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layoutlmv3-finetuned-cord

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layoutlmv3-finetuned-cord

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  1. README.md +25 -43
  2. model.safetensors +1 -1
README.md CHANGED
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  ---
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- base_model: layoutlmv3
 
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  tags:
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  - generated_from_trainer
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- datasets:
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- - mp-02/cord
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  metrics:
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  - precision
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  - recall
@@ -11,26 +10,7 @@ metrics:
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  - accuracy
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  model-index:
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  - name: layoutlmv3-finetuned-cord
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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: mp-02/cord
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- type: mp-02/cord
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- metrics:
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- - name: Precision
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- type: precision
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- value: 0.963984674329502
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- - name: Recall
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- type: recall
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- value: 0.9767080745341615
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- - name: F1
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- type: f1
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- value: 0.9703046664095644
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- - name: Accuracy
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- type: accuracy
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- value: 0.9690152801358234
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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
@@ -38,13 +18,13 @@ should probably proofread and complete it, then remove this comment. -->
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  # layoutlmv3-finetuned-cord
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- This model is a fine-tuned version of [layoutlmv3](https://huggingface.co/layoutlmv3) on the mp-02/cord dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2087
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- - Precision: 0.9640
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- - Recall: 0.9767
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- - F1: 0.9703
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- - Accuracy: 0.9690
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  ## Model description
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@@ -63,9 +43,9 @@ 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: 1e-05
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- - train_batch_size: 5
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- - eval_batch_size: 5
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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 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 | 1.5625 | 250 | 0.2302 | 0.9594 | 0.9720 | 0.9657 | 0.9656 |
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- | 0.041 | 3.125 | 500 | 0.2176 | 0.9542 | 0.9705 | 0.9623 | 0.9618 |
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- | 0.041 | 4.6875 | 750 | 0.1903 | 0.9573 | 0.9736 | 0.9654 | 0.9682 |
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- | 0.0302 | 6.25 | 1000 | 0.2027 | 0.9602 | 0.9744 | 0.9672 | 0.9660 |
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- | 0.0302 | 7.8125 | 1250 | 0.2174 | 0.9670 | 0.9775 | 0.9722 | 0.9703 |
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- | 0.019 | 9.375 | 1500 | 0.2018 | 0.9640 | 0.9775 | 0.9707 | 0.9711 |
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- | 0.019 | 10.9375 | 1750 | 0.2084 | 0.9677 | 0.9783 | 0.9730 | 0.9694 |
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- | 0.0115 | 12.5 | 2000 | 0.2087 | 0.9640 | 0.9767 | 0.9703 | 0.9690 |
 
 
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  ### Framework versions
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  - Transformers 4.42.4
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- - Pytorch 2.3.1+cu121
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  - Datasets 2.21.0
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  - Tokenizers 0.19.1
 
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  ---
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+ license: cc-by-nc-sa-4.0
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+ base_model: microsoft/layoutlmv3-base
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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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  - accuracy
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  model-index:
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  - name: layoutlmv3-finetuned-cord
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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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  # layoutlmv3-finetuned-cord
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+ This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2108
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+ - Precision: 0.9573
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+ - Recall: 0.9744
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+ - F1: 0.9658
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+ - Accuracy: 0.9652
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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: 5e-05
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+ - train_batch_size: 10
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+ - eval_batch_size: 10
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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 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 | 2.5 | 200 | 0.3004 | 0.9060 | 0.9425 | 0.9239 | 0.9304 |
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+ | No log | 5.0 | 400 | 0.1818 | 0.9351 | 0.9620 | 0.9483 | 0.9559 |
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+ | 0.4239 | 7.5 | 600 | 0.1737 | 0.9543 | 0.9728 | 0.9635 | 0.9677 |
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+ | 0.4239 | 10.0 | 800 | 0.1698 | 0.9594 | 0.9728 | 0.9661 | 0.9660 |
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+ | 0.0418 | 12.5 | 1000 | 0.1981 | 0.9602 | 0.9736 | 0.9668 | 0.9610 |
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+ | 0.0418 | 15.0 | 1200 | 0.2044 | 0.9565 | 0.9728 | 0.9646 | 0.9622 |
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+ | 0.0418 | 17.5 | 1400 | 0.1810 | 0.9581 | 0.9759 | 0.9669 | 0.9699 |
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+ | 0.014 | 20.0 | 1600 | 0.2161 | 0.9572 | 0.9720 | 0.9646 | 0.9665 |
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+ | 0.014 | 22.5 | 1800 | 0.2066 | 0.9610 | 0.9752 | 0.9680 | 0.9648 |
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+ | 0.0061 | 25.0 | 2000 | 0.2108 | 0.9573 | 0.9744 | 0.9658 | 0.9652 |
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  ### Framework versions
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  - Transformers 4.42.4
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+ - Pytorch 2.4.0+cu118
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  - Datasets 2.21.0
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  - Tokenizers 0.19.1
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