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--- |
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license: cc-by-nc-sa-4.0 |
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base_model: microsoft/layoutlmv3-base |
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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: primo_test |
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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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# primo_test |
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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: 0.0105 |
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- Precision: 0.9744 |
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- Recall: 0.9902 |
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- F1: 0.9822 |
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- Accuracy: 0.9979 |
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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: 1e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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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 | 0.03 | 100 | 0.4646 | 0.8275 | 0.8656 | 0.8461 | 0.9306 | |
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| No log | 0.06 | 200 | 0.0824 | 0.9614 | 0.9722 | 0.9667 | 0.9948 | |
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| No log | 0.08 | 300 | 0.0363 | 0.9622 | 0.9859 | 0.9739 | 0.9951 | |
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| No log | 0.11 | 400 | 0.0182 | 0.9756 | 0.9912 | 0.9833 | 0.9980 | |
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| 0.3067 | 0.14 | 500 | 0.0217 | 0.9578 | 0.9813 | 0.9694 | 0.9960 | |
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| 0.3067 | 0.17 | 600 | 0.0106 | 0.9913 | 0.9946 | 0.9929 | 0.9988 | |
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| 0.3067 | 0.19 | 700 | 0.0121 | 0.9733 | 0.9894 | 0.9812 | 0.9977 | |
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| 0.3067 | 0.22 | 800 | 0.0126 | 0.9699 | 0.9881 | 0.9789 | 0.9975 | |
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| 0.3067 | 0.25 | 900 | 0.0098 | 0.9778 | 0.9915 | 0.9846 | 0.9982 | |
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| 0.0105 | 0.28 | 1000 | 0.0105 | 0.9744 | 0.9902 | 0.9822 | 0.9979 | |
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### Framework versions |
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- Transformers 4.36.1 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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