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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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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: layoutlmv2-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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# layoutlmv2-er-ner |
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This model is a fine-tuned version of [renjithks/layoutlmv2-cord-ner](https://huggingface.co/renjithks/layoutlmv2-cord-ner) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1217 |
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- Precision: 0.7810 |
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- Recall: 0.8085 |
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- F1: 0.7945 |
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- Accuracy: 0.9747 |
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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 | 0.5441 | 0.0 | 0.0 | 0.0 | 0.8851 | |
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| No log | 2.0 | 82 | 0.4660 | 0.1019 | 0.0732 | 0.0852 | 0.8690 | |
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| No log | 3.0 | 123 | 0.2506 | 0.4404 | 0.4828 | 0.4606 | 0.9240 | |
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| No log | 4.0 | 164 | 0.1725 | 0.6120 | 0.6076 | 0.6098 | 0.9529 | |
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| No log | 5.0 | 205 | 0.1387 | 0.7204 | 0.7245 | 0.7225 | 0.9671 | |
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| No log | 6.0 | 246 | 0.1237 | 0.7742 | 0.7747 | 0.7745 | 0.9722 | |
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| No log | 7.0 | 287 | 0.1231 | 0.7619 | 0.7554 | 0.7586 | 0.9697 | |
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| No log | 8.0 | 328 | 0.1199 | 0.7994 | 0.7719 | 0.7854 | 0.9738 | |
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| No log | 9.0 | 369 | 0.1197 | 0.7937 | 0.8113 | 0.8024 | 0.9741 | |
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| No log | 10.0 | 410 | 0.1284 | 0.7581 | 0.7597 | 0.7589 | 0.9690 | |
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| No log | 11.0 | 451 | 0.1172 | 0.7792 | 0.7848 | 0.7820 | 0.9738 | |
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| No log | 12.0 | 492 | 0.1192 | 0.7913 | 0.7970 | 0.7941 | 0.9743 | |
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| 0.1858 | 13.0 | 533 | 0.1175 | 0.7960 | 0.8006 | 0.7983 | 0.9753 | |
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| 0.1858 | 14.0 | 574 | 0.1184 | 0.7724 | 0.8034 | 0.7876 | 0.9740 | |
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| 0.1858 | 15.0 | 615 | 0.1171 | 0.7882 | 0.8142 | 0.8010 | 0.9756 | |
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| 0.1858 | 16.0 | 656 | 0.1195 | 0.7829 | 0.8070 | 0.7948 | 0.9745 | |
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| 0.1858 | 17.0 | 697 | 0.1209 | 0.7810 | 0.8006 | 0.7906 | 0.9743 | |
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| 0.1858 | 18.0 | 738 | 0.1241 | 0.7806 | 0.7963 | 0.7884 | 0.9740 | |
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| 0.1858 | 19.0 | 779 | 0.1222 | 0.7755 | 0.8027 | 0.7889 | 0.9742 | |
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| 0.1858 | 20.0 | 820 | 0.1217 | 0.7810 | 0.8085 | 0.7945 | 0.9747 | |
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### Framework versions |
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- Transformers 4.16.2 |
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- Pytorch 1.9.0+cu111 |
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- Datasets 1.18.4 |
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- Tokenizers 0.11.6 |
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