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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: Output_LayoutLMv3_v1
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. -->
# Output_LayoutLMv3_v1
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: 0.2030
- Precision: 0.8
- Recall: 0.8319
- F1: 0.8156
- Accuracy: 0.9743
## 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: 1e-06
- 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: 3000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 2.27 | 100 | 0.3343 | 0.2051 | 0.0354 | 0.0604 | 0.8943 |
| No log | 4.55 | 200 | 0.1934 | 0.7143 | 0.6858 | 0.6998 | 0.9524 |
| No log | 6.82 | 300 | 0.1541 | 0.7344 | 0.7832 | 0.7580 | 0.9590 |
| No log | 9.09 | 400 | 0.1375 | 0.7542 | 0.8009 | 0.7768 | 0.9648 |
| 0.2233 | 11.36 | 500 | 0.1323 | 0.7915 | 0.8230 | 0.8069 | 0.9695 |
| 0.2233 | 13.64 | 600 | 0.1395 | 0.8 | 0.8142 | 0.8070 | 0.9695 |
| 0.2233 | 15.91 | 700 | 0.1495 | 0.7773 | 0.8186 | 0.7974 | 0.9686 |
| 0.2233 | 18.18 | 800 | 0.1444 | 0.8103 | 0.8319 | 0.8210 | 0.9752 |
| 0.2233 | 20.45 | 900 | 0.1732 | 0.7550 | 0.8319 | 0.7916 | 0.9676 |
| 0.0375 | 22.73 | 1000 | 0.1553 | 0.7966 | 0.8319 | 0.8139 | 0.9743 |
| 0.0375 | 25.0 | 1100 | 0.1639 | 0.7924 | 0.8274 | 0.8095 | 0.9724 |
| 0.0375 | 27.27 | 1200 | 0.1598 | 0.8034 | 0.8319 | 0.8174 | 0.9752 |
| 0.0375 | 29.55 | 1300 | 0.1723 | 0.8069 | 0.8319 | 0.8192 | 0.9743 |
| 0.0375 | 31.82 | 1400 | 0.1929 | 0.7810 | 0.8363 | 0.8077 | 0.9724 |
| 0.0188 | 34.09 | 1500 | 0.1940 | 0.7866 | 0.8319 | 0.8086 | 0.9714 |
| 0.0188 | 36.36 | 1600 | 0.1904 | 0.7932 | 0.8319 | 0.8121 | 0.9724 |
| 0.0188 | 38.64 | 1700 | 0.1910 | 0.7899 | 0.8319 | 0.8103 | 0.9724 |
| 0.0188 | 40.91 | 1800 | 0.2083 | 0.7801 | 0.8319 | 0.8051 | 0.9705 |
| 0.0188 | 43.18 | 1900 | 0.1880 | 0.8 | 0.8319 | 0.8156 | 0.9743 |
| 0.0123 | 45.45 | 2000 | 0.1902 | 0.8069 | 0.8319 | 0.8192 | 0.9752 |
| 0.0123 | 47.73 | 2100 | 0.1894 | 0.8095 | 0.8274 | 0.8184 | 0.9752 |
| 0.0123 | 50.0 | 2200 | 0.1833 | 0.8210 | 0.8319 | 0.8264 | 0.9771 |
| 0.0123 | 52.27 | 2300 | 0.1911 | 0.8069 | 0.8319 | 0.8192 | 0.9752 |
| 0.0123 | 54.55 | 2400 | 0.1972 | 0.8 | 0.8319 | 0.8156 | 0.9743 |
| 0.0086 | 56.82 | 2500 | 0.1924 | 0.8139 | 0.8319 | 0.8228 | 0.9762 |
| 0.0086 | 59.09 | 2600 | 0.1983 | 0.8 | 0.8319 | 0.8156 | 0.9743 |
| 0.0086 | 61.36 | 2700 | 0.2033 | 0.8 | 0.8319 | 0.8156 | 0.9743 |
| 0.0086 | 63.64 | 2800 | 0.2039 | 0.8 | 0.8319 | 0.8156 | 0.9743 |
| 0.0086 | 65.91 | 2900 | 0.2026 | 0.8 | 0.8319 | 0.8156 | 0.9743 |
| 0.0084 | 68.18 | 3000 | 0.2030 | 0.8 | 0.8319 | 0.8156 | 0.9743 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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