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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: layoutlmv3-cord-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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# layoutlmv3-cord-ner
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1215
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- Precision: 0.9448
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- Recall: 0.9520
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- F1: 0.9484
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- Accuracy: 0.9762
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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: 10
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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 | 113 | 0.1771 | 0.8485 | 0.8925 | 0.8700 | 0.9393 |
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| No log | 2.0 | 226 | 0.1584 | 0.8915 | 0.9146 | 0.9029 | 0.9524 |
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| No log | 3.0 | 339 | 0.1153 | 0.9160 | 0.9309 | 0.9234 | 0.9686 |
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| No log | 4.0 | 452 | 0.1477 | 0.9110 | 0.9136 | 0.9123 | 0.9592 |
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| 0.1562 | 5.0 | 565 | 0.0861 | 0.9363 | 0.9443 | 0.9403 | 0.9741 |
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| 0.1562 | 6.0 | 678 | 0.1165 | 0.9109 | 0.9415 | 0.9259 | 0.9673 |
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| 0.1562 | 7.0 | 791 | 0.1280 | 0.9278 | 0.9367 | 0.9322 | 0.9707 |
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| 0.1562 | 8.0 | 904 | 0.1122 | 0.9462 | 0.9453 | 0.9458 | 0.9762 |
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| 0.0224 | 9.0 | 1017 | 0.1265 | 0.9431 | 0.9539 | 0.9485 | 0.9771 |
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| 0.0224 | 10.0 | 1130 | 0.1215 | 0.9448 | 0.9520 | 0.9484 | 0.9762 |
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### Framework versions
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- Transformers 4.20.0.dev0
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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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