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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-large-finetuned-kinetics |
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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: MAE-CT-CPC-Dicotomized-v8-n0-m1 |
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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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# MAE-CT-CPC-Dicotomized-v8-n0-m1 |
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This model is a fine-tuned version of [MCG-NJU/videomae-large-finetuned-kinetics](https://huggingface.co./MCG-NJU/videomae-large-finetuned-kinetics) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.5946 |
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- Accuracy: 0.5753 |
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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: 1e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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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: 2500 |
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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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| 0.6826 | 0.0204 | 51 | 0.6157 | 0.7625 | |
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| 0.6549 | 1.0204 | 102 | 0.5930 | 0.7625 | |
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| 0.6486 | 2.0204 | 153 | 0.6797 | 0.5125 | |
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| 0.5595 | 3.0204 | 204 | 0.4902 | 0.6625 | |
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| 0.5586 | 4.0204 | 255 | 0.8195 | 0.475 | |
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| 0.4565 | 5.0204 | 306 | 0.5872 | 0.75 | |
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| 0.3697 | 6.0204 | 357 | 0.5017 | 0.775 | |
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| 0.6201 | 7.0204 | 408 | 0.6555 | 0.7 | |
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| 0.4333 | 8.0204 | 459 | 1.2277 | 0.6125 | |
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| 0.2148 | 9.0204 | 510 | 0.8115 | 0.7625 | |
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| 0.9458 | 10.0204 | 561 | 0.9873 | 0.6625 | |
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| 0.0651 | 11.0204 | 612 | 1.0840 | 0.7625 | |
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| 0.5756 | 12.0204 | 663 | 1.0489 | 0.8 | |
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| 0.354 | 13.0204 | 714 | 1.1601 | 0.7875 | |
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| 0.2888 | 14.0204 | 765 | 1.8144 | 0.625 | |
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| 0.2449 | 15.0204 | 816 | 1.3988 | 0.7125 | |
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| 0.1326 | 16.0204 | 867 | 1.7152 | 0.7125 | |
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| 0.0018 | 17.0204 | 918 | 2.1475 | 0.6375 | |
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| 0.3631 | 18.0204 | 969 | 1.8957 | 0.65 | |
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| 0.1252 | 19.0204 | 1020 | 1.1246 | 0.825 | |
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| 0.0943 | 20.0204 | 1071 | 1.9498 | 0.6625 | |
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| 0.3488 | 21.0204 | 1122 | 1.3457 | 0.7875 | |
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| 0.0008 | 22.0204 | 1173 | 1.7872 | 0.7125 | |
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| 0.009 | 23.0204 | 1224 | 1.5437 | 0.75 | |
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| 0.0274 | 24.0204 | 1275 | 1.9865 | 0.6875 | |
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| 0.0004 | 25.0204 | 1326 | 1.5100 | 0.7625 | |
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| 0.1007 | 26.0204 | 1377 | 1.9590 | 0.6875 | |
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| 0.0006 | 27.0204 | 1428 | 1.8346 | 0.7125 | |
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| 0.0006 | 28.0204 | 1479 | 1.4669 | 0.825 | |
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| 0.0001 | 29.0204 | 1530 | 1.5396 | 0.7875 | |
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| 0.0002 | 30.0204 | 1581 | 1.5716 | 0.7875 | |
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| 0.0001 | 31.0204 | 1632 | 1.6614 | 0.7625 | |
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| 0.0002 | 32.0204 | 1683 | 1.6356 | 0.7625 | |
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| 0.0001 | 33.0204 | 1734 | 1.5731 | 0.8 | |
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| 0.0001 | 34.0204 | 1785 | 2.0020 | 0.725 | |
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| 0.0001 | 35.0204 | 1836 | 1.8886 | 0.75 | |
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| 0.0001 | 36.0204 | 1887 | 1.8363 | 0.75 | |
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| 0.0001 | 37.0204 | 1938 | 1.6848 | 0.7625 | |
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| 0.0001 | 38.0204 | 1989 | 1.7188 | 0.75 | |
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| 0.0001 | 39.0204 | 2040 | 1.5820 | 0.8 | |
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| 0.0001 | 40.0204 | 2091 | 1.6061 | 0.7875 | |
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| 0.0001 | 41.0204 | 2142 | 2.2817 | 0.7 | |
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| 0.0001 | 42.0204 | 2193 | 2.1015 | 0.725 | |
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| 0.0001 | 43.0204 | 2244 | 1.6356 | 0.775 | |
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| 0.0001 | 44.0204 | 2295 | 1.5849 | 0.8125 | |
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| 0.0001 | 45.0204 | 2346 | 1.6463 | 0.775 | |
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| 0.0001 | 46.0204 | 2397 | 1.6641 | 0.775 | |
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| 0.0001 | 47.0204 | 2448 | 1.6123 | 0.7875 | |
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| 0.0001 | 48.0204 | 2499 | 1.6145 | 0.7875 | |
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| 0.0001 | 49.0004 | 2500 | 1.6145 | 0.7875 | |
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### Framework versions |
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- Transformers 4.45.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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