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
5.6348	0.0091	1.5865	4.2935	0.0093	0.9579	0
4.9212	0.0099	0.9054	4.1262	0.0097	0.9390	1
4.6819	0.0107	0.8319	3.9071	0.0103	0.8966	2
4.4443	0.0114	0.8310	3.7367	0.0106	0.8939	3
4.2479	0.0119	0.8226	3.6101	0.0109	0.8696	4
4.0911	0.0124	0.8103	3.5364	0.0110	0.8946	5
3.9590	0.0127	0.7913	3.4556	0.0113	0.8388	6
3.8513	0.0130	0.7794	3.4106	0.0114	0.8515	7
3.7607	0.0133	0.7657	3.3507	0.0115	0.8261	8
3.6757	0.0136	0.7548	3.3141	0.0116	0.8400	9
3.6023	0.0138	0.7454	3.2711	0.0117	0.8006	10
3.5261	0.0140	0.7348	3.2391	0.0119	0.8101	11
3.4534	0.0143	0.7212	3.2070	0.0120	0.7870	12
3.3814	0.0146	0.7080	3.1505	0.0122	0.7826	13
3.3069	0.0148	0.6961	3.1102	0.0124	0.7609	14
3.2229	0.0152	0.6781	3.0542	0.0125	0.7532	15
3.1334	0.0156	0.6614	2.9840	0.0127	0.7448	16
3.0313	0.0160	0.6425	2.9032	0.0130	0.7123	17
2.9122	0.0166	0.6202	2.7986	0.0134	0.6930	18
2.7559	0.0173	0.5940	2.6337	0.0139	0.6673	19
2.5649	0.0182	0.5674	2.4490	0.0145	0.6383	20
2.3414	0.0193	0.5299	2.2785	0.0150	0.6183	21
2.0966	0.0206	0.4903	2.0460	0.0158	0.5649	22
1.8283	0.0220	0.4459	1.8369	0.0165	0.5306	23
1.5547	0.0235	0.3996	1.6356	0.0172	0.4848	24
1.3218	0.0249	0.3581	1.4682	0.0179	0.4510	25
1.1383	0.0260	0.3211	1.3465	0.0183	0.4226	26
0.9876	0.0270	0.2920	1.2323	0.0188	0.3966	27
0.8635	0.0278	0.2651	1.1482	0.0191	0.3749	28
0.7620	0.0284	0.2435	1.0816	0.0194	0.3565	29
0.6749	0.0290	0.2234	1.0187	0.0196	0.3433	30
0.5998	0.0295	0.2025	0.9761	0.0198	0.3319	31
0.5325	0.0300	0.1827	0.9326	0.0200	0.3213	32
0.4735	0.0305	0.1665	0.8942	0.0201	0.3110	33
0.4228	0.0308	0.1466	0.8735	0.0202	0.3026	34
0.3747	0.0312	0.1293	0.8408	0.0203	0.2931	35
0.3331	0.0316	0.1111	0.8253	0.0204	0.2891	36
0.2947	0.0319	0.0962	0.8084	0.0205	0.2849	37
0.2601	0.0322	0.0817	0.7906	0.0205	0.2783	38
0.2291	0.0324	0.0706	0.7876	0.0206	0.2755	39
0.2009	0.0327	0.0596	0.7723	0.0207	0.2712	40
0.1750	0.0329	0.0504	0.7629	0.0207	0.2692	41
0.1510	0.0331	0.0410	0.7650	0.0207	0.2684	42
0.1319	0.0333	0.0367	0.7533	0.0207	0.2655	43
0.1121	0.0335	0.0292	0.7589	0.0207	0.2647	44
0.0956	0.0336	0.0253	0.7579	0.0208	0.2642	45
0.0812	0.0337	0.0254	0.7584	0.0208	0.2625	46
0.0694	0.0338	0.0332	0.7555	0.0208	0.2693	47
0.0592	0.0339	0.0319	0.7534	0.0208	0.2629	48
0.0499	0.0339	0.0487	0.7587	0.0208	0.3030	49
0.0409	0.0339	0.0615	0.7577	0.0208	0.2810	50
0.0347	0.0340	0.0859	0.7603	0.0208	0.3534	51
0.0286	0.0340	0.1928	0.7554	0.0209	0.5822	52
0.0267	0.0340	0.3131	0.7664	0.0208	1.7372	53
0.0243	0.0340	1.3154	0.7525	0.0209	0.7770	54
0.0206	0.0340	0.8121	0.7532	0.0209	0.9253	55
0.0174	0.0340	0.9253	0.7574	0.0209	1.4865	56
0.0135	0.0340	1.1761	0.7592	0.0209	1.5813	57
0.0111	0.0340	1.7125	0.7631	0.0209	1.8950	58
0.0096	0.0340	1.9230	0.7664	0.0209	2.4432	59

Framework versions

Transformers 4.34.0.dev0
TensorFlow 2.13.0
Tokenizers 0.13.3

bigmorning/whisper_input_decoder_shift_r_labels_no_force__0060

whisper_input_decoder_shift_r_labels_no_force__0060

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

bigmorning/
whisper_input_decoder_shift_r_labels_no_force__0060