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10-vit-mae-base-finetuned-spiderTraining20-500
zkdeng/10-vit-mae-base-finetuned-spiderTraining20-500
10-vit-mae-base-finetuned-spiderTraining20-500 is a machine learning model from zkdeng. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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Updated Aug 18, 2025
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At a glance
- Library
- transformers
- License
- apache-2.0
- Access
- Public
- Created
- Aug 15, 2025
- Updated
- Aug 18, 2025
- SHA
- ec722a30
Base models
Model card
From the Hugging Face model README
<!-- 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. -->
10-vit-mae-base-finetuned-spiderTraining20-500
This model is a fine-tuned version of facebook/vit-mae-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.1409
- Accuracy: 0.6466
- Precision: 0.6466
- Recall: 0.6439
- F1: 0.6402
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: 0.0005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 1.9593 | 1.0 | 125 | 1.9856 | 0.3604 | 0.4074 | 0.3494 | 0.3418 |
| 1.7366 | 2.0 | 250 | 1.9730 | 0.4004 | 0.4294 | 0.3887 | 0.3687 |
| 1.6263 | 3.0 | 375 | 1.6785 | 0.4585 | 0.4611 | 0.4575 | 0.4397 |
| 1.3865 | 4.0 | 500 | 1.5892 | 0.5085 | 0.5101 | 0.4932 | 0.4907 |
| 1.4461 | 5.0 | 625 | 1.4895 | 0.5355 | 0.5539 | 0.5294 | 0.5244 |
| 1.1613 | 6.0 | 750 | 1.3479 | 0.5676 | 0.5793 | 0.5606 | 0.5578 |
| 1.0639 | 7.0 | 875 | 1.3396 | 0.5686 | 0.5657 | 0.5643 | 0.5563 |
| 1.0971 | 8.0 | 1000 | 1.1978 | 0.6096 | 0.6117 | 0.6021 | 0.5991 |
| 0.7975 | 9.0 | 1125 | 1.1968 | 0.6206 | 0.6143 | 0.6152 | 0.6091 |
| 0.7041 | 10.0 | 1250 | 1.1409 | 0.6466 | 0.6466 | 0.6439 | 0.6402 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0
- Datasets 3.5.0
- Tokenizers 0.21.1
- Library
- transformers
- License
- apache-2.0
- Created
- Aug 15, 2025
- Updated
- Aug 18, 2025