videomae-base-finetuned-yt_short_classification-3

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: 0.4759
• Accuracy: 0.7968
• 0 Precision: 0.7671
• 0 Recall: 0.8232
• 0 F1-score: 0.7941
• 0 Support: 6322.0
• 1 Precision: 0.8279
• 1 Recall: 0.7729
• 1 F1-score: 0.7994
• 1 Support: 6957.0
• Accuracy F1-score: 0.7968
• Macro avg Precision: 0.7975
• Macro avg Recall: 0.7980
• Macro avg F1-score: 0.7968
• Macro avg Support: 13279.0
• Weighted avg Precision: 0.7989
• Weighted avg Recall: 0.7968
• Weighted avg F1-score: 0.7969
• 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: 8240

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
0.6086	0.0501	413	0.5774	0.7153	0.6893	0.7320	0.7100	6322.0	0.7420	0.7002	0.7205	6957.0	0.7153	0.7156	0.7161	0.7152	13279.0	0.7169	0.7153	0.7155	13279.0
0.7001	1.0501	826	0.5860	0.7059	0.7671	0.5487	0.6398	6322.0	0.6742	0.8486	0.7514	6957.0	0.7059	0.7207	0.6987	0.6956	13279.0	0.7184	0.7059	0.6983	13279.0
0.5946	2.0501	1239	0.5355	0.7391	0.7899	0.6156	0.6920	6322.0	0.7091	0.8512	0.7737	6957.0	0.7391	0.7495	0.7334	0.7328	13279.0	0.7476	0.7391	0.7348	13279.0
0.5304	3.0501	1652	0.5068	0.7592	0.7034	0.8543	0.7716	6322.0	0.8356	0.6727	0.7453	6957.0	0.7592	0.7695	0.7635	0.7585	13279.0	0.7727	0.7592	0.7578	13279.0
0.4982	4.0501	2065	0.5257	0.7595	0.7060	0.8481	0.7706	6322.0	0.8311	0.6790	0.7474	6957.0	0.7595	0.7685	0.7636	0.7590	13279.0	0.7715	0.7595	0.7584	13279.0
0.5006	5.0501	2478	0.4784	0.7736	0.8036	0.6939	0.7448	6322.0	0.7526	0.8459	0.7965	6957.0	0.7736	0.7781	0.7699	0.7706	13279.0	0.7769	0.7736	0.7719	13279.0
0.4356	6.0501	2891	0.4878	0.7772	0.7188	0.8738	0.7887	6322.0	0.8573	0.6894	0.7642	6957.0	0.7772	0.7881	0.7816	0.7765	13279.0	0.7914	0.7772	0.7759	13279.0
0.4393	7.0501	3304	0.4555	0.7884	0.7969	0.7455	0.7703	6322.0	0.7815	0.8274	0.8038	6957.0	0.7884	0.7892	0.7864	0.7871	13279.0	0.7889	0.7884	0.7879	13279.0
0.3447	8.0501	3717	0.4561	0.7946	0.8046	0.7509	0.7768	6322.0	0.7866	0.8343	0.8097	6957.0	0.7946	0.7956	0.7926	0.7933	13279.0	0.7951	0.7946	0.7940	13279.0
0.4447	9.0501	4130	0.4655	0.7793	0.7202	0.8771	0.7910	6322.0	0.8608	0.6904	0.7662	6957.0	0.7793	0.7905	0.7837	0.7786	13279.0	0.7938	0.7793	0.7780	13279.0
0.4503	10.0501	4543	0.4822	0.7748	0.8554	0.6343	0.7284	6322.0	0.7309	0.9025	0.8077	6957.0	0.7748	0.7931	0.7684	0.7681	13279.0	0.7902	0.7748	0.7700	13279.0
0.3794	11.0501	4956	0.5383	0.7577	0.6870	0.9018	0.7799	6322.0	0.8753	0.6267	0.7304	6957.0	0.7577	0.7812	0.7642	0.7552	13279.0	0.7857	0.7577	0.7540	13279.0
0.3636	12.0501	5369	0.4371	0.8049	0.7832	0.8160	0.7993	6322.0	0.8262	0.7947	0.8102	6957.0	0.8049	0.8047	0.8054	0.8047	13279.0	0.8057	0.8049	0.8050	13279.0
0.4918	13.0501	5782	0.4571	0.8010	0.8331	0.7278	0.7769	6322.0	0.7781	0.8675	0.8204	6957.0	0.8010	0.8056	0.7976	0.7986	13279.0	0.8043	0.8010	0.7997	13279.0
0.4904	14.0501	6195	0.4412	0.8047	0.7801	0.8211	0.8001	6322.0	0.8293	0.7897	0.8090	6957.0	0.8047	0.8047	0.8054	0.8046	13279.0	0.8059	0.8047	0.8048	13279.0
0.2887	15.0501	6608	0.4838	0.7829	0.7317	0.8589	0.7902	6322.0	0.8477	0.7138	0.7750	6957.0	0.7829	0.7897	0.7864	0.7826	13279.0	0.7925	0.7829	0.7823	13279.0
0.3773	16.0501	7021	0.5072	0.7778	0.7140	0.8896	0.7922	6322.0	0.8708	0.6762	0.7612	6957.0	0.7778	0.7924	0.7829	0.7767	13279.0	0.7961	0.7778	0.7760	13279.0
0.3193	17.0501	7434	0.4759	0.7968	0.7671	0.8232	0.7941	6322.0	0.8279	0.7729	0.7994	6957.0	0.7968	0.7975	0.7980	0.7968	13279.0	0.7989	0.7968	0.7969	13279.0

Framework versions

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