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--- |
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library_name: transformers |
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license: cc-by-nc-4.0 |
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base_model: MCG-NJU/videomae-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: Intoxicated_Classification |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Intoxicated_Classification |
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This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.7660 |
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- Accuracy: 0.6971 |
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- F1 Macro: 0.6962 |
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- F1 Weighted: 0.6990 |
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- Precision Macro: 0.7040 |
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- Precision Weighted: 0.7216 |
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- Recall Macro: 0.7094 |
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- Recall Weighted: 0.6971 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- training_steps: 6980 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Weighted | Precision Macro | Precision Weighted | Recall Macro | Recall Weighted | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:------------------:|:------------:|:---------------:| |
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| 0.3101 | 0.1 | 698 | 1.1498 | 0.6133 | 0.6127 | 0.6100 | 0.6448 | 0.6663 | 0.6396 | 0.6133 | |
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| 0.0223 | 1.1 | 1396 | 2.0621 | 0.6154 | 0.6114 | 0.6046 | 0.6727 | 0.6992 | 0.6528 | 0.6154 | |
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| 0.071 | 2.1 | 2094 | 1.1017 | 0.6650 | 0.6649 | 0.6641 | 0.6905 | 0.7124 | 0.6884 | 0.6650 | |
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| 0.1562 | 3.1 | 2792 | 0.9922 | 0.7803 | 0.7713 | 0.7792 | 0.7746 | 0.7791 | 0.7691 | 0.7803 | |
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| 0.1505 | 4.1 | 3490 | 0.6705 | 0.8203 | 0.8157 | 0.8208 | 0.8143 | 0.8217 | 0.8176 | 0.8203 | |
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| 0.0634 | 5.1 | 4188 | 2.0951 | 0.6155 | 0.6122 | 0.6059 | 0.6687 | 0.6945 | 0.6515 | 0.6155 | |
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| 0.0002 | 6.1 | 4886 | 2.2097 | 0.6473 | 0.6472 | 0.6483 | 0.6627 | 0.6818 | 0.6645 | 0.6473 | |
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| 0.0366 | 7.1 | 5584 | 1.7946 | 0.6842 | 0.6837 | 0.6858 | 0.6947 | 0.7133 | 0.6988 | 0.6842 | |
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| 0.0002 | 8.1 | 6282 | 1.8526 | 0.6838 | 0.6832 | 0.6856 | 0.6926 | 0.7107 | 0.6972 | 0.6838 | |
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| 0.0004 | 9.1 | 6980 | 1.7660 | 0.6971 | 0.6962 | 0.6990 | 0.7040 | 0.7216 | 0.7094 | 0.6971 | |
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### Framework versions |
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- Transformers 4.51.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.5.1 |
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- Tokenizers 0.21.1 |
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