optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
training_precision: float32

Training results

Train Loss	Train Accuracy	Train Wermet	Validation Loss	Validation Accuracy	Validation Wermet	Epoch
5.4122	0.0107	0.9328	3.9759	0.0114	0.9606	0
4.7176	0.0116	0.8683	3.9404	0.0114	0.9334	1
4.6750	0.0117	0.8478	3.9211	0.0115	0.9237	2
4.6511	0.0117	0.8413	3.8864	0.0115	0.9331	3
4.6294	0.0118	0.8270	3.8729	0.0115	0.9228	4
4.6134	0.0118	0.8199	3.8690	0.0114	0.9451	5
4.5980	0.0118	0.8102	3.8491	0.0115	0.9152	6
4.5759	0.0119	0.7890	3.8366	0.0116	0.8691	7
4.5518	0.0120	0.7694	3.8081	0.0116	0.9013	8
4.5219	0.0121	0.7591	3.7734	0.0118	0.8383	9
4.4761	0.0122	0.7400	3.7156	0.0120	0.8125	10
4.4139	0.0125	0.7257	3.6311	0.0121	0.8188	11
4.3113	0.0128	0.7127	3.5089	0.0124	0.8008	12
4.1608	0.0132	0.7088	3.3587	0.0127	0.7742	13
3.9595	0.0138	0.7012	3.1493	0.0132	0.7718	14
3.7188	0.0145	0.6820	2.8784	0.0139	0.7292	15
3.4775	0.0153	0.6678	2.6716	0.0144	0.7074	16
3.2575	0.0160	0.6481	2.4980	0.0149	0.6764	17
3.0615	0.0167	0.6314	2.3456	0.0153	0.6476	18
2.8715	0.0174	0.6094	2.2090	0.0158	0.6210	19
2.6930	0.0181	0.5931	2.0918	0.0162	0.5992	20
2.5383	0.0187	0.5739	1.9769	0.0166	0.5791	21
2.3952	0.0193	0.5512	1.9042	0.0168	0.5589	22
2.2427	0.0201	0.5333	1.8028	0.0172	0.5394	23
2.1236	0.0206	0.5174	1.7434	0.0174	0.5240	24
2.0315	0.0211	0.4978	1.6755	0.0177	0.5084	25
1.9066	0.0217	0.4773	1.6534	0.0178	0.4947	26
1.8279	0.0221	0.4596	1.5606	0.0182	0.4788	27
1.7325	0.0227	0.4412	1.5173	0.0184	0.4667	28
1.6416	0.0232	0.4199	1.4733	0.0186	0.4511	29
1.5702	0.0236	0.4028	1.4519	0.0187	0.4442	30
1.4787	0.0241	0.3839	1.4213	0.0188	0.4322	31
1.4238	0.0244	0.3700	1.3971	0.0190	0.4272	32
1.3561	0.0249	0.3594	1.3499	0.0192	0.4171	33
1.2828	0.0254	0.3431	1.3555	0.0192	0.4097	34
1.2318	0.0257	0.3277	1.3183	0.0194	0.4035	35
1.1668	0.0262	0.3201	1.3068	0.0195	0.3978	36
1.1571	0.0261	0.3105	1.2901	0.0195	0.3916	37
1.0812	0.0267	0.2989	1.2720	0.0197	0.3860	38
1.0134	0.0273	0.2863	1.2593	0.0197	0.3777	39
0.9986	0.0273	0.2769	1.2629	0.0198	0.3754	40
0.9322	0.0279	0.2653	1.2320	0.0199	0.3694	41
0.9021	0.0281	0.2552	1.2308	0.0200	0.3651	42
0.8583	0.0284	0.2444	1.2199	0.0200	0.3614	43
0.8101	0.0288	0.2355	1.2120	0.0200	0.3597	44
0.8045	0.0288	0.2299	1.2023	0.0201	0.3567	45
0.7823	0.0290	0.2213	1.2075	0.0201	0.3529	46
0.7186	0.0296	0.2107	1.1917	0.0202	0.3530	47
0.6949	0.0298	0.2028	1.1926	0.0202	0.3465	48
0.6669	0.0300	0.1943	1.1902	0.0203	0.3446	49
0.6125	0.0305	0.1842	1.1892	0.0203	0.3437	50
0.5926	0.0307	0.1778	1.2058	0.0203	0.3450	51
0.6055	0.0305	0.1738	1.1859	0.0203	0.3394	52
0.5828	0.0307	0.1653	1.1921	0.0203	0.3379	53
0.5507	0.0311	0.1569	1.1906	0.0204	0.3385	54
0.5050	0.0315	0.1485	1.1834	0.0205	0.3361	55
0.4878	0.0316	0.1447	1.1815	0.0205	0.3329	56
0.4825	0.0317	0.1410	1.2096	0.0204	0.3359	57
0.4987	0.0315	0.1374	1.2000	0.0204	0.3352	58
0.4576	0.0319	0.1305	1.1868	0.0205	0.3329	59
0.4185	0.0323	0.1215	1.2043	0.0205	0.3322	60
0.3889	0.0326	0.1156	1.1853	0.0206	0.3302	61
0.3790	0.0327	0.1101	1.2028	0.0205	0.3316	62
0.4072	0.0324	0.1110	1.2502	0.0203	0.3309	63
0.3519	0.0330	0.1020	1.1959	0.0206	0.3284	64
0.3861	0.0326	0.1034	1.1885	0.0206	0.3271	65
0.3789	0.0326	0.0961	1.1969	0.0206	0.3298	66
0.3233	0.0332	0.0905	1.1922	0.0207	0.3280	67
0.2956	0.0335	0.0854	1.2003	0.0207	0.3296	68
0.2666	0.0339	0.0796	1.2141	0.0207	0.3252	69
0.3181	0.0333	0.0813	1.2133	0.0207	0.3302	70
0.3032	0.0335	0.0770	1.2170	0.0207	0.3315	71
0.2746	0.0337	0.0741	1.2180	0.0207	0.3299	72
0.2549	0.0339	0.0705	1.2496	0.0206	0.3308	73
0.2529	0.0339	0.0685	1.2239	0.0207	0.3321	74
0.2427	0.0340	0.0671	1.2351	0.0207	0.3292	75
0.2166	0.0343	0.0623	1.2361	0.0207	0.3313	76
0.2030	0.0345	0.0585	1.2462	0.0207	0.3312	77
0.2126	0.0344	0.0566	1.2441	0.0207	0.3313	78
0.2166	0.0343	0.0569	1.2506	0.0207	0.3334	79
0.2088	0.0344	0.0562	1.2557	0.0207	0.3389	80
0.2212	0.0342	0.0560	1.2652	0.0207	0.3334	81
0.2256	0.0343	0.0543	1.2543	0.0207	0.3393	82
0.1915	0.0346	0.0501	1.2540	0.0207	0.3299	83
0.1544	0.0350	0.0443	1.2676	0.0207	0.3347	84
0.1567	0.0350	0.0435	1.2740	0.0207	0.3375	85
0.1329	0.0352	0.0405	1.2833	0.0207	0.3398	86
0.1261	0.0353	0.0392	1.3088	0.0206	0.3397	87
0.1547	0.0350	0.0438	1.2933	0.0207	0.3279	88
0.1288	0.0352	0.0377	1.2985	0.0208	0.3371	89
0.1400	0.0351	0.0391	1.3020	0.0208	0.3329	90
0.1077	0.0355	0.0351	1.3118	0.0207	0.3344	91
0.0982	0.0355	0.0348	1.3274	0.0207	0.3352	92
0.1041	0.0355	0.0356	1.3251	0.0208	0.3362	93
0.1573	0.0350	0.0364	1.3173	0.0208	0.3362	94
0.1478	0.0350	0.0365	1.3125	0.0208	0.3367	95
0.0929	0.0356	0.0336	1.3256	0.0208	0.3351	96
0.1106	0.0354	0.0341	1.3289	0.0208	0.3428	97
0.0916	0.0355	0.0322	1.3384	0.0208	0.3383	98
0.0708	0.0358	0.0313	1.3525	0.0208	0.3333	99
0.1271	0.0352	0.0326	1.3447	0.0208	0.3336	100
0.0963	0.0355	0.0329	1.3481	0.0208	0.3288	101
0.0698	0.0358	0.0294	1.3748	0.0207	0.3341	102
0.0844	0.0356	0.0313	1.3615	0.0208	0.3380	103
0.1045	0.0354	0.0319	1.3519	0.0208	0.3357	104
0.0877	0.0355	0.0326	1.3760	0.0207	0.3402	105
0.0713	0.0357	0.0303	1.3742	0.0208	0.3392	106
0.0686	0.0358	0.0292	1.3920	0.0207	0.3353	107
0.0466	0.0360	0.0287	1.3964	0.0208	0.3342	108
0.0353	0.0361	0.0258	1.3977	0.0208	0.3339	109

Framework versions

Transformers 4.33.0.dev0
TensorFlow 2.13.0
Tokenizers 0.13.3

bigmorning/whisper_input_decoder_no_lob__0110

whisper_input_decoder_no_lob__0110

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

bigmorning/
whisper_input_decoder_no_lob__0110