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--- |
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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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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: layoutlmv3-funsd |
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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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# layoutlmv3-funsd |
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8428 |
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- Precision: 0.8993 |
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- Recall: 0.9046 |
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- F1: 0.9019 |
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- Accuracy: 0.8354 |
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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: 4 |
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- eval_batch_size: 4 |
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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: 1000 |
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- mixed_precision_training: Native AMP |
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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 | 2.63 | 100 | 0.6294 | 0.7864 | 0.8286 | 0.8070 | 0.7966 | |
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| No log | 5.26 | 200 | 0.5034 | 0.8389 | 0.8793 | 0.8586 | 0.8343 | |
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| No log | 7.89 | 300 | 0.5673 | 0.8597 | 0.9011 | 0.8799 | 0.8416 | |
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| No log | 10.53 | 400 | 0.5730 | 0.8783 | 0.9106 | 0.8941 | 0.8395 | |
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| 0.4463 | 13.16 | 500 | 0.6630 | 0.8923 | 0.9016 | 0.8970 | 0.8412 | |
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| 0.4463 | 15.79 | 600 | 0.7048 | 0.8850 | 0.8947 | 0.8898 | 0.8329 | |
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| 0.4463 | 18.42 | 700 | 0.7772 | 0.8925 | 0.9071 | 0.8997 | 0.8317 | |
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| 0.4463 | 21.05 | 800 | 0.8408 | 0.8959 | 0.9016 | 0.8987 | 0.8313 | |
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| 0.4463 | 23.68 | 900 | 0.8580 | 0.8918 | 0.9051 | 0.8984 | 0.8313 | |
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| 0.0611 | 26.32 | 1000 | 0.8428 | 0.8993 | 0.9046 | 0.9019 | 0.8354 | |
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### Framework versions |
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- Transformers 4.36.0 |
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- Pytorch 2.0.0 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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