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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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+
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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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+
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+ # layoutlmv3-finetuned-registros_100
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+
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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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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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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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+
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+
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+ ### Framework versions
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+
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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