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---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-cord-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlmv3-cord-ner
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1215
- Precision: 0.9448
- Recall: 0.9520
- F1: 0.9484
- Accuracy: 0.9762
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 113 | 0.1771 | 0.8485 | 0.8925 | 0.8700 | 0.9393 |
| No log | 2.0 | 226 | 0.1584 | 0.8915 | 0.9146 | 0.9029 | 0.9524 |
| No log | 3.0 | 339 | 0.1153 | 0.9160 | 0.9309 | 0.9234 | 0.9686 |
| No log | 4.0 | 452 | 0.1477 | 0.9110 | 0.9136 | 0.9123 | 0.9592 |
| 0.1562 | 5.0 | 565 | 0.0861 | 0.9363 | 0.9443 | 0.9403 | 0.9741 |
| 0.1562 | 6.0 | 678 | 0.1165 | 0.9109 | 0.9415 | 0.9259 | 0.9673 |
| 0.1562 | 7.0 | 791 | 0.1280 | 0.9278 | 0.9367 | 0.9322 | 0.9707 |
| 0.1562 | 8.0 | 904 | 0.1122 | 0.9462 | 0.9453 | 0.9458 | 0.9762 |
| 0.0224 | 9.0 | 1017 | 0.1265 | 0.9431 | 0.9539 | 0.9485 | 0.9771 |
| 0.0224 | 10.0 | 1130 | 0.1215 | 0.9448 | 0.9520 | 0.9484 | 0.9762 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
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