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layoutlmv3-finetuned-sroie

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

  • Loss: 0.0426
  • Precision: 0.9371
  • Recall: 0.9438
  • F1: 0.9404
  • Accuracy: 0.9945

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: 2
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • training_steps: 5000

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 0.32 100 0.1127 0.6466 0.6102 0.6279 0.9729
No log 0.64 200 0.0663 0.8215 0.7428 0.7802 0.9821
No log 0.96 300 0.0563 0.8051 0.8718 0.8371 0.9855
No log 1.28 400 0.0470 0.8766 0.8595 0.8680 0.9895
0.1328 1.6 500 0.0419 0.8613 0.9128 0.8863 0.9906
0.1328 1.92 600 0.0338 0.8888 0.9099 0.8993 0.9926
0.1328 2.24 700 0.0320 0.8690 0.9467 0.9062 0.9929
0.1328 2.56 800 0.0348 0.8960 0.9438 0.9193 0.9931
0.1328 2.88 900 0.0300 0.9169 0.9460 0.9312 0.9942
0.029 3.19 1000 0.0281 0.9080 0.9452 0.9262 0.9942
0.029 3.51 1100 0.0259 0.9174 0.9438 0.9304 0.9945
0.029 3.83 1200 0.0309 0.9207 0.9532 0.9366 0.9944
0.029 4.15 1300 0.0366 0.9195 0.9388 0.9291 0.9940
0.029 4.47 1400 0.0302 0.9343 0.9424 0.9383 0.9949
0.0174 4.79 1500 0.0349 0.9142 0.9517 0.9326 0.9939
0.0174 5.11 1600 0.0327 0.9322 0.9510 0.9415 0.9950
0.0174 5.43 1700 0.0317 0.9215 0.9561 0.9385 0.9938
0.0174 5.75 1800 0.0385 0.9282 0.9316 0.9299 0.9940
0.0174 6.07 1900 0.0342 0.9235 0.9481 0.9357 0.9944
0.0117 6.39 2000 0.0344 0.9287 0.9474 0.9379 0.9944
0.0117 6.71 2100 0.0388 0.9232 0.9445 0.9338 0.9941
0.0117 7.03 2200 0.0325 0.9269 0.9496 0.9381 0.9949
0.0117 7.35 2300 0.0343 0.9225 0.9438 0.9330 0.9941
0.0117 7.67 2400 0.0372 0.9216 0.9481 0.9347 0.9944
0.0081 7.99 2500 0.0385 0.9192 0.9589 0.9386 0.9944
0.0081 8.31 2600 0.0376 0.9293 0.9467 0.9379 0.9944
0.0081 8.63 2700 0.0425 0.9261 0.9474 0.9366 0.9941
0.0081 8.95 2800 0.0407 0.9266 0.9452 0.9358 0.9941
0.0081 9.27 2900 0.0403 0.9280 0.9467 0.9372 0.9941
0.0055 9.58 3000 0.0364 0.9287 0.9474 0.9379 0.9948
0.0055 9.9 3100 0.0427 0.9122 0.9510 0.9312 0.9941
0.0055 10.22 3200 0.0394 0.9223 0.9488 0.9354 0.9943
0.0055 10.54 3300 0.0393 0.9247 0.9561 0.9401 0.9945
0.0055 10.86 3400 0.0413 0.9334 0.9496 0.9414 0.9945
0.0049 11.18 3500 0.0400 0.9290 0.9517 0.9402 0.9945
0.0049 11.5 3600 0.0412 0.9317 0.9539 0.9427 0.9945
0.0049 11.82 3700 0.0419 0.9314 0.9481 0.9397 0.9947
0.0049 12.14 3800 0.0452 0.9243 0.9503 0.9371 0.9941
0.0049 12.46 3900 0.0412 0.9334 0.9496 0.9414 0.9947
0.0039 12.78 4000 0.0438 0.9294 0.9481 0.9387 0.9941
0.0039 13.1 4100 0.0416 0.9326 0.9467 0.9396 0.9944
0.0039 13.42 4200 0.0418 0.9327 0.9488 0.9407 0.9948
0.0039 13.74 4300 0.0423 0.9345 0.9460 0.9402 0.9946
0.0039 14.06 4400 0.0419 0.9286 0.9467 0.9376 0.9947
0.0022 14.38 4500 0.0426 0.9371 0.9438 0.9404 0.9945
0.0022 14.7 4600 0.0424 0.9371 0.9445 0.9408 0.9947
0.0022 15.02 4700 0.0427 0.9372 0.9467 0.9419 0.9947
0.0022 15.34 4800 0.0431 0.9339 0.9460 0.9399 0.9945
0.0022 15.65 4900 0.0431 0.9346 0.9467 0.9406 0.9946
0.0015 15.97 5000 0.0434 0.9324 0.9445 0.9384 0.9945

Framework versions

  • Transformers 4.20.0.dev0
  • Pytorch 1.11.0+cu113
  • Datasets 2.2.2
  • Tokenizers 0.12.1
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Space using Theivaprakasham/layoutlmv3-finetuned-sroie

Evaluation results