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--- |
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library_name: transformers |
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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 |
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- f1 |
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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: 0.963084495488105 |
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- name: Recall |
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type: recall |
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value: 0.9726594863297432 |
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- name: F1 |
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type: f1 |
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value: 0.9678483099752679 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9688929551692589 |
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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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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: 0.1351 |
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- Precision: 0.9631 |
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- Recall: 0.9727 |
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- F1: 0.9678 |
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- Accuracy: 0.9689 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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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: 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_steps: 2000 |
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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 | 3.125 | 250 | 0.5767 | 0.8636 | 0.8915 | 0.8773 | 0.8925 | |
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| 1.0201 | 6.25 | 500 | 0.2739 | 0.9275 | 0.9536 | 0.9404 | 0.9465 | |
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| 1.0201 | 9.375 | 750 | 0.1894 | 0.9462 | 0.9611 | 0.9536 | 0.9602 | |
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| 0.1892 | 12.5 | 1000 | 0.1522 | 0.9592 | 0.9727 | 0.9659 | 0.9689 | |
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| 0.1892 | 15.625 | 1250 | 0.1537 | 0.9535 | 0.9677 | 0.9605 | 0.9652 | |
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| 0.0813 | 18.75 | 1500 | 0.1351 | 0.9631 | 0.9727 | 0.9678 | 0.9689 | |
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| 0.0813 | 21.875 | 1750 | 0.1406 | 0.9607 | 0.9718 | 0.9662 | 0.9689 | |
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| 0.0535 | 25.0 | 2000 | 0.1396 | 0.9599 | 0.9710 | 0.9654 | 0.9675 | |
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### Framework versions |
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- Transformers 4.44.2 |
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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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