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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-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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# layoutlmv3-er-ner
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This model is a fine-tuned version of [renjithks/layoutlmv3-cord-ner](https://huggingface.co/renjithks/layoutlmv3-cord-ner) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1905
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- Precision: 0.6189
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- Recall: 0.6591
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- F1: 0.6384
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- Accuracy: 0.9529
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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 | 22 | 0.2818 | 0.3420 | 0.2801 | 0.3079 | 0.9064 |
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| No log | 2.0 | 44 | 0.1898 | 0.5280 | 0.4540 | 0.4882 | 0.9380 |
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| No log | 3.0 | 66 | 0.1729 | 0.5992 | 0.6153 | 0.6071 | 0.9479 |
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| No log | 4.0 | 88 | 0.1880 | 0.6179 | 0.6040 | 0.6109 | 0.9457 |
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| No log | 5.0 | 110 | 0.1845 | 0.5607 | 0.5941 | 0.5769 | 0.9451 |
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| No log | 6.0 | 132 | 0.1869 | 0.6071 | 0.6294 | 0.6181 | 0.9501 |
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| No log | 7.0 | 154 | 0.1838 | 0.6288 | 0.6492 | 0.6388 | 0.9517 |
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| No log | 8.0 | 176 | 0.1928 | 0.5973 | 0.6167 | 0.6068 | 0.9503 |
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| No log | 9.0 | 198 | 0.1899 | 0.6179 | 0.6634 | 0.6398 | 0.9521 |
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| No log | 10.0 | 220 | 0.1905 | 0.6189 | 0.6591 | 0.6384 | 0.9529 |
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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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