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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/sroie |
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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-sroie |
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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/sroie |
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type: mp-02/sroie |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9232981783317353 |
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- name: Recall |
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type: recall |
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value: 0.9578912466843501 |
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- name: F1 |
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type: f1 |
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value: 0.9402766476810415 |
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- name: Accuracy |
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type: accuracy |
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value: 0.981485280541594 |
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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-sroie |
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This model is a fine-tuned version of [layoutlmv3](https://huggingface.co/layoutlmv3) on the mp-02/sroie dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0651 |
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- Precision: 0.9233 |
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- Recall: 0.9579 |
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- F1: 0.9403 |
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- Accuracy: 0.9815 |
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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: 2e-06 |
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- train_batch_size: 6 |
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- eval_batch_size: 6 |
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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: 1500 |
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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 | 2.3810 | 250 | 0.0957 | 0.9075 | 0.9304 | 0.9188 | 0.9752 | |
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| 0.1943 | 4.7619 | 500 | 0.0699 | 0.9260 | 0.9456 | 0.9357 | 0.9805 | |
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| 0.1943 | 7.1429 | 750 | 0.0657 | 0.9291 | 0.9513 | 0.9400 | 0.9817 | |
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| 0.0485 | 9.5238 | 1000 | 0.0651 | 0.9233 | 0.9579 | 0.9403 | 0.9815 | |
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| 0.0485 | 11.9048 | 1250 | 0.0661 | 0.9155 | 0.9625 | 0.9384 | 0.9808 | |
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| 0.0397 | 14.2857 | 1500 | 0.0660 | 0.9161 | 0.9632 | 0.9391 | 0.9810 | |
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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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