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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- sroie |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: layoutlmv3-finetuned-sroie |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: sroie |
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type: sroie |
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args: sroie |
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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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should probably proofread and complete it, then remove this comment. --> |
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# layoutlmv3-finetuned-sroie |
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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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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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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 |
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- Transformers 4.20.0.dev0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.2.2 |
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- Tokenizers 0.12.1 |
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