You can deploy this model and start earning money today!

alexziweiwang/
retrain2_oneTimeTraining_MTL-1epoch

transformers

pytorch

wav2vec2

generated_from_trainer

endpoints_compatible

Views

No views yet

retrain2_oneTimeTraining_MTL-1epoch

This model is a fine-tuned version of alexziweiwang/exp21-uaspeech-foundation on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 5.9312
Acc: 0.265
Wer: 1.0
Correct: 53
Total: 200
Strlen: 200

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: 9e-06
train_batch_size: 2
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss	Acc	Wer	Correct	Total	Strlen
No log	0.02	5	13.6638	0.005	1.6126	1	200	200
12.2282	0.04	10	13.4030	0.005	1.4743	1	200	200
12.2282	0.06	15	13.1289	0.005	1.3953	1	200	200
12.3565	0.08	20	12.8538	0.005	1.3043	1	200	200
12.3565	0.11	25	12.5711	0.005	1.2095	1	200	200
10.7997	0.13	30	12.2891	0.005	1.1462	1	200	200
10.7997	0.15	35	12.0060	0.005	1.0909	1	200	200
10.1556	0.17	40	11.7183	0.005	1.0632	1	200	200
10.1556	0.19	45	11.4347	0.01	1.0395	2	200	200
10.3187	0.21	50	11.1549	0.01	1.0178	2	200	200
10.3187	0.23	55	10.8828	0.01	1.0099	2	200	200
9.8042	0.25	60	10.6161	0.01	1.0040	2	200	200
9.8042	0.27	65	10.3539	0.01	0.9980	2	200	200
9.6489	0.3	70	10.0954	0.015	1.0	3	200	200
9.6489	0.32	75	9.8456	0.025	1.0	5	200	200
9.6112	0.34	80	9.5980	0.045	1.0	9	200	200
9.6112	0.36	85	9.3535	0.055	1.0	11	200	200
8.4257	0.38	90	9.1168	0.085	1.0	17	200	200
8.4257	0.4	95	8.8920	0.105	1.0	21	200	200
8.7311	0.42	100	8.6739	0.11	1.0	22	200	200
8.7311	0.44	105	8.4607	0.135	1.0	27	200	200
8.3653	0.46	110	8.2551	0.165	1.0	33	200	200
8.3653	0.48	115	8.0573	0.17	1.0	34	200	200
7.1342	0.51	120	7.8700	0.175	1.0	35	200	200
7.1342	0.53	125	7.6908	0.185	1.0	37	200	200
7.5411	0.55	130	7.5221	0.205	1.0	41	200	200
7.5411	0.57	135	7.3628	0.22	1.0	44	200	200
7.2449	0.59	140	7.2131	0.23	1.0	46	200	200
7.2449	0.61	145	7.0735	0.23	1.0	46	200	200
7.5166	0.63	150	6.9396	0.25	1.0	50	200	200
7.5166	0.65	155	6.8186	0.25	1.0	50	200	200
7.0016	0.67	160	6.7015	0.25	1.0	50	200	200
7.0016	0.7	165	6.5904	0.25	1.0	50	200	200
6.0715	0.72	170	6.4879	0.255	1.0	51	200	200
6.0715	0.74	175	6.3980	0.26	1.0	52	200	200
6.312	0.76	180	6.3198	0.26	1.0	52	200	200
6.312	0.78	185	6.2532	0.26	1.0	52	200	200
6.3694	0.8	190	6.1952	0.26	1.0	52	200	200
6.3694	0.82	195	6.1453	0.26	1.0	52	200	200
6.2196	0.84	200	6.0993	0.26	1.0	52	200	200
6.2196	0.86	205	6.0556	0.265	1.0	53	200	200
5.7131	0.89	210	6.0181	0.265	1.0	53	200	200
5.7131	0.91	215	5.9873	0.265	1.0	53	200	200
6.1827	0.93	220	5.9619	0.265	1.0	53	200	200
6.1827	0.95	225	5.9460	0.265	1.0	53	200	200
5.3823	0.97	230	5.9359	0.265	1.0	53	200	200
5.3823	0.99	235	5.9312	0.265	1.0	53	200	200

Framework versions

Transformers 4.23.1
Pytorch 1.12.1+cu113
Datasets 1.18.3
Tokenizers 0.13.2

alexziweiwang/retrain2_oneTimeTraining_MTL-1epoch

retrain2_oneTimeTraining_MTL-1epoch

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

alexziweiwang/
retrain2_oneTimeTraining_MTL-1epoch