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
5.4192	0.0107	1.1095	3.9799	0.0114	0.9502	0
4.7193	0.0116	0.8751	3.9335	0.0114	0.9414	1
4.6743	0.0117	0.8498	3.9949	0.0112	0.9682	2
4.6508	0.0117	0.8454	3.8900	0.0114	0.9453	3
4.6308	0.0118	0.8337	3.8853	0.0114	0.9495	4
4.6102	0.0118	0.8215	3.8884	0.0115	0.9205	5
4.5940	0.0118	0.8132	3.8409	0.0116	0.9007	6
4.5703	0.0119	0.7971	3.8224	0.0116	0.9098	7
4.5470	0.0120	0.7822	3.8013	0.0116	0.8938	8
4.5219	0.0120	0.7679	3.7776	0.0117	0.8829	9
4.4859	0.0121	0.7519	3.7360	0.0118	0.8411	10
4.4408	0.0123	0.7412	3.6972	0.0118	0.8593	11
4.3774	0.0124	0.7240	3.6035	0.0121	0.8234	12
4.2906	0.0127	0.7168	3.5057	0.0123	0.8130	13
4.1748	0.0130	0.7090	3.3528	0.0127	0.7856	14
4.0214	0.0135	0.7048	3.2086	0.0130	0.7786	15
3.8434	0.0140	0.6918	3.0436	0.0134	0.7466	16
3.6564	0.0146	0.6797	2.8693	0.0138	0.7348	17
3.4565	0.0152	0.6658	2.6967	0.0143	0.7131	18
3.2849	0.0158	0.6496	2.5221	0.0148	0.6792	19
3.0761	0.0165	0.6273	2.3796	0.0153	0.6550	20
2.9131	0.0171	0.6028	2.2468	0.0156	0.6282	21
2.7468	0.0178	0.5812	2.1322	0.0160	0.6123	22
2.6133	0.0183	0.5606	2.1131	0.0160	0.5950	23
2.4732	0.0189	0.5367	2.0006	0.0164	0.5730	24
2.3339	0.0196	0.5164	1.8895	0.0168	0.5532	25
2.2300	0.0200	0.4985	1.8183	0.0172	0.5430	26
2.1215	0.0206	0.4787	1.7413	0.0174	0.5230	27
2.0359	0.0210	0.4608	1.6732	0.0177	0.5099	28
1.9219	0.0216	0.4433	1.6287	0.0179	0.4979	29
1.8477	0.0220	0.4274	1.6129	0.0180	0.4896	30
1.7778	0.0224	0.4123	1.5705	0.0182	0.4769	31
1.6924	0.0228	0.3980	1.5096	0.0185	0.4666	32
1.6389	0.0231	0.3847	1.4837	0.0186	0.4558	33
1.5687	0.0235	0.3710	1.4419	0.0188	0.4475	34
1.4742	0.0242	0.3566	1.4365	0.0188	0.4385	35
1.4321	0.0244	0.3452	1.4011	0.0190	0.4300	36
1.3712	0.0248	0.3328	1.4145	0.0189	0.4245	37
1.3249	0.0251	0.3231	1.3604	0.0192	0.4188	38
1.2787	0.0253	0.3113	1.3424	0.0193	0.4133	39
1.1978	0.0259	0.2997	1.3275	0.0194	0.4068	40
1.1643	0.0261	0.2893	1.3122	0.0195	0.4019	41
1.1043	0.0266	0.2785	1.3011	0.0195	0.3964	42
1.0828	0.0267	0.2712	1.3235	0.0195	0.3932	43
1.0231	0.0272	0.2601	1.2947	0.0196	0.3873	44
0.9817	0.0275	0.2511	1.2785	0.0198	0.3855	45
0.9612	0.0276	0.2430	1.2755	0.0198	0.3831	46
0.9191	0.0280	0.2330	1.2522	0.0199	0.3744	47
0.8629	0.0284	0.2234	1.2679	0.0199	0.3759	48
0.8454	0.0285	0.2159	1.2575	0.0199	0.3732	49
0.7928	0.0290	0.2064	1.2505	0.0200	0.3693	50
0.7814	0.0291	0.2003	1.2450	0.0200	0.3669	51
0.7775	0.0291	0.1948	1.2965	0.0198	0.3662	52
0.7274	0.0295	0.1858	1.2472	0.0201	0.3663	53
0.7132	0.0296	0.1787	1.2389	0.0201	0.3591	54
0.6941	0.0298	0.1731	1.2386	0.0201	0.3564	55
0.6327	0.0304	0.1633	1.2437	0.0201	0.3557	56
0.6090	0.0306	0.1569	1.2333	0.0202	0.3532	57
0.6347	0.0303	0.1561	1.2425	0.0202	0.3564	58
0.5639	0.0310	0.1447	1.2261	0.0202	0.3526	59
0.5470	0.0311	0.1383	1.2476	0.0202	0.3497	60
0.5209	0.0314	0.1324	1.2304	0.0203	0.3489	61
0.5326	0.0313	0.1300	1.2410	0.0203	0.3482	62
0.5106	0.0314	0.1236	1.2508	0.0203	0.3462	63
0.4984	0.0315	0.1197	1.2628	0.0202	0.3489	64
0.4676	0.0319	0.1129	1.2352	0.0204	0.3446	65
0.4612	0.0319	0.1091	1.2369	0.0204	0.3441	66
0.4367	0.0321	0.1030	1.2393	0.0204	0.3440	67
0.4165	0.0323	0.0984	1.2412	0.0204	0.3413	68
0.4154	0.0323	0.0955	1.2445	0.0204	0.3423	69
0.4130	0.0323	0.0922	1.2361	0.0205	0.3408	70
0.3758	0.0327	0.0857	1.2439	0.0204	0.3406	71
0.3566	0.0329	0.0812	1.2406	0.0205	0.3392	72
0.3232	0.0333	0.0750	1.2572	0.0205	0.3386	73
0.3755	0.0327	0.0776	1.2465	0.0205	0.3372	74
0.3485	0.0330	0.0733	1.2488	0.0205	0.3366	75
0.2972	0.0335	0.0651	1.2797	0.0204	0.3408	76
0.3179	0.0334	0.0649	1.2714	0.0205	0.3368	77
0.2738	0.0338	0.0579	1.2693	0.0205	0.3384	78
0.2658	0.0339	0.0544	1.2769	0.0205	0.3385	79
0.2337	0.0342	0.0493	1.2862	0.0205	0.3363	80
0.2667	0.0338	0.0513	1.2851	0.0205	0.3379	81
0.2639	0.0338	0.0488	1.2911	0.0205	0.3382	82
0.2682	0.0338	0.0498	1.2893	0.0205	0.3370	83
0.2325	0.0341	0.0442	1.2965	0.0205	0.3380	84
0.2534	0.0339	0.0452	1.2888	0.0205	0.3366	85
0.2503	0.0340	0.0429	1.3043	0.0205	0.3373	86
0.1962	0.0346	0.0367	1.3363	0.0205	0.3396	87
0.1735	0.0348	0.0329	1.3139	0.0205	0.3372	88
0.1664	0.0349	0.0316	1.3461	0.0204	0.3397	89
0.1891	0.0346	0.0335	1.3275	0.0206	0.3378	90
0.2370	0.0343	0.0369	1.3073	0.0206	0.3356	91
0.2429	0.0341	0.0380	1.3116	0.0205	0.3351	92
0.1635	0.0349	0.0272	1.3342	0.0205	0.3385	93
0.1326	0.0352	0.0235	1.3294	0.0206	0.3370	94
0.1322	0.0352	0.0224	1.3418	0.0206	0.3357	95
0.1357	0.0352	0.0224	1.3476	0.0206	0.3372	96
0.1394	0.0351	0.0230	1.3578	0.0206	0.3364	97
0.1370	0.0351	0.0224	1.3559	0.0206	0.3366	98
0.1700	0.0349	0.0259	1.3587	0.0205	0.3371	99
0.1102	0.0354	0.0183	1.3664	0.0206	0.3364	100
0.1039	0.0355	0.0175	1.4739	0.0203	0.3433	101
0.1094	0.0354	0.0184	1.3800	0.0206	0.3378	102
0.1426	0.0350	0.0203	1.3797	0.0206	0.3370	103
0.1132	0.0353	0.0175	1.4130	0.0205	0.3384	104
0.1029	0.0354	0.0160	1.4131	0.0205	0.3381	105
0.1241	0.0352	0.0183	1.3905	0.0206	0.3361	106
0.0869	0.0356	0.0133	1.4050	0.0206	0.3369	107
0.0805	0.0356	0.0138	1.4236	0.0206	0.3361	108
0.0842	0.0357	0.0154	1.4232	0.0206	0.3348	109

Framework versions

Transformers 4.33.0.dev0
TensorFlow 2.13.0
Tokenizers 0.13.3

bigmorning/whisper_input_decoder_no_lob_with_force__0110

whisper_input_decoder_no_lob_with_force__0110

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

bigmorning/
whisper_input_decoder_no_lob_with_force__0110