generated_from_trainer

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

perioli_manifesti_v8.6

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 0.4 100 0.1273 0.7791 0.7842 0.7817 0.9675
No log 0.81 200 0.0645 0.8571 0.9121 0.8838 0.9842
No log 1.21 300 0.0435 0.8851 0.9290 0.9065 0.9876
No log 1.61 400 0.0301 0.9138 0.9500 0.9316 0.9912
0.1489 2.02 500 0.0266 0.9264 0.9542 0.9401 0.9923
0.1489 2.42 600 0.0326 0.8981 0.9511 0.9238 0.9905
0.1489 2.82 700 0.0239 0.9237 0.9555 0.9393 0.9925
0.1489 3.23 800 0.0225 0.9417 0.9628 0.9521 0.9939
0.1489 3.63 900 0.0230 0.9464 0.9673 0.9567 0.9946
0.0259 4.03 1000 0.0178 0.9554 0.9683 0.9618 0.9954
0.0259 4.44 1100 0.0232 0.9498 0.9662 0.9580 0.9948
0.0259 4.84 1200 0.0221 0.9510 0.9710 0.9609 0.9951
0.0259 5.24 1300 0.0207 0.9477 0.9690 0.9582 0.9950
0.0259 5.65 1400 0.0186 0.9555 0.9693 0.9624 0.9954
0.0143 6.05 1500 0.0176 0.9580 0.9676 0.9628 0.9956
0.0143 6.45 1600 0.0204 0.9570 0.9673 0.9621 0.9954
0.0143 6.85 1700 0.0204 0.9451 0.9728 0.9587 0.9951
0.0143 7.26 1800 0.0184 0.9566 0.9724 0.9644 0.9957
0.0143 7.66 1900 0.0193 0.9541 0.9752 0.9645 0.9957
0.0093 8.06 2000 0.0210 0.9485 0.9721 0.9602 0.9953
0.0093 8.47 2100 0.0197 0.9553 0.9728 0.9640 0.9957
0.0093 8.87 2200 0.0196 0.9560 0.9731 0.9645 0.9958
0.0093 9.27 2300 0.0198 0.9566 0.9735 0.9650 0.9958
0.0093 9.68 2400 0.0226 0.9465 0.9697 0.9579 0.9950
0.006 10.08 2500 0.0207 0.9541 0.9735 0.9637 0.9956
0.006 10.48 2600 0.0218 0.9531 0.9735 0.9632 0.9955
0.006 10.89 2700 0.0210 0.9602 0.9724 0.9663 0.9960
0.006 11.29 2800 0.0209 0.9590 0.9745 0.9667 0.9961
0.006 11.69 2900 0.0195 0.9572 0.9721 0.9646 0.9958
0.0045 12.1 3000 0.0214 0.9547 0.9731 0.9638 0.9956
0.0045 12.5 3100 0.0210 0.9511 0.9717 0.9613 0.9953
0.0045 12.9 3200 0.0237 0.9538 0.9741 0.9638 0.9956
0.0045 13.31 3300 0.0221 0.9583 0.9755 0.9669 0.9961
0.0045 13.71 3400 0.0207 0.9586 0.9731 0.9658 0.9959
0.0033 14.11 3500 0.0217 0.9562 0.9772 0.9666 0.9961
0.0033 14.52 3600 0.0207 0.9583 0.9748 0.9665 0.9961
0.0033 14.92 3700 0.0218 0.9590 0.9766 0.9677 0.9961
0.0033 15.32 3800 0.0213 0.9570 0.9735 0.9651 0.9959
0.0033 15.73 3900 0.0217 0.9577 0.9755 0.9665 0.9960
0.0027 16.13 4000 0.0216 0.9581 0.9762 0.9670 0.9961

Framework versions