generated_from_trainer

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perioli_manifesti_v9.2

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 0.35 100 0.1440 0.6850 0.6873 0.6862 0.9534
No log 0.71 200 0.0698 0.8219 0.8956 0.8571 0.9807
No log 1.06 300 0.0422 0.8893 0.9280 0.9082 0.9878
No log 1.41 400 0.0295 0.9195 0.9407 0.9300 0.9909
0.1669 1.77 500 0.0309 0.9044 0.9459 0.9247 0.9906
0.1669 2.12 600 0.0301 0.9113 0.9521 0.9312 0.9913
0.1669 2.47 700 0.0234 0.9482 0.9597 0.9539 0.9940
0.1669 2.83 800 0.0269 0.9289 0.9642 0.9462 0.9933
0.1669 3.18 900 0.0259 0.9291 0.9628 0.9457 0.9933
0.0254 3.53 1000 0.0218 0.9470 0.9673 0.9570 0.9948
0.0254 3.89 1100 0.0218 0.9557 0.9669 0.9613 0.9951
0.0254 4.24 1200 0.0198 0.9511 0.9731 0.9620 0.9955
0.0254 4.59 1300 0.0228 0.9458 0.9690 0.9573 0.9948
0.0254 4.95 1400 0.0222 0.9562 0.9697 0.9629 0.9953
0.0153 5.3 1500 0.0221 0.9438 0.9717 0.9575 0.9951
0.0153 5.65 1600 0.0235 0.9397 0.9662 0.9528 0.9945
0.0153 6.01 1700 0.0196 0.9582 0.9728 0.9654 0.9959
0.0153 6.36 1800 0.0209 0.9540 0.9728 0.9633 0.9956
0.0153 6.71 1900 0.0234 0.9402 0.9697 0.9547 0.9948
0.0107 7.07 2000 0.0202 0.9615 0.9728 0.9671 0.9960
0.0107 7.42 2100 0.0222 0.9554 0.9741 0.9647 0.9958
0.0107 7.77 2200 0.0223 0.9500 0.9686 0.9592 0.9952
0.0107 8.13 2300 0.0208 0.9497 0.9690 0.9592 0.9951
0.0107 8.48 2400 0.0184 0.9629 0.9741 0.9685 0.9962
0.0077 8.83 2500 0.0231 0.9463 0.9714 0.9587 0.9951
0.0077 9.19 2600 0.0249 0.9412 0.9717 0.9562 0.9950
0.0077 9.54 2700 0.0203 0.9625 0.9738 0.9681 0.9962
0.0077 9.89 2800 0.0240 0.9459 0.9704 0.9580 0.9951
0.0077 10.25 2900 0.0213 0.9573 0.9741 0.9657 0.9960
0.0054 10.6 3000 0.0208 0.9596 0.9741 0.9668 0.9961
0.0054 10.95 3100 0.0213 0.9596 0.9745 0.9670 0.9961
0.0054 11.31 3200 0.0213 0.9599 0.9728 0.9663 0.9961
0.0054 11.66 3300 0.0235 0.9511 0.9714 0.9611 0.9954
0.0054 12.01 3400 0.0227 0.9540 0.9724 0.9631 0.9957
0.0042 12.37 3500 0.0229 0.9533 0.9717 0.9624 0.9956

Framework versions