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PE_fb_v2
This model is a fine-tuned version of facebook/convnext-tiny-224 on an unknown dataset. It achieves the following results on the evaluation set:
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Accuracy: 1.0
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F1: 1.0
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Precision: 1.0
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Recall: 1.0
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Loss: 0.0012
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Classification Report: precision recall f1-score support
0 1.00 1.00 1.00 4 1 1.00 1.00 1.00 3 2 1.00 1.00 1.00 3 3 1.00 1.00 1.00 4 4 1.00 1.00 1.00 3 5 1.00 1.00 1.00 4 6 1.00 1.00 1.00 5 7 1.00 1.00 1.00 2accuracy 1.00 28 macro avg 1.00 1.00 1.00 28 weighted avg 1.00 1.00 1.00 28
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: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Accuracy | F1 | Precision | Recall | Validation Loss | Classification Report |
|---|---|---|---|---|---|---|---|---|
| 0.0588 | 1.0 | 172 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0097 | precision recall f1-score support |
0 1.00 1.00 1.00 4
1 1.00 1.00 1.00 3
2 1.00 1.00 1.00 3
3 1.00 1.00 1.00 4
4 1.00 1.00 1.00 3
5 1.00 1.00 1.00 4
6 1.00 1.00 1.00 5
7 1.00 1.00 1.00 2
accuracy 1.00 28
macro avg 1.00 1.00 1.00 28 weighted avg 1.00 1.00 1.00 28 | | 0.0425 | 2.0 | 344 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0015 | precision recall f1-score support
0 1.00 1.00 1.00 4
1 1.00 1.00 1.00 3
2 1.00 1.00 1.00 3
3 1.00 1.00 1.00 4
4 1.00 1.00 1.00 3
5 1.00 1.00 1.00 4
6 1.00 1.00 1.00 5
7 1.00 1.00 1.00 2
accuracy 1.00 28
macro avg 1.00 1.00 1.00 28 weighted avg 1.00 1.00 1.00 28 | | 0.0011 | 3.0 | 516 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0012 | precision recall f1-score support
0 1.00 1.00 1.00 4
1 1.00 1.00 1.00 3
2 1.00 1.00 1.00 3
3 1.00 1.00 1.00 4
4 1.00 1.00 1.00 3
5 1.00 1.00 1.00 4
6 1.00 1.00 1.00 5
7 1.00 1.00 1.00 2
accuracy 1.00 28
macro avg 1.00 1.00 1.00 28 weighted avg 1.00 1.00 1.00 28 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
