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: 0.0001
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1.5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.8767	0.01	10	1.8078	0.1684
1.7967	0.02	20	1.7544	0.2235
1.8173	0.02	30	1.7072	0.3032
1.7604	0.03	40	1.7162	0.2227
1.7271	0.04	50	1.6655	0.3032
1.764	0.05	60	1.5927	0.3599
1.55	0.06	70	1.5354	0.3657
1.5448	0.07	80	1.4057	0.4560
1.5118	0.07	90	1.3551	0.4733
1.354	0.08	100	1.2319	0.5596
1.3675	0.09	110	1.1786	0.5735
1.4058	0.1	120	1.0949	0.6105
1.1595	0.11	130	1.0964	0.5908
1.0444	0.12	140	1.1262	0.6212
1.0483	0.12	150	1.0863	0.5982
1.0439	0.13	160	1.0488	0.6491
1.0129	0.14	170	0.9045	0.6549
1.0171	0.15	180	1.0276	0.6270
1.0867	0.16	190	1.0888	0.6023
1.0646	0.16	200	0.9730	0.6311
1.0403	0.17	210	0.9315	0.6582
0.869	0.18	220	0.9686	0.6574
0.9193	0.19	230	0.9076	0.6960
1.0266	0.2	240	1.0796	0.6565
0.8563	0.21	250	1.0173	0.6426
0.8382	0.21	260	0.9155	0.6820
0.9275	0.22	270	0.9397	0.6689
0.9402	0.23	280	0.8919	0.6861
0.8636	0.24	290	0.9795	0.6680
1.2393	0.25	300	0.9872	0.6680
0.9537	0.25	310	0.8181	0.7247
0.7361	0.26	320	0.8470	0.7025
0.8452	0.27	330	0.8045	0.7198
0.9613	0.28	340	0.7530	0.7313
0.9335	0.29	350	0.9019	0.6902
0.9414	0.3	360	0.8981	0.6795
0.7473	0.3	370	0.7532	0.7321
0.8774	0.31	380	0.8953	0.7165
0.6989	0.32	390	0.7381	0.7387
0.9826	0.33	400	0.7128	0.7403
0.783	0.34	410	0.8292	0.6952
0.9668	0.35	420	0.7826	0.7239
0.7935	0.35	430	0.7081	0.7510
0.8284	0.36	440	0.7304	0.7264
0.9404	0.37	450	0.6761	0.7650
0.7735	0.38	460	0.6827	0.7469
0.6811	0.39	470	0.7926	0.7132
0.683	0.39	480	0.6883	0.7428
0.6779	0.4	490	0.6608	0.7486
0.6329	0.41	500	0.6578	0.7617
0.5824	0.42	510	0.7696	0.7420
0.6974	0.43	520	0.6755	0.7625
0.7716	0.44	530	0.6453	0.7716
0.7463	0.44	540	0.6644	0.7642
0.7993	0.45	550	0.6059	0.7864
0.606	0.46	560	0.6857	0.7461
0.8619	0.47	570	0.6570	0.7560
0.699	0.48	580	0.7400	0.7313
0.6619	0.49	590	0.7014	0.7494
0.7696	0.49	600	0.6621	0.7584
0.6544	0.5	610	0.6826	0.7650
0.5403	0.51	620	0.7464	0.7551
0.746	0.52	630	0.7323	0.7551
0.8129	0.53	640	0.7221	0.7634
0.7245	0.53	650	0.6306	0.7790
0.7062	0.54	660	0.6250	0.7896
0.741	0.55	670	0.6129	0.7938
0.7185	0.56	680	0.6332	0.7847
0.7706	0.57	690	0.5988	0.7954
0.8147	0.58	700	0.7032	0.7781
0.5144	0.58	710	0.6849	0.7634
0.9247	0.59	720	0.6088	0.7749
0.629	0.6	730	0.6393	0.7806
0.5908	0.61	740	0.5696	0.7913
0.4951	0.62	750	0.6370	0.7765
0.6358	0.62	760	0.6232	0.7979
0.6396	0.63	770	0.6707	0.7905
0.6947	0.64	780	0.6981	0.7683
0.6748	0.65	790	0.6761	0.7765
0.5607	0.66	800	0.6551	0.7921
0.6991	0.67	810	0.6134	0.7905
0.5793	0.67	820	0.5633	0.8118
0.4755	0.68	830	0.6031	0.7929
0.7645	0.69	840	0.5896	0.7962
0.742	0.7	850	0.5811	0.8036
0.5281	0.71	860	0.6449	0.7855
0.722	0.72	870	0.6593	0.7765
0.8174	0.72	880	0.5410	0.8003
0.5373	0.73	890	0.5802	0.7954
0.3868	0.74	900	0.6015	0.7954
0.5459	0.75	910	0.5485	0.7970
0.4629	0.76	920	0.6961	0.7584
0.6952	0.76	930	0.5608	0.8053
0.8452	0.77	940	0.5649	0.8044
0.6026	0.78	950	0.5330	0.8127
0.5131	0.79	960	0.5971	0.7888
0.6814	0.8	970	0.5594	0.8061
0.6001	0.81	980	0.5851	0.7954
0.5367	0.81	990	0.5716	0.8003
0.8356	0.82	1000	0.6519	0.7683
0.502	0.83	1010	0.6180	0.7749
0.5343	0.84	1020	0.5377	0.8053
0.5288	0.85	1030	0.5902	0.7962
0.5786	0.86	1040	0.6221	0.7905
0.6272	0.86	1050	0.6688	0.7831
0.5105	0.87	1060	0.6209	0.7880
0.5806	0.88	1070	0.6145	0.7929
0.5805	0.89	1080	0.6150	0.7847
0.4812	0.9	1090	0.5812	0.8061
0.5558	0.9	1100	0.6388	0.8044
0.7507	0.91	1110	0.5873	0.8044
0.7217	0.92	1120	0.5404	0.8085
0.8146	0.93	1130	0.5449	0.8003
0.6112	0.94	1140	0.5038	0.8151
0.7305	0.95	1150	0.4767	0.8316
0.3422	0.95	1160	0.5178	0.8127
0.4644	0.96	1170	0.5073	0.8200
0.4664	0.97	1180	0.4988	0.8184
0.6223	0.98	1190	0.5120	0.8283
0.6961	0.99	1200	0.5217	0.8118
0.6706	1.0	1210	0.5235	0.8094
0.3899	1.0	1220	0.5085	0.8184
0.418	1.01	1230	0.5171	0.8135
0.5011	1.02	1240	0.5056	0.8217
0.2969	1.03	1250	0.5209	0.8217
0.5093	1.04	1260	0.4921	0.8348
0.5167	1.04	1270	0.5081	0.8274
0.6382	1.05	1280	0.4851	0.8291
0.3493	1.06	1290	0.4946	0.8324
0.3471	1.07	1300	0.5122	0.8299
0.452	1.08	1310	0.5592	0.8291
0.4362	1.09	1320	0.5528	0.8266
0.4224	1.09	1330	0.5571	0.8192
0.333	1.1	1340	0.5714	0.8110
0.2944	1.11	1350	0.5156	0.8299
0.4004	1.12	1360	0.5208	0.8340
0.6824	1.13	1370	0.5426	0.8258
0.3746	1.13	1380	0.4902	0.8365
0.3679	1.14	1390	0.4868	0.8373
0.5009	1.15	1400	0.5192	0.8283
0.5577	1.16	1410	0.4937	0.8316
0.2566	1.17	1420	0.5043	0.8250
0.6625	1.18	1430	0.5416	0.8209
0.3251	1.18	1440	0.5146	0.8291
0.4306	1.19	1450	0.5313	0.8266
0.3159	1.2	1460	0.5308	0.8291
0.3598	1.21	1470	0.4869	0.8439
0.5498	1.22	1480	0.4670	0.8537
0.4947	1.23	1490	0.4928	0.8463
0.3948	1.23	1500	0.4816	0.8455
0.3137	1.24	1510	0.4755	0.8439
0.3525	1.25	1520	0.4972	0.8389
0.4821	1.26	1530	0.4954	0.8381
0.6099	1.27	1540	0.5096	0.8324
0.3172	1.27	1550	0.5029	0.8389
0.29	1.28	1560	0.4852	0.8455
0.288	1.29	1570	0.4916	0.8496
0.3771	1.3	1580	0.4734	0.8505
0.3106	1.31	1590	0.4746	0.8431
0.3494	1.32	1600	0.5069	0.8431
0.3183	1.32	1610	0.5155	0.8398
0.4353	1.33	1620	0.5242	0.8332
0.6207	1.34	1630	0.5161	0.8340
0.3241	1.35	1640	0.5037	0.8406
0.3646	1.36	1650	0.4890	0.8439
0.2341	1.37	1660	0.4884	0.8496
0.4874	1.37	1670	0.4688	0.8562
0.6701	1.38	1680	0.4589	0.8554
0.391	1.39	1690	0.4684	0.8537
0.3333	1.4	1700	0.4738	0.8513
0.2449	1.41	1710	0.4753	0.8488
0.361	1.41	1720	0.4946	0.8496
0.2229	1.42	1730	0.4971	0.8463
0.5915	1.43	1740	0.4904	0.8513
0.1812	1.44	1750	0.4782	0.8537
0.5887	1.45	1760	0.4702	0.8570
0.2823	1.46	1770	0.4665	0.8570
0.3397	1.46	1780	0.4673	0.8546
0.4727	1.47	1790	0.4638	0.8578
0.3303	1.48	1800	0.4636	0.8578
0.4544	1.49	1810	0.4646	0.8587
0.366	1.5	1820	0.4643	0.8587

Framework versions

Transformers 4.40.0.dev0
Pytorch 2.2.1+cu121
Datasets 2.18.1.dev0
Tokenizers 0.15.2

FarhadMadadzade/wav2vec2-large-xlsr-53-english-ser-linear

wav2vec2-large-xlsr-53-english-ser-linear

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

FarhadMadadzade/
wav2vec2-large-xlsr-53-english-ser-linear