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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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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-funsd2 |
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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-funsd2 |
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8330 |
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- Precision: 0.9046 |
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- Recall: 0.9105 |
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- F1: 0.9076 |
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- Accuracy: 0.8536 |
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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-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 16 |
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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: 500 |
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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 | 1.32 | 50 | 0.6163 | 0.8088 | 0.8965 | 0.8504 | 0.8088 | |
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| No log | 2.63 | 100 | 0.5416 | 0.8037 | 0.868 | 0.8346 | 0.8134 | |
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| No log | 3.95 | 150 | 0.5572 | 0.8446 | 0.8885 | 0.8660 | 0.8385 | |
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| No log | 5.26 | 200 | 0.7317 | 0.8458 | 0.8555 | 0.8506 | 0.8124 | |
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| No log | 6.58 | 250 | 0.7220 | 0.8877 | 0.8935 | 0.8906 | 0.8385 | |
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| No log | 7.89 | 300 | 0.8070 | 0.8778 | 0.9055 | 0.8915 | 0.8436 | |
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| No log | 9.21 | 350 | 0.7895 | 0.8969 | 0.913 | 0.9049 | 0.8477 | |
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| No log | 10.53 | 400 | 0.8168 | 0.8935 | 0.889 | 0.8912 | 0.8412 | |
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| No log | 11.84 | 450 | 0.8233 | 0.8955 | 0.917 | 0.9061 | 0.8521 | |
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| 0.2564 | 13.16 | 500 | 0.8330 | 0.9046 | 0.9105 | 0.9076 | 0.8536 | |
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### Framework versions |
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- Transformers 4.12.5 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 2.13.2 |
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- Tokenizers 0.10.1 |
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