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anderloh/
TestV3

Name: TestV3
Rating: 2.5 (22 reviews)

generated_from_trainer

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

finetune

endpoints_compatible

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TestV3

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: 0.9449
Accuracy: 0.6713

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: 350.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.92	3	1.6026	0.1608
No log	1.85	6	1.6024	0.1608
No log	2.77	9	1.6022	0.1608
No log	4.0	13	1.6018	0.1608
No log	4.92	16	1.6013	0.1608
No log	5.85	19	1.6008	0.1608
No log	6.77	22	1.6001	0.1608
No log	8.0	26	1.5991	0.1608
No log	8.92	29	1.5982	0.1643
No log	9.85	32	1.5973	0.1853
No log	10.77	35	1.5963	0.1888
No log	12.0	39	1.5947	0.2168
No log	12.92	42	1.5935	0.2308
No log	13.85	45	1.5921	0.2308
No log	14.77	48	1.5907	0.2483
1.5896	16.0	52	1.5887	0.2797
1.5896	16.92	55	1.5872	0.2937
1.5896	17.85	58	1.5856	0.3042
1.5896	18.77	61	1.5839	0.3357
1.5896	20.0	65	1.5815	0.3706
1.5896	20.92	68	1.5795	0.3811
1.5896	21.85	71	1.5774	0.3776
1.5896	22.77	74	1.5753	0.3601
1.5896	24.0	78	1.5723	0.3531
1.5896	24.92	81	1.5699	0.3392
1.5896	25.85	84	1.5675	0.3287
1.5896	26.77	87	1.5649	0.3217
1.5896	28.0	91	1.5612	0.3147
1.5896	28.92	94	1.5583	0.3112
1.5896	29.85	97	1.5553	0.3077
1.5478	30.77	100	1.5522	0.3112
1.5478	32.0	104	1.5478	0.3007
1.5478	32.92	107	1.5445	0.2937
1.5478	33.85	110	1.5413	0.2867
1.5478	34.77	113	1.5383	0.2762
1.5478	36.0	117	1.5340	0.2762
1.5478	36.92	120	1.5311	0.2657
1.5478	37.85	123	1.5282	0.2517
1.5478	38.77	126	1.5255	0.2448
1.5478	40.0	130	1.5224	0.2413
1.5478	40.92	133	1.5204	0.2343
1.5478	41.85	136	1.5191	0.2448
1.5478	42.77	139	1.5184	0.2378
1.5478	44.0	143	1.5181	0.2308
1.5478	44.92	146	1.5189	0.2308
1.5478	45.85	149	1.5199	0.2378
1.4365	46.77	152	1.5215	0.2483
1.4365	48.0	156	1.5236	0.2587
1.4365	48.92	159	1.5251	0.2657
1.4365	49.85	162	1.5259	0.2832
1.4365	50.77	165	1.5262	0.2797
1.4365	52.0	169	1.5249	0.2937
1.4365	52.92	172	1.5227	0.3007
1.4365	53.85	175	1.5190	0.3077
1.4365	54.77	178	1.5138	0.3217
1.4365	56.0	182	1.5053	0.3497
1.4365	56.92	185	1.4977	0.3601
1.4365	57.85	188	1.4910	0.3601
1.4365	58.77	191	1.4840	0.3671
1.4365	60.0	195	1.4755	0.3706
1.4365	60.92	198	1.4684	0.3811
1.2845	61.85	201	1.4627	0.3846
1.2845	62.77	204	1.4547	0.3881
1.2845	64.0	208	1.4456	0.4021
1.2845	64.92	211	1.4385	0.4056
1.2845	65.85	214	1.4316	0.4091
1.2845	66.77	217	1.4232	0.4161
1.2845	68.0	221	1.4133	0.4266
1.2845	68.92	224	1.4062	0.4301
1.2845	69.85	227	1.4003	0.4336
1.2845	70.77	230	1.3963	0.4336
1.2845	72.0	234	1.3880	0.4336
1.2845	72.92	237	1.3801	0.4371
1.2845	73.85	240	1.3725	0.4406
1.2845	74.77	243	1.3655	0.4476
1.2845	76.0	247	1.3561	0.4510
1.1752	76.92	250	1.3477	0.4545
1.1752	77.85	253	1.3386	0.4545
1.1752	78.77	256	1.3300	0.4545
1.1752	80.0	260	1.3187	0.4615
1.1752	80.92	263	1.3102	0.4720
1.1752	81.85	266	1.3016	0.4755
1.1752	82.77	269	1.2916	0.4825
1.1752	84.0	273	1.2802	0.4825
1.1752	84.92	276	1.2718	0.4860
1.1752	85.85	279	1.2626	0.4895
1.1752	86.77	282	1.2544	0.4930
1.1752	88.0	286	1.2429	0.4930
1.1752	88.92	289	1.2338	0.4965
1.1752	89.85	292	1.2237	0.5035
1.1752	90.77	295	1.2134	0.5140
1.1752	92.0	299	1.1997	0.5315
1.0336	92.92	302	1.1894	0.5350
1.0336	93.85	305	1.1795	0.5524
1.0336	94.77	308	1.1704	0.5629
1.0336	96.0	312	1.1574	0.5629
1.0336	96.92	315	1.1478	0.5804
1.0336	97.85	318	1.1388	0.5839
1.0336	98.77	321	1.1300	0.5874
1.0336	100.0	325	1.1172	0.5944
1.0336	100.92	328	1.1090	0.5979
1.0336	101.85	331	1.1001	0.5944
1.0336	102.77	334	1.0910	0.6049
1.0336	104.0	338	1.0769	0.6014
1.0336	104.92	341	1.0675	0.6049
1.0336	105.85	344	1.0602	0.6119
1.0336	106.77	347	1.0537	0.6154
0.8927	108.0	351	1.0456	0.6224
0.8927	108.92	354	1.0394	0.6294
0.8927	109.85	357	1.0331	0.6259
0.8927	110.77	360	1.0267	0.6259
0.8927	112.0	364	1.0193	0.6329
0.8927	112.92	367	1.0149	0.6364
0.8927	113.85	370	1.0100	0.6364
0.8927	114.77	373	1.0047	0.6399
0.8927	116.0	377	0.9991	0.6399
0.8927	116.92	380	0.9973	0.6434
0.8927	117.85	383	0.9936	0.6434
0.8927	118.77	386	0.9909	0.6434
0.8927	120.0	390	0.9878	0.6434
0.8927	120.92	393	0.9841	0.6469
0.8927	121.85	396	0.9810	0.6469
0.8927	122.77	399	0.9769	0.6503
0.7859	124.0	403	0.9750	0.6538
0.7859	124.92	406	0.9752	0.6538
0.7859	125.85	409	0.9752	0.6469
0.7859	126.77	412	0.9739	0.6469
0.7859	128.0	416	0.9697	0.6434
0.7859	128.92	419	0.9673	0.6469
0.7859	129.85	422	0.9655	0.6469
0.7859	130.77	425	0.9649	0.6469
0.7859	132.0	429	0.9624	0.6503
0.7859	132.92	432	0.9609	0.6503
0.7859	133.85	435	0.9589	0.6469
0.7859	134.77	438	0.9577	0.6469
0.7859	136.0	442	0.9576	0.6503
0.7859	136.92	445	0.9588	0.6469
0.7859	137.85	448	0.9580	0.6503
0.7428	138.77	451	0.9573	0.6469
0.7428	140.0	455	0.9572	0.6469
0.7428	140.92	458	0.9591	0.6469
0.7428	141.85	461	0.9620	0.6469
0.7428	142.77	464	0.9638	0.6503
0.7428	144.0	468	0.9599	0.6503
0.7428	144.92	471	0.9564	0.6573
0.7428	145.85	474	0.9549	0.6538
0.7428	146.77	477	0.9566	0.6469
0.7428	148.0	481	0.9600	0.6538
0.7428	148.92	484	0.9609	0.6573
0.7428	149.85	487	0.9582	0.6573
0.7428	150.77	490	0.9541	0.6573
0.7428	152.0	494	0.9551	0.6573
0.7428	152.92	497	0.9550	0.6538
0.7119	153.85	500	0.9533	0.6608
0.7119	154.77	503	0.9527	0.6608
0.7119	156.0	507	0.9555	0.6538
0.7119	156.92	510	0.9558	0.6608
0.7119	157.85	513	0.9578	0.6573
0.7119	158.77	516	0.9590	0.6573
0.7119	160.0	520	0.9553	0.6573
0.7119	160.92	523	0.9510	0.6573
0.7119	161.85	526	0.9447	0.6608
0.7119	162.77	529	0.9405	0.6608
0.7119	164.0	533	0.9429	0.6608
0.7119	164.92	536	0.9473	0.6608
0.7119	165.85	539	0.9522	0.6608
0.7119	166.77	542	0.9533	0.6608
0.7119	168.0	546	0.9498	0.6643
0.7119	168.92	549	0.9472	0.6608
0.6802	169.85	552	0.9484	0.6608
0.6802	170.77	555	0.9488	0.6608
0.6802	172.0	559	0.9508	0.6608
0.6802	172.92	562	0.9550	0.6608
0.6802	173.85	565	0.9578	0.6573
0.6802	174.77	568	0.9607	0.6538
0.6802	176.0	572	0.9590	0.6573
0.6802	176.92	575	0.9531	0.6608
0.6802	177.85	578	0.9498	0.6608
0.6802	178.77	581	0.9497	0.6608
0.6802	180.0	585	0.9547	0.6573
0.6802	180.92	588	0.9555	0.6573
0.6802	181.85	591	0.9561	0.6538
0.6802	182.77	594	0.9556	0.6573
0.6802	184.0	598	0.9522	0.6573
0.6609	184.92	601	0.9505	0.6573
0.6609	185.85	604	0.9509	0.6608
0.6609	186.77	607	0.9513	0.6608
0.6609	188.0	611	0.9521	0.6573
0.6609	188.92	614	0.9505	0.6573
0.6609	189.85	617	0.9492	0.6573
0.6609	190.77	620	0.9478	0.6538
0.6609	192.0	624	0.9458	0.6538
0.6609	192.92	627	0.9427	0.6573
0.6609	193.85	630	0.9434	0.6608
0.6609	194.77	633	0.9444	0.6573
0.6609	196.0	637	0.9477	0.6573
0.6609	196.92	640	0.9480	0.6573
0.6609	197.85	643	0.9454	0.6573
0.6609	198.77	646	0.9444	0.6573
0.6402	200.0	650	0.9394	0.6573
0.6402	200.92	653	0.9393	0.6573
0.6402	201.85	656	0.9409	0.6608
0.6402	202.77	659	0.9434	0.6608
0.6402	204.0	663	0.9422	0.6608
0.6402	204.92	666	0.9422	0.6608
0.6402	205.85	669	0.9415	0.6608
0.6402	206.77	672	0.9403	0.6608
0.6402	208.0	676	0.9444	0.6573
0.6402	208.92	679	0.9434	0.6573
0.6402	209.85	682	0.9393	0.6608
0.6402	210.77	685	0.9384	0.6573
0.6402	212.0	689	0.9406	0.6573
0.6402	212.92	692	0.9428	0.6573
0.6402	213.85	695	0.9420	0.6573
0.6402	214.77	698	0.9403	0.6538
0.632	216.0	702	0.9396	0.6608
0.632	216.92	705	0.9378	0.6608
0.632	217.85	708	0.9360	0.6608
0.632	218.77	711	0.9352	0.6608
0.632	220.0	715	0.9344	0.6678
0.632	220.92	718	0.9372	0.6678
0.632	221.85	721	0.9404	0.6643
0.632	222.77	724	0.9429	0.6643
0.632	224.0	728	0.9427	0.6643
0.632	224.92	731	0.9426	0.6643
0.632	225.85	734	0.9412	0.6678
0.632	226.77	737	0.9402	0.6678
0.632	228.0	741	0.9381	0.6678
0.632	228.92	744	0.9379	0.6678
0.632	229.85	747	0.9394	0.6678
0.6285	230.77	750	0.9396	0.6678
0.6285	232.0	754	0.9438	0.6643
0.6285	232.92	757	0.9464	0.6643
0.6285	233.85	760	0.9501	0.6643
0.6285	234.77	763	0.9518	0.6678
0.6285	236.0	767	0.9503	0.6678
0.6285	236.92	770	0.9495	0.6643
0.6285	237.85	773	0.9487	0.6643
0.6285	238.77	776	0.9492	0.6643
0.6285	240.0	780	0.9464	0.6678
0.6285	240.92	783	0.9433	0.6678
0.6285	241.85	786	0.9403	0.6643
0.6285	242.77	789	0.9371	0.6643
0.6285	244.0	793	0.9387	0.6643
0.6285	244.92	796	0.9423	0.6678
0.6285	245.85	799	0.9452	0.6678
0.6049	246.77	802	0.9475	0.6678
0.6049	248.0	806	0.9469	0.6678
0.6049	248.92	809	0.9463	0.6678
0.6049	249.85	812	0.9462	0.6678
0.6049	250.77	815	0.9460	0.6678
0.6049	252.0	819	0.9463	0.6678
0.6049	252.92	822	0.9468	0.6678
0.6049	253.85	825	0.9467	0.6678
0.6049	254.77	828	0.9466	0.6678
0.6049	256.0	832	0.9451	0.6678
0.6049	256.92	835	0.9441	0.6678
0.6049	257.85	838	0.9426	0.6678
0.6049	258.77	841	0.9439	0.6678
0.6049	260.0	845	0.9444	0.6678
0.6049	260.92	848	0.9435	0.6678
0.6024	261.85	851	0.9442	0.6678
0.6024	262.77	854	0.9441	0.6678
0.6024	264.0	858	0.9449	0.6713
0.6024	264.92	861	0.9438	0.6713
0.6024	265.85	864	0.9423	0.6713
0.6024	266.77	867	0.9406	0.6678
0.6024	268.0	871	0.9400	0.6678
0.6024	268.92	874	0.9407	0.6678
0.6024	269.85	877	0.9428	0.6713
0.6024	270.77	880	0.9454	0.6713
0.6024	272.0	884	0.9466	0.6713
0.6024	272.92	887	0.9472	0.6713
0.6024	273.85	890	0.9462	0.6713
0.6024	274.77	893	0.9464	0.6713
0.6024	276.0	897	0.9453	0.6678
0.5966	276.92	900	0.9435	0.6678
0.5966	277.85	903	0.9418	0.6713
0.5966	278.77	906	0.9401	0.6678
0.5966	280.0	910	0.9375	0.6678
0.5966	280.92	913	0.9366	0.6678
0.5966	281.85	916	0.9358	0.6678
0.5966	282.77	919	0.9364	0.6678
0.5966	284.0	923	0.9369	0.6713
0.5966	284.92	926	0.9384	0.6713
0.5966	285.85	929	0.9411	0.6713
0.5966	286.77	932	0.9424	0.6713
0.5966	288.0	936	0.9443	0.6678
0.5966	288.92	939	0.9451	0.6678
0.5966	289.85	942	0.9461	0.6713
0.5966	290.77	945	0.9465	0.6678
0.5966	292.0	949	0.9478	0.6713
0.5841	292.92	952	0.9480	0.6713
0.5841	293.85	955	0.9477	0.6713
0.5841	294.77	958	0.9466	0.6713
0.5841	296.0	962	0.9454	0.6678
0.5841	296.92	965	0.9449	0.6678
0.5841	297.85	968	0.9441	0.6678
0.5841	298.77	971	0.9439	0.6713
0.5841	300.0	975	0.9433	0.6713
0.5841	300.92	978	0.9433	0.6713
0.5841	301.85	981	0.9427	0.6713
0.5841	302.77	984	0.9423	0.6713
0.5841	304.0	988	0.9416	0.6713
0.5841	304.92	991	0.9412	0.6713
0.5841	305.85	994	0.9412	0.6713
0.5841	306.77	997	0.9410	0.6713
0.5913	308.0	1001	0.9409	0.6713
0.5913	308.92	1004	0.9412	0.6713
0.5913	309.85	1007	0.9415	0.6713
0.5913	310.77	1010	0.9419	0.6713
0.5913	312.0	1014	0.9426	0.6713
0.5913	312.92	1017	0.9430	0.6678
0.5913	313.85	1020	0.9434	0.6678
0.5913	314.77	1023	0.9436	0.6678
0.5913	316.0	1027	0.9439	0.6678
0.5913	316.92	1030	0.9439	0.6678
0.5913	317.85	1033	0.9439	0.6678
0.5913	318.77	1036	0.9440	0.6678
0.5913	320.0	1040	0.9440	0.6678
0.5913	320.92	1043	0.9440	0.6678
0.5913	321.85	1046	0.9441	0.6678
0.5913	322.77	1049	0.9442	0.6678
0.5798	323.08	1050	0.9442	0.6678

Framework versions

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

anderloh/TestV3

TestV3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

anderloh/
TestV3