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--- |
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library_name: transformers |
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license: mit |
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base_model: microsoft/layoutlm-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: layoutlm-receipts |
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results: [] |
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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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# layoutlm-receipts |
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This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2190 |
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- Precision: 0.2222 |
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- Recall: 0.4 |
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- F1: 0.2857 |
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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: 5e-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 OptimizerNames.ADAMW_TORCH_FUSED 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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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| |
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| 0.7275 | 1.0 | 8 | 0.8111 | 0.0 | 0.0 | 0.0 | |
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| 0.6014 | 2.0 | 16 | 0.6757 | 0.0 | 0.0 | 0.0 | |
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| 0.5061 | 3.0 | 24 | 0.5598 | 0.0 | 0.0 | 0.0 | |
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| 0.4025 | 4.0 | 32 | 0.4736 | 0.0 | 0.0 | 0.0 | |
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| 0.3486 | 5.0 | 40 | 0.4236 | 0.0571 | 0.1 | 0.0727 | |
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| 0.3318 | 6.0 | 48 | 0.3784 | 0.0377 | 0.1 | 0.0548 | |
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| 0.2649 | 7.0 | 56 | 0.3338 | 0.1064 | 0.25 | 0.1493 | |
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| 0.1982 | 8.0 | 64 | 0.2808 | 0.25 | 0.4 | 0.3077 | |
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| 0.1658 | 9.0 | 72 | 0.2388 | 0.1778 | 0.4 | 0.2462 | |
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| 0.1637 | 10.0 | 80 | 0.2190 | 0.2222 | 0.4 | 0.2857 | |
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### Framework versions |
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- Transformers 4.56.1 |
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- Pytorch 2.8.0+cu126 |
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- Datasets 4.0.0 |
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- Tokenizers 0.22.0 |
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