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layoutlmv3-finetuned-cord_100

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layoutlmv3-finetuned-cord_100

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  1. README.md +22 -20
  2. model.safetensors +1 -1
README.md CHANGED
@@ -25,16 +25,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.9428783382789317
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  - name: Recall
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  type: recall
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- value: 0.9513473053892215
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  - name: F1
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  type: f1
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- value: 0.9470938897168405
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  - name: Accuracy
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  type: accuracy
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- value: 0.952037351443124
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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
@@ -44,11 +44,11 @@ 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 cord-layoutlmv3 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2267
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- - Precision: 0.9429
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- - Recall: 0.9513
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- - F1: 0.9471
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- - Accuracy: 0.9520
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  ## Model description
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@@ -73,22 +73,24 @@ 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: 2500
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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 | 1.56 | 250 | 1.0513 | 0.6817 | 0.7597 | 0.7186 | 0.7806 |
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- | 1.4257 | 3.12 | 500 | 0.5744 | 0.8451 | 0.8660 | 0.8555 | 0.8697 |
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- | 1.4257 | 4.69 | 750 | 0.3979 | 0.8720 | 0.9027 | 0.8871 | 0.9062 |
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- | 0.4063 | 6.25 | 1000 | 0.3350 | 0.9107 | 0.9237 | 0.9171 | 0.9300 |
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- | 0.4063 | 7.81 | 1250 | 0.2638 | 0.9313 | 0.9431 | 0.9372 | 0.9402 |
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- | 0.2045 | 9.38 | 1500 | 0.2542 | 0.9205 | 0.9364 | 0.9284 | 0.9419 |
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- | 0.2045 | 10.94 | 1750 | 0.2417 | 0.9335 | 0.9454 | 0.9394 | 0.9469 |
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- | 0.1406 | 12.5 | 2000 | 0.2279 | 0.9371 | 0.9476 | 0.9423 | 0.9491 |
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- | 0.1406 | 14.06 | 2250 | 0.2267 | 0.9401 | 0.9513 | 0.9457 | 0.9550 |
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- | 0.1079 | 15.62 | 2500 | 0.2267 | 0.9429 | 0.9513 | 0.9471 | 0.9520 |
 
 
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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: 0.9561011904761905
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  - name: Recall
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  type: recall
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+ value: 0.9618263473053892
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  - name: F1
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  type: f1
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+ value: 0.958955223880597
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9702886247877759
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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 cord-layoutlmv3 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1726
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+ - Precision: 0.9561
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+ - Recall: 0.9618
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+ - F1: 0.9590
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+ - Accuracy: 0.9703
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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: 3000
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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 | 1.56 | 250 | 1.0075 | 0.7597 | 0.8046 | 0.7815 | 0.8145 |
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+ | 1.3907 | 3.12 | 500 | 0.5155 | 0.8388 | 0.8683 | 0.8533 | 0.8841 |
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+ | 1.3907 | 4.69 | 750 | 0.3486 | 0.8917 | 0.9117 | 0.9016 | 0.9283 |
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+ | 0.3755 | 6.25 | 1000 | 0.2722 | 0.9211 | 0.9356 | 0.9283 | 0.9435 |
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+ | 0.3755 | 7.81 | 1250 | 0.2399 | 0.9356 | 0.9461 | 0.9408 | 0.9533 |
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+ | 0.1857 | 9.38 | 1500 | 0.2170 | 0.9376 | 0.9454 | 0.9415 | 0.9542 |
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+ | 0.1857 | 10.94 | 1750 | 0.1917 | 0.9510 | 0.9588 | 0.9549 | 0.9660 |
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+ | 0.1236 | 12.5 | 2000 | 0.1821 | 0.9502 | 0.9573 | 0.9538 | 0.9652 |
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+ | 0.1236 | 14.06 | 2250 | 0.1870 | 0.9538 | 0.9588 | 0.9563 | 0.9669 |
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+ | 0.0858 | 15.62 | 2500 | 0.1741 | 0.9583 | 0.9633 | 0.9608 | 0.9711 |
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+ | 0.0858 | 17.19 | 2750 | 0.1726 | 0.9561 | 0.9611 | 0.9586 | 0.9690 |
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+ | 0.0708 | 18.75 | 3000 | 0.1726 | 0.9561 | 0.9618 | 0.9590 | 0.9703 |
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  ### Framework versions
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