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--- |
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license: cc-by-nc-sa-4.0 |
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base_model: microsoft/layoutlmv3-large |
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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: Output_LayoutLMv3_v1 |
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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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# Output_LayoutLMv3_v1 |
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This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2030 |
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- Precision: 0.8 |
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- Recall: 0.8319 |
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- F1: 0.8156 |
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- Accuracy: 0.9743 |
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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-06 |
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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: 3000 |
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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 | 2.27 | 100 | 0.3343 | 0.2051 | 0.0354 | 0.0604 | 0.8943 | |
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| No log | 4.55 | 200 | 0.1934 | 0.7143 | 0.6858 | 0.6998 | 0.9524 | |
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| No log | 6.82 | 300 | 0.1541 | 0.7344 | 0.7832 | 0.7580 | 0.9590 | |
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| No log | 9.09 | 400 | 0.1375 | 0.7542 | 0.8009 | 0.7768 | 0.9648 | |
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| 0.2233 | 11.36 | 500 | 0.1323 | 0.7915 | 0.8230 | 0.8069 | 0.9695 | |
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| 0.2233 | 13.64 | 600 | 0.1395 | 0.8 | 0.8142 | 0.8070 | 0.9695 | |
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| 0.2233 | 15.91 | 700 | 0.1495 | 0.7773 | 0.8186 | 0.7974 | 0.9686 | |
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| 0.2233 | 18.18 | 800 | 0.1444 | 0.8103 | 0.8319 | 0.8210 | 0.9752 | |
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| 0.2233 | 20.45 | 900 | 0.1732 | 0.7550 | 0.8319 | 0.7916 | 0.9676 | |
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| 0.0375 | 22.73 | 1000 | 0.1553 | 0.7966 | 0.8319 | 0.8139 | 0.9743 | |
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| 0.0375 | 25.0 | 1100 | 0.1639 | 0.7924 | 0.8274 | 0.8095 | 0.9724 | |
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| 0.0375 | 27.27 | 1200 | 0.1598 | 0.8034 | 0.8319 | 0.8174 | 0.9752 | |
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| 0.0375 | 29.55 | 1300 | 0.1723 | 0.8069 | 0.8319 | 0.8192 | 0.9743 | |
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| 0.0375 | 31.82 | 1400 | 0.1929 | 0.7810 | 0.8363 | 0.8077 | 0.9724 | |
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| 0.0188 | 34.09 | 1500 | 0.1940 | 0.7866 | 0.8319 | 0.8086 | 0.9714 | |
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| 0.0188 | 36.36 | 1600 | 0.1904 | 0.7932 | 0.8319 | 0.8121 | 0.9724 | |
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| 0.0188 | 38.64 | 1700 | 0.1910 | 0.7899 | 0.8319 | 0.8103 | 0.9724 | |
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| 0.0188 | 40.91 | 1800 | 0.2083 | 0.7801 | 0.8319 | 0.8051 | 0.9705 | |
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| 0.0188 | 43.18 | 1900 | 0.1880 | 0.8 | 0.8319 | 0.8156 | 0.9743 | |
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| 0.0123 | 45.45 | 2000 | 0.1902 | 0.8069 | 0.8319 | 0.8192 | 0.9752 | |
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| 0.0123 | 47.73 | 2100 | 0.1894 | 0.8095 | 0.8274 | 0.8184 | 0.9752 | |
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| 0.0123 | 50.0 | 2200 | 0.1833 | 0.8210 | 0.8319 | 0.8264 | 0.9771 | |
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| 0.0123 | 52.27 | 2300 | 0.1911 | 0.8069 | 0.8319 | 0.8192 | 0.9752 | |
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| 0.0123 | 54.55 | 2400 | 0.1972 | 0.8 | 0.8319 | 0.8156 | 0.9743 | |
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| 0.0086 | 56.82 | 2500 | 0.1924 | 0.8139 | 0.8319 | 0.8228 | 0.9762 | |
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| 0.0086 | 59.09 | 2600 | 0.1983 | 0.8 | 0.8319 | 0.8156 | 0.9743 | |
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| 0.0086 | 61.36 | 2700 | 0.2033 | 0.8 | 0.8319 | 0.8156 | 0.9743 | |
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| 0.0086 | 63.64 | 2800 | 0.2039 | 0.8 | 0.8319 | 0.8156 | 0.9743 | |
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| 0.0086 | 65.91 | 2900 | 0.2026 | 0.8 | 0.8319 | 0.8156 | 0.9743 | |
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| 0.0084 | 68.18 | 3000 | 0.2030 | 0.8 | 0.8319 | 0.8156 | 0.9743 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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