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
5.2930	0.0113	2.0658	3.9415	0.0117	0.9401	0
4.6215	0.0121	0.8917	3.7803	0.0120	0.9294	1
4.4086	0.0128	0.8403	3.6070	0.0124	0.9223	2
4.1842	0.0135	0.8337	3.4291	0.0128	0.8867	3
3.9981	0.0141	0.8182	3.3251	0.0131	0.8750	4
3.8531	0.0145	0.8058	3.2385	0.0133	0.8699	5
3.7345	0.0149	0.7925	3.1751	0.0134	0.8665	6
3.6307	0.0152	0.7851	3.1031	0.0136	0.8507	7
3.5437	0.0155	0.7717	3.0752	0.0138	0.8286	8
3.4649	0.0157	0.7651	3.0334	0.0139	0.8417	9
3.3926	0.0159	0.7531	3.0022	0.0139	0.8413	10
3.3262	0.0162	0.7462	2.9669	0.0140	0.8264	11
3.2625	0.0164	0.7367	2.9342	0.0141	0.8520	12
3.1979	0.0166	0.7231	2.9046	0.0144	0.8196	13
3.1319	0.0169	0.7133	2.8607	0.0145	0.8026	14
3.0616	0.0172	0.7007	2.8165	0.0146	0.7788	15
2.9792	0.0176	0.6816	2.7552	0.0149	0.7643	16
2.8905	0.0180	0.6641	2.6788	0.0151	0.7473	17
2.7749	0.0186	0.6424	2.5824	0.0155	0.7241	18
2.6263	0.0193	0.6159	2.4206	0.0161	0.7047	19
2.4352	0.0203	0.5829	2.2230	0.0168	0.6500	20
2.1941	0.0216	0.5411	2.0349	0.0175	0.5980	21
1.9184	0.0231	0.4922	1.7850	0.0184	0.5659	22
1.6174	0.0249	0.4371	1.5664	0.0192	0.5081	23
1.3542	0.0265	0.3851	1.3992	0.0199	0.4690	24
1.1499	0.0278	0.3408	1.2512	0.0205	0.4299	25
0.9878	0.0288	0.3029	1.1479	0.0209	0.4013	26
0.8600	0.0297	0.2735	1.0527	0.0213	0.3755	27
0.7516	0.0305	0.2441	0.9803	0.0216	0.3570	28
0.6626	0.0311	0.2197	0.9314	0.0219	0.3416	29
0.5863	0.0316	0.1993	0.8730	0.0221	0.3238	30
0.5187	0.0321	0.1775	0.8357	0.0223	0.3136	31
0.4608	0.0326	0.1610	0.8059	0.0224	0.3033	32
0.4087	0.0330	0.1467	0.7746	0.0226	0.2949	33
0.3642	0.0334	0.1298	0.7476	0.0227	0.2847	34
0.3221	0.0337	0.1168	0.7330	0.0228	0.2802	35
0.2837	0.0340	0.1030	0.7093	0.0229	0.2728	36
0.2509	0.0343	0.0882	0.6941	0.0229	0.2687	37
0.2209	0.0346	0.0747	0.6892	0.0230	0.2656	38
0.1934	0.0349	0.0670	0.6824	0.0230	0.2630	39
0.1688	0.0351	0.0542	0.6773	0.0230	0.2625	40
0.1469	0.0353	0.0429	0.6700	0.0231	0.2633	41
0.1268	0.0355	0.0365	0.6680	0.0231	0.2578	42
0.1086	0.0357	0.0284	0.6643	0.0231	0.2540	43
0.0920	0.0358	0.0221	0.6645	0.0231	0.2530	44
0.0783	0.0359	0.0169	0.6621	0.0232	0.2540	45
0.0667	0.0360	0.0121	0.6714	0.0232	0.2532	46
0.0563	0.0361	0.0094	0.6604	0.0232	0.2503	47
0.0477	0.0361	0.0072	0.6620	0.0232	0.2489	48
0.0397	0.0362	0.0055	0.6611	0.0232	0.2502	49
0.0330	0.0362	0.0045	0.6686	0.0232	0.2496	50
0.0283	0.0362	0.0033	0.6705	0.0232	0.2503	51
0.0242	0.0362	0.0034	0.6686	0.0232	0.2486	52
0.0212	0.0362	0.0031	0.6686	0.0232	0.2493	53
0.0197	0.0362	0.0028	0.6688	0.0232	0.2530	54
0.0226	0.0362	0.0041	0.6598	0.0233	0.2451	55
0.0158	0.0362	0.0024	0.6605	0.0233	0.2428	56
0.0115	0.0362	0.0018	0.6648	0.0233	0.2435	57
0.0094	0.0362	0.0017	0.6672	0.0233	0.2446	58
0.0081	0.0362	0.0018	0.6731	0.0233	0.2439	59
0.0071	0.0362	0.0017	0.6762	0.0233	0.2429	60
0.0062	0.0362	0.0017	0.6794	0.0233	0.2426	61
0.0055	0.0362	0.0017	0.6825	0.0233	0.2429	62
0.0048	0.0362	0.0017	0.6895	0.0233	0.2450	63
0.0042	0.0362	0.0019	0.6914	0.0233	0.2424	64

Framework versions

Transformers 4.33.0.dev0
TensorFlow 2.13.0
Tokenizers 0.13.3

bigmorning/whisper_syl_cv12_pad_lob100_low__0065

whisper_syl_cv12_pad_lob100_low__0065

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

bigmorning/
whisper_syl_cv12_pad_lob100_low__0065