ocr-v6

This model is a fine-tuned version of microsoft/layoutlm-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0242
• Axyear: {'precision': 0.9916666666666667, 'recall': 1.0, 'f1': 0.99581589958159, 'number': 119}
• Inemployeridentificationnumber: {'precision': 0.9668874172185431, 'recall': 0.9931972789115646, 'f1': 0.9798657718120806, 'number': 147}
• Mployeename: {'precision': 0.9919354838709677, 'recall': 0.9609375, 'f1': 0.9761904761904763, 'number': 128}
• Mployeraddresscity: {'precision': 0.9861111111111112, 'recall': 1.0, 'f1': 0.993006993006993, 'number': 142}
• Mployeraddressstate: {'precision': 0.9722222222222222, 'recall': 1.0, 'f1': 0.9859154929577464, 'number': 140}
• Mployeraddressstreet Name: {'precision': 0.9506172839506173, 'recall': 0.9746835443037974, 'f1': 0.9625, 'number': 158}
• Mployeraddresszip: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 141}
• Mployername: {'precision': 0.9548387096774194, 'recall': 0.9866666666666667, 'f1': 0.9704918032786887, 'number': 150}
• Ox16statewagestips: {'precision': 0.8192771084337349, 'recall': 0.7640449438202247, 'f1': 0.7906976744186045, 'number': 89}
• Ox17stateincometax: {'precision': 0.8470588235294118, 'recall': 0.8888888888888888, 'f1': 0.8674698795180723, 'number': 81}
• Ox1wagestipsandothercompensations: {'precision': 0.9182389937106918, 'recall': 0.8538011695906432, 'f1': 0.8848484848484848, 'number': 171}
• Ox2federalincometaxwithheld: {'precision': 0.9, 'recall': 0.9053254437869822, 'f1': 0.9026548672566372, 'number': 169}
• Ox3socialsecuritywages: {'precision': 0.8653846153846154, 'recall': 0.8598726114649682, 'f1': 0.8626198083067094, 'number': 157}
• Ox4socialsecuritytaxwithheld: {'precision': 0.916083916083916, 'recall': 0.8851351351351351, 'f1': 0.9003436426116838, 'number': 148}
• Snofemployee: {'precision': 0.9732142857142857, 'recall': 0.9732142857142857, 'f1': 0.9732142857142857, 'number': 112}
• Overall Precision: 0.9405
• Overall Recall: 0.9391
• Overall F1: 0.9398
• Overall Accuracy: 0.9947

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: 3e-05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 15
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Axyear	Inemployeridentificationnumber	Mployeename	Mployeraddresscity	Mployeraddressstate	Mployeraddressstreet Name	Mployeraddresszip	Mployername	Ox16statewagestips	Ox17stateincometax	Ox1wagestipsandothercompensations	Ox2federalincometaxwithheld	Ox3socialsecuritywages	Ox4socialsecuritytaxwithheld	Snofemployee	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.0074	1.0	30	0.3622	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 147}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 128}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 142}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 140}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 158}	{'precision': 1.0, 'recall': 0.03546099290780142, 'f1': 0.06849315068493152, 'number': 141}	{'precision': 0.07894736842105263, 'recall': 0.04, 'f1': 0.05309734513274336, 'number': 150}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 89}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 81}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 171}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 169}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 157}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 148}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 112}	0.1	0.0054	0.0102	0.8965
0.2619	2.0	60	0.1344	{'precision': 0.9764705882352941, 'recall': 0.6974789915966386, 'f1': 0.8137254901960784, 'number': 119}	{'precision': 0.8734939759036144, 'recall': 0.9863945578231292, 'f1': 0.926517571884984, 'number': 147}	{'precision': 0.9098360655737705, 'recall': 0.8671875, 'f1': 0.888, 'number': 128}	{'precision': 0.9324324324324325, 'recall': 0.971830985915493, 'f1': 0.9517241379310345, 'number': 142}	{'precision': 0.9716312056737588, 'recall': 0.9785714285714285, 'f1': 0.9750889679715302, 'number': 140}	{'precision': 0.8295454545454546, 'recall': 0.9240506329113924, 'f1': 0.874251497005988, 'number': 158}	{'precision': 0.9448275862068966, 'recall': 0.9716312056737588, 'f1': 0.958041958041958, 'number': 141}	{'precision': 0.9245283018867925, 'recall': 0.98, 'f1': 0.9514563106796116, 'number': 150}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 89}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 81}	{'precision': 0.215962441314554, 'recall': 0.26900584795321636, 'f1': 0.23958333333333334, 'number': 171}	{'precision': 0.26146788990825687, 'recall': 0.33727810650887574, 'f1': 0.2945736434108527, 'number': 169}	{'precision': 0.1951219512195122, 'recall': 0.15286624203821655, 'f1': 0.1714285714285714, 'number': 157}	{'precision': 0.20689655172413793, 'recall': 0.12162162162162163, 'f1': 0.15319148936170213, 'number': 148}	{'precision': 0.9767441860465116, 'recall': 0.75, 'f1': 0.8484848484848485, 'number': 112}	0.6811	0.6204	0.6493	0.9639
0.1153	3.0	90	0.0684	{'precision': 0.9646017699115044, 'recall': 0.9159663865546218, 'f1': 0.9396551724137931, 'number': 119}	{'precision': 0.96, 'recall': 0.9795918367346939, 'f1': 0.9696969696969697, 'number': 147}	{'precision': 0.96875, 'recall': 0.96875, 'f1': 0.96875, 'number': 128}	{'precision': 0.9659863945578231, 'recall': 1.0, 'f1': 0.9826989619377162, 'number': 142}	{'precision': 0.9790209790209791, 'recall': 1.0, 'f1': 0.989399293286219, 'number': 140}	{'precision': 0.9325153374233128, 'recall': 0.9620253164556962, 'f1': 0.9470404984423676, 'number': 158}	{'precision': 0.9724137931034482, 'recall': 1.0, 'f1': 0.9860139860139859, 'number': 141}	{'precision': 0.961038961038961, 'recall': 0.9866666666666667, 'f1': 0.9736842105263157, 'number': 150}	{'precision': 0.48148148148148145, 'recall': 0.29213483146067415, 'f1': 0.36363636363636365, 'number': 89}	{'precision': 0.31451612903225806, 'recall': 0.48148148148148145, 'f1': 0.3804878048780488, 'number': 81}	{'precision': 0.6020408163265306, 'recall': 0.6900584795321637, 'f1': 0.6430517711171663, 'number': 171}	{'precision': 0.8625954198473282, 'recall': 0.6686390532544378, 'f1': 0.7533333333333332, 'number': 169}	{'precision': 0.42592592592592593, 'recall': 0.4394904458598726, 'f1': 0.43260188087774293, 'number': 157}	{'precision': 0.7466666666666667, 'recall': 0.7567567567567568, 'f1': 0.7516778523489932, 'number': 148}	{'precision': 0.9714285714285714, 'recall': 0.9107142857142857, 'f1': 0.9400921658986174, 'number': 112}	0.8131	0.8182	0.8156	0.9832
0.0627	4.0	120	0.0390	{'precision': 0.9658119658119658, 'recall': 0.9495798319327731, 'f1': 0.9576271186440678, 'number': 119}	{'precision': 0.9664429530201343, 'recall': 0.9795918367346939, 'f1': 0.9729729729729729, 'number': 147}	{'precision': 0.9841269841269841, 'recall': 0.96875, 'f1': 0.9763779527559054, 'number': 128}	{'precision': 0.9793103448275862, 'recall': 1.0, 'f1': 0.9895470383275261, 'number': 142}	{'precision': 0.9790209790209791, 'recall': 1.0, 'f1': 0.989399293286219, 'number': 140}	{'precision': 0.9440993788819876, 'recall': 0.9620253164556962, 'f1': 0.9529780564263323, 'number': 158}	{'precision': 0.986013986013986, 'recall': 1.0, 'f1': 0.9929577464788732, 'number': 141}	{'precision': 0.9673202614379085, 'recall': 0.9866666666666667, 'f1': 0.9768976897689768, 'number': 150}	{'precision': 0.6935483870967742, 'recall': 0.48314606741573035, 'f1': 0.5695364238410596, 'number': 89}	{'precision': 0.532608695652174, 'recall': 0.6049382716049383, 'f1': 0.5664739884393064, 'number': 81}	{'precision': 0.8972602739726028, 'recall': 0.7660818713450293, 'f1': 0.8264984227129338, 'number': 171}	{'precision': 0.8918918918918919, 'recall': 0.7810650887573964, 'f1': 0.832807570977918, 'number': 169}	{'precision': 0.7604790419161677, 'recall': 0.8089171974522293, 'f1': 0.7839506172839507, 'number': 157}	{'precision': 0.8344827586206897, 'recall': 0.8175675675675675, 'f1': 0.825938566552901, 'number': 148}	{'precision': 0.9646017699115044, 'recall': 0.9732142857142857, 'f1': 0.9688888888888889, 'number': 112}	0.9035	0.8850	0.8941	0.9909
0.0386	5.0	150	0.0328	{'precision': 0.9669421487603306, 'recall': 0.9831932773109243, 'f1': 0.975, 'number': 119}	{'precision': 0.9668874172185431, 'recall': 0.9931972789115646, 'f1': 0.9798657718120806, 'number': 147}	{'precision': 0.9761904761904762, 'recall': 0.9609375, 'f1': 0.968503937007874, 'number': 128}	{'precision': 0.9726027397260274, 'recall': 1.0, 'f1': 0.9861111111111112, 'number': 142}	{'precision': 0.9722222222222222, 'recall': 1.0, 'f1': 0.9859154929577464, 'number': 140}	{'precision': 0.9386503067484663, 'recall': 0.9683544303797469, 'f1': 0.9532710280373832, 'number': 158}	{'precision': 0.9929577464788732, 'recall': 1.0, 'f1': 0.9964664310954063, 'number': 141}	{'precision': 0.961038961038961, 'recall': 0.9866666666666667, 'f1': 0.9736842105263157, 'number': 150}	{'precision': 0.7733333333333333, 'recall': 0.651685393258427, 'f1': 0.7073170731707319, 'number': 89}	{'precision': 0.6739130434782609, 'recall': 0.7654320987654321, 'f1': 0.7167630057803468, 'number': 81}	{'precision': 0.8375, 'recall': 0.783625730994152, 'f1': 0.8096676737160121, 'number': 171}	{'precision': 0.9240506329113924, 'recall': 0.863905325443787, 'f1': 0.8929663608562691, 'number': 169}	{'precision': 0.8125, 'recall': 0.8280254777070064, 'f1': 0.8201892744479495, 'number': 157}	{'precision': 0.8888888888888888, 'recall': 0.8648648648648649, 'f1': 0.8767123287671232, 'number': 148}	{'precision': 0.956140350877193, 'recall': 0.9732142857142857, 'f1': 0.9646017699115044, 'number': 112}	0.9156	0.9147	0.9152	0.9930
0.027	6.0	180	0.0275	{'precision': 0.9752066115702479, 'recall': 0.9915966386554622, 'f1': 0.9833333333333334, 'number': 119}	{'precision': 0.9668874172185431, 'recall': 0.9931972789115646, 'f1': 0.9798657718120806, 'number': 147}	{'precision': 0.9763779527559056, 'recall': 0.96875, 'f1': 0.9725490196078432, 'number': 128}	{'precision': 0.993006993006993, 'recall': 1.0, 'f1': 0.9964912280701755, 'number': 142}	{'precision': 0.9722222222222222, 'recall': 1.0, 'f1': 0.9859154929577464, 'number': 140}	{'precision': 0.9390243902439024, 'recall': 0.9746835443037974, 'f1': 0.9565217391304348, 'number': 158}	{'precision': 0.986013986013986, 'recall': 1.0, 'f1': 0.9929577464788732, 'number': 141}	{'precision': 0.961038961038961, 'recall': 0.9866666666666667, 'f1': 0.9736842105263157, 'number': 150}	{'precision': 0.8701298701298701, 'recall': 0.7528089887640449, 'f1': 0.8072289156626504, 'number': 89}	{'precision': 0.8888888888888888, 'recall': 0.8888888888888888, 'f1': 0.8888888888888888, 'number': 81}	{'precision': 0.9056603773584906, 'recall': 0.8421052631578947, 'f1': 0.8727272727272727, 'number': 171}	{'precision': 0.9041916167664671, 'recall': 0.893491124260355, 'f1': 0.8988095238095238, 'number': 169}	{'precision': 0.8758169934640523, 'recall': 0.8535031847133758, 'f1': 0.864516129032258, 'number': 157}	{'precision': 0.9154929577464789, 'recall': 0.8783783783783784, 'f1': 0.896551724137931, 'number': 148}	{'precision': 0.9821428571428571, 'recall': 0.9821428571428571, 'f1': 0.9821428571428571, 'number': 112}	0.9426	0.9362	0.9394	0.9944
0.0214	7.0	210	0.0257	{'precision': 0.9754098360655737, 'recall': 1.0, 'f1': 0.9875518672199171, 'number': 119}	{'precision': 0.9664429530201343, 'recall': 0.9795918367346939, 'f1': 0.9729729729729729, 'number': 147}	{'precision': 0.984, 'recall': 0.9609375, 'f1': 0.9723320158102766, 'number': 128}	{'precision': 0.993006993006993, 'recall': 1.0, 'f1': 0.9964912280701755, 'number': 142}	{'precision': 0.9722222222222222, 'recall': 1.0, 'f1': 0.9859154929577464, 'number': 140}	{'precision': 0.9390243902439024, 'recall': 0.9746835443037974, 'f1': 0.9565217391304348, 'number': 158}	{'precision': 0.986013986013986, 'recall': 1.0, 'f1': 0.9929577464788732, 'number': 141}	{'precision': 0.9673202614379085, 'recall': 0.9866666666666667, 'f1': 0.9768976897689768, 'number': 150}	{'precision': 0.8481012658227848, 'recall': 0.7528089887640449, 'f1': 0.7976190476190476, 'number': 89}	{'precision': 0.8488372093023255, 'recall': 0.9012345679012346, 'f1': 0.874251497005988, 'number': 81}	{'precision': 0.8963414634146342, 'recall': 0.8596491228070176, 'f1': 0.8776119402985074, 'number': 171}	{'precision': 0.9101796407185628, 'recall': 0.8994082840236687, 'f1': 0.9047619047619048, 'number': 169}	{'precision': 0.9121621621621622, 'recall': 0.8598726114649682, 'f1': 0.8852459016393441, 'number': 157}	{'precision': 0.8904109589041096, 'recall': 0.8783783783783784, 'f1': 0.8843537414965986, 'number': 148}	{'precision': 0.9649122807017544, 'recall': 0.9821428571428571, 'f1': 0.9734513274336283, 'number': 112}	0.9404	0.9381	0.9393	0.9945
0.0185	8.0	240	0.0279	{'precision': 0.9596774193548387, 'recall': 1.0, 'f1': 0.9794238683127572, 'number': 119}	{'precision': 0.9668874172185431, 'recall': 0.9931972789115646, 'f1': 0.9798657718120806, 'number': 147}	{'precision': 0.984, 'recall': 0.9609375, 'f1': 0.9723320158102766, 'number': 128}	{'precision': 0.9793103448275862, 'recall': 1.0, 'f1': 0.9895470383275261, 'number': 142}	{'precision': 0.9655172413793104, 'recall': 1.0, 'f1': 0.9824561403508771, 'number': 140}	{'precision': 0.9281437125748503, 'recall': 0.9810126582278481, 'f1': 0.9538461538461538, 'number': 158}	{'precision': 0.9929577464788732, 'recall': 1.0, 'f1': 0.9964664310954063, 'number': 141}	{'precision': 0.961038961038961, 'recall': 0.9866666666666667, 'f1': 0.9736842105263157, 'number': 150}	{'precision': 0.7674418604651163, 'recall': 0.7415730337078652, 'f1': 0.7542857142857143, 'number': 89}	{'precision': 0.7659574468085106, 'recall': 0.8888888888888888, 'f1': 0.8228571428571428, 'number': 81}	{'precision': 0.8944099378881988, 'recall': 0.8421052631578947, 'f1': 0.8674698795180723, 'number': 171}	{'precision': 0.9053254437869822, 'recall': 0.9053254437869822, 'f1': 0.9053254437869822, 'number': 169}	{'precision': 0.864516129032258, 'recall': 0.8535031847133758, 'f1': 0.858974358974359, 'number': 157}	{'precision': 0.9166666666666666, 'recall': 0.8918918918918919, 'f1': 0.9041095890410958, 'number': 148}	{'precision': 0.9646017699115044, 'recall': 0.9732142857142857, 'f1': 0.9688888888888889, 'number': 112}	0.9272	0.9376	0.9324	0.9935
0.016	9.0	270	0.0255	{'precision': 0.967479674796748, 'recall': 1.0, 'f1': 0.9834710743801653, 'number': 119}	{'precision': 0.9668874172185431, 'recall': 0.9931972789115646, 'f1': 0.9798657718120806, 'number': 147}	{'precision': 0.984, 'recall': 0.9609375, 'f1': 0.9723320158102766, 'number': 128}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 142}	{'precision': 0.9722222222222222, 'recall': 1.0, 'f1': 0.9859154929577464, 'number': 140}	{'precision': 0.9506172839506173, 'recall': 0.9746835443037974, 'f1': 0.9625, 'number': 158}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 141}	{'precision': 0.961038961038961, 'recall': 0.9866666666666667, 'f1': 0.9736842105263157, 'number': 150}	{'precision': 0.8170731707317073, 'recall': 0.7528089887640449, 'f1': 0.783625730994152, 'number': 89}	{'precision': 0.8372093023255814, 'recall': 0.8888888888888888, 'f1': 0.8622754491017963, 'number': 81}	{'precision': 0.9125, 'recall': 0.8538011695906432, 'f1': 0.8821752265861027, 'number': 171}	{'precision': 0.9047619047619048, 'recall': 0.8994082840236687, 'f1': 0.9020771513353116, 'number': 169}	{'precision': 0.8782051282051282, 'recall': 0.8726114649681529, 'f1': 0.8753993610223643, 'number': 157}	{'precision': 0.8791946308724832, 'recall': 0.8851351351351351, 'f1': 0.8821548821548821, 'number': 148}	{'precision': 0.9732142857142857, 'recall': 0.9732142857142857, 'f1': 0.9732142857142857, 'number': 112}	0.9377	0.9391	0.9384	0.9944
0.0143	10.0	300	0.0250	{'precision': 0.9916666666666667, 'recall': 1.0, 'f1': 0.99581589958159, 'number': 119}	{'precision': 0.9668874172185431, 'recall': 0.9931972789115646, 'f1': 0.9798657718120806, 'number': 147}	{'precision': 0.9919354838709677, 'recall': 0.9609375, 'f1': 0.9761904761904763, 'number': 128}	{'precision': 0.9861111111111112, 'recall': 1.0, 'f1': 0.993006993006993, 'number': 142}	{'precision': 0.9722222222222222, 'recall': 1.0, 'f1': 0.9859154929577464, 'number': 140}	{'precision': 0.9390243902439024, 'recall': 0.9746835443037974, 'f1': 0.9565217391304348, 'number': 158}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 141}	{'precision': 0.961038961038961, 'recall': 0.9866666666666667, 'f1': 0.9736842105263157, 'number': 150}	{'precision': 0.8170731707317073, 'recall': 0.7528089887640449, 'f1': 0.783625730994152, 'number': 89}	{'precision': 0.8888888888888888, 'recall': 0.8888888888888888, 'f1': 0.8888888888888888, 'number': 81}	{'precision': 0.9, 'recall': 0.8421052631578947, 'f1': 0.8700906344410877, 'number': 171}	{'precision': 0.9101796407185628, 'recall': 0.8994082840236687, 'f1': 0.9047619047619048, 'number': 169}	{'precision': 0.8831168831168831, 'recall': 0.8662420382165605, 'f1': 0.8745980707395498, 'number': 157}	{'precision': 0.9097222222222222, 'recall': 0.8851351351351351, 'f1': 0.8972602739726027, 'number': 148}	{'precision': 0.9732142857142857, 'recall': 0.9732142857142857, 'f1': 0.9732142857142857, 'number': 112}	0.9422	0.9376	0.9399	0.9946
0.0132	11.0	330	0.0278	{'precision': 0.9754098360655737, 'recall': 1.0, 'f1': 0.9875518672199171, 'number': 119}	{'precision': 0.9664429530201343, 'recall': 0.9795918367346939, 'f1': 0.9729729729729729, 'number': 147}	{'precision': 0.9919354838709677, 'recall': 0.9609375, 'f1': 0.9761904761904763, 'number': 128}	{'precision': 0.9861111111111112, 'recall': 1.0, 'f1': 0.993006993006993, 'number': 142}	{'precision': 0.9722222222222222, 'recall': 1.0, 'f1': 0.9859154929577464, 'number': 140}	{'precision': 0.9565217391304348, 'recall': 0.9746835443037974, 'f1': 0.9655172413793103, 'number': 158}	{'precision': 0.9929577464788732, 'recall': 1.0, 'f1': 0.9964664310954063, 'number': 141}	{'precision': 0.9548387096774194, 'recall': 0.9866666666666667, 'f1': 0.9704918032786887, 'number': 150}	{'precision': 0.723404255319149, 'recall': 0.7640449438202247, 'f1': 0.7431693989071038, 'number': 89}	{'precision': 0.7604166666666666, 'recall': 0.9012345679012346, 'f1': 0.824858757062147, 'number': 81}	{'precision': 0.8895705521472392, 'recall': 0.847953216374269, 'f1': 0.8682634730538922, 'number': 171}	{'precision': 0.9, 'recall': 0.9053254437869822, 'f1': 0.9026548672566372, 'number': 169}	{'precision': 0.9060402684563759, 'recall': 0.8598726114649682, 'f1': 0.8823529411764707, 'number': 157}	{'precision': 0.8979591836734694, 'recall': 0.8918918918918919, 'f1': 0.8949152542372881, 'number': 148}	{'precision': 0.9316239316239316, 'recall': 0.9732142857142857, 'f1': 0.9519650655021833, 'number': 112}	0.9273	0.9386	0.9329	0.9936
0.0122	12.0	360	0.0238	{'precision': 0.9916666666666667, 'recall': 1.0, 'f1': 0.99581589958159, 'number': 119}	{'precision': 0.9666666666666667, 'recall': 0.9863945578231292, 'f1': 0.9764309764309764, 'number': 147}	{'precision': 0.9919354838709677, 'recall': 0.9609375, 'f1': 0.9761904761904763, 'number': 128}	{'precision': 0.9861111111111112, 'recall': 1.0, 'f1': 0.993006993006993, 'number': 142}	{'precision': 0.9722222222222222, 'recall': 1.0, 'f1': 0.9859154929577464, 'number': 140}	{'precision': 0.9506172839506173, 'recall': 0.9746835443037974, 'f1': 0.9625, 'number': 158}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 141}	{'precision': 0.9548387096774194, 'recall': 0.9866666666666667, 'f1': 0.9704918032786887, 'number': 150}	{'precision': 0.8395061728395061, 'recall': 0.7640449438202247, 'f1': 0.8, 'number': 89}	{'precision': 0.9012345679012346, 'recall': 0.9012345679012346, 'f1': 0.9012345679012346, 'number': 81}	{'precision': 0.9245283018867925, 'recall': 0.8596491228070176, 'f1': 0.8909090909090909, 'number': 171}	{'precision': 0.9, 'recall': 0.9053254437869822, 'f1': 0.9026548672566372, 'number': 169}	{'precision': 0.8903225806451613, 'recall': 0.8789808917197452, 'f1': 0.8846153846153846, 'number': 157}	{'precision': 0.916083916083916, 'recall': 0.8851351351351351, 'f1': 0.9003436426116838, 'number': 148}	{'precision': 0.9478260869565217, 'recall': 0.9732142857142857, 'f1': 0.960352422907489, 'number': 112}	0.9447	0.9410	0.9429	0.9950
0.0119	13.0	390	0.0234	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 119}	{'precision': 0.9668874172185431, 'recall': 0.9931972789115646, 'f1': 0.9798657718120806, 'number': 147}	{'precision': 0.9919354838709677, 'recall': 0.9609375, 'f1': 0.9761904761904763, 'number': 128}	{'precision': 0.9861111111111112, 'recall': 1.0, 'f1': 0.993006993006993, 'number': 142}	{'precision': 0.9722222222222222, 'recall': 1.0, 'f1': 0.9859154929577464, 'number': 140}	{'precision': 0.9447852760736196, 'recall': 0.9746835443037974, 'f1': 0.9595015576323987, 'number': 158}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 141}	{'precision': 0.9548387096774194, 'recall': 0.9866666666666667, 'f1': 0.9704918032786887, 'number': 150}	{'precision': 0.8095238095238095, 'recall': 0.7640449438202247, 'f1': 0.7861271676300579, 'number': 89}	{'precision': 0.9125, 'recall': 0.9012345679012346, 'f1': 0.9068322981366459, 'number': 81}	{'precision': 0.9245283018867925, 'recall': 0.8596491228070176, 'f1': 0.8909090909090909, 'number': 171}	{'precision': 0.9107142857142857, 'recall': 0.9053254437869822, 'f1': 0.9080118694362018, 'number': 169}	{'precision': 0.8709677419354839, 'recall': 0.8598726114649682, 'f1': 0.8653846153846154, 'number': 157}	{'precision': 0.9290780141843972, 'recall': 0.8851351351351351, 'f1': 0.9065743944636678, 'number': 148}	{'precision': 0.9732142857142857, 'recall': 0.9732142857142857, 'f1': 0.9732142857142857, 'number': 112}	0.9456	0.9401	0.9428	0.9950
0.0112	14.0	420	0.0235	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 119}	{'precision': 0.9668874172185431, 'recall': 0.9931972789115646, 'f1': 0.9798657718120806, 'number': 147}	{'precision': 0.9919354838709677, 'recall': 0.9609375, 'f1': 0.9761904761904763, 'number': 128}	{'precision': 0.9861111111111112, 'recall': 1.0, 'f1': 0.993006993006993, 'number': 142}	{'precision': 0.9722222222222222, 'recall': 1.0, 'f1': 0.9859154929577464, 'number': 140}	{'precision': 0.9506172839506173, 'recall': 0.9746835443037974, 'f1': 0.9625, 'number': 158}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 141}	{'precision': 0.9548387096774194, 'recall': 0.9866666666666667, 'f1': 0.9704918032786887, 'number': 150}	{'precision': 0.8292682926829268, 'recall': 0.7640449438202247, 'f1': 0.7953216374269005, 'number': 89}	{'precision': 0.8674698795180723, 'recall': 0.8888888888888888, 'f1': 0.8780487804878048, 'number': 81}	{'precision': 0.9182389937106918, 'recall': 0.8538011695906432, 'f1': 0.8848484848484848, 'number': 171}	{'precision': 0.9058823529411765, 'recall': 0.9112426035502958, 'f1': 0.9085545722713865, 'number': 169}	{'precision': 0.8653846153846154, 'recall': 0.8598726114649682, 'f1': 0.8626198083067094, 'number': 157}	{'precision': 0.9225352112676056, 'recall': 0.8851351351351351, 'f1': 0.903448275862069, 'number': 148}	{'precision': 0.9732142857142857, 'recall': 0.9732142857142857, 'f1': 0.9732142857142857, 'number': 112}	0.9432	0.9396	0.9414	0.9949
0.0115	15.0	450	0.0242	{'precision': 0.9916666666666667, 'recall': 1.0, 'f1': 0.99581589958159, 'number': 119}	{'precision': 0.9668874172185431, 'recall': 0.9931972789115646, 'f1': 0.9798657718120806, 'number': 147}	{'precision': 0.9919354838709677, 'recall': 0.9609375, 'f1': 0.9761904761904763, 'number': 128}	{'precision': 0.9861111111111112, 'recall': 1.0, 'f1': 0.993006993006993, 'number': 142}	{'precision': 0.9722222222222222, 'recall': 1.0, 'f1': 0.9859154929577464, 'number': 140}	{'precision': 0.9506172839506173, 'recall': 0.9746835443037974, 'f1': 0.9625, 'number': 158}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 141}	{'precision': 0.9548387096774194, 'recall': 0.9866666666666667, 'f1': 0.9704918032786887, 'number': 150}	{'precision': 0.8192771084337349, 'recall': 0.7640449438202247, 'f1': 0.7906976744186045, 'number': 89}	{'precision': 0.8470588235294118, 'recall': 0.8888888888888888, 'f1': 0.8674698795180723, 'number': 81}	{'precision': 0.9182389937106918, 'recall': 0.8538011695906432, 'f1': 0.8848484848484848, 'number': 171}	{'precision': 0.9, 'recall': 0.9053254437869822, 'f1': 0.9026548672566372, 'number': 169}	{'precision': 0.8653846153846154, 'recall': 0.8598726114649682, 'f1': 0.8626198083067094, 'number': 157}	{'precision': 0.916083916083916, 'recall': 0.8851351351351351, 'f1': 0.9003436426116838, 'number': 148}	{'precision': 0.9732142857142857, 'recall': 0.9732142857142857, 'f1': 0.9732142857142857, 'number': 112}	0.9405	0.9391	0.9398	0.9947

Framework versions

• Transformers 4.44.0
• Pytorch 2.4.0
• Datasets 2.21.0
• Tokenizers 0.19.1