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---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test
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. -->
# test
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4675
- Precision: 0.8
- Recall: 0.8649
- F1: 0.8312
- Accuracy: 0.8318
## 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: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 3.85 | 50 | 1.1808 | 0.7013 | 0.7297 | 0.7152 | 0.7196 |
| No log | 7.69 | 100 | 0.7117 | 0.7317 | 0.8108 | 0.7692 | 0.8037 |
| No log | 11.54 | 150 | 0.5580 | 0.7778 | 0.8514 | 0.8129 | 0.8224 |
| No log | 15.38 | 200 | 0.5009 | 0.8228 | 0.8784 | 0.8497 | 0.8411 |
| No log | 19.23 | 250 | 0.4659 | 0.8228 | 0.8784 | 0.8497 | 0.8505 |
| No log | 23.08 | 300 | 0.4734 | 0.7901 | 0.8649 | 0.8258 | 0.8318 |
| No log | 26.92 | 350 | 0.4496 | 0.8205 | 0.8649 | 0.8421 | 0.8318 |
| No log | 30.77 | 400 | 0.4619 | 0.8 | 0.8649 | 0.8312 | 0.8318 |
| No log | 34.62 | 450 | 0.4560 | 0.8125 | 0.8784 | 0.8442 | 0.8411 |
| 0.3885 | 38.46 | 500 | 0.4675 | 0.8 | 0.8649 | 0.8312 | 0.8318 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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