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
5.1027	0.0113	52.5530	4.4267	0.0121	41.4796	0
4.3285	0.0126	38.6893	3.9835	0.0145	33.6050	1
3.4573	0.0168	30.7714	2.5568	0.0215	31.7559	2
2.0878	0.0226	20.5131	1.5738	0.0257	21.2159	3
1.3529	0.0258	17.4367	1.1712	0.0276	17.7695	4
0.9953	0.0275	18.7308	0.9389	0.0287	20.5259	5
0.7852	0.0286	18.5731	0.8074	0.0294	17.6576	6
0.6428	0.0293	18.2945	0.7219	0.0298	19.9850	7
0.5384	0.0299	18.9258	0.6610	0.0301	18.9327	8
0.4565	0.0304	19.0749	0.6117	0.0304	21.9796	9
0.3901	0.0308	19.2099	0.5693	0.0306	18.0965	10
0.3348	0.0312	20.4777	0.5449	0.0307	19.9518	11
0.2877	0.0315	20.3181	0.5232	0.0309	20.4017	12
0.2471	0.0318	19.2073	0.5057	0.0310	18.7612	13
0.2120	0.0320	19.0961	0.4925	0.0311	22.3187	14
0.1809	0.0323	20.7944	0.4849	0.0311	27.2314	15
0.1539	0.0325	22.0951	0.4787	0.0312	25.2171	16
0.1299	0.0327	22.7652	0.4733	0.0312	22.7492	17
0.1087	0.0329	25.2223	0.4701	0.0312	28.9044	18
0.0899	0.0330	24.8354	0.4715	0.0313	21.1618	19
0.0739	0.0332	25.4987	0.4680	0.0313	29.6304	20
0.0604	0.0333	27.6465	0.4693	0.0313	27.6937	21
0.0498	0.0333	27.7045	0.4711	0.0313	27.5013	22
0.0414	0.0334	28.0547	0.4689	0.0313	29.1776	23
0.0327	0.0334	27.5594	0.4718	0.0313	31.5623	24
0.0256	0.0335	27.3983	0.4710	0.0313	27.1071	25
0.0210	0.0335	24.7398	0.4736	0.0313	30.8282	26
0.0165	0.0335	25.1927	0.4773	0.0313	24.1750	27
0.0133	0.0335	25.6261	0.4807	0.0313	29.9520	28
0.0110	0.0335	25.8127	0.4825	0.0314	27.0813	29
0.0171	0.0335	26.0445	0.4858	0.0313	39.8503	30
0.0154	0.0335	28.6186	0.4766	0.0314	28.4465	31
0.0094	0.0335	27.8978	0.4778	0.0314	28.7775	32
0.0071	0.0335	27.8180	0.4775	0.0314	28.5229	33
0.0054	0.0335	27.4530	0.4807	0.0315	30.3598	34
0.0043	0.0335	27.2908	0.4833	0.0315	29.9185	35
0.0036	0.0335	27.9772	0.4870	0.0315	29.0761	36
0.0031	0.0335	29.0235	0.4901	0.0315	31.1068	37
0.0027	0.0335	28.2433	0.4930	0.0315	30.2512	38
0.0024	0.0335	33.0830	0.4968	0.0315	35.0547	39
0.0152	0.0334	30.0515	0.4999	0.0314	31.3169	40
0.0095	0.0335	30.0595	0.4917	0.0315	25.2631	41
0.0038	0.0335	23.3205	0.4882	0.0315	22.6513	42
0.0028	0.0335	23.9223	0.4847	0.0315	28.0730	43
0.0023	0.0335	28.1808	0.4919	0.0315	33.1382	44
0.0036	0.0335	32.2064	0.4898	0.0315	27.6573	45
0.0032	0.0335	31.5153	0.4964	0.0315	38.2573	46
0.0025	0.0335	35.6323	0.4925	0.0315	24.0359	47
0.0023	0.0335	30.3651	0.4937	0.0315	29.2069	48
0.0023	0.0335	32.4053	0.4968	0.0315	38.3220	49
0.0020	0.0335	39.4820	0.5002	0.0315	35.0649	50
0.0021	0.0335	33.6737	0.5188	0.0314	32.5078	51
0.0057	0.0335	31.5887	0.5069	0.0315	29.0739	52
0.0053	0.0335	30.3396	0.4958	0.0316	30.4228	53
0.0035	0.0335	30.4281	0.4911	0.0316	30.4131	54
0.0019	0.0335	36.0578	0.4909	0.0316	39.2006	55
0.0013	0.0335	36.1010	0.4927	0.0316	33.9386	56
0.0011	0.0335	30.4648	0.4979	0.0316	35.2314	57
0.0023	0.0335	37.5041	0.4983	0.0316	34.6155	58
0.0017	0.0335	34.4652	0.5023	0.0316	33.7523	59
0.0025	0.0335	38.1887	0.5096	0.0316	30.1588	60
0.0021	0.0335	35.3498	0.5017	0.0316	35.6420	61
0.0016	0.0335	36.5619	0.5123	0.0315	41.2840	62
0.0012	0.0335	35.5301	0.5119	0.0316	34.3617	63
0.0019	0.0335	36.1816	0.5121	0.0316	27.5481	64

Framework versions

Transformers 4.27.0.dev0
TensorFlow 2.11.0
Tokenizers 0.13.2

bigmorning/whisper_new_split_0065

whisper_new_split_0065

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

bigmorning/
whisper_new_split_0065