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README.md
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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: videomae-base-finetuned-yt_short_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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# videomae-base-finetuned-yt_short_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: 0.4704
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- Accuracy: 0.7815
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- 0 Precision: 0.7484
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- 0 Recall: 0.8149
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- 0 F1-score: 0.7803
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- 0 Support: 6322.0
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- 1 Precision: 0.8170
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- 1 Recall: 0.7510
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- 1 F1-score: 0.7827
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- 1 Support: 6957.0
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- Accuracy F1-score: 0.7815
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- Macro avg Precision: 0.7827
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- Macro avg Recall: 0.7830
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- Macro avg F1-score: 0.7815
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- Macro avg Support: 13279.0
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- Weighted avg Precision: 0.7844
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- Weighted avg Recall: 0.7815
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- Weighted avg F1-score: 0.7815
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- Weighted avg Support: 13279.0
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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: 16
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Use 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: 2060
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | 0 Precision | 0 Recall | 0 F1-score | 0 Support | 1 Precision | 1 Recall | 1 F1-score | 1 Support | Accuracy F1-score | Macro avg Precision | Macro avg Recall | Macro avg F1-score | Macro avg Support | Weighted avg Precision | Weighted avg Recall | Weighted avg F1-score | Weighted avg Support |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------------:|:-------------------:|:----------------:|:------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------------:|:--------------------:|
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| 0.6282 | 0.2005 | 413 | 0.6101 | 0.6848 | 0.7561 | 0.4991 | 0.6012 | 6322.0 | 0.6522 | 0.8537 | 0.7395 | 6957.0 | 0.6848 | 0.7041 | 0.6764 | 0.6704 | 13279.0 | 0.7016 | 0.6848 | 0.6737 | 13279.0 |
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| 0.6569 | 1.2005 | 826 | 0.5357 | 0.7290 | 0.7392 | 0.6655 | 0.7004 | 6322.0 | 0.7213 | 0.7867 | 0.7526 | 6957.0 | 0.7290 | 0.7303 | 0.7261 | 0.7265 | 13279.0 | 0.7298 | 0.7290 | 0.7277 | 13279.0 |
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| 0.5064 | 2.2005 | 1239 | 0.4839 | 0.7687 | 0.7517 | 0.7680 | 0.7597 | 6322.0 | 0.7849 | 0.7694 | 0.7771 | 6957.0 | 0.7687 | 0.7683 | 0.7687 | 0.7684 | 13279.0 | 0.7691 | 0.7687 | 0.7688 | 13279.0 |
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| 0.4293 | 3.2005 | 1652 | 0.5120 | 0.7518 | 0.6850 | 0.8861 | 0.7727 | 6322.0 | 0.8589 | 0.6297 | 0.7267 | 6957.0 | 0.7518 | 0.7719 | 0.7579 | 0.7497 | 13279.0 | 0.7761 | 0.7518 | 0.7486 | 13279.0 |
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| 0.421 | 4.1981 | 2060 | 0.4704 | 0.7815 | 0.7484 | 0.8149 | 0.7803 | 6322.0 | 0.8170 | 0.7510 | 0.7827 | 6957.0 | 0.7815 | 0.7827 | 0.7830 | 0.7815 | 13279.0 | 0.7844 | 0.7815 | 0.7815 | 13279.0 |
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### Framework versions
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- Transformers 4.46.3
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- Pytorch 2.0.0+cu117
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- Datasets 3.1.0
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- Tokenizers 0.20.3
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