| --- |
| license: cc-by-nc-sa-4.0 |
| base_model: microsoft/layoutlmv3-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: test |
| results: [] |
| --- |
| |
| <!-- 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. --> |
|
|
| # test |
|
|
| This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0314 |
| - Precision: 0.0 |
| - Recall: 0.0 |
| - F1: 0.0 |
| - Accuracy: 0.9933 |
|
|
| ## 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: 1e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - training_steps: 40000 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:---:|:--------:| |
| | 0.0381 | 0.13 | 500 | 0.0777 | 0.0 | 0.0 | 0.0 | 0.9745 | |
| | 0.0669 | 0.26 | 1000 | 0.0545 | 0.0 | 0.0 | 0.0 | 0.9790 | |
| | 0.0595 | 0.39 | 1500 | 0.0545 | 0.0 | 0.0 | 0.0 | 0.9803 | |
| | 0.054 | 0.52 | 2000 | 0.0555 | 0.0 | 0.0 | 0.0 | 0.9796 | |
| | 0.0502 | 0.65 | 2500 | 0.0451 | 0.0 | 0.0 | 0.0 | 0.9828 | |
| | 0.0474 | 0.78 | 3000 | 0.0486 | 0.0 | 0.0 | 0.0 | 0.9818 | |
| | 0.0458 | 0.92 | 3500 | 0.0417 | 0.0 | 0.0 | 0.0 | 0.9836 | |
| | 0.0415 | 1.05 | 4000 | 0.0440 | 0.0 | 0.0 | 0.0 | 0.9827 | |
| | 0.0372 | 1.18 | 4500 | 0.0432 | 0.0 | 0.0 | 0.0 | 0.9839 | |
| | 0.0391 | 1.31 | 5000 | 0.0442 | 0.0 | 0.0 | 0.0 | 0.9839 | |
| | 0.0368 | 1.44 | 5500 | 0.0377 | 0.0 | 0.0 | 0.0 | 0.9856 | |
| | 0.0388 | 1.57 | 6000 | 0.0417 | 0.0 | 0.0 | 0.0 | 0.9846 | |
| | 0.0351 | 1.7 | 6500 | 0.0363 | 0.0 | 0.0 | 0.0 | 0.9857 | |
| | 0.0357 | 1.83 | 7000 | 0.0383 | 0.0 | 0.0 | 0.0 | 0.9858 | |
| | 0.0336 | 1.96 | 7500 | 0.0371 | 0.0 | 0.0 | 0.0 | 0.9860 | |
| | 0.0309 | 2.09 | 8000 | 0.0373 | 0.0 | 0.0 | 0.0 | 0.9859 | |
| | 0.0288 | 2.22 | 8500 | 0.0355 | 0.0 | 0.0 | 0.0 | 0.9870 | |
| | 0.0288 | 2.35 | 9000 | 0.0359 | 0.0 | 0.0 | 0.0 | 0.9867 | |
| | 0.0285 | 2.49 | 9500 | 0.0369 | 0.0 | 0.0 | 0.0 | 0.9872 | |
| | 0.0307 | 2.62 | 10000 | 0.0322 | 0.0 | 0.0 | 0.0 | 0.9880 | |
| | 0.028 | 2.75 | 10500 | 0.0307 | 0.0 | 0.0 | 0.0 | 0.9886 | |
| | 0.0246 | 2.88 | 11000 | 0.0326 | 0.0 | 0.0 | 0.0 | 0.9881 | |
| | 0.0267 | 3.01 | 11500 | 0.0346 | 0.0 | 0.0 | 0.0 | 0.9882 | |
| | 0.022 | 3.14 | 12000 | 0.0316 | 0.0 | 0.0 | 0.0 | 0.9889 | |
| | 0.0218 | 3.27 | 12500 | 0.0357 | 0.0 | 0.0 | 0.0 | 0.9883 | |
| | 0.0217 | 3.4 | 13000 | 0.0363 | 0.0 | 0.0 | 0.0 | 0.9883 | |
| | 0.0208 | 3.53 | 13500 | 0.0340 | 0.0 | 0.0 | 0.0 | 0.9894 | |
| | 0.0223 | 3.66 | 14000 | 0.0304 | 0.0 | 0.0 | 0.0 | 0.9892 | |
| | 0.0232 | 3.79 | 14500 | 0.0319 | 0.0 | 0.0 | 0.0 | 0.9894 | |
| | 0.0229 | 3.92 | 15000 | 0.0307 | 0.0 | 0.0 | 0.0 | 0.9901 | |
| | 0.0192 | 4.06 | 15500 | 0.0310 | 0.0 | 0.0 | 0.0 | 0.9905 | |
| | 0.0178 | 4.19 | 16000 | 0.0345 | 0.0 | 0.0 | 0.0 | 0.9897 | |
| | 0.0178 | 4.32 | 16500 | 0.0309 | 0.0 | 0.0 | 0.0 | 0.9902 | |
| | 0.0173 | 4.45 | 17000 | 0.0328 | 0.0 | 0.0 | 0.0 | 0.9904 | |
| | 0.0176 | 4.58 | 17500 | 0.0316 | 0.0 | 0.0 | 0.0 | 0.9908 | |
| | 0.017 | 4.71 | 18000 | 0.0307 | 0.0 | 0.0 | 0.0 | 0.9912 | |
| | 0.0163 | 4.84 | 18500 | 0.0329 | 0.0 | 0.0 | 0.0 | 0.9909 | |
| | 0.018 | 4.97 | 19000 | 0.0295 | 0.0 | 0.0 | 0.0 | 0.9910 | |
| | 0.0143 | 5.1 | 19500 | 0.0367 | 0.0 | 0.0 | 0.0 | 0.9903 | |
| | 0.0144 | 5.23 | 20000 | 0.0317 | 0.0 | 0.0 | 0.0 | 0.9915 | |
| | 0.0158 | 5.36 | 20500 | 0.0290 | 0.0 | 0.0 | 0.0 | 0.9917 | |
| | 0.0143 | 5.49 | 21000 | 0.0315 | 0.0 | 0.0 | 0.0 | 0.9917 | |
| | 0.0137 | 5.63 | 21500 | 0.0310 | 0.0 | 0.0 | 0.0 | 0.9913 | |
| | 0.0135 | 5.76 | 22000 | 0.0310 | 0.0 | 0.0 | 0.0 | 0.9913 | |
| | 0.0128 | 5.89 | 22500 | 0.0290 | 0.0 | 0.0 | 0.0 | 0.9917 | |
| | 0.0132 | 6.02 | 23000 | 0.0314 | 0.0 | 0.0 | 0.0 | 0.9921 | |
| | 0.0124 | 6.15 | 23500 | 0.0274 | 0.0 | 0.0 | 0.0 | 0.9921 | |
| | 0.0114 | 6.28 | 24000 | 0.0300 | 0.0 | 0.0 | 0.0 | 0.9921 | |
| | 0.0111 | 6.41 | 24500 | 0.0291 | 0.0 | 0.0 | 0.0 | 0.9922 | |
| | 0.0109 | 6.54 | 25000 | 0.0307 | 0.0 | 0.0 | 0.0 | 0.9923 | |
| | 0.0117 | 6.67 | 25500 | 0.0328 | 0.0 | 0.0 | 0.0 | 0.9921 | |
| | 0.0112 | 6.8 | 26000 | 0.0293 | 0.0 | 0.0 | 0.0 | 0.9924 | |
| | 0.012 | 6.93 | 26500 | 0.0300 | 0.0 | 0.0 | 0.0 | 0.9924 | |
| | 0.0102 | 7.06 | 27000 | 0.0330 | 0.0 | 0.0 | 0.0 | 0.9921 | |
| | 0.0094 | 7.2 | 27500 | 0.0323 | 0.0 | 0.0 | 0.0 | 0.9922 | |
| | 0.0091 | 7.33 | 28000 | 0.0309 | 0.0 | 0.0 | 0.0 | 0.9924 | |
| | 0.0087 | 7.46 | 28500 | 0.0331 | 0.0 | 0.0 | 0.0 | 0.9920 | |
| | 0.0091 | 7.59 | 29000 | 0.0332 | 0.0 | 0.0 | 0.0 | 0.9923 | |
| | 0.0095 | 7.72 | 29500 | 0.0298 | 0.0 | 0.0 | 0.0 | 0.9925 | |
| | 0.0083 | 7.85 | 30000 | 0.0303 | 0.0 | 0.0 | 0.0 | 0.9929 | |
| | 0.0097 | 7.98 | 30500 | 0.0298 | 0.0 | 0.0 | 0.0 | 0.9928 | |
| | 0.0069 | 8.11 | 31000 | 0.0319 | 0.0 | 0.0 | 0.0 | 0.9926 | |
| | 0.0086 | 8.24 | 31500 | 0.0314 | 0.0 | 0.0 | 0.0 | 0.9929 | |
| | 0.0079 | 8.37 | 32000 | 0.0306 | 0.0 | 0.0 | 0.0 | 0.9929 | |
| | 0.0065 | 8.5 | 32500 | 0.0317 | 0.0 | 0.0 | 0.0 | 0.9926 | |
| | 0.0072 | 8.63 | 33000 | 0.0307 | 0.0 | 0.0 | 0.0 | 0.9927 | |
| | 0.0082 | 8.77 | 33500 | 0.0306 | 0.0 | 0.0 | 0.0 | 0.9929 | |
| | 0.0086 | 8.9 | 34000 | 0.0312 | 0.0 | 0.0 | 0.0 | 0.9931 | |
| | 0.0079 | 9.03 | 34500 | 0.0329 | 0.0 | 0.0 | 0.0 | 0.9929 | |
| | 0.0061 | 9.16 | 35000 | 0.0326 | 0.0 | 0.0 | 0.0 | 0.9928 | |
| | 0.0074 | 9.29 | 35500 | 0.0315 | 0.0 | 0.0 | 0.0 | 0.9928 | |
| | 0.0068 | 9.42 | 36000 | 0.0310 | 0.0 | 0.0 | 0.0 | 0.9931 | |
| | 0.0059 | 9.55 | 36500 | 0.0318 | 0.0 | 0.0 | 0.0 | 0.9930 | |
| | 0.0064 | 9.68 | 37000 | 0.0307 | 0.0 | 0.0 | 0.0 | 0.9933 | |
| | 0.0063 | 9.81 | 37500 | 0.0308 | 0.0 | 0.0 | 0.0 | 0.9930 | |
| | 0.0062 | 9.94 | 38000 | 0.0311 | 0.0 | 0.0 | 0.0 | 0.9931 | |
| | 0.0058 | 10.07 | 38500 | 0.0314 | 0.0 | 0.0 | 0.0 | 0.9932 | |
| | 0.0051 | 10.2 | 39000 | 0.0316 | 0.0 | 0.0 | 0.0 | 0.9933 | |
| | 0.0065 | 10.33 | 39500 | 0.0315 | 0.0 | 0.0 | 0.0 | 0.9933 | |
| | 0.0059 | 10.47 | 40000 | 0.0314 | 0.0 | 0.0 | 0.0 | 0.9933 | |
|
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|
| ### Framework versions |
|
|
| - Transformers 4.35.2 |
| - Pytorch 2.1.0+cu121 |
| - Datasets 2.15.0 |
| - Tokenizers 0.15.0 |
|
|
