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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_cedulas_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-cedulas_v3 |
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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_cedulas_layoutv3 |
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type: data_cedulas_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: 0.8991596638655462 |
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- name: Recall |
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type: recall |
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value: 0.9067796610169492 |
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- name: F1 |
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type: f1 |
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value: 0.9029535864978903 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9816565809379728 |
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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-cedulas_v3 |
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the data_cedulas_layoutv3 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0832 |
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- Precision: 0.8992 |
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- Recall: 0.9068 |
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- F1: 0.9030 |
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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: 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 | 3.12 | 250 | 0.7409 | 0.2850 | 0.2729 | 0.2788 | 0.8614 | |
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| 0.9048 | 6.25 | 500 | 0.3660 | 0.6222 | 0.6559 | 0.6386 | 0.9393 | |
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| 0.9048 | 9.38 | 750 | 0.2132 | 0.7492 | 0.7593 | 0.7542 | 0.9544 | |
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| 0.2923 | 12.5 | 1000 | 0.1467 | 0.7830 | 0.7949 | 0.7889 | 0.9661 | |
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| 0.2923 | 15.62 | 1250 | 0.1172 | 0.8114 | 0.8237 | 0.8175 | 0.9701 | |
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| 0.1445 | 18.75 | 1500 | 0.1013 | 0.8560 | 0.8763 | 0.8660 | 0.9766 | |
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| 0.1445 | 21.88 | 1750 | 0.0952 | 0.8811 | 0.8915 | 0.8863 | 0.9794 | |
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| 0.0956 | 25.0 | 2000 | 0.0876 | 0.8923 | 0.8983 | 0.8953 | 0.9807 | |
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| 0.0956 | 28.12 | 2250 | 0.0840 | 0.9005 | 0.9051 | 0.9028 | 0.9811 | |
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| 0.0766 | 31.25 | 2500 | 0.0832 | 0.8992 | 0.9068 | 0.9030 | 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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