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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-finetuned-cordv2 |
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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-finetuned-cordv2 |
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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: 0.1800 |
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- Precision: 0.9519 |
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- Recall: 0.9568 |
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- F1: 0.9544 |
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- Accuracy: 0.9656 |
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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: 5 |
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- eval_batch_size: 5 |
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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: 2500 |
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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 | 1.5625 | 250 | 0.7355 | 0.7503 | 0.7595 | 0.7549 | 0.8216 | |
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| 1.0406 | 3.125 | 500 | 0.4019 | 0.8576 | 0.8787 | 0.8680 | 0.9006 | |
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| 1.0406 | 4.6875 | 750 | 0.2671 | 0.9028 | 0.9260 | 0.9143 | 0.9384 | |
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| 0.2814 | 6.25 | 1000 | 0.2293 | 0.9380 | 0.9332 | 0.9356 | 0.9473 | |
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| 0.2814 | 7.8125 | 1250 | 0.1763 | 0.9426 | 0.9445 | 0.9435 | 0.9622 | |
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| 0.1349 | 9.375 | 1500 | 0.1926 | 0.9437 | 0.9476 | 0.9456 | 0.9613 | |
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| 0.1349 | 10.9375 | 1750 | 0.1848 | 0.9481 | 0.9579 | 0.9530 | 0.9647 | |
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| 0.082 | 12.5 | 2000 | 0.2028 | 0.9490 | 0.9558 | 0.9524 | 0.9626 | |
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| 0.082 | 14.0625 | 2250 | 0.1878 | 0.9510 | 0.9579 | 0.9544 | 0.9652 | |
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| 0.0584 | 15.625 | 2500 | 0.1800 | 0.9519 | 0.9568 | 0.9544 | 0.9656 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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