videomae-base-finetuned-yt_short_classification

This model is a fine-tuned version of MCG-NJU/videomae-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.4704
• Accuracy: 0.7815
• 0 Precision: 0.7484
• 0 Recall: 0.8149
• 0 F1-score: 0.7803
• 0 Support: 6322.0
• 1 Precision: 0.8170
• 1 Recall: 0.7510
• 1 F1-score: 0.7827
• 1 Support: 6957.0
• Accuracy F1-score: 0.7815
• Macro avg Precision: 0.7827
• Macro avg Recall: 0.7830
• Macro avg F1-score: 0.7815
• Macro avg Support: 13279.0
• Weighted avg Precision: 0.7844
• Weighted avg Recall: 0.7815
• Weighted avg F1-score: 0.7815
• Weighted avg Support: 13279.0

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: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use 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: 2060

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	0 Precision	0 Recall	0 F1-score	0 Support	1 Precision	1 Recall	1 F1-score	1 Support	Accuracy F1-score	Macro avg Precision	Macro avg Recall	Macro avg F1-score	Macro avg Support	Weighted avg Precision	Weighted avg Recall	Weighted avg F1-score	Weighted avg Support
0.6282	0.2005	413	0.6101	0.6848	0.7561	0.4991	0.6012	6322.0	0.6522	0.8537	0.7395	6957.0	0.6848	0.7041	0.6764	0.6704	13279.0	0.7016	0.6848	0.6737	13279.0
0.6569	1.2005	826	0.5357	0.7290	0.7392	0.6655	0.7004	6322.0	0.7213	0.7867	0.7526	6957.0	0.7290	0.7303	0.7261	0.7265	13279.0	0.7298	0.7290	0.7277	13279.0
0.5064	2.2005	1239	0.4839	0.7687	0.7517	0.7680	0.7597	6322.0	0.7849	0.7694	0.7771	6957.0	0.7687	0.7683	0.7687	0.7684	13279.0	0.7691	0.7687	0.7688	13279.0
0.4293	3.2005	1652	0.5120	0.7518	0.6850	0.8861	0.7727	6322.0	0.8589	0.6297	0.7267	6957.0	0.7518	0.7719	0.7579	0.7497	13279.0	0.7761	0.7518	0.7486	13279.0
0.421	4.1981	2060	0.4704	0.7815	0.7484	0.8149	0.7803	6322.0	0.8170	0.7510	0.7827	6957.0	0.7815	0.7827	0.7830	0.7815	13279.0	0.7844	0.7815	0.7815	13279.0

Framework versions

• Transformers 4.46.3
• Pytorch 2.0.0+cu117
• Datasets 3.1.0
• Tokenizers 0.20.3