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

Framework versions

Transformers 4.33.0.dev0
TensorFlow 2.13.0
Tokenizers 0.13.3

bigmorning/whisper_input_decoder_no_lob_with_force__0070

whisper_input_decoder_no_lob_with_force__0070

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__0070