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bert-base-uncased-test_16_107

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generated_from_trainer
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bert-base-uncased-test_16_107

This model is a fine-tuned version of bert-base-uncased on the glue dataset. It achieves the following results on the evaluation set:

  • Loss: 4.8422
  • F1: {'f1': 0.8146370725854829}
  • Accuracy: {'accuracy': 0.7037392138063279}

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.001
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 2000

Training results

Training Loss Epoch Step Validation Loss F1 Accuracy
No log 1.0 7 0.6308 {'f1': 0.8072009291521488} {'accuracy': 0.6816874400767018}
No log 2.0 14 0.6722 {'f1': 0.8186469584991471} {'accuracy': 0.6941514860977949}
No log 3.0 21 0.6415 {'f1': 0.8111239860950175} {'accuracy': 0.6874400767018217}
No log 4.0 28 0.8090 {'f1': 0.7658755612572161} {'accuracy': 0.6500479386385427}
No log 5.0 35 1.2079 {'f1': 0.810244470314319} {'accuracy': 0.6874400767018217}
No log 6.0 42 1.3409 {'f1': 0.8104956268221575} {'accuracy': 0.6883988494726749}
No log 7.0 49 1.1925 {'f1': 0.7536813922356091} {'accuracy': 0.6471716203259827}
No log 8.0 56 1.5031 {'f1': 0.8132930513595167} {'accuracy': 0.7037392138063279}
No log 9.0 63 1.6535 {'f1': 0.810126582278481} {'accuracy': 0.697986577181208}
No log 10.0 70 1.8059 {'f1': 0.7947598253275109} {'accuracy': 0.6845637583892618}
No log 11.0 77 2.1412 {'f1': 0.7772087067861716} {'accuracy': 0.6663470757430489}
No log 12.0 84 2.5238 {'f1': 0.8089887640449438} {'accuracy': 0.6903163950143816}
No log 13.0 91 2.0715 {'f1': 0.764} {'accuracy': 0.660594439117929}
No log 14.0 98 2.2280 {'f1': 0.7985392574558734} {'accuracy': 0.6826462128475551}
No log 15.0 105 2.1952 {'f1': 0.7785577536694321} {'accuracy': 0.6673058485139022}
No log 16.0 112 2.5146 {'f1': 0.8098303101228789} {'accuracy': 0.6883988494726749}
No log 17.0 119 2.4347 {'f1': 0.7007407407407408} {'accuracy': 0.6126558005752637}
No log 18.0 126 2.3472 {'f1': 0.8197674418604651} {'accuracy': 0.7027804410354745}
No log 19.0 133 1.6770 {'f1': 0.7657597876575978} {'accuracy': 0.6615532118887824}
No log 20.0 140 1.9552 {'f1': 0.8014616321559075} {'accuracy': 0.6874400767018217}
No log 21.0 147 2.0672 {'f1': 0.7898326100433974} {'accuracy': 0.6749760306807286}
No log 22.0 154 2.1661 {'f1': 0.7518493611297916} {'accuracy': 0.6462128475551294}
No log 23.0 161 2.2302 {'f1': 0.7783439490445861} {'accuracy': 0.6663470757430489}
No log 24.0 168 2.2864 {'f1': 0.7772756617172369} {'accuracy': 0.6692233940556088}
No log 25.0 175 2.3895 {'f1': 0.7693315858453472} {'accuracy': 0.6625119846596357}
No log 26.0 182 2.7993 {'f1': 0.8086600351082504} {'accuracy': 0.6864813039309684}
No log 27.0 189 2.4181 {'f1': 0.795525170913611} {'accuracy': 0.6845637583892618}
No log 28.0 196 2.2662 {'f1': 0.7619680851063829} {'accuracy': 0.6567593480345159}
No log 29.0 203 2.2569 {'f1': 0.773142112125163} {'accuracy': 0.6663470757430489}
No log 30.0 210 2.3350 {'f1': 0.7945034353529045} {'accuracy': 0.6845637583892618}
No log 31.0 217 2.4660 {'f1': 0.8004851425106124} {'accuracy': 0.6845637583892618}
No log 32.0 224 2.5017 {'f1': 0.7999999999999999} {'accuracy': 0.6836049856184084}
No log 33.0 231 2.4327 {'f1': 0.7857596948506039} {'accuracy': 0.6768935762224353}
No log 34.0 238 2.4516 {'f1': 0.7729342875731946} {'accuracy': 0.6653883029721956}
No log 35.0 245 2.4851 {'f1': 0.7725490196078432} {'accuracy': 0.6663470757430489}
No log 36.0 252 2.5146 {'f1': 0.7751134154244976} {'accuracy': 0.6673058485139022}
No log 37.0 259 2.6614 {'f1': 0.8055893074119077} {'accuracy': 0.6931927133269415}
No log 38.0 266 2.7001 {'f1': 0.7345505617977527} {'accuracy': 0.6375838926174496}
No log 39.0 273 2.6794 {'f1': 0.7286713286713286} {'accuracy': 0.6279961649089166}
No log 40.0 280 2.6036 {'f1': 0.7627230667547917} {'accuracy': 0.6558005752636625}
No log 41.0 287 2.5850 {'f1': 0.7849393746011487} {'accuracy': 0.6768935762224353}
No log 42.0 294 2.5937 {'f1': 0.7893738140417458} {'accuracy': 0.6807286673058485}
No log 43.0 301 2.6086 {'f1': 0.7949843260188089} {'accuracy': 0.6864813039309684}
No log 44.0 308 2.6312 {'f1': 0.7952706907280648} {'accuracy': 0.6845637583892618}
No log 45.0 315 2.6497 {'f1': 0.795765877957659} {'accuracy': 0.6855225311601151}
No log 46.0 322 2.6657 {'f1': 0.7929736511919698} {'accuracy': 0.6836049856184084}
No log 47.0 329 2.6802 {'f1': 0.7916928886091882} {'accuracy': 0.6826462128475551}
No log 48.0 336 2.6926 {'f1': 0.7901701323251417} {'accuracy': 0.6807286673058485}
No log 49.0 343 2.7032 {'f1': 0.7901701323251417} {'accuracy': 0.6807286673058485}
No log 50.0 350 2.7119 {'f1': 0.7909319899244333} {'accuracy': 0.6816874400767018}
No log 51.0 357 2.7591 {'f1': 0.7627906976744185} {'accuracy': 0.6577181208053692}
No log 52.0 364 2.6638 {'f1': 0.7969543147208121} {'accuracy': 0.6931927133269415}
No log 53.0 371 2.7258 {'f1': 0.8111718275652702} {'accuracy': 0.7018216682646213}
No log 54.0 378 2.5491 {'f1': 0.7729403884795712} {'accuracy': 0.6749760306807286}
No log 55.0 385 2.8775 {'f1': 0.8193883439122909} {'accuracy': 0.6999041227229147}
No log 56.0 392 2.0953 {'f1': 0.7896070975918885} {'accuracy': 0.6816874400767018}
No log 57.0 399 1.7301 {'f1': 0.8012578616352202} {'accuracy': 0.6970278044103547}
No log 58.0 406 1.7765 {'f1': 0.8147254073627037} {'accuracy': 0.7056567593480345}
No log 59.0 413 1.6425 {'f1': 0.7984445884640311} {'accuracy': 0.7018216682646213}
No log 60.0 420 1.8722 {'f1': 0.8066368857689854} {'accuracy': 0.7094918504314478}
No log 61.0 427 2.3662 {'f1': 0.8167664670658682} {'accuracy': 0.7066155321188878}
No log 62.0 434 2.3583 {'f1': 0.8107448107448108} {'accuracy': 0.7027804410354745}
No log 63.0 441 2.3069 {'f1': 0.8052763819095478} {'accuracy': 0.7027804410354745}
No log 64.0 448 2.3409 {'f1': 0.802555910543131} {'accuracy': 0.7037392138063279}
No log 65.0 455 2.4041 {'f1': 0.8038216560509553} {'accuracy': 0.7046979865771812}
No log 66.0 462 2.4409 {'f1': 0.8038095238095239} {'accuracy': 0.7037392138063279}
No log 67.0 469 2.4650 {'f1': 0.8048162230671737} {'accuracy': 0.7046979865771812}
No log 68.0 476 2.4834 {'f1': 0.8045540796963947} {'accuracy': 0.7037392138063279}
No log 69.0 483 2.5166 {'f1': 0.8017621145374451} {'accuracy': 0.697986577181208}
No log 70.0 490 2.5793 {'f1': 0.8046971569839309} {'accuracy': 0.6970278044103547}
No log 71.0 497 2.4983 {'f1': 0.806045340050378} {'accuracy': 0.7046979865771812}
0.054 72.0 504 2.4453 {'f1': 0.8077170418006431} {'accuracy': 0.713326941514861}
0.054 73.0 511 2.4421 {'f1': 0.8044184535412605} {'accuracy': 0.7114093959731543}
0.054 74.0 518 2.4477 {'f1': 0.8013071895424836} {'accuracy': 0.7085330776605945}
0.054 75.0 525 2.5000 {'f1': 0.8095537397862981} {'accuracy': 0.7094918504314478}
0.054 76.0 532 2.5767 {'f1': 0.8113790970933827} {'accuracy': 0.7075743048897412}
0.054 77.0 539 2.6187 {'f1': 0.8120393120393121} {'accuracy': 0.7066155321188878}
0.054 78.0 546 2.6423 {'f1': 0.8129981606376455} {'accuracy': 0.7075743048897412}
0.054 79.0 553 2.6559 {'f1': 0.814451928965095} {'accuracy': 0.7094918504314478}
0.054 80.0 560 2.6642 {'f1': 0.814451928965095} {'accuracy': 0.7094918504314478}
0.054 81.0 567 2.6704 {'f1': 0.814451928965095} {'accuracy': 0.7094918504314478}
0.054 82.0 574 2.6752 {'f1': 0.814451928965095} {'accuracy': 0.7094918504314478}
0.054 83.0 581 2.6794 {'f1': 0.814451928965095} {'accuracy': 0.7094918504314478}
0.054 84.0 588 2.6837 {'f1': 0.814451928965095} {'accuracy': 0.7094918504314478}
0.054 85.0 595 2.6456 {'f1': 0.8121137206427689} {'accuracy': 0.7085330776605945}
0.054 86.0 602 2.6218 {'f1': 0.8099688473520249} {'accuracy': 0.7075743048897412}
0.054 87.0 609 2.6126 {'f1': 0.81} {'accuracy': 0.7085330776605945}
0.054 88.0 616 2.6105 {'f1': 0.8107769423558898} {'accuracy': 0.710450623202301}
0.054 89.0 623 2.6131 {'f1': 0.8097928436911487} {'accuracy': 0.7094918504314478}
0.054 90.0 630 2.6170 {'f1': 0.8097928436911487} {'accuracy': 0.7094918504314478}
0.054 91.0 637 2.6215 {'f1': 0.8103015075376885} {'accuracy': 0.710450623202301}
0.054 92.0 644 2.6261 {'f1': 0.8103015075376885} {'accuracy': 0.710450623202301}
0.054 93.0 651 2.6308 {'f1': 0.8103015075376885} {'accuracy': 0.710450623202301}
0.054 94.0 658 2.6357 {'f1': 0.8097928436911487} {'accuracy': 0.7094918504314478}
0.054 95.0 665 2.6404 {'f1': 0.8105395232120451} {'accuracy': 0.710450623202301}
0.054 96.0 672 2.6450 {'f1': 0.8105395232120451} {'accuracy': 0.710450623202301}
0.054 97.0 679 2.6494 {'f1': 0.8105395232120451} {'accuracy': 0.710450623202301}
0.054 98.0 686 2.6534 {'f1': 0.8105395232120451} {'accuracy': 0.710450623202301}
0.054 99.0 693 2.6576 {'f1': 0.8105395232120451} {'accuracy': 0.710450623202301}
0.054 100.0 700 2.6620 {'f1': 0.8107769423558898} {'accuracy': 0.710450623202301}
0.054 101.0 707 2.6660 {'f1': 0.8107769423558898} {'accuracy': 0.710450623202301}
0.054 102.0 714 2.6706 {'f1': 0.8107769423558898} {'accuracy': 0.710450623202301}
0.054 103.0 721 2.6749 {'f1': 0.8102692548528492} {'accuracy': 0.7094918504314478}
0.054 104.0 728 2.6794 {'f1': 0.8097622027534418} {'accuracy': 0.7085330776605945}
0.054 105.0 735 2.6841 {'f1': 0.8097622027534418} {'accuracy': 0.7085330776605945}
0.054 106.0 742 2.6889 {'f1': 0.8097622027534418} {'accuracy': 0.7085330776605945}
0.054 107.0 749 2.6928 {'f1': 0.8097622027534418} {'accuracy': 0.7085330776605945}
0.054 108.0 756 2.7015 {'f1': 0.8105065666041276} {'accuracy': 0.7094918504314478}
0.054 109.0 763 2.7076 {'f1': 0.81} {'accuracy': 0.7085330776605945}
0.054 110.0 770 2.7125 {'f1': 0.81} {'accuracy': 0.7085330776605945}
0.054 111.0 777 2.7169 {'f1': 0.81} {'accuracy': 0.7085330776605945}
0.054 112.0 784 2.7207 {'f1': 0.81} {'accuracy': 0.7085330776605945}
0.054 113.0 791 2.7242 {'f1': 0.81} {'accuracy': 0.7085330776605945}
0.054 114.0 798 2.7278 {'f1': 0.81} {'accuracy': 0.7085330776605945}
0.054 115.0 805 2.7315 {'f1': 0.81} {'accuracy': 0.7085330776605945}
0.054 116.0 812 2.7356 {'f1': 0.8094940662086196} {'accuracy': 0.7075743048897412}
0.054 117.0 819 2.7398 {'f1': 0.8094940662086196} {'accuracy': 0.7075743048897412}
0.054 118.0 826 2.7439 {'f1': 0.8094940662086196} {'accuracy': 0.7075743048897412}
0.054 119.0 833 2.7477 {'f1': 0.8089887640449438} {'accuracy': 0.7066155321188878}
0.054 120.0 840 2.7516 {'f1': 0.8089887640449438} {'accuracy': 0.7066155321188878}
0.054 121.0 847 2.7549 {'f1': 0.8089887640449438} {'accuracy': 0.7066155321188878}
0.054 122.0 854 2.7587 {'f1': 0.8089887640449438} {'accuracy': 0.7066155321188878}
0.054 123.0 861 2.7645 {'f1': 0.8089887640449438} {'accuracy': 0.7066155321188878}
0.054 124.0 868 2.7735 {'f1': 0.8092269326683292} {'accuracy': 0.7066155321188878}
0.054 125.0 875 2.7812 {'f1': 0.8092269326683292} {'accuracy': 0.7066155321188878}
0.054 126.0 882 2.7944 {'f1': 0.8089607965152458} {'accuracy': 0.7056567593480345}
0.054 127.0 889 2.8023 {'f1': 0.8079552517091361} {'accuracy': 0.7037392138063279}
0.054 128.0 896 2.8077 {'f1': 0.8086956521739131} {'accuracy': 0.7046979865771812}
0.054 129.0 903 2.8116 {'f1': 0.8086956521739131} {'accuracy': 0.7046979865771812}
0.054 130.0 910 2.8152 {'f1': 0.8086956521739131} {'accuracy': 0.7046979865771812}
0.054 131.0 917 2.8183 {'f1': 0.8086956521739131} {'accuracy': 0.7046979865771812}
0.054 132.0 924 2.8213 {'f1': 0.8094351334574797} {'accuracy': 0.7056567593480345}
0.054 133.0 931 2.8245 {'f1': 0.8094351334574797} {'accuracy': 0.7056567593480345}
0.054 134.0 938 2.8278 {'f1': 0.8094351334574797} {'accuracy': 0.7056567593480345}
0.054 135.0 945 2.8313 {'f1': 0.8094351334574797} {'accuracy': 0.7056567593480345}
0.054 136.0 952 2.8345 {'f1': 0.8094351334574797} {'accuracy': 0.7056567593480345}
0.054 137.0 959 2.8375 {'f1': 0.8094351334574797} {'accuracy': 0.7056567593480345}
0.054 138.0 966 2.8408 {'f1': 0.8094351334574797} {'accuracy': 0.7056567593480345}
0.054 139.0 973 2.8438 {'f1': 0.8094351334574797} {'accuracy': 0.7056567593480345}
0.054 140.0 980 2.8466 {'f1': 0.8094351334574797} {'accuracy': 0.7056567593480345}
0.054 141.0 987 2.8492 {'f1': 0.8094351334574797} {'accuracy': 0.7056567593480345}
0.054 142.0 994 2.8519 {'f1': 0.8089330024813897} {'accuracy': 0.7046979865771812}
0.0 143.0 1001 2.8546 {'f1': 0.8096714197148172} {'accuracy': 0.7056567593480345}
0.0 144.0 1008 2.8573 {'f1': 0.8096714197148172} {'accuracy': 0.7056567593480345}
0.0 145.0 1015 2.8600 {'f1': 0.8096714197148172} {'accuracy': 0.7056567593480345}
0.0 146.0 1022 3.0634 {'f1': 0.7519884309472161} {'accuracy': 0.6711409395973155}
0.0 147.0 1029 2.6447 {'f1': 0.81} {'accuracy': 0.7085330776605945}
0.0 148.0 1036 2.8889 {'f1': 0.8230452674897119} {'accuracy': 0.7114093959731543}
0.0 149.0 1043 2.1697 {'f1': 0.8131720430107527} {'accuracy': 0.7334611697027804}
0.0 150.0 1050 2.1318 {'f1': 0.8240291262135921} {'accuracy': 0.7219558964525408}
0.0 151.0 1057 1.4035 {'f1': 0.8054474708171206} {'accuracy': 0.7123681687440077}
0.0 152.0 1064 1.6932 {'f1': 0.8216216216216216} {'accuracy': 0.7152444870565676}
0.0 153.0 1071 1.9637 {'f1': 0.737294201861131} {'accuracy': 0.6481303930968361}
0.0 154.0 1078 2.5085 {'f1': 0.8169668797210924} {'accuracy': 0.697986577181208}
0.0 155.0 1085 2.1312 {'f1': 0.7906976744186046} {'accuracy': 0.6807286673058485}
0.0 156.0 1092 1.6788 {'f1': 0.7508350033400133} {'accuracy': 0.6423777564717162}
0.0 157.0 1099 1.6363 {'f1': 0.8164196123147092} {'accuracy': 0.6912751677852349}
0.0 158.0 1106 1.7953 {'f1': 0.803921568627451} {'accuracy': 0.6931927133269415}
0.0 159.0 1113 2.7819 {'f1': 0.6291946308724833} {'accuracy': 0.576222435282838}
0.0 160.0 1120 2.6308 {'f1': 0.8154111558366878} {'accuracy': 0.6922339405560882}
0.0 161.0 1127 1.8606 {'f1': 0.7402777777777777} {'accuracy': 0.6414189837008629}
0.0 162.0 1134 1.9468 {'f1': 0.8004910988336403} {'accuracy': 0.6883988494726749}
0.0 163.0 1141 2.0791 {'f1': 0.7952261306532664} {'accuracy': 0.6874400767018217}
0.0 164.0 1148 2.3288 {'f1': 0.8084592145015107} {'accuracy': 0.6960690316395014}
0.0 165.0 1155 2.3496 {'f1': 0.7272727272727273} {'accuracy': 0.6375838926174496}
0.0 166.0 1162 2.2841 {'f1': 0.8012195121951221} {'accuracy': 0.6874400767018217}
0.0 167.0 1169 2.3575 {'f1': 0.8014660965180209} {'accuracy': 0.6883988494726749}
0.0 168.0 1176 2.2772 {'f1': 0.7770700636942676} {'accuracy': 0.6644295302013423}
0.0 169.0 1183 2.1862 {'f1': 0.7342123525329632} {'accuracy': 0.6327900287631831}
0.0 170.0 1190 2.3155 {'f1': 0.7983293556085919} {'accuracy': 0.675934803451582}
0.0 171.0 1197 2.1466 {'f1': 0.7573726541554959} {'accuracy': 0.6529242569511026}
0.0 172.0 1204 2.3600 {'f1': 0.8057901085645357} {'accuracy': 0.6912751677852349}
0.0 173.0 1211 2.3084 {'f1': 0.796558082360172} {'accuracy': 0.6826462128475551}
0.0 174.0 1218 1.8462 {'f1': 0.7369155617585484} {'accuracy': 0.638542665388303}
0.0 175.0 1225 1.6526 {'f1': 0.7948874010955569} {'accuracy': 0.6768935762224353}
0.0 176.0 1232 1.7791 {'f1': 0.7613412228796844} {'accuracy': 0.6519654841802492}
0.0 177.0 1239 2.3374 {'f1': 0.7914893617021277} {'accuracy': 0.6711409395973155}
0.0 178.0 1246 2.9717 {'f1': 0.8158803222094362} {'accuracy': 0.6931927133269415}
0.0 179.0 1253 3.0262 {'f1': 0.7995198079231692} {'accuracy': 0.6797698945349953}
0.0 180.0 1260 2.8010 {'f1': 0.7811271297509829} {'accuracy': 0.6797698945349953}
0.0 181.0 1267 2.8533 {'f1': 0.7992471769134253} {'accuracy': 0.6931927133269415}
0.0 182.0 1274 3.0398 {'f1': 0.8107462686567165} {'accuracy': 0.6960690316395014}
0.0 183.0 1281 2.7426 {'f1': 0.7588555858310626} {'accuracy': 0.660594439117929}
0.0 184.0 1288 2.6932 {'f1': 0.7929292929292929} {'accuracy': 0.6855225311601151}
0.0 185.0 1295 2.8641 {'f1': 0.8174322732626619} {'accuracy': 0.7027804410354745}
0.0 186.0 1302 2.4881 {'f1': 0.8031882280809319} {'accuracy': 0.6922339405560882}
0.0 187.0 1309 2.1634 {'f1': 0.7731755424063116} {'accuracy': 0.6692233940556088}
0.0 188.0 1316 2.1238 {'f1': 0.7844660194174757} {'accuracy': 0.6807286673058485}
0.0 189.0 1323 2.5103 {'f1': 0.8187683284457478} {'accuracy': 0.7037392138063279}
0.0 190.0 1330 1.9588 {'f1': 0.807906114885732} {'accuracy': 0.7018216682646213}
0.0 191.0 1337 1.9148 {'f1': 0.7457386363636365} {'accuracy': 0.6567593480345159}
0.0 192.0 1344 2.0834 {'f1': 0.7932598833441349} {'accuracy': 0.6941514860977949}
0.0 193.0 1351 2.5040 {'f1': 0.8147254073627037} {'accuracy': 0.7056567593480345}
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0.0 1896.0 13272 4.8287 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1897.0 13279 4.8290 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1898.0 13286 4.8292 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1899.0 13293 4.8294 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1900.0 13300 4.8296 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1901.0 13307 4.8299 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1902.0 13314 4.8301 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1903.0 13321 4.8304 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1904.0 13328 4.8306 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1905.0 13335 4.8308 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1906.0 13342 4.8310 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1907.0 13349 4.8313 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1908.0 13356 4.8315 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1909.0 13363 4.8317 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1910.0 13370 4.8319 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1911.0 13377 4.8321 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1912.0 13384 4.8323 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1913.0 13391 4.8325 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1914.0 13398 4.8327 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1915.0 13405 4.8329 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1916.0 13412 4.8331 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1917.0 13419 4.8333 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1918.0 13426 4.8335 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1919.0 13433 4.8337 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1920.0 13440 4.8339 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1921.0 13447 4.8341 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1922.0 13454 4.8343 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1923.0 13461 4.8345 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1924.0 13468 4.8346 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1925.0 13475 4.8348 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1926.0 13482 4.8350 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1927.0 13489 4.8352 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1928.0 13496 4.8354 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1929.0 13503 4.8356 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1930.0 13510 4.8357 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1931.0 13517 4.8359 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1932.0 13524 4.8361 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1933.0 13531 4.8363 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1934.0 13538 4.8365 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1935.0 13545 4.8366 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1936.0 13552 4.8368 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1937.0 13559 4.8369 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1938.0 13566 4.8371 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1939.0 13573 4.8373 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1940.0 13580 4.8374 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1941.0 13587 4.8376 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1942.0 13594 4.8377 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1943.0 13601 4.8379 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1944.0 13608 4.8380 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1945.0 13615 4.8382 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1946.0 13622 4.8383 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1947.0 13629 4.8384 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1948.0 13636 4.8386 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1949.0 13643 4.8387 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1950.0 13650 4.8388 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1951.0 13657 4.8389 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1952.0 13664 4.8391 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1953.0 13671 4.8392 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1954.0 13678 4.8393 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1955.0 13685 4.8394 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1956.0 13692 4.8395 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1957.0 13699 4.8396 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1958.0 13706 4.8397 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1959.0 13713 4.8399 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1960.0 13720 4.8400 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1961.0 13727 4.8401 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1962.0 13734 4.8402 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1963.0 13741 4.8403 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1964.0 13748 4.8404 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1965.0 13755 4.8405 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1966.0 13762 4.8406 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1967.0 13769 4.8407 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1968.0 13776 4.8408 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1969.0 13783 4.8409 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1970.0 13790 4.8409 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1971.0 13797 4.8410 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1972.0 13804 4.8411 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1973.0 13811 4.8412 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1974.0 13818 4.8413 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1975.0 13825 4.8413 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1976.0 13832 4.8414 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1977.0 13839 4.8415 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1978.0 13846 4.8415 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1979.0 13853 4.8416 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1980.0 13860 4.8416 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1981.0 13867 4.8417 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1982.0 13874 4.8418 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1983.0 13881 4.8418 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1984.0 13888 4.8418 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1985.0 13895 4.8419 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1986.0 13902 4.8419 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1987.0 13909 4.8420 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1988.0 13916 4.8420 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1989.0 13923 4.8420 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1990.0 13930 4.8421 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1991.0 13937 4.8421 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1992.0 13944 4.8421 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1993.0 13951 4.8422 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1994.0 13958 4.8422 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1995.0 13965 4.8422 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1996.0 13972 4.8422 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1997.0 13979 4.8422 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1998.0 13986 4.8422 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 1999.0 13993 4.8422 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}
0.0 2000.0 14000 4.8422 {'f1': 0.8146370725854829} {'accuracy': 0.7037392138063279}

Framework versions

  • Transformers 4.29.2
  • Pytorch 2.0.1+cu117
  • Datasets 2.12.0
  • Tokenizers 0.13.3
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Dataset used to train kwwww/bert-base-uncased-test_16_107

Evaluation results

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