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library_name: transformers
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
- f1
model-index:
- name: 3class_EfficientFormer30M_ForTesting
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. -->
# 3class_EfficientFormer30M_ForTesting
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0636
- Precision: 0.9798
- Recall: 0.9764
- Accuracy: 0.9818
- F1: 0.9781
- Roc Auc: 0.9983
## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 | Roc Auc |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:--------:|:------:|:-------:|
| 0.1199 | 0.3436 | 200 | 0.0955 | 0.9604 | 0.9551 | 0.9650 | 0.9576 | 0.9958 |
| 0.0656 | 0.6873 | 400 | 0.0722 | 0.9754 | 0.9699 | 0.9774 | 0.9725 | 0.9972 |
| 0.0418 | 1.0309 | 600 | 0.0797 | 0.9758 | 0.9740 | 0.9793 | 0.9749 | 0.9969 |
| 0.0744 | 1.3746 | 800 | 0.0636 | 0.9798 | 0.9764 | 0.9818 | 0.9781 | 0.9983 |
| 0.0044 | 1.7182 | 1000 | 0.0659 | 0.9793 | 0.9756 | 0.9814 | 0.9774 | 0.9983 |
| 0.0412 | 2.0619 | 1200 | 0.0690 | 0.9782 | 0.9779 | 0.9818 | 0.9780 | 0.9983 |
| 0.0029 | 2.4055 | 1400 | 0.0744 | 0.9808 | 0.9780 | 0.9830 | 0.9794 | 0.9984 |
| 0.0245 | 2.7491 | 1600 | 0.0872 | 0.9813 | 0.9755 | 0.9821 | 0.9782 | 0.9981 |
| 0.0006 | 3.0928 | 1800 | 0.0753 | 0.9811 | 0.9794 | 0.9837 | 0.9803 | 0.9985 |
| 0.0049 | 3.4364 | 2000 | 0.0844 | 0.9799 | 0.9773 | 0.9823 | 0.9785 | 0.9984 |
| 0.0011 | 3.7801 | 2200 | 0.0827 | 0.9806 | 0.9778 | 0.9828 | 0.9792 | 0.9984 |
### Framework versions
- Transformers 5.3.0
- Pytorch 2.10.0+cu128
- Datasets 4.7.0
- Tokenizers 0.22.2
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