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This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: Loss: 0.7167 Accuracy: 0.5230 Precision: 0.2735 Recall: 0.5230 F1: 0.3592
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This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
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1 sourceVérifié 5 sept.
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.7224 | 0.2526 | 97 | 0.6993 | 0.5467 | 0.7539 | 0.5467 | 0.3955 |
| 0.6813 | 1.2526 | 194 | 0.6902 | 0.5439 | 0.5614 | 0.5439 | 0.4120 |
--- library_name: transformers tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: videomae-base-finetuned-04-21-finetuned-06-12 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # videomae-base-finetuned-04-21-finetuned-06-12 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7167 - Accuracy: 0.5230 - Precision: 0.2735 - Recall: 0.5230 - F1: 0.3592 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 384 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.7224 | 0.2526 | 97 | 0.6993 | 0.5467 | 0.7539 | 0.5467 | 0.3955 | | 0.6813 | 1.2526 | 194 | 0.6902 | 0.5439 | 0.5614 | 0.5439 | 0.4120 | ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
Source context: 2 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo cocovani/videomae-base-finetuned-04-21-finetuned-06-12
