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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_v2 |
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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: 0.860632183908046 |
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- name: Recall |
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type: recall |
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value: 0.9374021909233177 |
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- name: F1 |
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type: f1 |
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value: 0.8973782771535581 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9816688664026983 |
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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_v2 |
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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: 0.1473 |
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- Precision: 0.8606 |
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- Recall: 0.9374 |
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- F1: 0.8974 |
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- Accuracy: 0.9817 |
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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: 1000 |
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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 | 0.4204 | 0.4257 | 0.4351 | 0.4303 | 0.9104 | |
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| 0.6077 | 21.74 | 500 | 0.2246 | 0.7957 | 0.8654 | 0.8291 | 0.9683 | |
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| 0.6077 | 32.61 | 750 | 0.1636 | 0.8438 | 0.9218 | 0.8811 | 0.9765 | |
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| 0.1638 | 43.48 | 1000 | 0.1473 | 0.8606 | 0.9374 | 0.8974 | 0.9817 | |
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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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