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bigmorning/
whisper_charsplit_new_round3__0036

Name: whisper_charsplit_new_round3__0036
Availability: InStock

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whisper_charsplit_new_round3__0036

This model is a fine-tuned version of bigmorning/whisper_charsplit_new_round2__0061 on an unknown dataset. It achieves the following results on the evaluation set:

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The following hyperparameters were used during training:

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

Train Loss	Train Accuracy	Train Wermet	Validation Loss	Validation Accuracy	Validation Wermet	Epoch
0.0009	0.0795	7.9492	0.5730	0.0769	7.2856	0
0.0015	0.0795	8.4221	0.5756	0.0769	7.1487	1
0.0012	0.0795	7.8476	0.5699	0.0769	6.5976	2
0.0010	0.0795	7.6843	0.5740	0.0769	6.9513	3
0.0014	0.0795	8.0796	0.5763	0.0768	7.4043	4
0.0019	0.0795	7.7274	0.5724	0.0769	6.4922	5
0.0008	0.0795	7.3468	0.5734	0.0769	6.1909	6
0.0009	0.0795	7.2393	0.5816	0.0769	6.5734	7
0.0010	0.0795	7.5822	0.5755	0.0769	6.6613	8
0.0004	0.0795	7.3807	0.5698	0.0770	7.0671	9
0.0001	0.0795	7.7157	0.5681	0.0771	6.8391	10
0.0001	0.0795	7.7540	0.5725	0.0771	6.9281	11
0.0001	0.0795	7.7721	0.5726	0.0771	6.8911	12
0.0000	0.0795	7.8163	0.5721	0.0771	6.8876	13
0.0000	0.0795	7.7745	0.5741	0.0771	6.8770	14
0.0000	0.0795	7.7277	0.5752	0.0771	6.8671	15
0.0000	0.0795	7.7355	0.5765	0.0771	6.8447	16
0.0000	0.0795	7.7109	0.5784	0.0771	6.8560	17
0.0000	0.0795	7.7427	0.5796	0.0771	6.8406	18
0.0003	0.0795	7.6709	0.6610	0.0762	7.0119	19
0.0115	0.0793	8.3288	0.5580	0.0769	7.1457	20
0.0013	0.0795	8.2537	0.5574	0.0770	6.7708	21
0.0004	0.0795	8.0507	0.5619	0.0770	7.0678	22
0.0003	0.0795	8.0534	0.5593	0.0771	7.0433	23
0.0002	0.0795	8.1738	0.5604	0.0771	7.1617	24
0.0001	0.0795	8.1494	0.5589	0.0771	7.1609	25
0.0000	0.0795	8.2151	0.5614	0.0771	7.1972	26
0.0000	0.0795	8.2332	0.5633	0.0771	7.1736	27
0.0000	0.0795	8.2573	0.5648	0.0771	7.2086	28
0.0000	0.0795	8.2571	0.5667	0.0771	7.1787	29
0.0000	0.0795	8.2607	0.5689	0.0771	7.2107	30
0.0000	0.0795	8.2992	0.5700	0.0772	7.2006	31
0.0000	0.0795	8.3059	0.5721	0.0772	7.2341	32
0.0000	0.0795	8.2872	0.5744	0.0772	7.2069	33
0.0080	0.0794	8.3693	0.5947	0.0762	7.3034	34
0.0063	0.0794	8.2517	0.5491	0.0769	7.1324	35