You can deploy this model and start earning money today!

anderloh/
FinetunedWav2vec5ClassProblemNew

Name: FinetunedWav2vec5ClassProblemNew
Availability: InStock

generated_from_trainer

anderloh/Hugginhface-master-wav2vec-pretreined-5-class-train-test

finetune

endpoints_compatible

Views

No views yet

wav2vec2-5Class-train-test-finetune-Medium

This model is a fine-tuned version of anderloh/Hugginhface-master-wav2vec-pretreined-5-class-train-test on the anderloh/Master5Class dataset. It achieves the following results on the evaluation set:

Loss: 0.7855
Accuracy: 0.7448

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: 3e-05
train_batch_size: 16
eval_batch_size: 16
seed: 0
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 250.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.6852	0.95	14	1.5987	0.3427
1.5721	1.97	29	1.5976	0.3427
1.5696	2.98	44	1.5957	0.3427
1.5671	4.0	59	1.5932	0.3357
1.6757	4.95	73	1.5902	0.3252
1.5595	5.97	88	1.5864	0.3217
1.5536	6.98	103	1.5818	0.3182
1.5484	8.0	118	1.5765	0.3112
1.6506	8.95	132	1.5708	0.3077
1.5317	9.97	147	1.5641	0.3007
1.5226	10.98	162	1.5564	0.2867
1.5116	12.0	177	1.5478	0.2692
1.6046	12.95	191	1.5385	0.2622
1.4822	13.97	206	1.5285	0.2552
1.4614	14.98	221	1.5184	0.2552
1.4396	16.0	236	1.5101	0.2517
1.5047	16.95	250	1.5104	0.2448
1.3741	17.97	265	1.5250	0.2517
1.3512	18.98	280	1.5329	0.2657
1.3286	20.0	295	1.5095	0.3252
1.3967	20.95	309	1.4829	0.3497
1.2779	21.97	324	1.4598	0.3846
1.2449	22.98	339	1.4284	0.4161
1.2118	24.0	354	1.4162	0.4231
1.2521	24.95	368	1.3798	0.4476
1.1183	25.97	383	1.3246	0.4790
1.0778	26.98	398	1.2761	0.4930
1.0306	28.0	413	1.2300	0.5105
1.0808	28.95	427	1.2064	0.5140
0.9767	29.97	442	1.1670	0.5524
0.9589	30.98	457	1.1264	0.5734
0.9193	32.0	472	1.1013	0.5874
0.9462	32.95	486	1.0736	0.6049
0.85	33.97	501	1.0628	0.6049
0.8294	34.98	516	1.0473	0.6189
0.8025	36.0	531	1.0030	0.6329
0.8206	36.95	545	0.9964	0.6399
0.7541	37.97	560	0.9605	0.6608
0.7413	38.98	575	0.9467	0.6643
0.709	40.0	590	0.9348	0.6678
0.7817	40.95	604	0.9366	0.6678
0.7034	41.97	619	0.9109	0.6818
0.6856	42.98	634	0.9277	0.6573
0.6625	44.0	649	0.8980	0.6783
0.7207	44.95	663	0.9050	0.6713
0.6684	45.97	678	0.8973	0.6748
0.6651	46.98	693	0.8935	0.6783
0.6451	48.0	708	0.8945	0.6748
0.6774	48.95	722	0.8879	0.6818
0.6308	49.97	737	0.8883	0.6818
0.6199	50.98	752	0.8826	0.6818
0.6379	52.0	767	0.8582	0.6923
0.6588	52.95	781	0.8825	0.6818
0.5857	53.97	796	0.8808	0.6748
0.6076	54.98	811	0.8555	0.6958
0.5934	56.0	826	0.8654	0.6888
0.6427	56.95	840	0.8616	0.6853
0.5782	57.97	855	0.8711	0.6678
0.5819	58.98	870	0.8689	0.6748
0.5918	60.0	885	0.8602	0.6923
0.5845	60.95	899	0.8459	0.6993
0.5667	61.97	914	0.8467	0.7028
0.5327	62.98	929	0.8540	0.6923
0.523	64.0	944	0.8323	0.7063
0.548	64.95	958	0.8407	0.6993
0.5399	65.97	973	0.8379	0.6993
0.5324	66.98	988	0.8119	0.7028
0.5171	68.0	1003	0.8445	0.6923
0.538	68.95	1017	0.8196	0.7098
0.5312	69.97	1032	0.8415	0.6853
0.4914	70.98	1047	0.8184	0.6958
0.5055	72.0	1062	0.8218	0.6923
0.5401	72.95	1076	0.8160	0.7028
0.4966	73.97	1091	0.8238	0.6888
0.4768	74.98	1106	0.8185	0.6993
0.4789	76.0	1121	0.8261	0.7028
0.5176	76.95	1135	0.8110	0.7098
0.466	77.97	1150	0.8141	0.6993
0.4736	78.98	1165	0.7970	0.7168
0.4785	80.0	1180	0.8062	0.7098
0.5309	80.95	1194	0.8051	0.6958
0.4571	81.97	1209	0.8024	0.7098
0.47	82.98	1224	0.8031	0.7168
0.4525	84.0	1239	0.7911	0.7133
0.5058	84.95	1253	0.7877	0.7133
0.4627	85.97	1268	0.7918	0.7063
0.4343	86.98	1283	0.7882	0.7168
0.4442	88.0	1298	0.8058	0.7133
0.4745	88.95	1312	0.7810	0.7238
0.4282	89.97	1327	0.7951	0.7098
0.4307	90.98	1342	0.7739	0.7168
0.4403	92.0	1357	0.7788	0.7203
0.4567	92.95	1371	0.7927	0.7168
0.4233	93.97	1386	0.7885	0.7203
0.4347	94.98	1401	0.7849	0.7203
0.4167	96.0	1416	0.7880	0.7238
0.4394	96.95	1430	0.7889	0.7203
0.4359	97.97	1445	0.7785	0.7203
0.4085	98.98	1460	0.7852	0.7133
0.3965	100.0	1475	0.7785	0.7273
0.445	100.95	1489	0.7826	0.7203
0.3988	101.97	1504	0.8045	0.7098
0.4129	102.98	1519	0.7686	0.7273
0.3937	104.0	1534	0.7912	0.7133
0.4356	104.95	1548	0.7922	0.7133
0.3969	105.97	1563	0.7752	0.7203
0.4051	106.98	1578	0.7917	0.7133
0.3982	108.0	1593	0.7917	0.7098
0.4117	108.95	1607	0.8071	0.7063
0.3666	109.97	1622	0.7840	0.7203
0.3894	110.98	1637	0.7790	0.7238
0.3858	112.0	1652	0.7961	0.7098
0.4037	112.95	1666	0.7822	0.7203
0.3886	113.97	1681	0.7748	0.7238
0.3762	114.98	1696	0.7782	0.7168
0.3444	116.0	1711	0.7746	0.7308
0.3961	116.95	1725	0.7842	0.7203
0.3578	117.97	1740	0.7819	0.7203
0.3578	118.98	1755	0.7806	0.7203
0.3489	120.0	1770	0.7809	0.7238
0.3622	120.95	1784	0.7947	0.7098
0.3545	121.97	1799	0.7878	0.7168
0.3361	122.98	1814	0.7855	0.7168
0.3618	124.0	1829	0.7890	0.7133
0.3472	124.95	1843	0.7810	0.7168
0.3511	125.97	1858	0.7897	0.7133
0.3389	126.98	1873	0.7923	0.7133
0.3391	128.0	1888	0.7782	0.7273
0.3746	128.95	1902	0.7838	0.7203
0.3238	129.97	1917	0.7943	0.7168
0.3601	130.98	1932	0.7863	0.7168
0.3339	132.0	1947	0.7949	0.7133
0.3805	132.95	1961	0.7823	0.7238
0.3524	133.97	1976	0.8052	0.7098
0.3103	134.98	1991	0.7809	0.7238
0.3484	136.0	2006	0.7879	0.7203
0.3424	136.95	2020	0.7875	0.7273
0.316	137.97	2035	0.7829	0.7273
0.3171	138.98	2050	0.7882	0.7203
0.3155	140.0	2065	0.7830	0.7168
0.3382	140.95	2079	0.7826	0.7273
0.3175	141.97	2094	0.7964	0.7203
0.3444	142.98	2109	0.7859	0.7238
0.3208	144.0	2124	0.7860	0.7273
0.3286	144.95	2138	0.7869	0.7273
0.3319	145.97	2153	0.7916	0.7168
0.2954	146.98	2168	0.7938	0.7238
0.3283	148.0	2183	0.7974	0.7168
0.3306	148.95	2197	0.7795	0.7308
0.3073	149.97	2212	0.7910	0.7203
0.3089	150.98	2227	0.7942	0.7203
0.2915	152.0	2242	0.7934	0.7168
0.3286	152.95	2256	0.7808	0.7308
0.2817	153.97	2271	0.7788	0.7308
0.3118	154.98	2286	0.7898	0.7273
0.3155	156.0	2301	0.7966	0.7203
0.3156	156.95	2315	0.7947	0.7203
0.2936	157.97	2330	0.7917	0.7168
0.3049	158.98	2345	0.7780	0.7308
0.2896	160.0	2360	0.7926	0.7273
0.3194	160.95	2374	0.8023	0.7203
0.2918	161.97	2389	0.7933	0.7273
0.2992	162.98	2404	0.7829	0.7413
0.3	164.0	2419	0.7946	0.7203
0.322	164.95	2433	0.7969	0.7273
0.2994	165.97	2448	0.8076	0.7238
0.2849	166.98	2463	0.7951	0.7308
0.2745	168.0	2478	0.7892	0.7343
0.2974	168.95	2492	0.7916	0.7308
0.2656	169.97	2507	0.7995	0.7343
0.295	170.98	2522	0.8026	0.7238
0.2791	172.0	2537	0.7973	0.7343
0.2836	172.95	2551	0.8023	0.7308
0.2806	173.97	2566	0.8013	0.7308
0.2661	174.98	2581	0.7965	0.7308
0.2695	176.0	2596	0.8063	0.7273
0.286	176.95	2610	0.7963	0.7238
0.2743	177.97	2625	0.7929	0.7413
0.2775	178.98	2640	0.7855	0.7448
0.2878	180.0	2655	0.7894	0.7378
0.2757	180.95	2669	0.8013	0.7273
0.3067	181.97	2684	0.8015	0.7308
0.2412	182.98	2699	0.7975	0.7273
0.2686	184.0	2714	0.8037	0.7238
0.3176	184.95	2728	0.8017	0.7308
0.269	185.97	2743	0.8060	0.7308
0.2668	186.98	2758	0.8030	0.7308
0.2761	188.0	2773	0.8007	0.7413
0.2731	188.95	2787	0.8039	0.7343
0.2678	189.97	2802	0.8054	0.7308
0.2686	190.98	2817	0.8042	0.7343
0.2721	192.0	2832	0.8063	0.7308
0.3111	192.95	2846	0.8075	0.7378
0.2525	193.97	2861	0.8114	0.7273
0.2589	194.98	2876	0.8071	0.7413
0.2597	196.0	2891	0.8161	0.7378
0.3034	196.95	2905	0.8163	0.7378
0.2721	197.97	2920	0.8151	0.7308
0.2783	198.98	2935	0.8093	0.7448
0.259	200.0	2950	0.8026	0.7413
0.2896	200.95	2964	0.8070	0.7308
0.2584	201.97	2979	0.8113	0.7308
0.2672	202.98	2994	0.8096	0.7343
0.2622	204.0	3009	0.8060	0.7413
0.2677	204.95	3023	0.8084	0.7343
0.263	205.97	3038	0.8010	0.7378
0.2608	206.98	3053	0.7989	0.7448
0.2528	208.0	3068	0.7954	0.7448
0.2553	208.95	3082	0.7965	0.7413
0.2652	209.97	3097	0.7995	0.7413
0.246	210.98	3112	0.8026	0.7378
0.2665	212.0	3127	0.8049	0.7378
0.2731	212.95	3141	0.8052	0.7378
0.2539	213.97	3156	0.8089	0.7378
0.2376	214.98	3171	0.8116	0.7378
0.2667	216.0	3186	0.8099	0.7413
0.2768	216.95	3200	0.8079	0.7413
0.2551	217.97	3215	0.8065	0.7448
0.256	218.98	3230	0.8080	0.7413
0.2387	220.0	3245	0.8076	0.7448
0.2736	220.95	3259	0.8081	0.7448
0.2551	221.97	3274	0.8092	0.7448
0.2482	222.98	3289	0.8085	0.7448
0.2432	224.0	3304	0.8085	0.7448
0.2857	224.95	3318	0.8079	0.7448
0.2434	225.97	3333	0.8079	0.7448
0.2531	226.98	3348	0.8078	0.7448
0.222	228.0	3363	0.8093	0.7448
0.2651	228.95	3377	0.8105	0.7448
0.2886	229.97	3392	0.8116	0.7448
0.2534	230.98	3407	0.8116	0.7448
0.2483	232.0	3422	0.8116	0.7448
0.272	232.95	3436	0.8118	0.7448
0.2562	233.97	3451	0.8120	0.7448
0.2531	234.98	3466	0.8122	0.7448
0.2397	236.0	3481	0.8123	0.7448
0.2573	236.95	3495	0.8123	0.7448
0.2023	237.29	3500	0.8122	0.7448

Framework versions

Transformers 4.39.0.dev0
Pytorch 2.2.1+cu121
Datasets 2.17.1
Tokenizers 0.15.2

anderloh/FinetunedWav2vec5ClassProblemNew

wav2vec2-5Class-train-test-finetune-Medium

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

anderloh/
FinetunedWav2vec5ClassProblemNew