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---
base_model: MBZUAI/swiftformer-xs
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: swiftformer-xs-dmae-va-U-40
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-40
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: 0.6423
- Accuracy: 0.8165
## 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 | 1.3883 | 0.3211 |
| 1.4011 | 1.94 | 15 | 1.3383 | 0.3578 |
| 1.3646 | 2.97 | 23 | 1.2802 | 0.4404 |
| 1.315 | 4.0 | 31 | 1.2194 | 0.4495 |
| 1.315 | 4.9 | 38 | 1.1718 | 0.5229 |
| 1.2634 | 5.94 | 46 | 1.1279 | 0.5046 |
| 1.1949 | 6.97 | 54 | 1.0761 | 0.5872 |
| 1.1136 | 8.0 | 62 | 1.0224 | 0.6330 |
| 1.1136 | 8.9 | 69 | 0.9976 | 0.6239 |
| 1.0824 | 9.94 | 77 | 0.9518 | 0.6606 |
| 1.0212 | 10.97 | 85 | 0.9117 | 0.6697 |
| 0.9566 | 12.0 | 93 | 0.8973 | 0.6881 |
| 0.935 | 12.9 | 100 | 0.8705 | 0.7064 |
| 0.935 | 13.94 | 108 | 0.8559 | 0.7156 |
| 0.8826 | 14.97 | 116 | 0.8371 | 0.7156 |
| 0.8688 | 16.0 | 124 | 0.8252 | 0.7156 |
| 0.8436 | 16.9 | 131 | 0.8211 | 0.6972 |
| 0.8436 | 17.94 | 139 | 0.8040 | 0.7339 |
| 0.8155 | 18.97 | 147 | 0.7625 | 0.7431 |
| 0.7831 | 20.0 | 155 | 0.7452 | 0.7431 |
| 0.7826 | 20.9 | 162 | 0.7279 | 0.7431 |
| 0.7499 | 21.94 | 170 | 0.7148 | 0.7431 |
| 0.7499 | 22.97 | 178 | 0.7061 | 0.7523 |
| 0.7539 | 24.0 | 186 | 0.7026 | 0.7523 |
| 0.7453 | 24.9 | 193 | 0.6819 | 0.7890 |
| 0.7174 | 25.94 | 201 | 0.6837 | 0.7706 |
| 0.7174 | 26.97 | 209 | 0.6743 | 0.7798 |
| 0.7083 | 28.0 | 217 | 0.6706 | 0.7798 |
| 0.6813 | 28.9 | 224 | 0.6644 | 0.8073 |
| 0.7107 | 29.94 | 232 | 0.6423 | 0.8165 |
| 0.6912 | 30.97 | 240 | 0.6419 | 0.7890 |
| 0.6912 | 32.0 | 248 | 0.6465 | 0.7890 |
| 0.7031 | 32.9 | 255 | 0.6346 | 0.8073 |
| 0.6647 | 33.94 | 263 | 0.6347 | 0.8073 |
| 0.6799 | 34.97 | 271 | 0.6476 | 0.7982 |
| 0.6799 | 36.0 | 279 | 0.6429 | 0.7982 |
| 0.6774 | 36.13 | 280 | 0.6518 | 0.7890 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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