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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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model-index: |
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- name: lmv2ubiai-pan8doc-06-11 |
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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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# lmv2ubiai-pan8doc-06-11 |
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This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9633 |
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- Dob Precision: 1.0 |
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- Dob Recall: 1.0 |
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- Dob F1: 1.0 |
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- Dob Number: 2 |
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- Fname Precision: 0.6667 |
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- Fname Recall: 1.0 |
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- Fname F1: 0.8 |
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- Fname Number: 2 |
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- Name Precision: 1.0 |
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- Name Recall: 1.0 |
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- Name F1: 1.0 |
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- Name Number: 2 |
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- Pan Precision: 1.0 |
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- Pan Recall: 1.0 |
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- Pan F1: 1.0 |
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- Pan Number: 2 |
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- Overall Precision: 0.8889 |
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- Overall Recall: 1.0 |
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- Overall F1: 0.9412 |
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- Overall Accuracy: 0.9821 |
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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: 4e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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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: constant |
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- num_epochs: 30 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Dob Precision | Dob Recall | Dob F1 | Dob Number | Fname Precision | Fname Recall | Fname F1 | Fname Number | Name Precision | Name Recall | Name F1 | Name Number | Pan Precision | Pan Recall | Pan F1 | Pan Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:-------------:|:----------:|:------:|:----------:|:---------------:|:------------:|:--------:|:------------:|:--------------:|:-----------:|:-------:|:-----------:|:-------------:|:----------:|:------:|:----------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 2.1195 | 1.0 | 6 | 1.7519 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 0.7857 | |
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| 1.6994 | 2.0 | 12 | 1.5117 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 0.7857 | |
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| 1.5521 | 3.0 | 18 | 1.4130 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 0.7857 | |
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| 1.4726 | 4.0 | 24 | 1.3410 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 0.7857 | |
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| 1.395 | 5.0 | 30 | 1.2693 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 0.7857 | |
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| 1.3131 | 6.0 | 36 | 1.2079 | 1.0 | 1.0 | 1.0 | 2 | 0.1667 | 0.5 | 0.25 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 2 | 0.3 | 0.375 | 0.3333 | 0.8929 | |
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| 1.2474 | 7.0 | 42 | 1.1495 | 1.0 | 1.0 | 1.0 | 2 | 0.2 | 0.5 | 0.2857 | 2 | 0.0 | 0.0 | 0.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.4167 | 0.625 | 0.5 | 0.9286 | |
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| 1.1869 | 8.0 | 48 | 1.0942 | 1.0 | 1.0 | 1.0 | 2 | 0.2 | 0.5 | 0.2857 | 2 | 0.0 | 0.0 | 0.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.4167 | 0.625 | 0.5 | 0.9286 | |
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| 1.1369 | 9.0 | 54 | 1.0453 | 1.0 | 1.0 | 1.0 | 2 | 0.4 | 1.0 | 0.5714 | 2 | 0.0 | 0.0 | 0.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5455 | 0.75 | 0.6316 | 0.9464 | |
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| 1.0882 | 10.0 | 60 | 1.0054 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 1.0 | 0.6667 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.7 | 0.875 | 0.7778 | 0.9643 | |
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| 1.0482 | 11.0 | 66 | 0.9633 | 1.0 | 1.0 | 1.0 | 2 | 0.6667 | 1.0 | 0.8 | 2 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.8889 | 1.0 | 0.9412 | 0.9821 | |
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| 1.017 | 12.0 | 72 | 0.9368 | 1.0 | 1.0 | 1.0 | 2 | 0.6667 | 1.0 | 0.8 | 2 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.8889 | 1.0 | 0.9412 | 0.9643 | |
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| 0.9825 | 13.0 | 78 | 0.9139 | 1.0 | 1.0 | 1.0 | 2 | 0.6667 | 1.0 | 0.8 | 2 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.8889 | 1.0 | 0.9412 | 0.9821 | |
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| 0.9459 | 14.0 | 84 | 0.8837 | 1.0 | 1.0 | 1.0 | 2 | 0.6667 | 1.0 | 0.8 | 2 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.8889 | 1.0 | 0.9412 | 0.9643 | |
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| 0.9155 | 15.0 | 90 | 0.8472 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 | |
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| 0.8819 | 16.0 | 96 | 0.8231 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 | |
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| 0.8523 | 17.0 | 102 | 0.7957 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.6667 | 1.0 | 0.8 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.8889 | 1.0 | 0.9412 | 0.9821 | |
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| 0.8251 | 18.0 | 108 | 0.7681 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 | |
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| 0.7982 | 19.0 | 114 | 0.7533 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 | |
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| 0.7762 | 20.0 | 120 | 0.7283 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 | |
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| 0.7558 | 21.0 | 126 | 0.7114 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 | |
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| 0.7346 | 22.0 | 132 | 0.6889 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 | |
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| 0.7116 | 23.0 | 138 | 0.6697 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 | |
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| 0.6898 | 24.0 | 144 | 0.6593 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 | |
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| 0.6748 | 25.0 | 150 | 0.6356 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 | |
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| 0.6487 | 26.0 | 156 | 0.6142 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 | |
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| 0.6312 | 27.0 | 162 | 0.6008 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 | |
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| 0.6156 | 28.0 | 168 | 0.5855 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 | |
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| 0.5961 | 29.0 | 174 | 0.5625 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 | |
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| 0.5781 | 30.0 | 180 | 0.5553 | 1.0 | 1.0 | 1.0 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.5 | 0.5 | 0.5 | 2 | 1.0 | 1.0 | 1.0 | 2 | 0.875 | 0.875 | 0.875 | 0.9643 | |
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### Framework versions |
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- Transformers 4.20.0.dev0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.2.2 |
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- Tokenizers 0.12.1 |
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