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anderloh/
wav2vec2-5Class-Validation-Mobil

Name: wav2vec2-5Class-Validation-Mobil
Availability: InStock

generated_from_trainer

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

finetune

endpoints_compatible

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wav2vec2-5Class-Validation-Mobil

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

Loss: 1.2514
Accuracy: 0.5836

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: 128
eval_batch_size: 128
seed: 0
gradient_accumulation_steps: 4
total_train_batch_size: 512
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 300.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.92	3	1.6024	0.3203
No log	1.85	6	1.6022	0.3167
No log	2.77	9	1.6020	0.3167
No log	4.0	13	1.6015	0.3167
No log	4.92	16	1.6009	0.3167
No log	5.85	19	1.6003	0.3132
No log	6.77	22	1.5996	0.3060
No log	8.0	26	1.5984	0.2989
No log	8.92	29	1.5974	0.2918
No log	9.85	32	1.5964	0.2740
No log	10.77	35	1.5952	0.2598
No log	12.0	39	1.5934	0.2633
No log	12.92	42	1.5920	0.2740
No log	13.85	45	1.5905	0.2989
No log	14.77	48	1.5889	0.2989
No log	16.0	52	1.5868	0.2847
No log	16.92	55	1.5851	0.2847
No log	17.85	58	1.5833	0.2847
No log	18.77	61	1.5816	0.2633
No log	20.0	65	1.5790	0.2456
No log	20.92	68	1.5770	0.2420
No log	21.85	71	1.5748	0.2349
No log	22.77	74	1.5728	0.2313
No log	24.0	78	1.5699	0.2278
No log	24.92	81	1.5678	0.2313
No log	25.85	84	1.5657	0.2313
No log	26.77	87	1.5638	0.2313
No log	28.0	91	1.5613	0.2313
No log	28.92	94	1.5597	0.2313
No log	29.85	97	1.5588	0.2313
1.561	30.77	100	1.5586	0.2313
1.561	32.0	104	1.5597	0.2313
1.561	32.92	107	1.5619	0.2313
1.561	33.85	110	1.5661	0.2313
1.561	34.77	113	1.5720	0.2313
1.561	36.0	117	1.5833	0.2313
1.561	36.92	120	1.5957	0.2313
1.561	37.85	123	1.6120	0.2313
1.561	38.77	126	1.6318	0.2313
1.561	40.0	130	1.6638	0.2313
1.561	40.92	133	1.6905	0.2313
1.561	41.85	136	1.7197	0.2313
1.561	42.77	139	1.7503	0.2313
1.561	44.0	143	1.7803	0.2313
1.561	44.92	146	1.7917	0.2313
1.561	45.85	149	1.7920	0.2313
1.561	46.77	152	1.7869	0.2313
1.561	48.0	156	1.7700	0.2598
1.561	48.92	159	1.7525	0.2740
1.561	49.85	162	1.7407	0.2776
1.561	50.77	165	1.7307	0.2918
1.561	52.0	169	1.7241	0.3096
1.561	52.92	172	1.7243	0.3167
1.561	53.85	175	1.7254	0.3167
1.561	54.77	178	1.7233	0.3238
1.561	56.0	182	1.7225	0.3238
1.561	56.92	185	1.7187	0.3274
1.561	57.85	188	1.7172	0.3274
1.561	58.77	191	1.7146	0.3345
1.561	60.0	195	1.7120	0.3488
1.561	60.92	198	1.7049	0.3559
1.3094	61.85	201	1.7022	0.3594
1.3094	62.77	204	1.6912	0.3737
1.3094	64.0	208	1.6798	0.3772
1.3094	64.92	211	1.6687	0.3808
1.3094	65.85	214	1.6569	0.3843
1.3094	66.77	217	1.6427	0.3915
1.3094	68.0	221	1.6301	0.3915
1.3094	68.92	224	1.6217	0.3950
1.3094	69.85	227	1.6203	0.3950
1.3094	70.77	230	1.6257	0.3950
1.3094	72.0	234	1.6192	0.4021
1.3094	72.92	237	1.6044	0.4093
1.3094	73.85	240	1.5868	0.4306
1.3094	74.77	243	1.5787	0.4377
1.3094	76.0	247	1.5762	0.4342
1.3094	76.92	250	1.5717	0.4377
1.3094	77.85	253	1.5674	0.4342
1.3094	78.77	256	1.5684	0.4270
1.3094	80.0	260	1.5619	0.4270
1.3094	80.92	263	1.5555	0.4306
1.3094	81.85	266	1.5505	0.4342
1.3094	82.77	269	1.5386	0.4413
1.3094	84.0	273	1.5362	0.4377
1.3094	84.92	276	1.5411	0.4342
1.3094	85.85	279	1.5453	0.4342
1.3094	86.77	282	1.5611	0.4270
1.3094	88.0	286	1.5766	0.4199
1.3094	88.92	289	1.5781	0.4199
1.3094	89.85	292	1.5675	0.4235
1.3094	90.77	295	1.5588	0.4270
1.3094	92.0	299	1.5496	0.4270
1.0538	92.92	302	1.5493	0.4270
1.0538	93.85	305	1.5540	0.4235
1.0538	94.77	308	1.5620	0.4164
1.0538	96.0	312	1.5648	0.4164
1.0538	96.92	315	1.5617	0.4164
1.0538	97.85	318	1.5461	0.4235
1.0538	98.77	321	1.5348	0.4306
1.0538	100.0	325	1.5346	0.4306
1.0538	100.92	328	1.5466	0.4164
1.0538	101.85	331	1.5547	0.4128
1.0538	102.77	334	1.5560	0.4128
1.0538	104.0	338	1.5315	0.4306
1.0538	104.92	341	1.5124	0.4448
1.0538	105.85	344	1.5044	0.4448
1.0538	106.77	347	1.5010	0.4484
1.0538	108.0	351	1.5005	0.4448
1.0538	108.92	354	1.4992	0.4448
1.0538	109.85	357	1.4994	0.4484
1.0538	110.77	360	1.4988	0.4520
1.0538	112.0	364	1.5005	0.4662
1.0538	112.92	367	1.5010	0.4733
1.0538	113.85	370	1.4969	0.4698
1.0538	114.77	373	1.4776	0.4733
1.0538	116.0	377	1.4528	0.4769
1.0538	116.92	380	1.4395	0.4947
1.0538	117.85	383	1.4310	0.4982
1.0538	118.77	386	1.4315	0.4947
1.0538	120.0	390	1.4389	0.4947
1.0538	120.92	393	1.4375	0.4982
1.0538	121.85	396	1.4381	0.4982
1.0538	122.77	399	1.4247	0.4982
0.8509	124.0	403	1.4196	0.4982
0.8509	124.92	406	1.4179	0.5053
0.8509	125.85	409	1.4091	0.5053
0.8509	126.77	412	1.3958	0.5053
0.8509	128.0	416	1.3736	0.5089
0.8509	128.92	419	1.3661	0.5089
0.8509	129.85	422	1.3694	0.5125
0.8509	130.77	425	1.3808	0.5125
0.8509	132.0	429	1.3819	0.5125
0.8509	132.92	432	1.3859	0.5125
0.8509	133.85	435	1.3780	0.5231
0.8509	134.77	438	1.3696	0.5231
0.8509	136.0	442	1.3564	0.5302
0.8509	136.92	445	1.3421	0.5338
0.8509	137.85	448	1.3256	0.5374
0.8509	138.77	451	1.3274	0.5374
0.8509	140.0	455	1.3402	0.5409
0.8509	140.92	458	1.3517	0.5409
0.8509	141.85	461	1.3585	0.5409
0.8509	142.77	464	1.3592	0.5374
0.8509	144.0	468	1.3329	0.5480
0.8509	144.92	471	1.3126	0.5480
0.8509	145.85	474	1.3076	0.5445
0.8509	146.77	477	1.3146	0.5480
0.8509	148.0	481	1.3345	0.5445
0.8509	148.92	484	1.3409	0.5445
0.8509	149.85	487	1.3374	0.5445
0.8509	150.77	490	1.3227	0.5480
0.8509	152.0	494	1.3201	0.5445
0.8509	152.92	497	1.3174	0.5445
0.7118	153.85	500	1.3073	0.5445
0.7118	154.77	503	1.2984	0.5552
0.7118	156.0	507	1.2975	0.5516
0.7118	156.92	510	1.3027	0.5516
0.7118	157.85	513	1.3089	0.5480
0.7118	158.77	516	1.3139	0.5480
0.7118	160.0	520	1.3068	0.5552
0.7118	160.92	523	1.3011	0.5552
0.7118	161.85	526	1.2957	0.5552
0.7118	162.77	529	1.2960	0.5552
0.7118	164.0	533	1.3159	0.5516
0.7118	164.92	536	1.3257	0.5516
0.7118	165.85	539	1.3312	0.5516
0.7118	166.77	542	1.3222	0.5516
0.7118	168.0	546	1.2986	0.5552
0.7118	168.92	549	1.2898	0.5587
0.7118	169.85	552	1.2938	0.5552
0.7118	170.77	555	1.2902	0.5552
0.7118	172.0	559	1.2879	0.5658
0.7118	172.92	562	1.2838	0.5658
0.7118	173.85	565	1.2812	0.5658
0.7118	174.77	568	1.2864	0.5658
0.7118	176.0	572	1.2934	0.5552
0.7118	176.92	575	1.2940	0.5587
0.7118	177.85	578	1.2988	0.5587
0.7118	178.77	581	1.2953	0.5623
0.7118	180.0	585	1.2972	0.5587
0.7118	180.92	588	1.2936	0.5658
0.7118	181.85	591	1.2928	0.5658
0.7118	182.77	594	1.2913	0.5658
0.7118	184.0	598	1.2825	0.5658
0.6473	184.92	601	1.2736	0.5694
0.6473	185.85	604	1.2715	0.5694
0.6473	186.77	607	1.2704	0.5694
0.6473	188.0	611	1.2717	0.5694
0.6473	188.92	614	1.2724	0.5658
0.6473	189.85	617	1.2763	0.5658
0.6473	190.77	620	1.2812	0.5658
0.6473	192.0	624	1.2791	0.5658
0.6473	192.92	627	1.2698	0.5694
0.6473	193.85	630	1.2695	0.5694
0.6473	194.77	633	1.2704	0.5694
0.6473	196.0	637	1.2737	0.5658
0.6473	196.92	640	1.2782	0.5658
0.6473	197.85	643	1.2814	0.5623
0.6473	198.77	646	1.2819	0.5623
0.6473	200.0	650	1.2746	0.5658
0.6473	200.92	653	1.2694	0.5658
0.6473	201.85	656	1.2625	0.5765
0.6473	202.77	659	1.2575	0.5801
0.6473	204.0	663	1.2549	0.5801
0.6473	204.92	666	1.2623	0.5730
0.6473	205.85	669	1.2665	0.5658
0.6473	206.77	672	1.2684	0.5658
0.6473	208.0	676	1.2770	0.5623
0.6473	208.92	679	1.2808	0.5623
0.6473	209.85	682	1.2762	0.5730
0.6473	210.77	685	1.2759	0.5730
0.6473	212.0	689	1.2752	0.5730
0.6473	212.92	692	1.2754	0.5730
0.6473	213.85	695	1.2722	0.5765
0.6473	214.77	698	1.2739	0.5765
0.613	216.0	702	1.2783	0.5765
0.613	216.92	705	1.2775	0.5765
0.613	217.85	708	1.2741	0.5765
0.613	218.77	711	1.2706	0.5765
0.613	220.0	715	1.2628	0.5765
0.613	220.92	718	1.2581	0.5801
0.613	221.85	721	1.2568	0.5765
0.613	222.77	724	1.2559	0.5730
0.613	224.0	728	1.2503	0.5765
0.613	224.92	731	1.2498	0.5765
0.613	225.85	734	1.2500	0.5765
0.613	226.77	737	1.2490	0.5765
0.613	228.0	741	1.2532	0.5765
0.613	228.92	744	1.2572	0.5765
0.613	229.85	747	1.2599	0.5765
0.613	230.77	750	1.2601	0.5730
0.613	232.0	754	1.2625	0.5730
0.613	232.92	757	1.2636	0.5765
0.613	233.85	760	1.2629	0.5765
0.613	234.77	763	1.2600	0.5765
0.613	236.0	767	1.2559	0.5801
0.613	236.92	770	1.2534	0.5801
0.613	237.85	773	1.2514	0.5836
0.613	238.77	776	1.2508	0.5836
0.613	240.0	780	1.2488	0.5836
0.613	240.92	783	1.2483	0.5836
0.613	241.85	786	1.2500	0.5836
0.613	242.77	789	1.2504	0.5801
0.613	244.0	793	1.2521	0.5801
0.613	244.92	796	1.2533	0.5801
0.613	245.85	799	1.2513	0.5801
0.5946	246.77	802	1.2513	0.5801
0.5946	248.0	806	1.2507	0.5801
0.5946	248.92	809	1.2492	0.5836
0.5946	249.85	812	1.2500	0.5801
0.5946	250.77	815	1.2505	0.5801
0.5946	252.0	819	1.2519	0.5801
0.5946	252.92	822	1.2531	0.5801
0.5946	253.85	825	1.2538	0.5801
0.5946	254.77	828	1.2532	0.5801
0.5946	256.0	832	1.2528	0.5801
0.5946	256.92	835	1.2528	0.5801
0.5946	257.85	838	1.2521	0.5836
0.5946	258.77	841	1.2526	0.5836
0.5946	260.0	845	1.2528	0.5836
0.5946	260.92	848	1.2529	0.5836
0.5946	261.85	851	1.2528	0.5836
0.5946	262.77	854	1.2517	0.5836
0.5946	264.0	858	1.2512	0.5836
0.5946	264.92	861	1.2512	0.5836
0.5946	265.85	864	1.2504	0.5836
0.5946	266.77	867	1.2499	0.5836
0.5946	268.0	871	1.2496	0.5836
0.5946	268.92	874	1.2497	0.5836
0.5946	269.85	877	1.2500	0.5836
0.5946	270.77	880	1.2500	0.5836
0.5946	272.0	884	1.2499	0.5836
0.5946	272.92	887	1.2501	0.5836
0.5946	273.85	890	1.2504	0.5836
0.5946	274.77	893	1.2506	0.5836
0.5946	276.0	897	1.2506	0.5836
0.588	276.92	900	1.2506	0.5836

Framework versions

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

anderloh/wav2vec2-5Class-Validation-Mobil

wav2vec2-5Class-Validation-Mobil

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

anderloh/
wav2vec2-5Class-Validation-Mobil