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--- |
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license: cc-by-nc-sa-4.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- data_registros_layoutv3 |
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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-registros_100 |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: data_registros_layoutv3 |
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type: data_registros_layoutv3 |
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config: default |
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split: test |
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args: default |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9871382636655949 |
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- name: Recall |
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type: recall |
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value: 0.9935275080906149 |
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- name: F1 |
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type: f1 |
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value: 0.9903225806451612 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9992192379762649 |
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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-registros_100 |
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the data_registros_layoutv3 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0110 |
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- Precision: 0.9871 |
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- Recall: 0.9935 |
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- F1: 0.9903 |
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- Accuracy: 0.9992 |
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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-06 |
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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: 3000 |
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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 | 10.87 | 250 | 0.4325 | 0.2663 | 0.2638 | 0.2650 | 0.8982 | |
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| 0.6304 | 21.74 | 500 | 0.2065 | 0.7715 | 0.8139 | 0.7921 | 0.9622 | |
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| 0.6304 | 32.61 | 750 | 0.1058 | 0.9048 | 0.9385 | 0.9214 | 0.9866 | |
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| 0.1413 | 43.48 | 1000 | 0.0600 | 0.9314 | 0.9660 | 0.9484 | 0.9944 | |
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| 0.1413 | 54.35 | 1250 | 0.0377 | 0.9451 | 0.9741 | 0.9594 | 0.9969 | |
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| 0.0558 | 65.22 | 1500 | 0.0277 | 0.9697 | 0.9838 | 0.9767 | 0.9981 | |
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| 0.0558 | 76.09 | 1750 | 0.0199 | 0.9792 | 0.9903 | 0.9847 | 0.9988 | |
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| 0.0307 | 86.96 | 2000 | 0.0160 | 0.9824 | 0.9919 | 0.9871 | 0.9989 | |
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| 0.0307 | 97.83 | 2250 | 0.0147 | 0.9823 | 0.9903 | 0.9863 | 0.9988 | |
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| 0.0211 | 108.7 | 2500 | 0.0122 | 0.9871 | 0.9935 | 0.9903 | 0.9992 | |
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| 0.0211 | 119.57 | 2750 | 0.0113 | 0.9871 | 0.9935 | 0.9903 | 0.9992 | |
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| 0.0174 | 130.43 | 3000 | 0.0110 | 0.9871 | 0.9935 | 0.9903 | 0.9992 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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