generated_from_trainer

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passive_invoices_v2.3

This model is a fine-tuned version of microsoft/layoutlmv3-base on the sroie dataset. It achieves the following results on the evaluation set:

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:

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 0.15 100 1.0186 0.3274 0.0121 0.0233 0.7863
No log 0.29 200 0.6958 0.3536 0.2604 0.2999 0.8433
No log 0.44 300 0.4969 0.5328 0.4943 0.5128 0.8973
No log 0.59 400 0.3566 0.7209 0.6777 0.6986 0.9374
0.8847 0.74 500 0.2788 0.7946 0.7569 0.7753 0.9550
0.8847 0.88 600 0.2184 0.8252 0.8058 0.8154 0.9628
0.8847 1.03 700 0.1919 0.8528 0.8183 0.8352 0.9657
0.8847 1.18 800 0.1752 0.8390 0.8346 0.8368 0.9647
0.8847 1.33 900 0.1566 0.8661 0.8387 0.8522 0.9705
0.2412 1.47 1000 0.1383 0.8710 0.8561 0.8635 0.9731
0.2412 1.62 1100 0.1324 0.8584 0.8594 0.8589 0.9715
0.2412 1.77 1200 0.1248 0.8799 0.8743 0.8771 0.9746
0.2412 1.91 1300 0.1243 0.8617 0.8763 0.8689 0.9728
0.2412 2.06 1400 0.1090 0.8771 0.8787 0.8779 0.9758
0.1444 2.21 1500 0.1063 0.8927 0.8820 0.8873 0.9774
0.1444 2.36 1600 0.1047 0.8798 0.8943 0.8870 0.9762
0.1444 2.5 1700 0.1002 0.9025 0.8940 0.8983 0.9788
0.1444 2.65 1800 0.0937 0.9078 0.8896 0.8986 0.9801
0.1444 2.8 1900 0.0952 0.9003 0.8958 0.8981 0.9800
0.1168 2.95 2000 0.0867 0.9127 0.8945 0.9035 0.9815
0.1168 3.09 2100 0.0919 0.9156 0.8881 0.9017 0.9814
0.1168 3.24 2200 0.0845 0.9089 0.9035 0.9062 0.9818
0.1168 3.39 2300 0.0785 0.9155 0.9107 0.9131 0.9836
0.1168 3.53 2400 0.0785 0.9118 0.9122 0.9120 0.9831
0.0876 3.68 2500 0.0816 0.9087 0.9144 0.9115 0.9816
0.0876 3.83 2600 0.0794 0.9103 0.9107 0.9105 0.9820
0.0876 3.98 2700 0.0734 0.9145 0.9179 0.9162 0.9834
0.0876 4.12 2800 0.0719 0.9182 0.9212 0.9197 0.9846
0.0876 4.27 2900 0.0732 0.9235 0.9166 0.9201 0.9844
0.0707 4.42 3000 0.0673 0.9160 0.9256 0.9208 0.9846
0.0707 4.57 3100 0.0787 0.9312 0.9085 0.9197 0.9843
0.0707 4.71 3200 0.0644 0.9298 0.9241 0.9269 0.9856
0.0707 4.86 3300 0.0676 0.9308 0.9153 0.9230 0.9852
0.0707 5.01 3400 0.0629 0.9240 0.9201 0.9221 0.9857
0.0635 5.15 3500 0.0691 0.9206 0.9153 0.9179 0.9848
0.0635 5.3 3600 0.0715 0.9269 0.9215 0.9242 0.9854
0.0635 5.45 3700 0.0659 0.9230 0.9206 0.9218 0.9853
0.0635 5.6 3800 0.0652 0.9194 0.9239 0.9216 0.9852
0.0635 5.74 3900 0.0652 0.9253 0.9182 0.9217 0.9859
0.052 5.89 4000 0.0646 0.9259 0.9212 0.9236 0.9863
0.052 6.04 4100 0.0601 0.9300 0.9298 0.9299 0.9869
0.052 6.19 4200 0.0643 0.9285 0.9234 0.9260 0.9866
0.052 6.33 4300 0.0611 0.9173 0.9243 0.9208 0.9862
0.052 6.48 4400 0.0605 0.9286 0.9267 0.9276 0.9871
0.0496 6.63 4500 0.0604 0.9257 0.9296 0.9276 0.9872
0.0496 6.77 4600 0.0616 0.9261 0.9322 0.9291 0.9874
0.0496 6.92 4700 0.0609 0.9290 0.9276 0.9283 0.9873
0.0496 7.07 4800 0.0599 0.9259 0.9318 0.9288 0.9877
0.0496 7.22 4900 0.0587 0.9307 0.9315 0.9311 0.9881
0.0383 7.36 5000 0.0594 0.9292 0.9305 0.9298 0.9877
0.0383 7.51 5100 0.0594 0.9357 0.9291 0.9324 0.9880
0.0383 7.66 5200 0.0597 0.9350 0.9305 0.9327 0.9878
0.0383 7.81 5300 0.0572 0.9288 0.9333 0.9311 0.9884
0.0383 7.95 5400 0.0569 0.9331 0.9340 0.9336 0.9885
0.0397 8.1 5500 0.0567 0.9346 0.9346 0.9346 0.9886
0.0397 8.25 5600 0.0567 0.9359 0.9355 0.9357 0.9889
0.0397 8.39 5700 0.0560 0.9307 0.9344 0.9326 0.9885
0.0397 8.54 5800 0.0570 0.9342 0.9342 0.9342 0.9888
0.0397 8.69 5900 0.0565 0.9325 0.9340 0.9332 0.9887
0.0329 8.84 6000 0.0566 0.9324 0.9348 0.9336 0.9886

Framework versions