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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-cord_100 |
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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-cord_100 |
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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: 0.1687 |
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- Precision: 0.9382 |
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- Recall: 0.9574 |
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- F1: 0.9477 |
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- Accuracy: 0.9597 |
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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 | 1.56 | 250 | 0.3730 | 0.8662 | 0.8708 | 0.8685 | 0.9042 | |
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| 0.3943 | 3.12 | 500 | 0.2683 | 0.8939 | 0.9027 | 0.8983 | 0.9279 | |
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| 0.3943 | 4.69 | 750 | 0.2232 | 0.9248 | 0.9339 | 0.9293 | 0.9474 | |
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| 0.1559 | 6.25 | 1000 | 0.2129 | 0.9301 | 0.9407 | 0.9354 | 0.9504 | |
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| 0.1559 | 7.81 | 1250 | 0.1782 | 0.9289 | 0.9529 | 0.9407 | 0.9563 | |
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| 0.082 | 9.38 | 1500 | 0.1876 | 0.9327 | 0.9483 | 0.9405 | 0.9555 | |
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| 0.082 | 10.94 | 1750 | 0.1746 | 0.9416 | 0.9559 | 0.9487 | 0.9606 | |
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| 0.0486 | 12.5 | 2000 | 0.1848 | 0.9349 | 0.9498 | 0.9423 | 0.9550 | |
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| 0.0486 | 14.06 | 2250 | 0.1739 | 0.9439 | 0.9590 | 0.9514 | 0.9623 | |
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| 0.0351 | 15.62 | 2500 | 0.1687 | 0.9382 | 0.9574 | 0.9477 | 0.9597 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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