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tuanio/
w2v2_ablation_with_ling_head-drop0.1-not-load-best-wer-best_on_tp0.025_tl10_fp0.001_fl16

Name: w2v2_ablation_with_ling_head-drop0.1-not-load-best-wer-best_on_tp0.025_tl10_fp0.001_fl16
Availability: InStock

transformers

safetensors

wav2vec2

generated_from_trainer

nguyenvulebinh/wav2vec2-base-vietnamese-250h

finetune

cc-by-nc-4.0

endpoints_compatible

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w2v2_ablation_with_ling_head-drop0.1-not-load-best-wer-best_on_tp0.025_tl10_fp0.001_fl16

This model is a fine-tuned version of nguyenvulebinh/wav2vec2-base-vietnamese-250h on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.4141
Wer: 0.0914

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: 2e-05
train_batch_size: 8
eval_batch_size: 16
seed: 42
distributed_type: multi-GPU
num_devices: 4
total_train_batch_size: 32
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 100
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
119.415	0.94	100	91.5112	18.6364
74.7916	1.89	200	12.2928	0.9951
6.9068	2.83	300	5.2345	1.0
5.1207	3.77	400	5.0365	1.0
4.7306	4.72	500	4.9152	1.0
4.4974	5.66	600	4.9315	1.0
4.3923	6.6	700	4.7918	1.0
4.3447	7.55	800	4.6447	1.0
4.225	8.49	900	4.6061	1.0
3.9805	9.43	1000	3.6422	0.8733
2.8303	10.38	1100	1.7824	0.3489
1.5807	11.32	1200	1.0908	0.2162
1.1284	12.26	1300	0.8473	0.1640
0.8703	13.21	1400	0.7322	0.1423
0.7576	14.15	1500	0.6551	0.1325
0.6256	15.09	1600	0.6027	0.1387
0.594	16.04	1700	0.5550	0.1300
0.5492	16.98	1800	0.5200	0.1159
0.476	17.92	1900	0.5012	0.1091
0.4822	18.87	2000	0.5112	0.1074
0.4351	19.81	2100	0.4985	0.1179
0.4169	20.75	2200	0.4712	0.1061
0.3957	21.7	2300	0.4613	0.0988
0.3885	22.64	2400	0.4610	0.1025
0.3827	23.58	2500	0.4509	0.0978
0.3468	24.53	2600	0.4549	0.0951
0.3451	25.47	2700	0.4556	0.1019
0.3234	26.42	2800	0.4554	0.1104
0.31	27.36	2900	0.4568	0.0988
0.3026	28.3	3000	0.4211	0.0965
0.2905	29.25	3100	0.4305	0.0911
0.2964	30.19	3200	0.4379	0.0990
0.302	31.13	3300	0.4379	0.0943
0.2576	32.08	3400	0.4293	0.0933
0.2771	33.02	3500	0.4239	0.0928
0.268	33.96	3600	0.4228	0.0894
0.2458	34.91	3700	0.4288	0.0899
0.2553	35.85	3800	0.4312	0.0966
0.2424	36.79	3900	0.4162	0.0917
0.2501	37.74	4000	0.4088	0.0840
0.2498	38.68	4100	0.4144	0.0921
0.2273	39.62	4200	0.4154	0.0863
0.23	40.57	4300	0.4157	0.0868
0.2409	41.51	4400	0.4033	0.0826
0.248	42.45	4500	0.4122	0.0847
0.218	43.4	4600	0.4052	0.0848
0.1979	44.34	4700	0.4063	0.0887
0.2091	45.28	4800	0.4078	0.0823
0.2097	46.23	4900	0.4177	0.0893
0.2017	47.17	5000	0.4295	0.0887
0.1899	48.11	5100	0.4177	0.0919
0.195	49.06	5200	0.4109	0.0880
0.179	50.0	5300	0.4089	0.0879
0.1773	50.94	5400	0.4071	0.0843
0.1889	51.89	5500	0.4072	0.0885
0.1987	52.83	5600	0.4033	0.0873
0.1979	53.77	5700	0.4033	0.0928
0.1777	54.72	5800	0.4077	0.0898
0.1742	55.66	5900	0.3969	0.0838
0.1678	56.6	6000	0.3997	0.0806
0.1726	57.55	6100	0.3978	0.0885
0.1602	58.49	6200	0.3967	0.0860
0.1681	59.43	6300	0.4039	0.0901
0.1594	60.38	6400	0.3992	0.0856
0.171	61.32	6500	0.4058	0.0890
0.1691	62.26	6600	0.4078	0.0842
0.1724	63.21	6700	0.4161	0.0903
0.172	64.15	6800	0.4121	0.0899
0.1717	65.09	6900	0.4111	0.0878
0.1775	66.04	7000	0.4109	0.0926
0.1607	66.98	7100	0.4080	0.0908
0.1606	67.92	7200	0.4070	0.0930
0.1801	68.87	7300	0.4096	0.0908
0.16	69.81	7400	0.4030	0.0933
0.1433	70.75	7500	0.4059	0.0920
0.1473	71.7	7600	0.4120	0.0979
0.1396	72.64	7700	0.4062	0.0922
0.1429	73.58	7800	0.4079	0.0899
0.1332	74.53	7900	0.4055	0.0851
0.1429	75.47	8000	0.4081	0.0922
0.1528	76.42	8100	0.4083	0.0853
0.1547	77.36	8200	0.4139	0.0945
0.1384	78.3	8300	0.4111	0.0933
0.1696	79.25	8400	0.4132	0.0943
0.1483	80.19	8500	0.4139	0.0906
0.1547	81.13	8600	0.4156	0.0959
0.149	82.08	8700	0.4119	0.0905
0.1294	83.02	8800	0.4145	0.0945
0.1383	83.96	8900	0.4151	0.0917
0.1356	84.91	9000	0.4165	0.0952
0.1491	85.85	9100	0.4188	0.0950
0.1395	86.79	9200	0.4174	0.0950
0.1439	87.74	9300	0.4151	0.0919
0.1421	88.68	9400	0.4152	0.0931
0.1443	89.62	9500	0.4160	0.0944
0.1429	90.57	9600	0.4138	0.0928
0.1397	91.51	9700	0.4149	0.0918
0.155	92.45	9800	0.4144	0.0915
0.1406	93.4	9900	0.4139	0.0921
0.1328	94.34	10000	0.4140	0.0929
0.1461	95.28	10100	0.4142	0.0914
0.1455	96.23	10200	0.4142	0.0913
0.155	97.17	10300	0.4139	0.0914
0.147	98.11	10400	0.4140	0.0918
0.1298	99.06	10500	0.4140	0.0917
0.1508	100.0	10600	0.4141	0.0914

Framework versions

Transformers 4.35.2
Pytorch 1.13.1+cu117
Datasets 2.12.0
Tokenizers 0.14.1

tuanio/w2v2_ablation_with_ling_head-drop0.1-not-load-best-wer-best_on_tp0.025_tl10_fp0.001_fl16

w2v2_ablation_with_ling_head-drop0.1-not-load-best-wer-best_on_tp0.025_tl10_fp0.001_fl16

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

tuanio/
w2v2_ablation_with_ling_head-drop0.1-not-load-best-wer-best_on_tp0.025_tl10_fp0.001_fl16