timesformer-base-finetuned-k400-finetuned-yt_short_classification-3

This model is a fine-tuned version of facebook/timesformer-base-finetuned-k400 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.4771
• Accuracy: 0.8724
• 0 Precision: 0.8472
• 0 Recall: 0.8938
• 0 F1-score: 0.8699
• 0 Support: 24395.0
• 1 Precision: 0.8979
• 1 Recall: 0.8528
• 1 F1-score: 0.8748
• 1 Support: 26720.0
• Accuracy F1-score: 0.8724
• Macro avg Precision: 0.8726
• Macro avg Recall: 0.8733
• Macro avg F1-score: 0.8723
• Macro avg Support: 51115.0
• Weighted avg Precision: 0.8737
• Weighted avg Recall: 0.8724
• Weighted avg F1-score: 0.8725
• Weighted avg Support: 51115.0

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: 4
• eval_batch_size: 4
• 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: 39620

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
0.6564	0.0500	1982	0.4807	0.7684	0.7250	0.8294	0.7737	24395.0	0.8206	0.7128	0.7629	26720.0	0.7684	0.7728	0.7711	0.7683	51115.0	0.7750	0.7684	0.7680	51115.0
0.6013	1.0500	3964	0.5336	0.7612	0.7301	0.7929	0.7602	24395.0	0.7948	0.7323	0.7623	26720.0	0.7612	0.7624	0.7626	0.7612	51115.0	0.7639	0.7612	0.7613	51115.0
0.4629	2.0500	5946	0.5388	0.7692	0.7280	0.8244	0.7732	24395.0	0.8176	0.7188	0.7650	26720.0	0.7692	0.7728	0.7716	0.7691	51115.0	0.7748	0.7692	0.7689	51115.0
0.6739	3.0500	7928	0.4304	0.8098	0.7891	0.8207	0.8046	24395.0	0.8301	0.7998	0.8147	26720.0	0.8098	0.8096	0.8102	0.8096	51115.0	0.8105	0.8098	0.8099	51115.0
0.2837	4.0500	9910	0.5067	0.8136	0.7818	0.8455	0.8124	24395.0	0.8476	0.7845	0.8148	26720.0	0.8136	0.8147	0.8150	0.8136	51115.0	0.8162	0.8136	0.8136	51115.0
0.6485	5.0500	11892	0.5121	0.8072	0.8035	0.7890	0.7962	24395.0	0.8105	0.8238	0.8171	26720.0	0.8072	0.8070	0.8064	0.8066	51115.0	0.8071	0.8072	0.8071	51115.0
0.5415	6.0500	13874	0.8758	0.6895	0.6096	0.9716	0.7492	24395.0	0.9434	0.4320	0.5926	26720.0	0.6895	0.7765	0.7018	0.6709	51115.0	0.7841	0.6895	0.6673	51115.0
0.3286	7.0500	15856	0.5110	0.8262	0.8450	0.7788	0.8105	24395.0	0.8115	0.8695	0.8395	26720.0	0.8262	0.8282	0.8242	0.8250	51115.0	0.8275	0.8262	0.8257	51115.0
0.3357	8.0500	17838	0.4913	0.8278	0.8414	0.7876	0.8136	24395.0	0.8168	0.8644	0.8399	26720.0	0.8278	0.8291	0.8260	0.8268	51115.0	0.8285	0.8278	0.8274	51115.0
0.3095	9.0500	19820	0.5020	0.8467	0.8483	0.8267	0.8374	24395.0	0.8454	0.8650	0.8551	26720.0	0.8467	0.8468	0.8458	0.8462	51115.0	0.8468	0.8467	0.8466	51115.0
0.6872	10.0500	21802	0.6839	0.7834	0.7049	0.9395	0.8055	24395.0	0.9207	0.6409	0.7558	26720.0	0.7834	0.8128	0.7902	0.7806	51115.0	0.8177	0.7834	0.7795	51115.0
0.2417	11.0500	23784	0.7490	0.8001	0.7235	0.9408	0.8180	24395.0	0.9256	0.6717	0.7784	26720.0	0.8001	0.8245	0.8063	0.7982	51115.0	0.8291	0.8001	0.7973	51115.0
0.6484	12.0500	25766	0.4507	0.8448	0.8540	0.8139	0.8335	24395.0	0.8371	0.8730	0.8547	26720.0	0.8448	0.8456	0.8435	0.8441	51115.0	0.8452	0.8448	0.8446	51115.0
0.4147	13.0500	27748	0.4223	0.8620	0.8307	0.8927	0.8606	24395.0	0.8949	0.8339	0.8633	26720.0	0.8620	0.8628	0.8633	0.8620	51115.0	0.8643	0.8620	0.8620	51115.0
0.6485	14.0500	29730	0.4759	0.8548	0.8523	0.8416	0.8470	24395.0	0.8571	0.8669	0.8619	26720.0	0.8548	0.8547	0.8543	0.8545	51115.0	0.8548	0.8548	0.8548	51115.0
0.3193	15.0500	31712	0.5955	0.8311	0.7551	0.9561	0.8438	24395.0	0.9471	0.7170	0.8161	26720.0	0.8311	0.8511	0.8365	0.8300	51115.0	0.8555	0.8311	0.8293	51115.0
0.4384	16.0500	33694	0.4914	0.8567	0.8308	0.8788	0.8541	24395.0	0.8832	0.8366	0.8592	26720.0	0.8567	0.8570	0.8577	0.8567	51115.0	0.8582	0.8567	0.8568	51115.0
0.2316	17.0500	35676	0.4951	0.8621	0.8282	0.8971	0.8613	24395.0	0.8983	0.8301	0.8629	26720.0	0.8621	0.8633	0.8636	0.8621	51115.0	0.8649	0.8621	0.8621	51115.0
0.3014	18.0500	37658	0.5001	0.8654	0.8245	0.9122	0.8662	24395.0	0.9113	0.8227	0.8647	26720.0	0.8654	0.8679	0.8675	0.8654	51115.0	0.8698	0.8654	0.8654	51115.0
0.2855	19.0495	39620	0.4771	0.8724	0.8472	0.8938	0.8699	24395.0	0.8979	0.8528	0.8748	26720.0	0.8724	0.8726	0.8733	0.8723	51115.0	0.8737	0.8724	0.8725	51115.0

Framework versions

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