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README.md
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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-ucf101-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-ucf101-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: 0.1199
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- Accuracy: 0.9714
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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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- gradient_accumulation_steps: 4
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- total_train_batch_size: 8
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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: 600
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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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| 2.2409 | 0.06 | 37 | 2.2371 | 0.1429 |
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| 1.3169 | 1.06 | 75 | 1.1241 | 0.6571 |
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| 0.5831 | 2.06 | 112 | 0.5958 | 0.7857 |
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| 0.5517 | 3.06 | 150 | 0.4112 | 0.8143 |
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| 0.398 | 4.06 | 187 | 0.3376 | 0.8429 |
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| 0.1959 | 5.06 | 225 | 0.4228 | 0.8857 |
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| 0.1159 | 6.06 | 262 | 0.3382 | 0.8571 |
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| 0.015 | 7.06 | 300 | 0.3205 | 0.9 |
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| 0.0316 | 8.06 | 337 | 0.3495 | 0.8857 |
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| 0.0242 | 9.06 | 375 | 0.1675 | 0.9429 |
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| 0.005 | 10.06 | 412 | 0.2990 | 0.9286 |
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| 0.0047 | 11.06 | 450 | 0.1553 | 0.9429 |
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| 0.0044 | 12.06 | 487 | 0.1390 | 0.9571 |
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| 0.0039 | 13.06 | 525 | 0.1406 | 0.9429 |
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| 0.0107 | 14.06 | 562 | 0.1184 | 0.9571 |
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| 0.0034 | 15.06 | 600 | 0.1199 | 0.9714 |
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### Framework versions
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- Transformers 4.31.0
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.0
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- Tokenizers 0.13.3
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