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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: 0.8860759493670886 |
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- name: Recall |
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type: recall |
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value: 0.9210526315789473 |
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- name: F1 |
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type: f1 |
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value: 0.9032258064516129 |
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- name: Accuracy |
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type: accuracy |
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value: 0.94375 |
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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: 0.3028 |
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- Precision: 0.8861 |
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- Recall: 0.9211 |
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- F1: 0.9032 |
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- Accuracy: 0.9437 |
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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 | 4.3478 | 100 | 0.6867 | 0.6842 | 0.6842 | 0.6842 | 0.8063 | |
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| No log | 8.6957 | 200 | 0.2381 | 0.8625 | 0.9079 | 0.8846 | 0.9313 | |
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| No log | 13.0435 | 300 | 0.2598 | 0.8846 | 0.9079 | 0.8961 | 0.9313 | |
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| No log | 17.3913 | 400 | 0.2165 | 0.8625 | 0.9079 | 0.8846 | 0.9375 | |
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| 0.3281 | 21.7391 | 500 | 0.2037 | 0.8625 | 0.9079 | 0.8846 | 0.9375 | |
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| 0.3281 | 26.0870 | 600 | 0.2571 | 0.8861 | 0.9211 | 0.9032 | 0.9437 | |
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| 0.3281 | 30.4348 | 700 | 0.2735 | 0.8861 | 0.9211 | 0.9032 | 0.9437 | |
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| 0.3281 | 34.7826 | 800 | 0.2993 | 0.8861 | 0.9211 | 0.9032 | 0.9437 | |
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| 0.3281 | 39.1304 | 900 | 0.3044 | 0.8861 | 0.9211 | 0.9032 | 0.9437 | |
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| 0.012 | 43.4783 | 1000 | 0.3028 | 0.8861 | 0.9211 | 0.9032 | 0.9437 | |
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### Framework versions |
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- Transformers 4.47.0.dev0 |
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- Pytorch 2.5.0+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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