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---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-large-cord
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-large-cord
This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1616
- Precision: 0.9526
- Recall: 0.9482
- F1: 0.9504
- Accuracy: 0.9677
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.25 | 100 | 0.5321 | 0.7584 | 0.7859 | 0.7719 | 0.8224 |
| No log | 0.5 | 200 | 0.4949 | 0.8091 | 0.8354 | 0.8221 | 0.8683 |
| No log | 0.75 | 300 | 0.3478 | 0.8668 | 0.8648 | 0.8658 | 0.8916 |
| No log | 1.0 | 400 | 0.5194 | 0.75 | 0.7117 | 0.7304 | 0.8513 |
| 0.6065 | 1.25 | 500 | 0.3052 | 0.9059 | 0.9003 | 0.9031 | 0.9341 |
| 0.6065 | 1.5 | 600 | 0.2427 | 0.9245 | 0.9173 | 0.9209 | 0.9443 |
| 0.6065 | 1.75 | 700 | 0.2372 | 0.9174 | 0.9181 | 0.9177 | 0.9477 |
| 0.6065 | 2.0 | 800 | 0.2044 | 0.9247 | 0.9212 | 0.9230 | 0.9494 |
| 0.6065 | 2.25 | 900 | 0.1847 | 0.9442 | 0.9413 | 0.9427 | 0.9613 |
| 0.1862 | 2.5 | 1000 | 0.1616 | 0.9526 | 0.9482 | 0.9504 | 0.9677 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.16.1
- Tokenizers 0.15.0
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