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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: 0.3299 |
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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: 960 |
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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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| 3.9613 | 0.06 | 60 | 3.7190 | 0.05 | |
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| 4.0386 | 1.06 | 120 | 3.6877 | 0.05 | |
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| 3.7601 | 2.06 | 180 | 3.6471 | 0.05 | |
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| 3.7248 | 3.06 | 240 | 3.5456 | 0.05 | |
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| 3.8879 | 4.06 | 300 | 3.5123 | 0.05 | |
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| 3.7959 | 5.06 | 360 | 3.4910 | 0.075 | |
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| 3.5278 | 6.06 | 420 | 3.4499 | 0.175 | |
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| 3.4031 | 7.06 | 480 | 3.3234 | 0.35 | |
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| 3.3702 | 8.06 | 540 | 2.9582 | 0.475 | |
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| 2.9523 | 9.06 | 600 | 2.1683 | 0.8 | |
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| 2.0989 | 10.06 | 660 | 1.5218 | 0.85 | |
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| 1.5045 | 11.06 | 720 | 0.9658 | 0.975 | |
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| 0.9524 | 12.06 | 780 | 0.6727 | 1.0 | |
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| 0.6987 | 13.06 | 840 | 0.4638 | 1.0 | |
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| 0.4222 | 14.06 | 900 | 0.3591 | 1.0 | |
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| 0.366 | 15.06 | 960 | 0.3299 | 1.0 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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