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license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LayoutLMv3_97_2
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_97_2
This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5892
- Precision: 0.8315
- Recall: 0.7721
- F1: 0.8007
- Accuracy: 0.9122
## 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-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: 2000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 2.56 | 100 | 0.4807 | 0.6058 | 0.4966 | 0.5458 | 0.8297 |
| No log | 5.13 | 200 | 0.3940 | 0.7553 | 0.6088 | 0.6742 | 0.8771 |
| No log | 7.69 | 300 | 0.3804 | 0.7438 | 0.7109 | 0.7270 | 0.9008 |
| No log | 10.26 | 400 | 0.3900 | 0.8185 | 0.8129 | 0.8157 | 0.9096 |
| 0.2035 | 12.82 | 500 | 0.4102 | 0.8255 | 0.7721 | 0.7979 | 0.9087 |
| 0.2035 | 15.38 | 600 | 0.4077 | 0.8095 | 0.8095 | 0.8095 | 0.9148 |
| 0.2035 | 17.95 | 700 | 0.4915 | 0.7867 | 0.7653 | 0.7759 | 0.8982 |
| 0.2035 | 20.51 | 800 | 0.4861 | 0.8269 | 0.7959 | 0.8111 | 0.9131 |
| 0.2035 | 23.08 | 900 | 0.5051 | 0.7818 | 0.7313 | 0.7557 | 0.9052 |
| 0.0117 | 25.64 | 1000 | 0.5404 | 0.8303 | 0.7653 | 0.7965 | 0.9069 |
| 0.0117 | 28.21 | 1100 | 0.6110 | 0.8492 | 0.7279 | 0.7839 | 0.9061 |
| 0.0117 | 30.77 | 1200 | 0.5379 | 0.8014 | 0.7823 | 0.7917 | 0.9096 |
| 0.0117 | 33.33 | 1300 | 0.5343 | 0.8057 | 0.7755 | 0.7903 | 0.9131 |
| 0.0117 | 35.9 | 1400 | 0.5590 | 0.8333 | 0.7653 | 0.7979 | 0.9140 |
| 0.0013 | 38.46 | 1500 | 0.6296 | 0.8488 | 0.7449 | 0.7935 | 0.9122 |
| 0.0013 | 41.03 | 1600 | 0.6089 | 0.8421 | 0.7619 | 0.8 | 0.9122 |
| 0.0013 | 43.59 | 1700 | 0.5869 | 0.8291 | 0.7755 | 0.8014 | 0.9140 |
| 0.0013 | 46.15 | 1800 | 0.5847 | 0.8291 | 0.7755 | 0.8014 | 0.9140 |
| 0.0013 | 48.72 | 1900 | 0.5881 | 0.8285 | 0.7721 | 0.7993 | 0.9131 |
| 0.0004 | 51.28 | 2000 | 0.5892 | 0.8315 | 0.7721 | 0.8007 | 0.9122 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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