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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_v3
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_v3
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: 0.1344
- Precision: 0.7699
- Recall: 0.8142
- F1: 0.7914
- Accuracy: 0.9695
## 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: 3e-07
- train_batch_size: 4
- 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 | 4.55 | 100 | 0.5786 | 0.0 | 0.0 | 0.0 | 0.8867 |
| No log | 9.09 | 200 | 0.4032 | 0.0 | 0.0 | 0.0 | 0.8867 |
| No log | 13.64 | 300 | 0.2908 | 0.4091 | 0.1593 | 0.2293 | 0.9067 |
| No log | 18.18 | 400 | 0.2300 | 0.5858 | 0.4381 | 0.5013 | 0.9267 |
| 0.5251 | 22.73 | 500 | 0.1981 | 0.685 | 0.6062 | 0.6432 | 0.9438 |
| 0.5251 | 27.27 | 600 | 0.1790 | 0.7130 | 0.6814 | 0.6968 | 0.9505 |
| 0.5251 | 31.82 | 700 | 0.1689 | 0.7249 | 0.7345 | 0.7297 | 0.9581 |
| 0.5251 | 36.36 | 800 | 0.1593 | 0.7478 | 0.7478 | 0.7478 | 0.9619 |
| 0.5251 | 40.91 | 900 | 0.1582 | 0.75 | 0.7832 | 0.7662 | 0.9638 |
| 0.129 | 45.45 | 1000 | 0.1527 | 0.7306 | 0.7920 | 0.7601 | 0.9619 |
| 0.129 | 50.0 | 1100 | 0.1470 | 0.7429 | 0.8053 | 0.7728 | 0.9638 |
| 0.129 | 54.55 | 1200 | 0.1418 | 0.7552 | 0.8053 | 0.7794 | 0.9657 |
| 0.129 | 59.09 | 1300 | 0.1404 | 0.7657 | 0.8097 | 0.7871 | 0.9667 |
| 0.129 | 63.64 | 1400 | 0.1368 | 0.7741 | 0.8186 | 0.7957 | 0.9695 |
| 0.0799 | 68.18 | 1500 | 0.1316 | 0.7741 | 0.8186 | 0.7957 | 0.9705 |
| 0.0799 | 72.73 | 1600 | 0.1301 | 0.7764 | 0.8142 | 0.7948 | 0.9705 |
| 0.0799 | 77.27 | 1700 | 0.1326 | 0.7699 | 0.8142 | 0.7914 | 0.9695 |
| 0.0799 | 81.82 | 1800 | 0.1357 | 0.7552 | 0.8053 | 0.7794 | 0.9676 |
| 0.0799 | 86.36 | 1900 | 0.1304 | 0.7699 | 0.8142 | 0.7914 | 0.9695 |
| 0.0561 | 90.91 | 2000 | 0.1326 | 0.7699 | 0.8142 | 0.7914 | 0.9695 |
| 0.0561 | 95.45 | 2100 | 0.1340 | 0.7689 | 0.8097 | 0.7888 | 0.9695 |
| 0.0561 | 100.0 | 2200 | 0.1371 | 0.7635 | 0.8142 | 0.7880 | 0.9686 |
| 0.0561 | 104.55 | 2300 | 0.1337 | 0.7764 | 0.8142 | 0.7948 | 0.9705 |
| 0.0561 | 109.09 | 2400 | 0.1310 | 0.7764 | 0.8142 | 0.7948 | 0.9705 |
| 0.0451 | 113.64 | 2500 | 0.1353 | 0.7657 | 0.8097 | 0.7871 | 0.9686 |
| 0.0451 | 118.18 | 2600 | 0.1357 | 0.7657 | 0.8097 | 0.7871 | 0.9686 |
| 0.0451 | 122.73 | 2700 | 0.1361 | 0.7699 | 0.8142 | 0.7914 | 0.9695 |
| 0.0451 | 127.27 | 2800 | 0.1358 | 0.7667 | 0.8142 | 0.7897 | 0.9686 |
| 0.0451 | 131.82 | 2900 | 0.1347 | 0.7699 | 0.8142 | 0.7914 | 0.9695 |
| 0.0414 | 136.36 | 3000 | 0.1344 | 0.7699 | 0.8142 | 0.7914 | 0.9695 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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