heartbeat-detection

This model is a fine-tuned version of ntu-spml/distilhubert on the audiofolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.6877
• Accuracy: 1.0

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: 1e-06
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.01
• num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.5999	1.0	8	1.5962	0.2222
1.5827	2.0	16	1.5749	0.8519
1.5617	3.0	24	1.5546	0.9630
1.541	4.0	32	1.5346	1.0
1.5215	5.0	40	1.5153	1.0
1.5014	6.0	48	1.4962	1.0
1.4817	7.0	56	1.4774	1.0
1.4622	8.0	64	1.4588	1.0
1.4444	9.0	72	1.4404	1.0
1.4244	10.0	80	1.4221	1.0
1.4068	11.0	88	1.4041	1.0
1.389	12.0	96	1.3862	1.0
1.3706	13.0	104	1.3685	1.0
1.3526	14.0	112	1.3510	1.0
1.3348	15.0	120	1.3337	1.0
1.3176	16.0	128	1.3165	1.0
1.3003	17.0	136	1.2997	1.0
1.283	18.0	144	1.2829	1.0
1.2667	19.0	152	1.2664	1.0
1.2508	20.0	160	1.2501	1.0
1.2345	21.0	168	1.2341	1.0
1.2189	22.0	176	1.2183	1.0
1.2052	23.0	184	1.2029	1.0
1.1882	24.0	192	1.1876	1.0
1.1734	25.0	200	1.1725	1.0
1.159	26.0	208	1.1579	1.0
1.1449	27.0	216	1.1436	1.0
1.1302	28.0	224	1.1295	1.0
1.1177	29.0	232	1.1156	1.0
1.1032	30.0	240	1.1021	1.0
1.0902	31.0	248	1.0887	1.0
1.0776	32.0	256	1.0758	1.0
1.0656	33.0	264	1.0628	1.0
1.0519	34.0	272	1.0502	1.0
1.0412	35.0	280	1.0378	1.0
1.0277	36.0	288	1.0255	1.0
1.0178	37.0	296	1.0135	1.0
1.0067	38.0	304	1.0021	1.0
0.9948	39.0	312	0.9907	1.0
0.9852	40.0	320	0.9799	1.0
0.973	41.0	328	0.9690	1.0
0.9635	42.0	336	0.9586	1.0
0.953	43.0	344	0.9484	1.0
0.943	44.0	352	0.9384	1.0
0.935	45.0	360	0.9284	1.0
0.9249	46.0	368	0.9188	1.0
0.916	47.0	376	0.9092	1.0
0.9068	48.0	384	0.9001	1.0
0.8989	49.0	392	0.8912	1.0
0.8904	50.0	400	0.8826	1.0
0.8831	51.0	408	0.8741	1.0
0.8744	52.0	416	0.8659	1.0
0.8676	53.0	424	0.8580	1.0
0.8592	54.0	432	0.8501	1.0
0.8531	55.0	440	0.8427	1.0
0.8445	56.0	448	0.8352	1.0
0.8382	57.0	456	0.8281	1.0
0.8308	58.0	464	0.8212	1.0
0.8264	59.0	472	0.8145	1.0
0.819	60.0	480	0.8079	1.0
0.8132	61.0	488	0.8016	1.0
0.8078	62.0	496	0.7954	1.0
0.801	63.0	504	0.7895	1.0
0.7971	64.0	512	0.7838	1.0
0.791	65.0	520	0.7783	1.0
0.7861	66.0	528	0.7729	1.0
0.7801	67.0	536	0.7677	1.0
0.7756	68.0	544	0.7627	1.0
0.7719	69.0	552	0.7579	1.0
0.7671	70.0	560	0.7533	1.0
0.762	71.0	568	0.7488	1.0
0.7581	72.0	576	0.7446	1.0
0.7538	73.0	584	0.7405	1.0
0.7512	74.0	592	0.7365	1.0
0.7467	75.0	600	0.7327	1.0
0.7435	76.0	608	0.7291	1.0
0.7412	77.0	616	0.7256	1.0
0.7368	78.0	624	0.7223	1.0
0.7345	79.0	632	0.7192	1.0
0.7313	80.0	640	0.7162	1.0
0.7285	81.0	648	0.7134	1.0
0.7261	82.0	656	0.7107	1.0
0.724	83.0	664	0.7082	1.0
0.7221	84.0	672	0.7058	1.0
0.7189	85.0	680	0.7036	1.0
0.7171	86.0	688	0.7016	1.0
0.7148	87.0	696	0.6997	1.0
0.7131	88.0	704	0.6979	1.0
0.7116	89.0	712	0.6963	1.0
0.7107	90.0	720	0.6948	1.0
0.7089	91.0	728	0.6934	1.0
0.7077	92.0	736	0.6922	1.0
0.7069	93.0	744	0.6911	1.0
0.7071	94.0	752	0.6902	1.0
0.705	95.0	760	0.6895	1.0
0.7045	96.0	768	0.6888	1.0
0.7038	97.0	776	0.6883	1.0
0.7029	98.0	784	0.6880	1.0
0.7041	99.0	792	0.6878	1.0
0.7029	100.0	800	0.6877	1.0

Framework versions

• Transformers 4.39.3
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2