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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-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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# layoutlmv2-cord-ner
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This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0952
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- Precision: 0.9639
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- Recall: 0.9741
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- F1: 0.9690
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- Accuracy: 0.9911
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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 | 0.5962 | 0.8714 | 0.8973 | 0.8842 | 0.9405 |
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| No log | 2.0 | 226 | 0.4064 | 0.8713 | 0.9098 | 0.8901 | 0.9511 |
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| No log | 3.0 | 339 | 0.2687 | 0.9314 | 0.9386 | 0.9350 | 0.9754 |
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| No log | 4.0 | 452 | 0.2007 | 0.9355 | 0.9472 | 0.9413 | 0.9792 |
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| 0.4677 | 5.0 | 565 | 0.1625 | 0.9497 | 0.9597 | 0.9547 | 0.9834 |
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| 0.4677 | 6.0 | 678 | 0.1326 | 0.9526 | 0.9645 | 0.9585 | 0.9868 |
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| 0.4677 | 7.0 | 791 | 0.1212 | 0.9508 | 0.9645 | 0.9576 | 0.9851 |
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| 0.4677 | 8.0 | 904 | 0.1019 | 0.9675 | 0.9712 | 0.9693 | 0.9911 |
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| 0.1131 | 9.0 | 1017 | 0.1029 | 0.9545 | 0.9664 | 0.9604 | 0.9881 |
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| 0.1131 | 10.0 | 1130 | 0.0952 | 0.9639 | 0.9741 | 0.9690 | 0.9911 |
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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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