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--- |
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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-SLT-subset |
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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-SLT-subset |
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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: 0.4349 |
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- Accuracy: 1.0 |
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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: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- training_steps: 944 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 3.8217 | 0.06 | 59 | 3.7146 | 0.025 | |
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| 3.8922 | 1.06 | 118 | 3.6380 | 0.05 | |
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| 3.8685 | 2.06 | 177 | 3.5008 | 0.075 | |
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| 3.5993 | 3.06 | 236 | 3.3747 | 0.075 | |
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| 3.5955 | 4.06 | 295 | 3.3114 | 0.1 | |
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| 3.3868 | 5.06 | 354 | 3.2517 | 0.15 | |
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| 3.0407 | 6.06 | 413 | 3.1527 | 0.375 | |
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| 3.2339 | 7.06 | 472 | 2.9500 | 0.625 | |
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| 2.964 | 8.06 | 531 | 2.4629 | 0.6 | |
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| 2.6435 | 9.06 | 590 | 1.9360 | 0.875 | |
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| 1.8166 | 10.06 | 649 | 1.3224 | 0.925 | |
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| 1.5438 | 11.06 | 708 | 0.9461 | 0.925 | |
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| 1.0153 | 12.06 | 767 | 0.6873 | 1.0 | |
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| 0.8273 | 13.06 | 826 | 0.5575 | 1.0 | |
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| 0.5994 | 14.06 | 885 | 0.4687 | 0.975 | |
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| 0.5846 | 15.06 | 944 | 0.4349 | 1.0 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.0 |
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