pure-start-epoch2

alexziweiwang/pure-start-epoch2

pure-start-epoch2 is a machine learning model from alexziweiwang. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: transformers
Model type: wav2vec2
Access: Public
Created: Nov 28, 2022
Updated: Nov 28, 2022
SHA: 1e6b3acc

Model card

From the Hugging Face model README

pure-start-epoch2

This model is a fine-tuned version of alexziweiwang/pure-start-epoch1 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 7.7447
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: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss	Acc	Wer	Correct	Total	Strlen
No log	0.01	2	20.4002	0.095	1.0	19	200	200
No log	0.02	4	19.9080	0.095	1.0	19	200	200
No log	0.03	6	19.4711	0.095	1.0	19	200	200
No log	0.03	8	19.1535	0.095	1.0	19	200	200
46.6007	0.04	10	18.6684	0.095	1.0	19	200	200
46.6007	0.05	12	18.1640	0.095	1.0	19	200	200
46.6007	0.06	14	17.6937	0.095	1.0	19	200	200
46.6007	0.07	16	17.2710	0.095	1.0	19	200	200
46.6007	0.08	18	16.8469	0.095	1.0	19	200	200
49.1547	0.08	20	16.4418	0.095	1.0	19	200	200
49.1547	0.09	22	16.0409	0.095	1.0	19	200	200
49.1547	0.1	24	15.6677	0.095	1.0	19	200	200
49.1547	0.11	26	15.3291	0.095	1.0	19	200	200
49.1547	0.12	28	15.0097	0.095	1.0	19	200	200
35.1416	0.13	30	14.6776	0.095	1.0	19	200	200
35.1416	0.13	32	14.3788	0.095	1.0	19	200	200
35.1416	0.14	34	14.0924	0.095	1.0	19	200	200
35.1416	0.15	36	13.8133	0.095	1.0	19	200	200
35.1416	0.16	38	13.5539	0.095	1.0	19	200	200
34.4057	0.17	40	13.3095	0.095	1.0	19	200	200
34.4057	0.18	42	13.0804	0.095	1.0	19	200	200
34.4057	0.19	44	12.8580	0.105	1.0	21	200	200
34.4057	0.19	46	12.6532	0.115	1.0	23	200	200
34.4057	0.2	48	12.4532	0.13	1.0	26	200	200
33.2759	0.21	50	12.2452	0.14	1.0	28	200	200
33.2759	0.22	52	12.0666	0.13	1.0	26	200	200
33.2759	0.23	54	11.8976	0.165	1.0	33	200	200
33.2759	0.24	56	11.7373	0.175	1.0	35	200	200
33.2759	0.24	58	11.5933	0.17	1.0	34	200	200
29.8129	0.25	60	11.4281	0.15	1.0	30	200	200
29.8129	0.26	62	11.2665	0.14	1.0	28	200	200
29.8129	0.27	64	11.1158	0.145	1.0	29	200	200
29.8129	0.28	66	10.9840	0.135	1.0	27	200	200
29.8129	0.29	68	10.8502	0.15	1.0	30	200	200
38.792	0.3	70	10.7341	0.15	1.0	30	200	200
38.792	0.3	72	10.6082	0.165	1.0	33	200	200
38.792	0.31	74	10.4944	0.18	1.0	36	200	200
38.792	0.32	76	10.3818	0.21	1.0	42	200	200
38.792	0.33	78	10.2719	0.235	1.0	47	200	200
28.0092	0.34	80	10.1636	0.235	1.0	47	200	200
28.0092	0.35	82	10.0709	0.24	1.0	48	200	200
28.0092	0.35	84	9.9797	0.24	1.0	48	200	200
28.0092	0.36	86	9.8958	0.24	1.0	48	200	200
28.0092	0.37	88	9.7977	0.24	1.0	48	200	200
21.6175	0.38	90	9.7015	0.24	1.0	48	200	200
21.6175	0.39	92	9.6150	0.24	1.0	48	200	200
21.6175	0.4	94	9.5304	0.24	1.0	48	200	200
21.6175	0.4	96	9.4521	0.24	1.0	48	200	200
21.6175	0.41	98	9.3832	0.24	1.0	48	200	200
26.3434	0.42	100	9.3148	0.24	1.0	48	200	200
26.3434	0.43	102	9.2563	0.24	1.0	48	200	200
26.3434	0.44	104	9.1944	0.24	1.0	48	200	200
26.3434	0.45	106	9.1323	0.24	1.0	48	200	200
26.3434	0.46	108	9.0717	0.24	1.0	48	200	200
24.4387	0.46	110	9.0245	0.24	1.0	48	200	200
24.4387	0.47	112	8.9772	0.24	1.0	48	200	200
24.4387	0.48	114	8.9390	0.24	1.0	48	200	200
24.4387	0.49	116	8.9013	0.24	1.0	48	200	200
24.4387	0.5	118	8.8605	0.24	1.0	48	200	200
21.7305	0.51	120	8.8126	0.24	1.0	48	200	200
21.7305	0.51	122	8.7503	0.24	1.0	48	200	200
21.7305	0.52	124	8.6921	0.24	1.0	48	200	200
21.7305	0.53	126	8.6378	0.24	1.0	48	200	200
21.7305	0.54	128	8.5927	0.24	1.0	48	200	200
21.5989	0.55	130	8.5520	0.24	1.0	48	200	200
21.5989	0.56	132	8.5126	0.24	1.0	48	200	200
21.5989	0.56	134	8.4743	0.24	1.0	48	200	200
21.5989	0.57	136	8.4369	0.24	1.0	48	200	200
21.5989	0.58	138	8.3993	0.24	1.0	48	200	200
21.8372	0.59	140	8.3636	0.24	1.0	48	200	200
21.8372	0.6	142	8.3311	0.24	1.0	48	200	200
21.8372	0.61	144	8.2983	0.24	1.0	48	200	200
21.8372	0.62	146	8.2652	0.24	1.0	48	200	200
21.8372	0.62	148	8.2345	0.24	1.0	48	200	200
20.1716	0.63	150	8.2064	0.24	1.0	48	200	200
20.1716	0.64	152	8.1818	0.24	1.0	48	200	200
20.1716	0.65	154	8.1603	0.24	1.0	48	200	200
20.1716	0.66	156	8.1403	0.24	1.0	48	200	200
20.1716	0.67	158	8.1180	0.24	1.0	48	200	200
24.5655	0.67	160	8.0997	0.24	1.0	48	200	200
24.5655	0.68	162	8.0791	0.24	1.0	48	200	200
24.5655	0.69	164	8.0563	0.24	1.0	48	200	200
24.5655	0.7	166	8.0342	0.24	1.0	48	200	200
24.5655	0.71	168	8.0130	0.24	1.0	48	200	200
17.3768	0.72	170	7.9936	0.24	1.0	48	200	200
17.3768	0.72	172	7.9756	0.24	1.0	48	200	200
17.3768	0.73	174	7.9594	0.24	1.0	48	200	200
17.3768	0.74	176	7.9439	0.24	1.0	48	200	200
17.3768	0.75	178	7.9298	0.24	1.0	48	200	200
19.7473	0.76	180	7.9157	0.24	1.0	48	200	200
19.7473	0.77	182	7.9021	0.24	1.0	48	200	200
19.7473	0.78	184	7.8899	0.24	1.0	48	200	200
19.7473	0.78	186	7.8796	0.24	1.0	48	200	200
19.7473	0.79	188	7.8697	0.24	1.0	48	200	200
15.7279	0.8	190	7.8598	0.24	1.0	48	200	200
15.7279	0.81	192	7.8490	0.24	1.0	48	200	200
15.7279	0.82	194	7.8390	0.24	1.0	48	200	200
15.7279	0.83	196	7.8293	0.24	1.0	48	200	200
15.7279	0.83	198	7.8211	0.24	1.0	48	200	200
18.5034	0.84	200	7.8135	0.24	1.0	48	200	200
18.5034	0.85	202	7.8064	0.24	1.0	48	200	200
18.5034	0.86	204	7.7991	0.24	1.0	48	200	200
18.5034	0.87	206	7.7924	0.24	1.0	48	200	200
18.5034	0.88	208	7.7862	0.24	1.0	48	200	200
17.1983	0.89	210	7.7803	0.24	1.0	48	200	200
17.1983	0.89	212	7.7749	0.24	1.0	48	200	200
17.1983	0.9	214	7.7701	0.24	1.0	48	200	200
17.1983	0.91	216	7.7657	0.24	1.0	48	200	200
17.1983	0.92	218	7.7628	0.24	1.0	48	200	200
17.7276	0.93	220	7.7595	0.24	1.0	48	200	200
17.7276	0.94	222	7.7567	0.24	1.0	48	200	200
17.7276	0.94	224	7.7541	0.24	1.0	48	200	200
17.7276	0.95	226	7.7518	0.24	1.0	48	200	200
17.7276	0.96	228	7.7497	0.24	1.0	48	200	200
17.8692	0.97	230	7.7479	0.24	1.0	48	200	200
17.8692	0.98	232	7.7463	0.24	1.0	48	200	200
17.8692	0.99	234	7.7453	0.24	1.0	48	200	200
17.8692	0.99	236	7.7447	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

Library: transformers
Type: wav2vec2
Created: Nov 28, 2022
Updated: Nov 28, 2022