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--- |
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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: layoutlmv1-er-ner |
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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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# layoutlmv1-er-ner |
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This model is a fine-tuned version of [renjithks/layoutlmv1-cord-ner](https://huggingface.co/renjithks/layoutlmv1-cord-ner) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2092 |
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- Precision: 0.7202 |
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- Recall: 0.7238 |
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- F1: 0.7220 |
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- Accuracy: 0.9639 |
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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: 8 |
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- eval_batch_size: 8 |
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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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- num_epochs: 20 |
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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 | 1.0 | 41 | 0.2444 | 0.4045 | 0.3996 | 0.4020 | 0.9226 | |
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| No log | 2.0 | 82 | 0.1640 | 0.5319 | 0.6098 | 0.5682 | 0.9455 | |
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| No log | 3.0 | 123 | 0.1531 | 0.6324 | 0.6614 | 0.6466 | 0.9578 | |
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| No log | 4.0 | 164 | 0.1440 | 0.6927 | 0.6743 | 0.6834 | 0.9620 | |
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| No log | 5.0 | 205 | 0.1520 | 0.6750 | 0.6958 | 0.6853 | 0.9613 | |
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| No log | 6.0 | 246 | 0.1597 | 0.6840 | 0.6987 | 0.6913 | 0.9605 | |
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| No log | 7.0 | 287 | 0.1910 | 0.7002 | 0.6887 | 0.6944 | 0.9605 | |
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| No log | 8.0 | 328 | 0.1860 | 0.6834 | 0.6923 | 0.6878 | 0.9609 | |
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| No log | 9.0 | 369 | 0.1665 | 0.6785 | 0.7102 | 0.6940 | 0.9624 | |
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| No log | 10.0 | 410 | 0.1816 | 0.7016 | 0.7052 | 0.7034 | 0.9624 | |
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| No log | 11.0 | 451 | 0.1808 | 0.6913 | 0.7166 | 0.7038 | 0.9638 | |
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| No log | 12.0 | 492 | 0.2165 | 0.712 | 0.7023 | 0.7071 | 0.9628 | |
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| 0.1014 | 13.0 | 533 | 0.2135 | 0.6979 | 0.7109 | 0.7043 | 0.9613 | |
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| 0.1014 | 14.0 | 574 | 0.2154 | 0.6906 | 0.7109 | 0.7006 | 0.9612 | |
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| 0.1014 | 15.0 | 615 | 0.2118 | 0.6902 | 0.7016 | 0.6958 | 0.9615 | |
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| 0.1014 | 16.0 | 656 | 0.2091 | 0.6985 | 0.7080 | 0.7032 | 0.9623 | |
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| 0.1014 | 17.0 | 697 | 0.2104 | 0.7118 | 0.7123 | 0.7121 | 0.9630 | |
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| 0.1014 | 18.0 | 738 | 0.2081 | 0.7129 | 0.7231 | 0.7179 | 0.9638 | |
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| 0.1014 | 19.0 | 779 | 0.2093 | 0.7205 | 0.7231 | 0.7218 | 0.9638 | |
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| 0.1014 | 20.0 | 820 | 0.2092 | 0.7202 | 0.7238 | 0.7220 | 0.9639 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.11.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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