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layoutlmv3-finetuned-sroie

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update model card README.md

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@@ -21,16 +21,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.9362154500354358
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  - name: Recall
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  type: recall
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- value: 0.9517291066282421
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  - name: F1
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  type: f1
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- value: 0.9439085387638442
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  - name: Accuracy
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  type: accuracy
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- value: 0.9951776838044365
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -40,11 +40,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the sroie dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0288
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- - Precision: 0.9362
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- - Recall: 0.9517
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- - F1: 0.9439
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- - Accuracy: 0.9952
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  ## Model description
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@@ -69,32 +69,62 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - training_steps: 2000
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 0.32 | 100 | 0.1063 | 0.6851 | 0.6599 | 0.6723 | 0.9739 |
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- | No log | 0.64 | 200 | 0.0583 | 0.7849 | 0.7860 | 0.7855 | 0.9843 |
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- | No log | 0.96 | 300 | 0.0475 | 0.8463 | 0.8610 | 0.8536 | 0.9884 |
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- | No log | 1.28 | 400 | 0.0437 | 0.8566 | 0.8739 | 0.8652 | 0.9894 |
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- | 0.1215 | 1.6 | 500 | 0.0424 | 0.8616 | 0.9063 | 0.8834 | 0.9895 |
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- | 0.1215 | 1.92 | 600 | 0.0332 | 0.8702 | 0.9323 | 0.9002 | 0.9924 |
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- | 0.1215 | 2.24 | 700 | 0.0318 | 0.8979 | 0.9373 | 0.9172 | 0.9932 |
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- | 0.1215 | 2.56 | 800 | 0.0316 | 0.9092 | 0.9445 | 0.9265 | 0.9936 |
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- | 0.1215 | 2.88 | 900 | 0.0295 | 0.8982 | 0.9467 | 0.9218 | 0.9937 |
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- | 0.0286 | 3.19 | 1000 | 0.0329 | 0.8685 | 0.9517 | 0.9082 | 0.9930 |
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- | 0.0286 | 3.51 | 1100 | 0.0289 | 0.9298 | 0.9352 | 0.9325 | 0.9945 |
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- | 0.0286 | 3.83 | 1200 | 0.0287 | 0.9202 | 0.9474 | 0.9336 | 0.9946 |
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- | 0.0286 | 4.15 | 1300 | 0.0301 | 0.9174 | 0.9524 | 0.9346 | 0.9947 |
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- | 0.0286 | 4.47 | 1400 | 0.0268 | 0.9212 | 0.9431 | 0.9320 | 0.9946 |
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- | 0.017 | 4.79 | 1500 | 0.0307 | 0.9236 | 0.9488 | 0.9360 | 0.9944 |
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- | 0.017 | 5.11 | 1600 | 0.0286 | 0.9335 | 0.9503 | 0.9418 | 0.9951 |
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- | 0.017 | 5.43 | 1700 | 0.0287 | 0.9284 | 0.9618 | 0.9448 | 0.9951 |
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- | 0.017 | 5.75 | 1800 | 0.0278 | 0.9334 | 0.9496 | 0.9414 | 0.9952 |
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- | 0.017 | 6.07 | 1900 | 0.0289 | 0.9337 | 0.9539 | 0.9437 | 0.9952 |
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- | 0.0111 | 6.39 | 2000 | 0.0288 | 0.9362 | 0.9517 | 0.9439 | 0.9952 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.9370529327610873
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  - name: Recall
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  type: recall
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+ value: 0.9438040345821326
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  - name: F1
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  type: f1
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+ value: 0.9404163675520459
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9945347083116948
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the sroie dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0426
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+ - Precision: 0.9371
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+ - Recall: 0.9438
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+ - F1: 0.9404
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+ - Accuracy: 0.9945
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  ## Model description
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - training_steps: 5000
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 0.32 | 100 | 0.1127 | 0.6466 | 0.6102 | 0.6279 | 0.9729 |
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+ | No log | 0.64 | 200 | 0.0663 | 0.8215 | 0.7428 | 0.7802 | 0.9821 |
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+ | No log | 0.96 | 300 | 0.0563 | 0.8051 | 0.8718 | 0.8371 | 0.9855 |
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+ | No log | 1.28 | 400 | 0.0470 | 0.8766 | 0.8595 | 0.8680 | 0.9895 |
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+ | 0.1328 | 1.6 | 500 | 0.0419 | 0.8613 | 0.9128 | 0.8863 | 0.9906 |
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+ | 0.1328 | 1.92 | 600 | 0.0338 | 0.8888 | 0.9099 | 0.8993 | 0.9926 |
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+ | 0.1328 | 2.24 | 700 | 0.0320 | 0.8690 | 0.9467 | 0.9062 | 0.9929 |
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+ | 0.1328 | 2.56 | 800 | 0.0348 | 0.8960 | 0.9438 | 0.9193 | 0.9931 |
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+ | 0.1328 | 2.88 | 900 | 0.0300 | 0.9169 | 0.9460 | 0.9312 | 0.9942 |
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+ | 0.029 | 3.19 | 1000 | 0.0281 | 0.9080 | 0.9452 | 0.9262 | 0.9942 |
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+ | 0.029 | 3.51 | 1100 | 0.0259 | 0.9174 | 0.9438 | 0.9304 | 0.9945 |
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+ | 0.029 | 3.83 | 1200 | 0.0309 | 0.9207 | 0.9532 | 0.9366 | 0.9944 |
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+ | 0.029 | 4.15 | 1300 | 0.0366 | 0.9195 | 0.9388 | 0.9291 | 0.9940 |
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+ | 0.029 | 4.47 | 1400 | 0.0302 | 0.9343 | 0.9424 | 0.9383 | 0.9949 |
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+ | 0.0174 | 4.79 | 1500 | 0.0349 | 0.9142 | 0.9517 | 0.9326 | 0.9939 |
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+ | 0.0174 | 5.11 | 1600 | 0.0327 | 0.9322 | 0.9510 | 0.9415 | 0.9950 |
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+ | 0.0174 | 5.43 | 1700 | 0.0317 | 0.9215 | 0.9561 | 0.9385 | 0.9938 |
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+ | 0.0174 | 5.75 | 1800 | 0.0385 | 0.9282 | 0.9316 | 0.9299 | 0.9940 |
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+ | 0.0174 | 6.07 | 1900 | 0.0342 | 0.9235 | 0.9481 | 0.9357 | 0.9944 |
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+ | 0.0117 | 6.39 | 2000 | 0.0344 | 0.9287 | 0.9474 | 0.9379 | 0.9944 |
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+ | 0.0117 | 6.71 | 2100 | 0.0388 | 0.9232 | 0.9445 | 0.9338 | 0.9941 |
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+ | 0.0117 | 7.03 | 2200 | 0.0325 | 0.9269 | 0.9496 | 0.9381 | 0.9949 |
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+ | 0.0117 | 7.35 | 2300 | 0.0343 | 0.9225 | 0.9438 | 0.9330 | 0.9941 |
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+ | 0.0117 | 7.67 | 2400 | 0.0372 | 0.9216 | 0.9481 | 0.9347 | 0.9944 |
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+ | 0.0081 | 7.99 | 2500 | 0.0385 | 0.9192 | 0.9589 | 0.9386 | 0.9944 |
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+ | 0.0081 | 8.31 | 2600 | 0.0376 | 0.9293 | 0.9467 | 0.9379 | 0.9944 |
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+ | 0.0081 | 8.63 | 2700 | 0.0425 | 0.9261 | 0.9474 | 0.9366 | 0.9941 |
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+ | 0.0081 | 8.95 | 2800 | 0.0407 | 0.9266 | 0.9452 | 0.9358 | 0.9941 |
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+ | 0.0081 | 9.27 | 2900 | 0.0403 | 0.9280 | 0.9467 | 0.9372 | 0.9941 |
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+ | 0.0055 | 9.58 | 3000 | 0.0364 | 0.9287 | 0.9474 | 0.9379 | 0.9948 |
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+ | 0.0055 | 9.9 | 3100 | 0.0427 | 0.9122 | 0.9510 | 0.9312 | 0.9941 |
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+ | 0.0055 | 10.22 | 3200 | 0.0394 | 0.9223 | 0.9488 | 0.9354 | 0.9943 |
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+ | 0.0055 | 10.54 | 3300 | 0.0393 | 0.9247 | 0.9561 | 0.9401 | 0.9945 |
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+ | 0.0055 | 10.86 | 3400 | 0.0413 | 0.9334 | 0.9496 | 0.9414 | 0.9945 |
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+ | 0.0049 | 11.18 | 3500 | 0.0400 | 0.9290 | 0.9517 | 0.9402 | 0.9945 |
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+ | 0.0049 | 11.5 | 3600 | 0.0412 | 0.9317 | 0.9539 | 0.9427 | 0.9945 |
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+ | 0.0049 | 11.82 | 3700 | 0.0419 | 0.9314 | 0.9481 | 0.9397 | 0.9947 |
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+ | 0.0049 | 12.14 | 3800 | 0.0452 | 0.9243 | 0.9503 | 0.9371 | 0.9941 |
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+ | 0.0049 | 12.46 | 3900 | 0.0412 | 0.9334 | 0.9496 | 0.9414 | 0.9947 |
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+ | 0.0039 | 12.78 | 4000 | 0.0438 | 0.9294 | 0.9481 | 0.9387 | 0.9941 |
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+ | 0.0039 | 13.1 | 4100 | 0.0416 | 0.9326 | 0.9467 | 0.9396 | 0.9944 |
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+ | 0.0039 | 13.42 | 4200 | 0.0418 | 0.9327 | 0.9488 | 0.9407 | 0.9948 |
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+ | 0.0039 | 13.74 | 4300 | 0.0423 | 0.9345 | 0.9460 | 0.9402 | 0.9946 |
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+ | 0.0039 | 14.06 | 4400 | 0.0419 | 0.9286 | 0.9467 | 0.9376 | 0.9947 |
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+ | 0.0022 | 14.38 | 4500 | 0.0426 | 0.9371 | 0.9438 | 0.9404 | 0.9945 |
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+ | 0.0022 | 14.7 | 4600 | 0.0424 | 0.9371 | 0.9445 | 0.9408 | 0.9947 |
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+ | 0.0022 | 15.02 | 4700 | 0.0427 | 0.9372 | 0.9467 | 0.9419 | 0.9947 |
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+ | 0.0022 | 15.34 | 4800 | 0.0431 | 0.9339 | 0.9460 | 0.9399 | 0.9945 |
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+ | 0.0022 | 15.65 | 4900 | 0.0431 | 0.9346 | 0.9467 | 0.9406 | 0.9946 |
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+ | 0.0015 | 15.97 | 5000 | 0.0434 | 0.9324 | 0.9445 | 0.9384 | 0.9945 |
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  ### Framework versions