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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_v3 |
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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_v3 |
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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: 0.1344 |
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- Precision: 0.7699 |
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- Recall: 0.8142 |
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- F1: 0.7914 |
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- Accuracy: 0.9695 |
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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: 3e-07 |
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- train_batch_size: 4 |
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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 | 4.55 | 100 | 0.5786 | 0.0 | 0.0 | 0.0 | 0.8867 | |
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| No log | 9.09 | 200 | 0.4032 | 0.0 | 0.0 | 0.0 | 0.8867 | |
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| No log | 13.64 | 300 | 0.2908 | 0.4091 | 0.1593 | 0.2293 | 0.9067 | |
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| No log | 18.18 | 400 | 0.2300 | 0.5858 | 0.4381 | 0.5013 | 0.9267 | |
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| 0.5251 | 22.73 | 500 | 0.1981 | 0.685 | 0.6062 | 0.6432 | 0.9438 | |
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| 0.5251 | 27.27 | 600 | 0.1790 | 0.7130 | 0.6814 | 0.6968 | 0.9505 | |
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| 0.5251 | 31.82 | 700 | 0.1689 | 0.7249 | 0.7345 | 0.7297 | 0.9581 | |
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| 0.5251 | 36.36 | 800 | 0.1593 | 0.7478 | 0.7478 | 0.7478 | 0.9619 | |
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| 0.5251 | 40.91 | 900 | 0.1582 | 0.75 | 0.7832 | 0.7662 | 0.9638 | |
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| 0.129 | 45.45 | 1000 | 0.1527 | 0.7306 | 0.7920 | 0.7601 | 0.9619 | |
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| 0.129 | 50.0 | 1100 | 0.1470 | 0.7429 | 0.8053 | 0.7728 | 0.9638 | |
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| 0.129 | 54.55 | 1200 | 0.1418 | 0.7552 | 0.8053 | 0.7794 | 0.9657 | |
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| 0.129 | 59.09 | 1300 | 0.1404 | 0.7657 | 0.8097 | 0.7871 | 0.9667 | |
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| 0.129 | 63.64 | 1400 | 0.1368 | 0.7741 | 0.8186 | 0.7957 | 0.9695 | |
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| 0.0799 | 68.18 | 1500 | 0.1316 | 0.7741 | 0.8186 | 0.7957 | 0.9705 | |
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| 0.0799 | 72.73 | 1600 | 0.1301 | 0.7764 | 0.8142 | 0.7948 | 0.9705 | |
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| 0.0799 | 77.27 | 1700 | 0.1326 | 0.7699 | 0.8142 | 0.7914 | 0.9695 | |
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| 0.0799 | 81.82 | 1800 | 0.1357 | 0.7552 | 0.8053 | 0.7794 | 0.9676 | |
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| 0.0799 | 86.36 | 1900 | 0.1304 | 0.7699 | 0.8142 | 0.7914 | 0.9695 | |
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| 0.0561 | 90.91 | 2000 | 0.1326 | 0.7699 | 0.8142 | 0.7914 | 0.9695 | |
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| 0.0561 | 95.45 | 2100 | 0.1340 | 0.7689 | 0.8097 | 0.7888 | 0.9695 | |
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| 0.0561 | 100.0 | 2200 | 0.1371 | 0.7635 | 0.8142 | 0.7880 | 0.9686 | |
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| 0.0561 | 104.55 | 2300 | 0.1337 | 0.7764 | 0.8142 | 0.7948 | 0.9705 | |
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| 0.0561 | 109.09 | 2400 | 0.1310 | 0.7764 | 0.8142 | 0.7948 | 0.9705 | |
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| 0.0451 | 113.64 | 2500 | 0.1353 | 0.7657 | 0.8097 | 0.7871 | 0.9686 | |
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| 0.0451 | 118.18 | 2600 | 0.1357 | 0.7657 | 0.8097 | 0.7871 | 0.9686 | |
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| 0.0451 | 122.73 | 2700 | 0.1361 | 0.7699 | 0.8142 | 0.7914 | 0.9695 | |
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| 0.0451 | 127.27 | 2800 | 0.1358 | 0.7667 | 0.8142 | 0.7897 | 0.9686 | |
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| 0.0451 | 131.82 | 2900 | 0.1347 | 0.7699 | 0.8142 | 0.7914 | 0.9695 | |
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| 0.0414 | 136.36 | 3000 | 0.1344 | 0.7699 | 0.8142 | 0.7914 | 0.9695 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.2.2+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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