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anderloh/
NyTestModell

Name: NyTestModell
Rating: 2.5 (5 reviews)

generated_from_trainer

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

finetune

endpoints_compatible

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HuggingfaceTest

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: 0.8156
Accuracy: 0.7028

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
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.92	3	1.5989	0.3427
No log	1.85	6	1.5988	0.3427
No log	2.77	9	1.5986	0.3427
No log	4.0	13	1.5981	0.3427
No log	4.92	16	1.5976	0.3357
No log	5.85	19	1.5970	0.3427
No log	6.77	22	1.5963	0.3392
No log	8.0	26	1.5953	0.3357
No log	8.92	29	1.5943	0.3287
No log	9.85	32	1.5933	0.3287
No log	10.77	35	1.5922	0.3217
No log	12.0	39	1.5906	0.3182
No log	12.92	42	1.5892	0.3147
No log	13.85	45	1.5877	0.3007
No log	14.77	48	1.5862	0.2937
1.5907	16.0	52	1.5841	0.2972
1.5907	16.92	55	1.5824	0.2832
1.5907	17.85	58	1.5806	0.2797
1.5907	18.77	61	1.5788	0.2692
1.5907	20.0	65	1.5762	0.2692
1.5907	20.92	68	1.5740	0.2657
1.5907	21.85	71	1.5717	0.2552
1.5907	22.77	74	1.5694	0.2517
1.5907	24.0	78	1.5661	0.2378
1.5907	24.92	81	1.5635	0.2343
1.5907	25.85	84	1.5608	0.2238
1.5907	26.77	87	1.5581	0.2238
1.5907	28.0	91	1.5542	0.2273
1.5907	28.92	94	1.5511	0.2273
1.5907	29.85	97	1.5479	0.2273
1.5431	30.77	100	1.5448	0.2273
1.5431	32.0	104	1.5408	0.2273
1.5431	32.92	107	1.5380	0.2273
1.5431	33.85	110	1.5359	0.2273
1.5431	34.77	113	1.5345	0.2273
1.5431	36.0	117	1.5335	0.2273
1.5431	36.92	120	1.5341	0.2273
1.5431	37.85	123	1.5361	0.2273
1.5431	38.77	126	1.5397	0.2273
1.5431	40.0	130	1.5479	0.2273
1.5431	40.92	133	1.5564	0.2273
1.5431	41.85	136	1.5679	0.2273
1.5431	42.77	139	1.5822	0.2273
1.5431	44.0	143	1.6002	0.2273
1.5431	44.92	146	1.6109	0.2273
1.5431	45.85	149	1.6146	0.2273
1.4033	46.77	152	1.6131	0.2273
1.4033	48.0	156	1.6008	0.2273
1.4033	48.92	159	1.5862	0.2413
1.4033	49.85	162	1.5726	0.2692
1.4033	50.77	165	1.5599	0.2692
1.4033	52.0	169	1.5459	0.2867
1.4033	52.92	172	1.5383	0.2937
1.4033	53.85	175	1.5311	0.3147
1.4033	54.77	178	1.5242	0.3252
1.4033	56.0	182	1.5169	0.3357
1.4033	56.92	185	1.5103	0.3427
1.4033	57.85	188	1.5056	0.3462
1.4033	58.77	191	1.4995	0.3462
1.4033	60.0	195	1.4939	0.3497
1.4033	60.92	198	1.4870	0.3601
1.2485	61.85	201	1.4829	0.3671
1.2485	62.77	204	1.4735	0.3741
1.2485	64.0	208	1.4612	0.3811
1.2485	64.92	211	1.4492	0.3986
1.2485	65.85	214	1.4365	0.4126
1.2485	66.77	217	1.4227	0.4231
1.2485	68.0	221	1.4096	0.4336
1.2485	68.92	224	1.4010	0.4371
1.2485	69.85	227	1.3950	0.4406
1.2485	70.77	230	1.3920	0.4371
1.2485	72.0	234	1.3799	0.4406
1.2485	72.92	237	1.3669	0.4476
1.2485	73.85	240	1.3515	0.4545
1.2485	74.77	243	1.3401	0.4720
1.2485	76.0	247	1.3286	0.4825
1.1198	76.92	250	1.3175	0.4860
1.1198	77.85	253	1.3067	0.4895
1.1198	78.77	256	1.3013	0.4825
1.1198	80.0	260	1.2954	0.4790
1.1198	80.92	263	1.2897	0.4860
1.1198	81.85	266	1.2832	0.4860
1.1198	82.77	269	1.2712	0.4825
1.1198	84.0	273	1.2584	0.4930
1.1198	84.92	276	1.2516	0.4965
1.1198	85.85	279	1.2456	0.5
1.1198	86.77	282	1.2444	0.5105
1.1198	88.0	286	1.2373	0.5105
1.1198	88.92	289	1.2309	0.5140
1.1198	89.85	292	1.2219	0.5210
1.1198	90.77	295	1.2145	0.5210
1.1198	92.0	299	1.2054	0.5280
0.9915	92.92	302	1.1982	0.5350
0.9915	93.85	305	1.1913	0.5385
0.9915	94.77	308	1.1859	0.5455
0.9915	96.0	312	1.1794	0.5490
0.9915	96.92	315	1.1734	0.5455
0.9915	97.85	318	1.1638	0.5524
0.9915	98.77	321	1.1550	0.5524
0.9915	100.0	325	1.1465	0.5490
0.9915	100.92	328	1.1444	0.5594
0.9915	101.85	331	1.1359	0.5629
0.9915	102.77	334	1.1271	0.5664
0.9915	104.0	338	1.1090	0.5769
0.9915	104.92	341	1.0972	0.5944
0.9915	105.85	344	1.0901	0.6014
0.9915	106.77	347	1.0809	0.6084
0.8834	108.0	351	1.0683	0.6119
0.8834	108.92	354	1.0605	0.6224
0.8834	109.85	357	1.0563	0.6259
0.8834	110.77	360	1.0538	0.6224
0.8834	112.0	364	1.0491	0.6154
0.8834	112.92	367	1.0441	0.6119
0.8834	113.85	370	1.0358	0.6119
0.8834	114.77	373	1.0194	0.6224
0.8834	116.0	377	1.0034	0.6294
0.8834	116.92	380	0.9991	0.6259
0.8834	117.85	383	0.9960	0.6259
0.8834	118.77	386	0.9911	0.6294
0.8834	120.0	390	0.9834	0.6434
0.8834	120.92	393	0.9776	0.6434
0.8834	121.85	396	0.9773	0.6434
0.8834	122.77	399	0.9735	0.6434
0.7786	124.0	403	0.9731	0.6399
0.7786	124.92	406	0.9728	0.6434
0.7786	125.85	409	0.9657	0.6573
0.7786	126.77	412	0.9548	0.6573
0.7786	128.0	416	0.9424	0.6643
0.7786	128.92	419	0.9391	0.6678
0.7786	129.85	422	0.9418	0.6678
0.7786	130.77	425	0.9476	0.6608
0.7786	132.0	429	0.9457	0.6643
0.7786	132.92	432	0.9413	0.6643
0.7786	133.85	435	0.9334	0.6678
0.7786	134.77	438	0.9329	0.6678
0.7786	136.0	442	0.9334	0.6713
0.7786	136.92	445	0.9265	0.6713
0.7786	137.85	448	0.9187	0.6713
0.7133	138.77	451	0.9169	0.6678
0.7133	140.0	455	0.9142	0.6713
0.7133	140.92	458	0.9131	0.6713
0.7133	141.85	461	0.9161	0.6783
0.7133	142.77	464	0.9224	0.6678
0.7133	144.0	468	0.9139	0.6748
0.7133	144.92	471	0.9090	0.6748
0.7133	145.85	474	0.9073	0.6713
0.7133	146.77	477	0.9110	0.6608
0.7133	148.0	481	0.9167	0.6573
0.7133	148.92	484	0.9118	0.6643
0.7133	149.85	487	0.8996	0.6713
0.7133	150.77	490	0.8904	0.6748
0.7133	152.0	494	0.8889	0.6748
0.7133	152.92	497	0.8899	0.6713
0.6674	153.85	500	0.8874	0.6748
0.6674	154.77	503	0.8874	0.6748
0.6674	156.0	507	0.8905	0.6748
0.6674	156.92	510	0.8881	0.6783
0.6674	157.85	513	0.8829	0.6748
0.6674	158.77	516	0.8809	0.6783
0.6674	160.0	520	0.8781	0.6783
0.6674	160.92	523	0.8776	0.6818
0.6674	161.85	526	0.8796	0.6783
0.6674	162.77	529	0.8795	0.6818
0.6674	164.0	533	0.8797	0.6783
0.6674	164.92	536	0.8707	0.6783
0.6674	165.85	539	0.8697	0.6783
0.6674	166.77	542	0.8724	0.6783
0.6674	168.0	546	0.8704	0.6748
0.6674	168.92	549	0.8694	0.6748
0.6305	169.85	552	0.8740	0.6748
0.6305	170.77	555	0.8713	0.6748
0.6305	172.0	559	0.8682	0.6783
0.6305	172.92	562	0.8688	0.6783
0.6305	173.85	565	0.8693	0.6818
0.6305	174.77	568	0.8744	0.6783
0.6305	176.0	572	0.8760	0.6783
0.6305	176.92	575	0.8696	0.6853
0.6305	177.85	578	0.8669	0.6853
0.6305	178.77	581	0.8641	0.6853
0.6305	180.0	585	0.8697	0.6713
0.6305	180.92	588	0.8678	0.6748
0.6305	181.85	591	0.8621	0.6818
0.6305	182.77	594	0.8557	0.6888
0.6305	184.0	598	0.8481	0.6888
0.6095	184.92	601	0.8429	0.6888
0.6095	185.85	604	0.8413	0.6888
0.6095	186.77	607	0.8402	0.6923
0.6095	188.0	611	0.8415	0.6888
0.6095	188.92	614	0.8410	0.6923
0.6095	189.85	617	0.8389	0.6853
0.6095	190.77	620	0.8354	0.6853
0.6095	192.0	624	0.8357	0.6888
0.6095	192.92	627	0.8401	0.6958
0.6095	193.85	630	0.8449	0.6958
0.6095	194.77	633	0.8479	0.6958
0.6095	196.0	637	0.8455	0.6923
0.6095	196.92	640	0.8422	0.6923
0.6095	197.85	643	0.8425	0.6923
0.6095	198.77	646	0.8437	0.6923
0.5908	200.0	650	0.8367	0.6958
0.5908	200.92	653	0.8347	0.6993
0.5908	201.85	656	0.8287	0.6958
0.5908	202.77	659	0.8260	0.6923
0.5908	204.0	663	0.8264	0.6958
0.5908	204.92	666	0.8295	0.6958
0.5908	205.85	669	0.8302	0.6923
0.5908	206.77	672	0.8285	0.6923
0.5908	208.0	676	0.8311	0.6923
0.5908	208.92	679	0.8321	0.6923
0.5908	209.85	682	0.8306	0.6923
0.5908	210.77	685	0.8303	0.6923
0.5908	212.0	689	0.8256	0.6993
0.5908	212.92	692	0.8230	0.6958
0.5908	213.85	695	0.8194	0.6958
0.5908	214.77	698	0.8183	0.6958
0.5763	216.0	702	0.8232	0.6958
0.5763	216.92	705	0.8237	0.6888
0.5763	217.85	708	0.8196	0.6993
0.5763	218.77	711	0.8142	0.6993
0.5763	220.0	715	0.8115	0.6993
0.5763	220.92	718	0.8130	0.6993
0.5763	221.85	721	0.8156	0.7028
0.5763	222.77	724	0.8201	0.6958
0.5763	224.0	728	0.8227	0.6958
0.5763	224.92	731	0.8232	0.6958
0.5763	225.85	734	0.8198	0.6923
0.5763	226.77	737	0.8151	0.6923
0.5763	228.0	741	0.8136	0.6923
0.5763	228.92	744	0.8134	0.6923
0.5763	229.85	747	0.8123	0.6958
0.57	230.77	750	0.8095	0.6958
0.57	232.0	754	0.8082	0.6958
0.57	232.92	757	0.8084	0.6958
0.57	233.85	760	0.8114	0.6923
0.57	234.77	763	0.8130	0.6923
0.57	236.0	767	0.8154	0.6923
0.57	236.92	770	0.8160	0.6923
0.57	237.85	773	0.8126	0.6888
0.57	238.77	776	0.8114	0.6888
0.57	240.0	780	0.8041	0.6923
0.57	240.92	783	0.8006	0.6923
0.57	241.85	786	0.7987	0.6958
0.57	242.77	789	0.7977	0.6993
0.57	244.0	793	0.8001	0.6993
0.57	244.92	796	0.8044	0.6958
0.57	245.85	799	0.8082	0.6958
0.5456	246.77	802	0.8121	0.6888
0.5456	248.0	806	0.8107	0.6888
0.5456	248.92	809	0.8064	0.6958
0.5456	249.85	812	0.8042	0.6958
0.5456	250.77	815	0.8006	0.6958
0.5456	252.0	819	0.7969	0.6958
0.5456	252.92	822	0.7955	0.6993
0.5456	253.85	825	0.7973	0.6958
0.5456	254.77	828	0.8001	0.6958
0.5456	256.0	832	0.8035	0.6888
0.5456	256.92	835	0.8035	0.6853
0.5456	257.85	838	0.8012	0.6923
0.5456	258.77	841	0.8000	0.6923
0.5456	260.0	845	0.7963	0.6888
0.5456	260.92	848	0.7928	0.6958
0.5369	261.85	851	0.7919	0.6923
0.5369	262.77	854	0.7913	0.6888
0.5369	264.0	858	0.7929	0.6888
0.5369	264.92	861	0.7955	0.6818
0.5369	265.85	864	0.7963	0.6853
0.5369	266.77	867	0.7952	0.6888
0.5369	268.0	871	0.7936	0.6888
0.5369	268.92	874	0.7929	0.6853
0.5369	269.85	877	0.7933	0.6853
0.5369	270.77	880	0.7941	0.6853
0.5369	272.0	884	0.7940	0.6853
0.5369	272.92	887	0.7929	0.6853
0.5369	273.85	890	0.7930	0.6853
0.5369	274.77	893	0.7943	0.6853
0.5369	276.0	897	0.7944	0.6853
0.5388	276.92	900	0.7933	0.6853
0.5388	277.85	903	0.7914	0.6853
0.5388	278.77	906	0.7904	0.6853
0.5388	280.0	910	0.7888	0.6853
0.5388	280.92	913	0.7900	0.6853
0.5388	281.85	916	0.7906	0.6853
0.5388	282.77	919	0.7911	0.6853
0.5388	284.0	923	0.7907	0.6853
0.5388	284.92	926	0.7907	0.6853
0.5388	285.85	929	0.7905	0.6818
0.5388	286.77	932	0.7900	0.6818
0.5388	288.0	936	0.7901	0.6853
0.5388	288.92	939	0.7902	0.6853
0.5388	289.85	942	0.7910	0.6853
0.5388	290.77	945	0.7914	0.6888
0.5388	292.0	949	0.7920	0.6888
0.5261	292.92	952	0.7928	0.6853
0.5261	293.85	955	0.7932	0.6888
0.5261	294.77	958	0.7925	0.6888
0.5261	296.0	962	0.7922	0.6888
0.5261	296.92	965	0.7919	0.6888
0.5261	297.85	968	0.7922	0.6888
0.5261	298.77	971	0.7921	0.6888
0.5261	300.0	975	0.7912	0.6853
0.5261	300.92	978	0.7907	0.6853
0.5261	301.85	981	0.7896	0.6853
0.5261	302.77	984	0.7885	0.6888
0.5261	304.0	988	0.7877	0.6888
0.5261	304.92	991	0.7874	0.6888
0.5261	305.85	994	0.7876	0.6888
0.5261	306.77	997	0.7879	0.6888
0.5188	308.0	1001	0.7884	0.6888
0.5188	308.92	1004	0.7887	0.6888
0.5188	309.85	1007	0.7890	0.6888
0.5188	310.77	1010	0.7894	0.6888
0.5188	312.0	1014	0.7899	0.6888
0.5188	312.92	1017	0.7904	0.6888
0.5188	313.85	1020	0.7907	0.6923
0.5188	314.77	1023	0.7910	0.6923
0.5188	316.0	1027	0.7912	0.6923
0.5188	316.92	1030	0.7912	0.6923
0.5188	317.85	1033	0.7912	0.6923
0.5188	318.77	1036	0.7913	0.6923
0.5188	320.0	1040	0.7913	0.6923
0.5188	320.92	1043	0.7912	0.6923
0.5188	321.85	1046	0.7912	0.6923
0.5188	322.77	1049	0.7911	0.6923
0.5194	323.08	1050	0.7911	0.6923

Framework versions

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

anderloh/NyTestModell

HuggingfaceTest

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

anderloh/
NyTestModell