---
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