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Output_LayoutLMv3

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layoutlmv3
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  1. README.md +45 -5
  2. runs/Mar31_17-52-32_bf0b87ba8723/events.out.tfevents.1711907581.bf0b87ba8723.420.0 +2 -2
README.md CHANGED
@@ -3,12 +3,14 @@ license: cc-by-nc-sa-4.0
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  base_model: microsoft/layoutlmv3-large
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  tags:
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  - generated_from_trainer
 
 
 
 
 
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  model-index:
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  - name: Output_LayoutLMv3
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  results: []
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- metrics:
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- - accuracy
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- - f1
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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
@@ -17,6 +19,12 @@ should probably proofread and complete it, then remove this comment. -->
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  # Output_LayoutLMv3
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  This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on an unknown dataset.
 
 
 
 
 
 
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  ## Model description
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@@ -41,10 +49,42 @@ 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: 20
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  ### Training results
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  ### Framework versions
@@ -52,4 +92,4 @@ The following hyperparameters were used during training:
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  - Transformers 4.38.2
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  - Pytorch 2.2.1+cu121
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  - Datasets 2.18.0
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- - Tokenizers 0.15.2
 
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  base_model: microsoft/layoutlmv3-large
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  tags:
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  - generated_from_trainer
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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: Output_LayoutLMv3
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  results: []
 
 
 
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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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  # Output_LayoutLMv3
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  This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2507
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+ - Precision: 0.8319
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+ - Recall: 0.8319
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+ - F1: 0.8319
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+ - Accuracy: 0.9771
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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 | 2.27 | 100 | 0.1116 | 0.7705 | 0.8319 | 0.8 | 0.9676 |
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+ | No log | 4.55 | 200 | 0.1130 | 0.8319 | 0.8540 | 0.8428 | 0.9762 |
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+ | No log | 6.82 | 300 | 0.1707 | 0.7931 | 0.8142 | 0.8035 | 0.9686 |
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+ | No log | 9.09 | 400 | 0.1998 | 0.7521 | 0.7920 | 0.7716 | 0.9648 |
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+ | 0.0744 | 11.36 | 500 | 0.1633 | 0.8210 | 0.8319 | 0.8264 | 0.9752 |
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+ | 0.0744 | 13.64 | 600 | 0.1784 | 0.8182 | 0.8363 | 0.8271 | 0.9752 |
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+ | 0.0744 | 15.91 | 700 | 0.1909 | 0.8095 | 0.8274 | 0.8184 | 0.9724 |
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+ | 0.0744 | 18.18 | 800 | 0.1962 | 0.7974 | 0.8186 | 0.8079 | 0.9724 |
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+ | 0.0744 | 20.45 | 900 | 0.1723 | 0.8412 | 0.8673 | 0.8540 | 0.9781 |
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+ | 0.0081 | 22.73 | 1000 | 0.2109 | 0.8210 | 0.8319 | 0.8264 | 0.9733 |
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+ | 0.0081 | 25.0 | 1100 | 0.2194 | 0.8087 | 0.8230 | 0.8158 | 0.9743 |
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+ | 0.0081 | 27.27 | 1200 | 0.2076 | 0.8465 | 0.8540 | 0.8502 | 0.9771 |
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+ | 0.0081 | 29.55 | 1300 | 0.1883 | 0.8688 | 0.8496 | 0.8591 | 0.9819 |
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+ | 0.0081 | 31.82 | 1400 | 0.2042 | 0.8170 | 0.8496 | 0.8330 | 0.9771 |
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+ | 0.0034 | 34.09 | 1500 | 0.2144 | 0.8261 | 0.8407 | 0.8333 | 0.9771 |
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+ | 0.0034 | 36.36 | 1600 | 0.1953 | 0.8205 | 0.8496 | 0.8348 | 0.9771 |
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+ | 0.0034 | 38.64 | 1700 | 0.2259 | 0.8267 | 0.8230 | 0.8248 | 0.9762 |
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+ | 0.0034 | 40.91 | 1800 | 0.2553 | 0.7974 | 0.8186 | 0.8079 | 0.9714 |
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+ | 0.0034 | 43.18 | 1900 | 0.2238 | 0.8377 | 0.8451 | 0.8414 | 0.9781 |
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+ | 0.0006 | 45.45 | 2000 | 0.2245 | 0.8451 | 0.8451 | 0.8451 | 0.9790 |
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+ | 0.0006 | 47.73 | 2100 | 0.2389 | 0.8326 | 0.8142 | 0.8233 | 0.9762 |
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+ | 0.0006 | 50.0 | 2200 | 0.2500 | 0.8251 | 0.8142 | 0.8196 | 0.9752 |
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+ | 0.0006 | 52.27 | 2300 | 0.2537 | 0.8304 | 0.8451 | 0.8377 | 0.9762 |
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+ | 0.0006 | 54.55 | 2400 | 0.2410 | 0.8319 | 0.8319 | 0.8319 | 0.9771 |
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+ | 0.0001 | 56.82 | 2500 | 0.2484 | 0.8319 | 0.8319 | 0.8319 | 0.9771 |
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+ | 0.0001 | 59.09 | 2600 | 0.2517 | 0.8319 | 0.8319 | 0.8319 | 0.9771 |
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+ | 0.0001 | 61.36 | 2700 | 0.2524 | 0.8319 | 0.8319 | 0.8319 | 0.9771 |
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+ | 0.0001 | 63.64 | 2800 | 0.2531 | 0.8319 | 0.8319 | 0.8319 | 0.9771 |
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+ | 0.0001 | 65.91 | 2900 | 0.2528 | 0.8319 | 0.8319 | 0.8319 | 0.9771 |
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+ | 0.0 | 68.18 | 3000 | 0.2507 | 0.8319 | 0.8319 | 0.8319 | 0.9771 |
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  ### Framework versions
 
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  - Transformers 4.38.2
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  - Pytorch 2.2.1+cu121
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  - Datasets 2.18.0
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+ - Tokenizers 0.15.2
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