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--- |
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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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datasets: |
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- layoutlmv4 |
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model-index: |
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- name: Layoutlm_invoices |
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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_invoices |
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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 layoutlmv4 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0603 |
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- Customer Address: {'precision': 0.7692307692307693, 'recall': 0.9090909090909091, 'f1': 0.8333333333333333, 'number': 11} |
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- Customer Name: {'precision': 0.8333333333333334, 'recall': 0.9090909090909091, 'f1': 0.8695652173913043, 'number': 11} |
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- Invoice Number: {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11} |
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- Tax Amount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} |
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- Total Amount: {'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11} |
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- Vendor Name: {'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 11} |
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- Overall Precision: 0.8986 |
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- Overall Recall: 0.9394 |
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- Overall F1: 0.9185 |
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- Overall Accuracy: 0.9831 |
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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: 6 |
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- eval_batch_size: 3 |
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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: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Customer Address | Customer Name | Invoice Number | Tax Amount | Total Amount | Vendor Name | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 0.0714 | 1.25 | 10 | 0.0752 | {'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 11} | {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11} | {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11} | {'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 11} | 0.9403 | 0.9545 | 0.9474 | 0.9864 | |
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| 0.0572 | 2.5 | 20 | 0.0603 | {'precision': 0.7692307692307693, 'recall': 0.9090909090909091, 'f1': 0.8333333333333333, 'number': 11} | {'precision': 0.8333333333333334, 'recall': 0.9090909090909091, 'f1': 0.8695652173913043, 'number': 11} | {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11} | {'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 11} | 0.8986 | 0.9394 | 0.9185 | 0.9831 | |
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### Framework versions |
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- Transformers 4.32.1 |
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- Pytorch 2.2.0+cpu |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.2 |
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