lilt_240307

This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0115
  • Escription: {'precision': 0.999605367008682, 'recall': 0.9988170347003155, 'f1': 0.9992110453648916, 'number': 7608}
  • Otalprice: {'precision': 0.9985453583708014, 'recall': 0.9986774236212141, 'f1': 0.998611386629637, 'number': 7561}
  • Rice: {'precision': 0.9904117315284828, 'recall': 0.9848569826135727, 'f1': 0.9876265466816648, 'number': 1783}
  • Roductcode: {'precision': 0.9869678540399652, 'recall': 0.9964912280701754, 'f1': 0.9917066783064163, 'number': 1140}
  • Uantity: {'precision': 0.9971945137157108, 'recall': 0.9981279251170047, 'f1': 0.9976610010915329, 'number': 3205}
  • Uantityunit: {'precision': 0.9994643813604713, 'recall': 1.0, 'f1': 0.999732118939191, 'number': 1866}
  • Overall Precision: 0.9976
  • Overall Recall: 0.9976
  • Overall F1: 0.9976
  • Overall Accuracy: 0.9985

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: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • training_steps: 10000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Escription Otalprice Rice Roductcode Uantity Uantityunit Overall Precision Overall Recall Overall F1 Overall Accuracy
0.129 0.22 200 0.0369 {'precision': 0.9989463979981562, 'recall': 0.9969768664563617, 'f1': 0.997960660482863, 'number': 7608} {'precision': 0.9970330411328389, 'recall': 0.9777807168363973, 'f1': 0.9873130341880342, 'number': 7561} {'precision': 0.9133010231556273, 'recall': 0.9512058328659563, 'f1': 0.9318681318681319, 'number': 1783} {'precision': 0.915, 'recall': 0.9631578947368421, 'f1': 0.9384615384615383, 'number': 1140} {'precision': 0.9952963311382879, 'recall': 0.9903276131045242, 'f1': 0.9928057553956834, 'number': 3205} {'precision': 0.997836668469443, 'recall': 0.9887459807073955, 'f1': 0.993270524899058, 'number': 1866} 0.9865 0.9839 0.9852 0.9907
0.0269 0.44 400 0.0287 {'precision': 0.9994720865778013, 'recall': 0.9953995793901157, 'f1': 0.9974316759960488, 'number': 7608} {'precision': 0.9937945603379984, 'recall': 0.995503240312128, 'f1': 0.9946481665014867, 'number': 7561} {'precision': 0.9386767079074771, 'recall': 0.978687605159843, 'f1': 0.958264689730917, 'number': 1783} {'precision': 0.9805013927576601, 'recall': 0.9263157894736842, 'f1': 0.9526387009472259, 'number': 1140} {'precision': 0.9959170854271356, 'recall': 0.9893915756630265, 'f1': 0.9926436061981532, 'number': 3205} {'precision': 0.9973204715969989, 'recall': 0.9973204715969989, 'f1': 0.9973204715969989, 'number': 1866} 0.9912 0.9901 0.9906 0.9940
0.0179 0.66 600 0.0229 {'precision': 0.9996033844526705, 'recall': 0.9938222923238696, 'f1': 0.9967044555760611, 'number': 7608} {'precision': 0.9964290437772781, 'recall': 0.9964290437772781, 'f1': 0.9964290437772781, 'number': 7561} {'precision': 0.9769533445756042, 'recall': 0.9747616376892877, 'f1': 0.9758562605277933, 'number': 1783} {'precision': 0.9438860971524288, 'recall': 0.9885964912280701, 'f1': 0.9657240788346187, 'number': 1140} {'precision': 0.996551724137931, 'recall': 0.9918876755070203, 'f1': 0.9942142298670836, 'number': 3205} {'precision': 0.9989287627209427, 'recall': 0.9994640943193998, 'f1': 0.999196356817573, 'number': 1866} 0.9935 0.9931 0.9933 0.9959
0.0188 0.88 800 0.0171 {'precision': 0.9992083388309804, 'recall': 0.9953995793901157, 'f1': 0.9973003226443669, 'number': 7608} {'precision': 0.9958983858163535, 'recall': 0.995503240312128, 'f1': 0.995700773860705, 'number': 7561} {'precision': 0.9959607616849394, 'recall': 0.9680314077397645, 'f1': 0.981797497155859, 'number': 1783} {'precision': 0.9522613065326633, 'recall': 0.9973684210526316, 'f1': 0.9742930591259639, 'number': 1140} {'precision': 0.9940902021772939, 'recall': 0.997191887675507, 'f1': 0.9956386292834891, 'number': 3205} {'precision': 0.9946666666666667, 'recall': 0.9994640943193998, 'f1': 0.9970596097300188, 'number': 1866} 0.9944 0.9940 0.9942 0.9962
0.0117 1.1 1000 0.0111 {'precision': 0.9989472298986709, 'recall': 0.9977655099894848, 'f1': 0.9983560202538305, 'number': 7608} {'precision': 0.9978816364358533, 'recall': 0.9968258166909139, 'f1': 0.9973534471351064, 'number': 7561} {'precision': 0.9870713884204609, 'recall': 0.9848569826135727, 'f1': 0.9859629421673217, 'number': 1783} {'precision': 0.9766637856525497, 'recall': 0.9912280701754386, 'f1': 0.9838920330866348, 'number': 1140} {'precision': 0.9962628464652756, 'recall': 0.9981279251170047, 'f1': 0.9971945137157107, 'number': 3205} {'precision': 0.9989276139410188, 'recall': 0.9983922829581994, 'f1': 0.9986598767086573, 'number': 1866} 0.9962 0.9962 0.9962 0.9976
0.0146 1.32 1200 0.0148 {'precision': 0.9971102062261921, 'recall': 0.9977655099894848, 'f1': 0.9974377504763156, 'number': 7608} {'precision': 0.9954972851277977, 'recall': 0.9941806639333421, 'f1': 0.9948385389094759, 'number': 7561} {'precision': 0.9880613985218875, 'recall': 0.9747616376892877, 'f1': 0.9813664596273293, 'number': 1783} {'precision': 0.9808529155787642, 'recall': 0.9885964912280701, 'f1': 0.9847094801223241, 'number': 1140} {'precision': 0.9950031230480949, 'recall': 0.9940717628705148, 'f1': 0.9945372249102544, 'number': 3205} {'precision': 0.9924812030075187, 'recall': 0.9903536977491961, 'f1': 0.9914163090128755, 'number': 1866} 0.9944 0.9933 0.9938 0.9965
0.0128 1.54 1400 0.0183 {'precision': 0.999604064933351, 'recall': 0.9955310199789695, 'f1': 0.9975633849193283, 'number': 7608} {'precision': 0.9962982548915917, 'recall': 0.9966935590530354, 'f1': 0.996495867768595, 'number': 7561} {'precision': 0.9909348441926346, 'recall': 0.9809310151430174, 'f1': 0.9859075535512964, 'number': 1783} {'precision': 0.9655172413793104, 'recall': 0.9824561403508771, 'f1': 0.9739130434782608, 'number': 1140} {'precision': 0.996571072319202, 'recall': 0.9975039001560062, 'f1': 0.9970372680492747, 'number': 3205} {'precision': 0.997327632282202, 'recall': 1.0, 'f1': 0.9986620283649986, 'number': 1866} 0.9955 0.9948 0.9952 0.9967
0.01 1.76 1600 0.0117 {'precision': 0.9994731296101159, 'recall': 0.9973711882229233, 'f1': 0.9984210526315789, 'number': 7608} {'precision': 0.9974857747783512, 'recall': 0.9969580743287925, 'f1': 0.9972218547426909, 'number': 7561} {'precision': 0.9843225083986562, 'recall': 0.9859786876051598, 'f1': 0.9851499019333148, 'number': 1783} {'precision': 0.9754385964912281, 'recall': 0.9754385964912281, 'f1': 0.9754385964912281, 'number': 1140} {'precision': 0.9975015615240475, 'recall': 0.9965678627145086, 'f1': 0.9970344935227097, 'number': 3205} {'precision': 0.9978598180845372, 'recall': 0.9994640943193998, 'f1': 0.9986613119143238, 'number': 1866} 0.9961 0.9953 0.9957 0.9972
0.0134 1.98 1800 0.0128 {'precision': 0.9990791896869244, 'recall': 0.9982912723449001, 'f1': 0.9986850756081526, 'number': 7608} {'precision': 0.9966957441184245, 'recall': 0.9973548472424283, 'f1': 0.997025186752165, 'number': 7561} {'precision': 0.9903737259343148, 'recall': 0.9809310151430174, 'f1': 0.9856297548605241, 'number': 1783} {'precision': 0.970714900947459, 'recall': 0.9885964912280701, 'f1': 0.979574098218166, 'number': 1140} {'precision': 0.9984350547730829, 'recall': 0.9953198127925117, 'f1': 0.9968750000000001, 'number': 3205} {'precision': 0.9994640943193998, 'recall': 0.9994640943193998, 'f1': 0.9994640943193998, 'number': 1866} 0.9962 0.9959 0.9960 0.9976
0.0099 2.2 2000 0.0140 {'precision': 0.9993413252535898, 'recall': 0.9971083070452156, 'f1': 0.9982235673399565, 'number': 7608} {'precision': 0.9970868644067796, 'recall': 0.9959000132257638, 'f1': 0.9964930854231456, 'number': 7561} {'precision': 0.9903791737408036, 'recall': 0.981491867638811, 'f1': 0.9859154929577465, 'number': 1783} {'precision': 0.9716251074806534, 'recall': 0.9912280701754386, 'f1': 0.9813287016934433, 'number': 1140} {'precision': 0.9968837644125896, 'recall': 0.9981279251170047, 'f1': 0.9975054568132211, 'number': 3205} {'precision': 0.9957310565635006, 'recall': 1.0, 'f1': 0.9978609625668449, 'number': 1866} 0.9959 0.9956 0.9957 0.9972
0.008 2.42 2200 0.0156 {'precision': 0.9993417588204319, 'recall': 0.9977655099894848, 'f1': 0.998553012365167, 'number': 7608} {'precision': 0.9982763192787059, 'recall': 0.9957677555878852, 'f1': 0.9970204595113553, 'number': 7561} {'precision': 0.9920769666100736, 'recall': 0.9831744251261918, 'f1': 0.987605633802817, 'number': 1783} {'precision': 0.9667519181585678, 'recall': 0.9947368421052631, 'f1': 0.980544747081712, 'number': 1140} {'precision': 0.9919354838709677, 'recall': 0.9978159126365055, 'f1': 0.9948670088660756, 'number': 3205} {'precision': 0.9983940042826552, 'recall': 0.9994640943193998, 'f1': 0.9989287627209427, 'number': 1866} 0.9957 0.9960 0.9958 0.9975
0.0147 2.64 2400 0.0110 {'precision': 0.998290373487638, 'recall': 0.9977655099894848, 'f1': 0.9980278727320536, 'number': 7608} {'precision': 0.9986760227724083, 'recall': 0.9976193625181854, 'f1': 0.9981474129945745, 'number': 7561} {'precision': 0.9926010244735345, 'recall': 0.9781267526640494, 'f1': 0.9853107344632769, 'number': 1783} {'precision': 0.9718189581554227, 'recall': 0.9982456140350877, 'f1': 0.9848550411077457, 'number': 1140} {'precision': 0.9981279251170047, 'recall': 0.9981279251170047, 'f1': 0.9981279251170047, 'number': 3205} {'precision': 0.9994643813604713, 'recall': 1.0, 'f1': 0.999732118939191, 'number': 1866} 0.9967 0.9965 0.9966 0.9979
0.0095 2.86 2600 0.0106 {'precision': 0.9998684210526316, 'recall': 0.9988170347003155, 'f1': 0.9993424513413993, 'number': 7608} {'precision': 0.9948508053868498, 'recall': 0.9965613014151568, 'f1': 0.9957053187974892, 'number': 7561} {'precision': 0.9765494137353434, 'recall': 0.9809310151430174, 'f1': 0.9787353105763851, 'number': 1783} {'precision': 0.9963302752293578, 'recall': 0.9526315789473684, 'f1': 0.9739910313901345, 'number': 1140} {'precision': 0.9956440572495333, 'recall': 0.9984399375975039, 'f1': 0.9970400373890015, 'number': 3205} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1866} 0.9957 0.9945 0.9951 0.9970
0.0068 3.08 2800 0.0092 {'precision': 0.9996052112120016, 'recall': 0.998422712933754, 'f1': 0.9990136121522984, 'number': 7608} {'precision': 0.9988087359364659, 'recall': 0.9980161354318212, 'f1': 0.998412278380524, 'number': 7561} {'precision': 0.9821528165086447, 'recall': 0.9876612450925406, 'f1': 0.9848993288590603, 'number': 1783} {'precision': 0.9876434245366285, 'recall': 0.9815789473684211, 'f1': 0.9846018477782666, 'number': 1140} {'precision': 0.9971945137157108, 'recall': 0.9981279251170047, 'f1': 0.9976610010915329, 'number': 3205} {'precision': 0.9989293361884368, 'recall': 1.0, 'f1': 0.9994643813604713, 'number': 1866} 0.9970 0.9967 0.9969 0.9980
0.0054 3.3 3000 0.0100 {'precision': 0.9997367035281727, 'recall': 0.9981598317560463, 'f1': 0.9989476453564851, 'number': 7608} {'precision': 0.9984131182226924, 'recall': 0.9985451659833355, 'f1': 0.9984791377372215, 'number': 7561} {'precision': 0.9960227272727272, 'recall': 0.9831744251261918, 'f1': 0.9895568727067456, 'number': 1783} {'precision': 0.9767241379310345, 'recall': 0.993859649122807, 'f1': 0.9852173913043478, 'number': 1140} {'precision': 0.9956413449564134, 'recall': 0.9978159126365055, 'f1': 0.9967274427302478, 'number': 3205} {'precision': 0.9994643813604713, 'recall': 1.0, 'f1': 0.999732118939191, 'number': 1866} 0.9973 0.9970 0.9972 0.9982
0.0073 3.52 3200 0.0125 {'precision': 0.999079673941625, 'recall': 0.9988170347003155, 'f1': 0.9989483370579728, 'number': 7608} {'precision': 0.9985449735449735, 'recall': 0.9984129083454569, 'f1': 0.9984789365782686, 'number': 7561} {'precision': 0.9832026875699889, 'recall': 0.9848569826135727, 'f1': 0.9840291398150743, 'number': 1783} {'precision': 0.9833479404031551, 'recall': 0.9842105263157894, 'f1': 0.9837790442788251, 'number': 1140} {'precision': 0.9971927635683094, 'recall': 0.9975039001560062, 'f1': 0.9973483075963188, 'number': 3205} {'precision': 0.9989276139410188, 'recall': 0.9983922829581994, 'f1': 0.9986598767086573, 'number': 1866} 0.9966 0.9967 0.9967 0.9979
0.0109 3.74 3400 0.0132 {'precision': 0.9997367381861261, 'recall': 0.9982912723449001, 'f1': 0.999013482407103, 'number': 7608} {'precision': 0.9970949425590915, 'recall': 0.9986774236212141, 'f1': 0.9978855557023919, 'number': 7561} {'precision': 0.9915158371040724, 'recall': 0.9831744251261918, 'f1': 0.9873275133765137, 'number': 1783} {'precision': 0.9801381692573402, 'recall': 0.9956140350877193, 'f1': 0.9878154917319407, 'number': 1140} {'precision': 0.9984345648090169, 'recall': 0.9950078003120125, 'f1': 0.9967182372245664, 'number': 3205} {'precision': 0.9989293361884368, 'recall': 1.0, 'f1': 0.9994643813604713, 'number': 1866} 0.9970 0.9968 0.9969 0.9979
0.0063 3.96 3600 0.0121 {'precision': 0.9980281319836992, 'recall': 0.9978969505783386, 'f1': 0.9979625369700953, 'number': 7608} {'precision': 0.9988084204951675, 'recall': 0.997751620156064, 'f1': 0.9982797406378192, 'number': 7561} {'precision': 0.9814606741573034, 'recall': 0.9798093101514301, 'f1': 0.9806342969407803, 'number': 1783} {'precision': 0.9833770778652668, 'recall': 0.9859649122807017, 'f1': 0.9846692947875603, 'number': 1140} {'precision': 0.9968857053877297, 'recall': 0.9987519500780031, 'f1': 0.9978179551122195, 'number': 3205} {'precision': 0.9989293361884368, 'recall': 1.0, 'f1': 0.9994643813604713, 'number': 1866} 0.9962 0.9962 0.9962 0.9978
0.006 4.18 3800 0.0118 {'precision': 0.9993423648559779, 'recall': 0.9986855941114616, 'f1': 0.9990138715403327, 'number': 7608} {'precision': 0.9981479031617939, 'recall': 0.9978838777939426, 'f1': 0.998015873015873, 'number': 7561} {'precision': 0.9937321937321937, 'recall': 0.9781267526640494, 'f1': 0.9858677218767665, 'number': 1783} {'precision': 0.9784668389319552, 'recall': 0.9964912280701754, 'f1': 0.9873967840069536, 'number': 1140} {'precision': 0.9956440572495333, 'recall': 0.9984399375975039, 'f1': 0.9970400373890015, 'number': 3205} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1866} 0.9970 0.9968 0.9969 0.9982
0.0063 4.4 4000 0.0117 {'precision': 0.9989472298986709, 'recall': 0.9977655099894848, 'f1': 0.9983560202538305, 'number': 7608} {'precision': 0.9980150853513299, 'recall': 0.9974871048803068, 'f1': 0.9977510252678925, 'number': 7561} {'precision': 0.9864940911648846, 'recall': 0.9831744251261918, 'f1': 0.9848314606741573, 'number': 1783} {'precision': 0.9750859106529209, 'recall': 0.9956140350877193, 'f1': 0.9852430555555555, 'number': 1140} {'precision': 0.9981244138793373, 'recall': 0.9962558502340093, 'f1': 0.9971892567145534, 'number': 3205} {'precision': 1.0, 'recall': 0.9994640943193998, 'f1': 0.9997319753417314, 'number': 1866} 0.9965 0.9964 0.9964 0.9978
0.0042 4.62 4200 0.0144 {'precision': 0.9993423648559779, 'recall': 0.9986855941114616, 'f1': 0.9990138715403327, 'number': 7608} {'precision': 0.9965631196298744, 'recall': 0.9970903319666711, 'f1': 0.9968266560888536, 'number': 7561} {'precision': 0.9853685987619584, 'recall': 0.9820527201346047, 'f1': 0.9837078651685394, 'number': 1783} {'precision': 0.9750859106529209, 'recall': 0.9956140350877193, 'f1': 0.9852430555555555, 'number': 1140} {'precision': 0.9987472596304416, 'recall': 0.9950078003120125, 'f1': 0.9968740231322288, 'number': 3205} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1866} 0.9961 0.9963 0.9962 0.9978
0.0081 4.84 4400 0.0116 {'precision': 0.9988161010260458, 'recall': 0.9980283911671924, 'f1': 0.998422090729783, 'number': 7608} {'precision': 0.9984131182226924, 'recall': 0.9985451659833355, 'f1': 0.9984791377372215, 'number': 7561} {'precision': 0.9886621315192744, 'recall': 0.9781267526640494, 'f1': 0.9833662249788554, 'number': 1783} {'precision': 0.9800693240901213, 'recall': 0.9921052631578947, 'f1': 0.986050566695728, 'number': 1140} {'precision': 0.9962570180910792, 'recall': 0.9965678627145086, 'f1': 0.9964124161597255, 'number': 3205} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1866} 0.9967 0.9963 0.9965 0.9979
0.008 5.05 4600 0.0134 {'precision': 0.9994738226782426, 'recall': 0.9986855941114616, 'f1': 0.9990795529257067, 'number': 7608} {'precision': 0.9943309162821358, 'recall': 0.9974871048803068, 'f1': 0.9959065099696289, 'number': 7561} {'precision': 0.9931623931623932, 'recall': 0.9775659001682557, 'f1': 0.9853024307518372, 'number': 1783} {'precision': 0.9783737024221453, 'recall': 0.9921052631578947, 'f1': 0.985191637630662, 'number': 1140} {'precision': 0.9987421383647799, 'recall': 0.9909516380655227, 'f1': 0.9948316366483946, 'number': 3205} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1866} 0.9962 0.9954 0.9958 0.9974
0.007 5.27 4800 0.0148 {'precision': 0.9978955675391293, 'recall': 0.9972397476340694, 'f1': 0.9975675497994873, 'number': 7608} {'precision': 0.9988090512107979, 'recall': 0.9982806507075783, 'f1': 0.9985447810556951, 'number': 7561} {'precision': 0.9718232044198895, 'recall': 0.9865395401009535, 'f1': 0.9791260784859448, 'number': 1783} {'precision': 0.9891696750902527, 'recall': 0.9614035087719298, 'f1': 0.9750889679715302, 'number': 1140} {'precision': 0.9971901342491414, 'recall': 0.9965678627145086, 'f1': 0.9968789013732835, 'number': 3205} {'precision': 0.9994643813604713, 'recall': 1.0, 'f1': 0.999732118939191, 'number': 1866} 0.9958 0.9951 0.9954 0.9972
0.0046 5.49 5000 0.0110 {'precision': 0.9986849026827985, 'recall': 0.9981598317560463, 'f1': 0.998422298185643, 'number': 7608} {'precision': 0.9976218787158145, 'recall': 0.9986774236212141, 'f1': 0.9981493721083939, 'number': 7561} {'precision': 0.9926303854875284, 'recall': 0.9820527201346047, 'f1': 0.9873132224414999, 'number': 1783} {'precision': 0.983435047951177, 'recall': 0.9894736842105263, 'f1': 0.9864451246174027, 'number': 1140} {'precision': 0.9981244138793373, 'recall': 0.9962558502340093, 'f1': 0.9971892567145534, 'number': 3205} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1866} 0.9971 0.9965 0.9968 0.9980
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Framework versions

  • Transformers 4.38.2
  • Pytorch 2.0.1+cu118
  • Datasets 2.18.0
  • Tokenizers 0.15.2
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