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--- |
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library_name: transformers |
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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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datasets: |
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- layoutlmv3 |
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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: test |
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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: layoutlmv3 |
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type: layoutlmv3 |
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config: InvoiceExtraction |
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split: test |
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args: InvoiceExtraction |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9735537190082645 |
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- name: Recall |
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type: recall |
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value: 0.9751655629139073 |
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- name: F1 |
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type: f1 |
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value: 0.9743589743589743 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9924257137308216 |
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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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# test |
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the layoutlmv3 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0899 |
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- Precision: 0.9736 |
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- Recall: 0.9752 |
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- F1: 0.9744 |
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- Accuracy: 0.9924 |
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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: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- training_steps: 1000 |
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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 | 0.5291 | 100 | 0.4956 | 0.7821 | 0.7724 | 0.7772 | 0.9413 | |
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| No log | 1.0582 | 200 | 0.1802 | 0.9285 | 0.9247 | 0.9266 | 0.9761 | |
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| No log | 1.5873 | 300 | 0.1465 | 0.9334 | 0.9512 | 0.9422 | 0.9841 | |
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| No log | 2.1164 | 400 | 0.1309 | 0.9447 | 0.9611 | 0.9528 | 0.9876 | |
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| 0.3392 | 2.6455 | 500 | 0.1095 | 0.9516 | 0.9594 | 0.9555 | 0.9891 | |
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| 0.3392 | 3.1746 | 600 | 0.1022 | 0.9573 | 0.9652 | 0.9613 | 0.9915 | |
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| 0.3392 | 3.7037 | 700 | 0.1081 | 0.9573 | 0.9661 | 0.9617 | 0.9918 | |
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| 0.3392 | 4.2328 | 800 | 0.0922 | 0.9726 | 0.9694 | 0.9710 | 0.9920 | |
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| 0.3392 | 4.7619 | 900 | 0.0930 | 0.9702 | 0.9702 | 0.9702 | 0.9916 | |
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| 0.0282 | 5.2910 | 1000 | 0.0899 | 0.9736 | 0.9752 | 0.9744 | 0.9924 | |
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### Framework versions |
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- Transformers 4.47.0.dev0 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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