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---
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Intoxicated_Classification
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. -->
# Intoxicated_Classification
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7660
- Accuracy: 0.6971
- F1 Macro: 0.6962
- F1 Weighted: 0.6990
- Precision Macro: 0.7040
- Precision Weighted: 0.7216
- Recall Macro: 0.7094
- Recall Weighted: 0.6971
## 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: 8
- eval_batch_size: 8
- seed: 42
- 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
- training_steps: 6980
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Weighted | Precision Macro | Precision Weighted | Recall Macro | Recall Weighted |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:------------------:|:------------:|:---------------:|
| 0.3101 | 0.1 | 698 | 1.1498 | 0.6133 | 0.6127 | 0.6100 | 0.6448 | 0.6663 | 0.6396 | 0.6133 |
| 0.0223 | 1.1 | 1396 | 2.0621 | 0.6154 | 0.6114 | 0.6046 | 0.6727 | 0.6992 | 0.6528 | 0.6154 |
| 0.071 | 2.1 | 2094 | 1.1017 | 0.6650 | 0.6649 | 0.6641 | 0.6905 | 0.7124 | 0.6884 | 0.6650 |
| 0.1562 | 3.1 | 2792 | 0.9922 | 0.7803 | 0.7713 | 0.7792 | 0.7746 | 0.7791 | 0.7691 | 0.7803 |
| 0.1505 | 4.1 | 3490 | 0.6705 | 0.8203 | 0.8157 | 0.8208 | 0.8143 | 0.8217 | 0.8176 | 0.8203 |
| 0.0634 | 5.1 | 4188 | 2.0951 | 0.6155 | 0.6122 | 0.6059 | 0.6687 | 0.6945 | 0.6515 | 0.6155 |
| 0.0002 | 6.1 | 4886 | 2.2097 | 0.6473 | 0.6472 | 0.6483 | 0.6627 | 0.6818 | 0.6645 | 0.6473 |
| 0.0366 | 7.1 | 5584 | 1.7946 | 0.6842 | 0.6837 | 0.6858 | 0.6947 | 0.7133 | 0.6988 | 0.6842 |
| 0.0002 | 8.1 | 6282 | 1.8526 | 0.6838 | 0.6832 | 0.6856 | 0.6926 | 0.7107 | 0.6972 | 0.6838 |
| 0.0004 | 9.1 | 6980 | 1.7660 | 0.6971 | 0.6962 | 0.6990 | 0.7040 | 0.7216 | 0.7094 | 0.6971 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
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