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---
base_model: MBZUAI/swiftformer-xs
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: swiftformer-xs-dmae-va-U-SF
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. -->
# swiftformer-xs-dmae-va-U-SF
This model is a fine-tuned version of [MBZUAI/swiftformer-xs](https://huggingface.co./MBZUAI/swiftformer-xs) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6987
- Accuracy: 0.7431
## 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: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.9 | 7 | 1.3887 | 0.3119 |
| 1.4383 | 1.94 | 15 | 1.3440 | 0.4128 |
| 1.3956 | 2.97 | 23 | 1.3159 | 0.3761 |
| 1.36 | 4.0 | 31 | 1.2907 | 0.3853 |
| 1.36 | 4.9 | 38 | 1.2488 | 0.4404 |
| 1.2912 | 5.94 | 46 | 1.2129 | 0.4037 |
| 1.2387 | 6.97 | 54 | 1.1734 | 0.4679 |
| 1.1607 | 8.0 | 62 | 1.1436 | 0.5138 |
| 1.1607 | 8.9 | 69 | 1.0991 | 0.4954 |
| 1.1224 | 9.94 | 77 | 1.0479 | 0.5505 |
| 1.0547 | 10.97 | 85 | 0.9993 | 0.5963 |
| 1.0137 | 12.0 | 93 | 0.9860 | 0.6147 |
| 0.9652 | 12.9 | 100 | 0.9698 | 0.6147 |
| 0.9652 | 13.94 | 108 | 0.9519 | 0.6055 |
| 0.9217 | 14.97 | 116 | 0.9242 | 0.6055 |
| 0.9122 | 16.0 | 124 | 0.9062 | 0.6147 |
| 0.8763 | 16.9 | 131 | 0.8873 | 0.6422 |
| 0.8763 | 17.94 | 139 | 0.8477 | 0.6514 |
| 0.8471 | 18.97 | 147 | 0.8427 | 0.6514 |
| 0.8331 | 20.0 | 155 | 0.8257 | 0.6881 |
| 0.8167 | 20.9 | 162 | 0.8025 | 0.6881 |
| 0.8022 | 21.94 | 170 | 0.8011 | 0.6972 |
| 0.8022 | 22.97 | 178 | 0.8078 | 0.6972 |
| 0.7996 | 24.0 | 186 | 0.7920 | 0.7064 |
| 0.7962 | 24.9 | 193 | 0.7604 | 0.7248 |
| 0.7268 | 25.94 | 201 | 0.7597 | 0.6972 |
| 0.7268 | 26.97 | 209 | 0.7462 | 0.6972 |
| 0.7477 | 28.0 | 217 | 0.7316 | 0.7064 |
| 0.7411 | 28.9 | 224 | 0.7275 | 0.7523 |
| 0.7415 | 29.94 | 232 | 0.7210 | 0.7248 |
| 0.7159 | 30.97 | 240 | 0.7271 | 0.7248 |
| 0.7159 | 32.0 | 248 | 0.7005 | 0.7431 |
| 0.7322 | 32.9 | 255 | 0.7012 | 0.7431 |
| 0.7124 | 33.94 | 263 | 0.7052 | 0.7523 |
| 0.7194 | 34.97 | 271 | 0.6964 | 0.7615 |
| 0.7194 | 36.0 | 279 | 0.7007 | 0.7523 |
| 0.6903 | 36.13 | 280 | 0.6987 | 0.7431 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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