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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-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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# layoutlmv1-cord-ner
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This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1438
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- Precision: 0.9336
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- Recall: 0.9453
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- F1: 0.9394
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- Accuracy: 0.9767
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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 | 0.1251 | 0.9054 | 0.9184 | 0.9119 | 0.9651 |
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| No log | 2.0 | 226 | 0.1343 | 0.9002 | 0.9261 | 0.9130 | 0.9635 |
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| No log | 3.0 | 339 | 0.1264 | 0.9189 | 0.9357 | 0.9272 | 0.9647 |
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| No log | 4.0 | 452 | 0.1235 | 0.9122 | 0.9376 | 0.9248 | 0.9681 |
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| 0.1371 | 5.0 | 565 | 0.1353 | 0.9378 | 0.9405 | 0.9391 | 0.9717 |
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| 0.1371 | 6.0 | 678 | 0.1431 | 0.9233 | 0.9357 | 0.9295 | 0.9709 |
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| 0.1371 | 7.0 | 791 | 0.1473 | 0.9289 | 0.9405 | 0.9347 | 0.9759 |
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| 0.1371 | 8.0 | 904 | 0.1407 | 0.9473 | 0.9491 | 0.9482 | 0.9784 |
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| 0.0106 | 9.0 | 1017 | 0.1440 | 0.9301 | 0.9453 | 0.9376 | 0.9769 |
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| 0.0106 | 10.0 | 1130 | 0.1438 | 0.9336 | 0.9453 | 0.9394 | 0.9767 |
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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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