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---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: dit-base-Document_Classification-Desafio_1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: validation
split: train
args: validation
metrics:
- name: Accuracy
type: accuracy
value: 0.9865
---
<!-- 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. -->
# dit-base-Document_Classification-Desafio_1
This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0436
- Accuracy: 0.9865
- Weighted f1: 0.9865
- Micro f1: 0.9865
- Macro f1: 0.9863
- Weighted recall: 0.9865
- Micro recall: 0.9865
- Macro recall: 0.9861
- Weighted precision: 0.9869
- Micro precision: 0.9865
- Macro precision: 0.9870
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 0.8316 | 0.99 | 62 | 0.7519 | 0.743 | 0.7020 | 0.743 | 0.7015 | 0.743 | 0.743 | 0.7430 | 0.6827 | 0.743 | 0.6819 |
| 0.3561 | 2.0 | 125 | 0.2302 | 0.9395 | 0.9401 | 0.9395 | 0.9400 | 0.9395 | 0.9395 | 0.9394 | 0.9482 | 0.9395 | 0.9480 |
| 0.2222 | 2.99 | 187 | 0.1350 | 0.956 | 0.9564 | 0.956 | 0.9561 | 0.956 | 0.956 | 0.9551 | 0.9598 | 0.956 | 0.9600 |
| 0.1705 | 4.0 | 250 | 0.0873 | 0.9725 | 0.9727 | 0.9725 | 0.9725 | 0.9725 | 0.9725 | 0.9721 | 0.9740 | 0.9725 | 0.9740 |
| 0.1541 | 4.99 | 312 | 0.0642 | 0.9825 | 0.9825 | 0.9825 | 0.9824 | 0.9825 | 0.9825 | 0.9822 | 0.9830 | 0.9825 | 0.9830 |
| 0.1253 | 6.0 | 375 | 0.0330 | 0.9915 | 0.9915 | 0.9915 | 0.9914 | 0.9915 | 0.9915 | 0.9913 | 0.9916 | 0.9915 | 0.9916 |
| 0.1196 | 6.99 | 437 | 0.0524 | 0.982 | 0.9822 | 0.982 | 0.9820 | 0.982 | 0.982 | 0.9817 | 0.9832 | 0.982 | 0.9832 |
| 0.0896 | 7.94 | 496 | 0.0436 | 0.9865 | 0.9865 | 0.9865 | 0.9863 | 0.9865 | 0.9865 | 0.9861 | 0.9869 | 0.9865 | 0.9870 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3
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