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layoutlmv3-base-cord-sroie

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Files changed (6) hide show
  1. README.md +17 -32
  2. all_results.json +8 -8
  3. predict_results.json +8 -8
  4. predictions.txt +0 -0
  5. runs/Nov17_22-07-29_bernini/events.out.tfevents.1731877651.bernini.3234.0 +2 -2
  6. runs/Nov17_22-07-29_bernini/events.out.tfevents.1731878982.bernini.3234.1 +3 -0
README.md CHANGED
@@ -22,16 +22,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.9539082917557614
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  - name: Recall
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  type: recall
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- value: 0.951196398957593
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  - name: F1
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  type: f1
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- value: 0.9525504151838672
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  - name: Accuracy
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  type: accuracy
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- value: 0.9836608621693004
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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
@@ -41,11 +41,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [layoutlmv3](https://huggingface.co/layoutlmv3) on the mp-02/cord-sroie dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0748
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- - Precision: 0.9539
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- - Recall: 0.9512
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- - F1: 0.9526
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- - Accuracy: 0.9837
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  ## Model description
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@@ -64,7 +64,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 6e-05
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  - train_batch_size: 16
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  - eval_batch_size: 16
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  - seed: 42
@@ -76,28 +76,13 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 0.5556 | 50 | 0.5599 | 0.8723 | 0.5451 | 0.6709 | 0.8791 |
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- | No log | 1.1111 | 100 | 0.4348 | 0.9023 | 0.6408 | 0.7494 | 0.8977 |
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- | No log | 1.6667 | 150 | 0.2920 | 0.8816 | 0.8024 | 0.8401 | 0.9298 |
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- | No log | 2.2222 | 200 | 0.2370 | 0.8846 | 0.8448 | 0.8643 | 0.9416 |
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- | No log | 2.7778 | 250 | 0.1776 | 0.8491 | 0.8676 | 0.8582 | 0.9615 |
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- | No log | 3.3333 | 300 | 0.1181 | 0.8935 | 0.9282 | 0.9105 | 0.9735 |
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- | No log | 3.8889 | 350 | 0.0779 | 0.9355 | 0.9382 | 0.9368 | 0.9806 |
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- | No log | 4.4444 | 400 | 0.0785 | 0.9444 | 0.9505 | 0.9475 | 0.9831 |
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- | No log | 5.0 | 450 | 0.0675 | 0.9536 | 0.9552 | 0.9544 | 0.9848 |
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- | 0.4435 | 5.5556 | 500 | 0.0756 | 0.9508 | 0.9469 | 0.9488 | 0.9829 |
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- | 0.4435 | 6.1111 | 550 | 0.0708 | 0.9546 | 0.9555 | 0.9550 | 0.9847 |
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- | 0.4435 | 6.6667 | 600 | 0.0707 | 0.9576 | 0.9472 | 0.9524 | 0.9841 |
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- | 0.4435 | 7.2222 | 650 | 0.0630 | 0.9577 | 0.9552 | 0.9565 | 0.9854 |
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- | 0.4435 | 7.7778 | 700 | 0.0679 | 0.9548 | 0.9614 | 0.9581 | 0.9860 |
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- | 0.4435 | 8.3333 | 750 | 0.0665 | 0.9505 | 0.9642 | 0.9573 | 0.9858 |
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- | 0.4435 | 8.8889 | 800 | 0.0687 | 0.9480 | 0.9628 | 0.9553 | 0.9850 |
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- | 0.4435 | 9.4444 | 850 | 0.0730 | 0.9577 | 0.9555 | 0.9566 | 0.9850 |
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- | 0.4435 | 10.0 | 900 | 0.0905 | 0.9634 | 0.9346 | 0.9488 | 0.9816 |
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- | 0.4435 | 10.5556 | 950 | 0.0755 | 0.9523 | 0.9611 | 0.9567 | 0.9851 |
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- | 0.0363 | 11.1111 | 1000 | 0.0748 | 0.9539 | 0.9512 | 0.9526 | 0.9837 |
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- | 0.0363 | 11.6667 | 1050 | 0.0768 | 0.9531 | 0.9583 | 0.9557 | 0.9844 |
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- | 0.0363 | 12.2222 | 1100 | 0.0759 | 0.9562 | 0.9611 | 0.9586 | 0.9855 |
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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: 0.9399720800372267
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  - name: Recall
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  type: recall
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+ value: 0.9465791940018744
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  - name: F1
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  type: f1
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+ value: 0.9432640672425869
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9813340410474168
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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 [layoutlmv3](https://huggingface.co/layoutlmv3) on the mp-02/cord-sroie dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0970
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+ - Precision: 0.9400
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+ - Recall: 0.9466
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+ - F1: 0.9433
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+ - Accuracy: 0.9813
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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  - train_batch_size: 16
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  - eval_batch_size: 16
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  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 2.2222 | 100 | 0.3258 | 0.8171 | 0.7685 | 0.7921 | 0.9363 |
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+ | No log | 4.4444 | 200 | 0.1516 | 0.9078 | 0.8946 | 0.9011 | 0.9694 |
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+ | No log | 6.6667 | 300 | 0.1085 | 0.9315 | 0.9175 | 0.9245 | 0.9761 |
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+ | No log | 8.8889 | 400 | 0.1000 | 0.9382 | 0.9456 | 0.9419 | 0.9817 |
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+ | 0.4015 | 11.1111 | 500 | 0.0970 | 0.9400 | 0.9466 | 0.9433 | 0.9813 |
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+ | 0.4015 | 13.3333 | 600 | 0.1064 | 0.9505 | 0.9358 | 0.9431 | 0.9814 |
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+ | 0.4015 | 15.5556 | 700 | 0.1095 | 0.9465 | 0.9372 | 0.9418 | 0.9812 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
all_results.json CHANGED
@@ -1,10 +1,10 @@
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  {
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- "predict_accuracy": 0.9820627802690582,
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- "predict_loss": 0.08279602229595184,
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- "predict_samples_per_second": 10.992,
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- "predict_steps_per_second": 0.725
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  }
 
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  {
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+ "predict_samples_per_second": 11.279,
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+ "predict_steps_per_second": 0.741
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  }
predict_results.json CHANGED
@@ -1,10 +1,10 @@
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  {
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- "predict_recall": 0.9544609665427509,
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- "predict_runtime": 24.8356,
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- "predict_samples_per_second": 10.992,
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- "predict_steps_per_second": 0.725
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  }
 
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  {
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+ "predict_runtime": 12.1468,
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+ "predict_samples_per_second": 11.279,
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+ "predict_steps_per_second": 0.741
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  }
predictions.txt CHANGED
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