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--- |
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license: cc-by-nc-sa-4.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- invoices |
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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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base_model: microsoft/layoutlmv3-base |
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model-index: |
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- name: layoutlmv3-finetuned-invoice |
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results: |
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- task: |
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type: token-classification |
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name: Token Classification |
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dataset: |
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name: invoices |
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type: invoices |
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config: sroie |
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split: train |
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args: sroie |
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metrics: |
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- type: precision |
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value: 0.975 |
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name: Precision |
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- type: recall |
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value: 0.975 |
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name: Recall |
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- type: f1 |
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value: 0.975 |
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name: F1 |
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- type: accuracy |
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value: 0.975 |
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name: Accuracy |
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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-invoice |
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the invoices dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2299 |
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- Precision: 0.975 |
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- Recall: 0.975 |
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- F1: 0.975 |
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- Accuracy: 0.975 |
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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: 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 | 14.29 | 100 | 0.1616 | 0.975 | 0.975 | 0.975 | 0.975 | |
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| No log | 28.57 | 200 | 0.1909 | 0.975 | 0.975 | 0.975 | 0.975 | |
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| No log | 42.86 | 300 | 0.2046 | 0.975 | 0.975 | 0.975 | 0.975 | |
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| No log | 57.14 | 400 | 0.2134 | 0.975 | 0.975 | 0.975 | 0.975 | |
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| 0.1239 | 71.43 | 500 | 0.2299 | 0.975 | 0.975 | 0.975 | 0.975 | |
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| 0.1239 | 85.71 | 600 | 0.2309 | 0.975 | 0.975 | 0.975 | 0.975 | |
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| 0.1239 | 100.0 | 700 | 0.2342 | 0.975 | 0.975 | 0.975 | 0.975 | |
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| 0.1239 | 114.29 | 800 | 0.2407 | 0.975 | 0.975 | 0.975 | 0.975 | |
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| 0.1239 | 128.57 | 900 | 0.2428 | 0.975 | 0.975 | 0.975 | 0.975 | |
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| 0.0007 | 142.86 | 1000 | 0.2449 | 0.975 | 0.975 | 0.975 | 0.975 | |
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| 0.0007 | 157.14 | 1100 | 0.2465 | 0.975 | 0.975 | 0.975 | 0.975 | |
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| 0.0007 | 171.43 | 1200 | 0.2488 | 0.975 | 0.975 | 0.975 | 0.975 | |
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| 0.0007 | 185.71 | 1300 | 0.2515 | 0.975 | 0.975 | 0.975 | 0.975 | |
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| 0.0007 | 200.0 | 1400 | 0.2525 | 0.975 | 0.975 | 0.975 | 0.975 | |
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| 0.0004 | 214.29 | 1500 | 0.2540 | 0.975 | 0.975 | 0.975 | 0.975 | |
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| 0.0004 | 228.57 | 1600 | 0.2557 | 0.975 | 0.975 | 0.975 | 0.975 | |
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| 0.0004 | 242.86 | 1700 | 0.2564 | 0.975 | 0.975 | 0.975 | 0.975 | |
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| 0.0004 | 257.14 | 1800 | 0.2570 | 0.975 | 0.975 | 0.975 | 0.975 | |
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| 0.0004 | 271.43 | 1900 | 0.2573 | 0.975 | 0.975 | 0.975 | 0.975 | |
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| 0.0003 | 285.71 | 2000 | 0.2574 | 0.975 | 0.975 | 0.975 | 0.975 | |
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### Framework versions |
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- Transformers 4.23.0.dev0 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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