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