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End of training

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README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: mit
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+ base_model: google/vivit-b-16x2-kinetics400
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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: VIVIT-d2
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+ results: []
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+ ---
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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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+
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+ # VIVIT-d2
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+
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+ This model is a fine-tuned version of [google/vivit-b-16x2-kinetics400](https://huggingface.co/google/vivit-b-16x2-kinetics400) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 2.9103
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+ - Accuracy: 0.4210
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 1
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+ - eval_batch_size: 1
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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: 6650
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 2.556 | 0.1 | 665 | 2.3470 | 0.2123 |
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+ | 2.0142 | 1.1 | 1330 | 2.1601 | 0.3180 |
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+ | 2.122 | 2.1 | 1995 | 2.0851 | 0.4047 |
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+ | 1.7405 | 3.1 | 2660 | 2.3452 | 0.4205 |
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+ | 1.2998 | 4.1 | 3325 | 2.3814 | 0.4557 |
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+ | 1.4591 | 5.1 | 3990 | 2.7093 | 0.3820 |
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+ | 0.8984 | 6.1 | 4655 | 2.5562 | 0.3584 |
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+ | 0.3971 | 7.1 | 5320 | 3.1583 | 0.4057 |
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+ | 0.5996 | 8.1 | 5985 | 2.9134 | 0.4154 |
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+ | 0.8684 | 9.1 | 6650 | 2.9103 | 0.4210 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.46.2
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+ - Pytorch 2.5.1+cu124
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+ - Datasets 3.1.0
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+ - Tokenizers 0.20.3
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