| 741 |
+
epoch=25, iter=59, iter2=1812, loss=0.10026, spearmanr=0.18986,p_value=0.3974091335777127 times:3.71s, 22 samples/iter
|
| 742 |
+
Validation: epoch=25, loss=0.09076
|
| 743 |
+
Pearson Correlation: 0.62379,p_value: 1.3929404860314743e-20
|
| 744 |
+
Spearman Correlation: 0.63980,p_value; 7.024710686687356e-22
|
| 745 |
+
epoch=25,iter=59,iter2=1812,spearman_corr=0.6397986264595541
|
| 746 |
+
|
| 747 |
+
epoch=25, iter=69, iter2=1822, loss=0.10031, spearmanr=0.22865,p_value=0.30606862687373276 times:5.67s, 22 samples/iter
|
| 748 |
+
####################epoch:27
|
| 749 |
+
epoch=26, iter=9, iter2=1835, loss=0.10033, spearmanr=0.73209,p_value=0.00010746740920896853 times:5.04s, 22 samples/iter
|
| 750 |
+
Validation: epoch=26, loss=0.09112
|
| 751 |
+
Pearson Correlation: 0.64758,p_value: 1.5419774499756285e-22
|
| 752 |
+
Spearman Correlation: 0.66803,p_value; 2.2964698220713202e-24
|
| 753 |
+
epoch=26,iter=14,iter2=1840,spearman_corr=0.6680339882950223
|
| 754 |
+
|
| 755 |
+
epoch=26, iter=19, iter2=1845, loss=0.10037, spearmanr=0.46256,p_value=0.030188492318916772 times:5.84s, 22 samples/iter
|
| 756 |
+
epoch=26, iter=29, iter2=1855, loss=0.10029, spearmanr=0.13097,p_value=0.5612812878322839 times:3.82s, 22 samples/iter
|
| 757 |
+
Validation: epoch=26, loss=0.09068
|
| 758 |
+
Pearson Correlation: 0.61675,p_value: 4.9180789107641726e-20
|
| 759 |
+
Spearman Correlation: 0.62792,p_value; 6.554880620159224e-21
|
| 760 |
+
epoch=26,iter=29,iter2=1855,spearman_corr=0.6279216528614219
|
| 761 |
+
|
| 762 |
+
epoch=26, iter=39, iter2=1865, loss=0.10022, spearmanr=-0.01809,p_value=0.9363168349253017 times:6.07s, 22 samples/iter
|
| 763 |
+
Validation: epoch=26, loss=0.09064
|
| 764 |
+
Pearson Correlation: 0.61298,p_value: 9.524125261192883e-20
|
| 765 |
+
Spearman Correlation: 0.61420,p_value; 7.696425535927488e-20
|
| 766 |
+
epoch=26,iter=44,iter2=1870,spearman_corr=0.6142019756073663
|
| 767 |
+
|
| 768 |
+
epoch=26, iter=49, iter2=1875, loss=0.10020, spearmanr=0.51754,p_value=0.013628864359883454 times:5.75s, 22 samples/iter
|
| 769 |
+
epoch=26, iter=59, iter2=1885, loss=0.10021, spearmanr=0.53735,p_value=0.009909062567471299 times:3.74s, 22 samples/iter
|
| 770 |
+
Validation: epoch=26, loss=0.09081
|
| 771 |
+
Pearson Correlation: 0.61832,p_value: 3.7186566828046255e-20
|
| 772 |
+
Spearman Correlation: 0.62369,p_value; 1.419333197480726e-20
|
| 773 |
+
epoch=26,iter=59,iter2=1885,spearman_corr=0.6236907923307868
|
| 774 |
+
|
| 775 |
+
epoch=26, iter=69, iter2=1895, loss=0.10025, spearmanr=0.49130,p_value=0.020232040077103806 times:5.62s, 22 samples/iter
|
| 776 |
+
####################epoch:28
|
| 777 |
+
epoch=27, iter=9, iter2=1908, loss=0.10027, spearmanr=0.45846,p_value=0.0318776215060935 times:5.39s, 22 samples/iter
|
| 778 |
+
Validation: epoch=27, loss=0.09122
|
| 779 |
+
Pearson Correlation: 0.58790,p_value: 6.2745367639000575e-18
|
| 780 |
+
Spearman Correlation: 0.59490,p_value; 2.0229882428464035e-18
|
| 781 |
+
epoch=27,iter=14,iter2=1913,spearman_corr=0.5948972495129098
|
| 782 |
+
|
| 783 |
+
epoch=27, iter=19, iter2=1918, loss=0.10031, spearmanr=0.27848,p_value=0.20949550117837754 times:6.00s, 22 samples/iter
|
| 784 |
+
epoch=27, iter=29, iter2=1928, loss=0.10023, spearmanr=0.49546,p_value=0.019041570514930974 times:3.87s, 22 samples/iter
|
| 785 |
+
Validation: epoch=27, loss=0.09070
|
| 786 |
+
Pearson Correlation: 0.62095,p_value: 2.3265650262532984e-20
|
| 787 |
+
Spearman Correlation: 0.63347,p_value; 2.3387227789451868e-21
|
| 788 |
+
epoch=27,iter=29,iter2=1928,spearman_corr=0.6334665028948924
|
| 789 |
+
|
| 790 |
+
epoch=27, iter=39, iter2=1938, loss=0.10017, spearmanr=0.37982,p_value=0.08123830433822879 times:5.75s, 22 samples/iter
|
| 791 |
+
Validation: epoch=27, loss=0.09067
|
| 792 |
+
Pearson Correlation: 0.60020,p_value: 8.419805797716147e-19
|
| 793 |
+
Spearman Correlation: 0.60952,p_value; 1.736206016607545e-19
|
| 794 |
+
epoch=27,iter=44,iter2=1943,spearman_corr=0.609518902886113
|
| 795 |
+
|
| 796 |
+
epoch=27, iter=49, iter2=1948, loss=0.10014, spearmanr=0.59576,p_value=0.0034374379431936298 times:5.63s, 22 samples/iter
|
| 797 |
+
epoch=27, iter=59, iter2=1958, loss=0.10016, spearmanr=0.38285,p_value=0.07864015755063058 times:3.84s, 22 samples/iter
|
| 798 |
+
Validation: epoch=27, loss=0.09073
|
| 799 |
+
Pearson Correlation: 0.58831,p_value: 5.8727159969692544e-18
|
| 800 |
+
Spearman Correlation: 0.60945,p_value; 1.7555066836621532e-19
|
| 801 |
+
epoch=27,iter=59,iter2=1958,spearman_corr=0.6094547259723831
|
| 802 |
+
|
| 803 |
+
epoch=27, iter=69, iter2=1968, loss=0.10020, spearmanr=-0.00542,p_value=0.9808878791887714 times:5.86s, 22 samples/iter
|
| 804 |
+
####################epoch:29
|
| 805 |
+
epoch=28, iter=9, iter2=1981, loss=0.10022, spearmanr=0.09685,p_value=0.6681051977945289 times:5.45s, 22 samples/iter
|
| 806 |
+
Validation: epoch=28, loss=0.09125
|
| 807 |
+
Pearson Correlation: 0.61205,p_value: 1.1194735415456777e-19
|
| 808 |
+
Spearman Correlation: 0.62821,p_value; 6.216655303913151e-21
|
| 809 |
+
epoch=28,iter=14,iter2=1986,spearman_corr=0.6282094234319382
|
| 810 |
+
|
| 811 |
+
epoch=28, iter=19, iter2=1991, loss=0.10025, spearmanr=0.46637,p_value=0.0286797851722083 times:5.61s, 22 samples/iter
|
| 812 |
+
Checkpoint saved at epoch 28, batch_idx 28, total_train_iter 1999
|
| 813 |
+
iter 2000 save model /home/chipan/shuffle_token_pan/checkpoint/2025_01_14_09_47_45/last_add_1999.pth
|
| 814 |
+
epoch=28, iter=29, iter2=2001, loss=0.10018, spearmanr=0.52413,p_value=0.012282553121680369 times:14.61s, 22 samples/iter
|
| 815 |
+
Validation: epoch=28, loss=0.09742
|
| 816 |
+
Pearson Correlation: 0.59902,p_value: 1.0253906649087499e-18
|
| 817 |
+
Spearman Correlation: 0.60213,p_value; 6.102676546523705e-19
|
| 818 |
+
epoch=28,iter=29,iter2=2001,spearman_corr=0.6021271703863238
|
| 819 |
+
|
| 820 |
+
epoch=28, iter=39, iter2=2011, loss=0.10012, spearmanr=0.50311,p_value=0.016998200508253902 times:6.02s, 22 samples/iter
|
| 821 |
+
Validation: epoch=28, loss=0.09784
|
| 822 |
+
Pearson Correlation: 0.56002,p_value: 4.39474521413862e-16
|
| 823 |
+
Spearman Correlation: 0.56176,p_value; 3.4080926186565337e-16
|
| 824 |
+
epoch=28,iter=44,iter2=2016,spearman_corr=0.5617612403378215
|
| 825 |
+
|
| 826 |
+
epoch=28, iter=49, iter2=2021, loss=0.10009, spearmanr=0.20606,p_value=0.3575700677337238 times:5.98s, 22 samples/iter
|
| 827 |
+
epoch=28, iter=59, iter2=2031, loss=0.10011, spearmanr=0.60515,p_value=0.002845110736186952 times:3.63s, 22 samples/iter
|
| 828 |
+
Validation: epoch=28, loss=0.09741
|
| 829 |
+
Pearson Correlation: 0.55994,p_value: 4.445668305531956e-16
|
| 830 |
+
Spearman Correlation: 0.56843,p_value; 1.2720107847441177e-16
|
| 831 |
+
epoch=28,iter=59,iter2=2031,spearman_corr=0.5684280065740264
|
| 832 |
+
|
| 833 |
+
epoch=28, iter=69, iter2=2041, loss=0.10015, spearmanr=0.57389,p_value=0.005226281404106174 times:5.99s, 22 samples/iter
|
| 834 |
+
####################epoch:30
|
| 835 |
+
epoch=29, iter=9, iter2=2054, loss=0.10012, spearmanr=0.56465,p_value=0.006185362521450508 times:5.12s, 22 samples/iter
|
| 836 |
+
Validation: epoch=29, loss=0.09776
|
| 837 |
+
Pearson Correlation: 0.58500,p_value: 9.943346237597833e-18
|
| 838 |
+
Spearman Correlation: 0.59470,p_value; 2.0894415788295464e-18
|
| 839 |
+
epoch=29,iter=14,iter2=2059,spearman_corr=0.5946997202474497
|
| 840 |
+
|
| 841 |
+
epoch=29, iter=19, iter2=2064, loss=0.10008, spearmanr=0.40272,p_value=0.06313468536669764 times:5.46s, 22 samples/iter
|
| 842 |
+
epoch=29, iter=29, iter2=2074, loss=0.09997, spearmanr=0.64498,p_value=0.0011914708136180915 times:3.81s, 22 samples/iter
|
| 843 |
+
Validation: epoch=29, loss=0.09903
|
| 844 |
+
Pearson Correlation: 0.59266,p_value: 2.9152144348471593e-18
|
| 845 |
+
Spearman Correlation: 0.59471,p_value; 2.0843035576795214e-18
|
| 846 |
+
epoch=29,iter=29,iter2=2074,spearman_corr=0.5947147718619696
|
| 847 |
+
|
| 848 |
+
epoch=29, iter=39, iter2=2084, loss=0.10006, spearmanr=0.68497,p_value=0.00043609284508473343 times:5.50s, 22 samples/iter
|
| 849 |
+
Validation: epoch=29, loss=0.09863
|
| 850 |
+
Pearson Correlation: 0.63151,p_value: 3.3694592368204538e-21
|
| 851 |
+
Spearman Correlation: 0.63784,p_value; 1.0220206740666307e-21
|
| 852 |
+
epoch=29,iter=44,iter2=2089,spearman_corr=0.6378404753455845
|
| 853 |
+
|
| 854 |
+
epoch=29, iter=49, iter2=2094, loss=0.09998, spearmanr=0.43934,p_value=0.04077717752711833 times:5.96s, 22 samples/iter
|
| 855 |
+
epoch=29, iter=59, iter2=2104, loss=0.09998, spearmanr=0.45522,p_value=0.03326982428894075 times:3.91s, 22 samples/iter
|
| 856 |
+
Validation: epoch=29, loss=0.09964
|
| 857 |
+
Pearson Correlation: 0.61614,p_value: 5.475839366107863e-20
|
| 858 |
+
Spearman Correlation: 0.61711,p_value; 4.6102686852325554e-20
|
| 859 |
+
epoch=29,iter=59,iter2=2104,spearman_corr=0.6171125032013768
|
| 860 |
+
|
| 861 |
+
epoch=29, iter=69, iter2=2114, loss=0.10002, spearmanr=0.32372,p_value=0.14165932258705455 times:5.84s, 22 samples/iter
|
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ADDED
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Binary file (18.2 kB). View file
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LLMs_Test_Results/Jet Tagging in Particle Physics/General Purpose LLMs on Jet Tagging.xlsx
ADDED
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