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alexziweiwang/
retrain_epoch2and3

Name: retrain_epoch2and3
Rating: 2.5 (5 reviews)

transformers

pytorch

wav2vec2

generated_from_trainer

endpoints_compatible

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retrain_epoch2and3

This model is a fine-tuned version of alexziweiwang/retrain_first1epoch on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.4888
Acc: 0.24
Wer: 1.0
Correct: 48
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: 2.0

Training results

Training Loss	Epoch	Step	Validation Loss	Acc	Wer	Correct	Total	Strlen
No log	0.02	5	7.8479	0.24	1.0	48	200	200
7.6019	0.04	10	7.4765	0.24	1.0	48	200	200
7.6019	0.06	15	7.1196	0.24	1.0	48	200	200
7.3222	0.08	20	6.8029	0.24	1.0	48	200	200
7.3222	0.11	25	6.5210	0.24	1.0	48	200	200
6.2645	0.13	30	6.2630	0.24	1.0	48	200	200
6.2645	0.15	35	6.0213	0.24	1.0	48	200	200
5.8699	0.17	40	5.8096	0.24	1.0	48	200	200
5.8699	0.19	45	5.5831	0.24	1.0	48	200	200
5.7145	0.21	50	5.3644	0.24	1.0	48	200	200
5.7145	0.23	55	5.1777	0.24	1.0	48	200	200
5.3702	0.25	60	5.0257	0.24	1.0	48	200	200
5.3702	0.27	65	4.8642	0.24	1.0	48	200	200
5.1896	0.3	70	4.7205	0.24	1.0	48	200	200
5.1896	0.32	75	4.5846	0.24	1.0	48	200	200
5.0615	0.34	80	4.4313	0.24	1.0	48	200	200
5.0615	0.36	85	4.2923	0.24	1.0	48	200	200
4.5189	0.38	90	4.1662	0.24	1.0	48	200	200
4.5189	0.4	95	4.0545	0.24	1.0	48	200	200
4.4911	0.42	100	3.9585	0.24	1.0	48	200	200
4.4911	0.44	105	3.8489	0.24	1.0	48	200	200
4.1997	0.46	110	3.7573	0.24	1.0	48	200	200
4.1997	0.48	115	3.6722	0.24	1.0	48	200	200
3.7348	0.51	120	3.5844	0.24	1.0	48	200	200
3.7348	0.53	125	3.4980	0.24	1.0	48	200	200
3.8042	0.55	130	3.4318	0.24	1.0	48	200	200
3.8042	0.57	135	3.3690	0.24	1.0	48	200	200
3.705	0.59	140	3.3126	0.24	1.0	48	200	200
3.705	0.61	145	3.2630	0.24	1.0	48	200	200
3.763	0.63	150	3.2063	0.24	1.0	48	200	200
3.763	0.65	155	3.1562	0.24	1.0	48	200	200
3.5585	0.67	160	3.1096	0.24	1.0	48	200	200
3.5585	0.7	165	3.0719	0.24	1.0	48	200	200
3.213	0.72	170	3.0373	0.24	1.0	48	200	200
3.213	0.74	175	3.0035	0.24	1.0	48	200	200
3.2874	0.76	180	2.9712	0.24	1.0	48	200	200
3.2874	0.78	185	2.9405	0.24	1.0	48	200	200
3.3327	0.8	190	2.9134	0.24	1.0	48	200	200
3.3327	0.82	195	2.8910	0.24	1.0	48	200	200
3.2382	0.84	200	2.8672	0.24	1.0	48	200	200
3.2382	0.86	205	2.8462	0.24	1.0	48	200	200
3.0069	0.89	210	2.8260	0.24	1.0	48	200	200
3.0069	0.91	215	2.8087	0.24	1.0	48	200	200
3.2288	0.93	220	2.7920	0.24	1.0	48	200	200
3.2288	0.95	225	2.7750	0.24	1.0	48	200	200
2.787	0.97	230	2.7557	0.24	1.0	48	200	200
2.787	0.99	235	2.7367	0.24	1.0	48	200	200
2.9717	1.01	240	2.7207	0.24	1.0	48	200	200
2.9717	1.03	245	2.7063	0.24	1.0	48	200	200
2.9269	1.05	250	2.6939	0.24	1.0	48	200	200
2.9269	1.08	255	2.6831	0.24	1.0	48	200	200
2.8771	1.1	260	2.6709	0.24	1.0	48	200	200
2.8771	1.12	265	2.6594	0.24	1.0	48	200	200
3.0474	1.14	270	2.6472	0.24	1.0	48	200	200
3.0474	1.16	275	2.6361	0.24	1.0	48	200	200
2.7652	1.18	280	2.6268	0.24	1.0	48	200	200
2.7652	1.2	285	2.6184	0.24	1.0	48	200	200
2.8322	1.22	290	2.6106	0.24	1.0	48	200	200
2.8322	1.24	295	2.6034	0.24	1.0	48	200	200
2.6464	1.27	300	2.5957	0.24	1.0	48	200	200
2.6464	1.29	305	2.5877	0.24	1.0	48	200	200
2.7974	1.31	310	2.5805	0.24	1.0	48	200	200
2.7974	1.33	315	2.5748	0.24	1.0	48	200	200
2.797	1.35	320	2.5698	0.24	1.0	48	200	200
2.797	1.37	325	2.5644	0.24	1.0	48	200	200
2.7508	1.39	330	2.5595	0.24	1.0	48	200	200
2.7508	1.41	335	2.5537	0.24	1.0	48	200	200
2.7188	1.43	340	2.5486	0.24	1.0	48	200	200
2.7188	1.46	345	2.5434	0.24	1.0	48	200	200
2.6889	1.48	350	2.5377	0.24	1.0	48	200	200
2.6889	1.5	355	2.5336	0.24	1.0	48	200	200
2.6373	1.52	360	2.5300	0.24	1.0	48	200	200
2.6373	1.54	365	2.5258	0.24	1.0	48	200	200
2.765	1.56	370	2.5219	0.24	1.0	48	200	200
2.765	1.58	375	2.5181	0.24	1.0	48	200	200
2.6407	1.6	380	2.5144	0.24	1.0	48	200	200
2.6407	1.62	385	2.5113	0.24	1.0	48	200	200
2.7727	1.64	390	2.5093	0.24	1.0	48	200	200
2.7727	1.67	395	2.5076	0.24	1.0	48	200	200
2.8091	1.69	400	2.5060	0.24	1.0	48	200	200
2.8091	1.71	405	2.5042	0.24	1.0	48	200	200
2.7204	1.73	410	2.5027	0.24	1.0	48	200	200
2.7204	1.75	415	2.5011	0.24	1.0	48	200	200
2.6168	1.77	420	2.4987	0.24	1.0	48	200	200
2.6168	1.79	425	2.4965	0.24	1.0	48	200	200
2.6947	1.81	430	2.4947	0.24	1.0	48	200	200
2.6947	1.83	435	2.4932	0.24	1.0	48	200	200
2.7495	1.86	440	2.4921	0.24	1.0	48	200	200
2.7495	1.88	445	2.4911	0.24	1.0	48	200	200
2.7413	1.9	450	2.4904	0.24	1.0	48	200	200
2.7413	1.92	455	2.4897	0.24	1.0	48	200	200
2.6498	1.94	460	2.4893	0.24	1.0	48	200	200
2.6498	1.96	465	2.4890	0.24	1.0	48	200	200
2.6891	1.98	470	2.4888	0.24	1.0	48	200	200

Framework versions

Transformers 4.23.1
Pytorch 1.12.1+cu113
Datasets 1.18.3
Tokenizers 0.13.2

alexziweiwang/retrain_epoch2and3

retrain_epoch2and3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

alexziweiwang/
retrain_epoch2and3