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layoutlmv2-finetuned-sroie

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update model card README.md

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  license: cc-by-nc-sa-4.0
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  tags:
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  - generated_from_trainer
 
 
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  model-index:
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  - name: layoutlmv2-finetuned-sroie
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  results: []
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  # layoutlmv2-finetuned-sroie
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- This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Model description
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@@ -38,16 +62,38 @@ The following hyperparameters were used during training:
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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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  - lr_scheduler_warmup_ratio: 0.1
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- - training_steps: 1000
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  - mixed_precision_training: Native AMP
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  ### Training results
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  ### Framework versions
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  - Transformers 4.16.2
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  - Pytorch 1.8.0+cu101
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- - Datasets 1.18.3
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- - Tokenizers 0.11.0
 
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  license: cc-by-nc-sa-4.0
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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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  model-index:
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  - name: layoutlmv2-finetuned-sroie
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  results: []
 
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  # layoutlmv2-finetuned-sroie
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+ This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on the sroie dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0291
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+ - Address Precision: 0.9341
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+ - Address Recall: 0.9395
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+ - Address F1: 0.9368
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+ - Address Number: 347
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+ - Company Precision: 0.9570
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+ - Company Recall: 0.9625
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+ - Company F1: 0.9598
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+ - Company Number: 347
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+ - Date Precision: 0.9885
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+ - Date Recall: 0.9885
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+ - Date F1: 0.9885
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+ - Date Number: 347
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+ - Total Precision: 0.9253
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+ - Total Recall: 0.9280
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+ - Total F1: 0.9266
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+ - Total Number: 347
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+ - Overall Precision: 0.9512
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+ - Overall Recall: 0.9546
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+ - Overall F1: 0.9529
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+ - Overall Accuracy: 0.9961
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  ## Model description
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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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  - lr_scheduler_warmup_ratio: 0.1
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+ - training_steps: 3000
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  - mixed_precision_training: Native AMP
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Address Precision | Address Recall | Address F1 | Address Number | Company Precision | Company Recall | Company F1 | Company Number | Date Precision | Date Recall | Date F1 | Date Number | Total Precision | Total Recall | Total F1 | Total Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:--------------:|:-----------------:|:--------------:|:----------:|:--------------:|:--------------:|:-----------:|:-------:|:-----------:|:---------------:|:------------:|:--------:|:------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | No log | 0.05 | 157 | 0.8162 | 0.3670 | 0.7233 | 0.4869 | 347 | 0.0617 | 0.0144 | 0.0234 | 347 | 0.0 | 0.0 | 0.0 | 347 | 0.0 | 0.0 | 0.0 | 347 | 0.3346 | 0.1844 | 0.2378 | 0.9342 |
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+ | No log | 1.05 | 314 | 0.3490 | 0.8564 | 0.8934 | 0.8745 | 347 | 0.8610 | 0.9280 | 0.8932 | 347 | 0.7297 | 0.8559 | 0.7878 | 347 | 0.0 | 0.0 | 0.0 | 347 | 0.8128 | 0.6693 | 0.7341 | 0.9826 |
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+ | No log | 2.05 | 471 | 0.1845 | 0.7970 | 0.9049 | 0.8475 | 347 | 0.9211 | 0.9424 | 0.9316 | 347 | 0.9885 | 0.9885 | 0.9885 | 347 | 0.0 | 0.0 | 0.0 | 347 | 0.8978 | 0.7089 | 0.7923 | 0.9835 |
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+ | 0.7027 | 3.05 | 628 | 0.1194 | 0.9040 | 0.9222 | 0.9130 | 347 | 0.8880 | 0.9135 | 0.9006 | 347 | 0.9885 | 0.9885 | 0.9885 | 347 | 0.0 | 0.0 | 0.0 | 347 | 0.9263 | 0.7061 | 0.8013 | 0.9853 |
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+ | 0.7027 | 4.05 | 785 | 0.0762 | 0.9397 | 0.9424 | 0.9410 | 347 | 0.8889 | 0.9222 | 0.9052 | 347 | 0.9885 | 0.9885 | 0.9885 | 347 | 0.7740 | 0.9078 | 0.8355 | 347 | 0.8926 | 0.9402 | 0.9158 | 0.9928 |
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+ | 0.7027 | 5.05 | 942 | 0.0564 | 0.9282 | 0.9308 | 0.9295 | 347 | 0.9296 | 0.9510 | 0.9402 | 347 | 0.9885 | 0.9885 | 0.9885 | 347 | 0.7801 | 0.8588 | 0.8176 | 347 | 0.9036 | 0.9323 | 0.9177 | 0.9946 |
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+ | 0.0935 | 6.05 | 1099 | 0.0548 | 0.9222 | 0.9222 | 0.9222 | 347 | 0.6975 | 0.7378 | 0.7171 | 347 | 0.9885 | 0.9885 | 0.9885 | 347 | 0.8608 | 0.8732 | 0.8670 | 347 | 0.8648 | 0.8804 | 0.8725 | 0.9921 |
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+ | 0.0935 | 7.05 | 1256 | 0.0410 | 0.92 | 0.9280 | 0.9240 | 347 | 0.9486 | 0.9568 | 0.9527 | 347 | 0.9885 | 0.9885 | 0.9885 | 347 | 0.9091 | 0.9222 | 0.9156 | 347 | 0.9414 | 0.9488 | 0.9451 | 0.9961 |
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+ | 0.0935 | 8.05 | 1413 | 0.0369 | 0.9368 | 0.9395 | 0.9381 | 347 | 0.9569 | 0.9597 | 0.9583 | 347 | 0.9772 | 0.9885 | 0.9828 | 347 | 0.9143 | 0.9222 | 0.9182 | 347 | 0.9463 | 0.9524 | 0.9494 | 0.9960 |
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+ | 0.038 | 9.05 | 1570 | 0.0343 | 0.9282 | 0.9308 | 0.9295 | 347 | 0.9624 | 0.9597 | 0.9610 | 347 | 0.9885 | 0.9885 | 0.9885 | 347 | 0.9206 | 0.9020 | 0.9112 | 347 | 0.9500 | 0.9452 | 0.9476 | 0.9958 |
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+ | 0.038 | 10.05 | 1727 | 0.0317 | 0.9395 | 0.9395 | 0.9395 | 347 | 0.9598 | 0.9625 | 0.9612 | 347 | 0.9885 | 0.9885 | 0.9885 | 347 | 0.9280 | 0.9280 | 0.9280 | 347 | 0.9539 | 0.9546 | 0.9543 | 0.9963 |
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+ | 0.038 | 11.05 | 1884 | 0.0312 | 0.9368 | 0.9395 | 0.9381 | 347 | 0.9514 | 0.9597 | 0.9555 | 347 | 0.9885 | 0.9885 | 0.9885 | 347 | 0.9226 | 0.9280 | 0.9253 | 347 | 0.9498 | 0.9539 | 0.9518 | 0.9960 |
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+ | 0.0236 | 12.05 | 2041 | 0.0318 | 0.9368 | 0.9395 | 0.9381 | 347 | 0.9570 | 0.9625 | 0.9598 | 347 | 0.9885 | 0.9885 | 0.9885 | 347 | 0.9043 | 0.8991 | 0.9017 | 347 | 0.9467 | 0.9474 | 0.9471 | 0.9956 |
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+ | 0.0236 | 13.05 | 2198 | 0.0291 | 0.9337 | 0.9337 | 0.9337 | 347 | 0.9598 | 0.9625 | 0.9612 | 347 | 0.9885 | 0.9885 | 0.9885 | 347 | 0.9164 | 0.9164 | 0.9164 | 347 | 0.9496 | 0.9503 | 0.9499 | 0.9960 |
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+ | 0.0236 | 14.05 | 2355 | 0.0300 | 0.9286 | 0.9366 | 0.9326 | 347 | 0.9459 | 0.9568 | 0.9513 | 347 | 0.9885 | 0.9885 | 0.9885 | 347 | 0.9275 | 0.9222 | 0.9249 | 347 | 0.9476 | 0.9510 | 0.9493 | 0.9959 |
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+ | 0.0178 | 15.05 | 2512 | 0.0307 | 0.9366 | 0.9366 | 0.9366 | 347 | 0.9513 | 0.9568 | 0.9540 | 347 | 0.9885 | 0.9885 | 0.9885 | 347 | 0.9275 | 0.9222 | 0.9249 | 347 | 0.9510 | 0.9510 | 0.9510 | 0.9959 |
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+ | 0.0178 | 16.05 | 2669 | 0.0300 | 0.9312 | 0.9366 | 0.9339 | 347 | 0.9543 | 0.9625 | 0.9584 | 347 | 0.9885 | 0.9885 | 0.9885 | 347 | 0.9171 | 0.9251 | 0.9211 | 347 | 0.9477 | 0.9532 | 0.9504 | 0.9959 |
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+ | 0.0178 | 17.05 | 2826 | 0.0292 | 0.9368 | 0.9395 | 0.9381 | 347 | 0.9570 | 0.9625 | 0.9598 | 347 | 0.9885 | 0.9885 | 0.9885 | 347 | 0.9253 | 0.9280 | 0.9266 | 347 | 0.9519 | 0.9546 | 0.9532 | 0.9961 |
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+ | 0.0178 | 18.05 | 2983 | 0.0291 | 0.9341 | 0.9395 | 0.9368 | 347 | 0.9570 | 0.9625 | 0.9598 | 347 | 0.9885 | 0.9885 | 0.9885 | 347 | 0.9253 | 0.9280 | 0.9266 | 347 | 0.9512 | 0.9546 | 0.9529 | 0.9961 |
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+ | 0.0149 | 19.01 | 3000 | 0.0291 | 0.9341 | 0.9395 | 0.9368 | 347 | 0.9570 | 0.9625 | 0.9598 | 347 | 0.9885 | 0.9885 | 0.9885 | 347 | 0.9253 | 0.9280 | 0.9266 | 347 | 0.9512 | 0.9546 | 0.9529 | 0.9961 |
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  ### Framework versions
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  - Transformers 4.16.2
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  - Pytorch 1.8.0+cu101
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+ - Datasets 1.18.4.dev0
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+ - Tokenizers 0.11.6