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nik1509/
xlsrm_300m_finetunedtelugu_epoch_ablation_ep30

Name: xlsrm_300m_finetunedtelugu_epoch_ablation_ep30
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xlsrm_300m_finetunedtelugu_epoch_ablation_ep30

This model is a fine-tuned version of nik1509/xlsr300m_finetuned_telugu_basemodel on the None dataset. It achieves the following results on the evaluation set:

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The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 30
mixed_precision_training: Native AMP

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
311.8045	1.0	1152	453.8935	0.4002	0.1668
310.2089	2.0	2304	456.3084	0.4019	0.1679
303.6436	3.0	3456	457.9976	0.3998	0.1670
307.857	4.0	4608	471.1595	0.4019	0.1671
296.6655	5.0	5760	481.4806	0.4035	0.1681
296.3349	6.0	6912	476.3087	0.3991	0.1673
292.5621	7.0	8064	490.1333	0.4030	0.1669
283.9695	8.0	9216	497.3463	0.3977	0.1663
277.2706	9.0	10368	502.9385	0.4016	0.1677
281.4092	10.0	11520	523.4032	0.3986	0.1668
272.3868	11.0	12672	514.6779	0.4011	0.1680
273.5133	12.0	13824	527.3060	0.3990	0.1683
260.919	13.0	14976	536.7178	0.3971	0.1674
259.2489	14.0	16128	546.4207	0.3971	0.1675
259.2039	15.0	17280	555.5333	0.3982	0.1678
251.7688	16.0	18432	561.7747	0.3964	0.1682
248.3311	17.0	19584	562.4690	0.3984	0.1690
244.0257	18.0	20736	572.6827	0.3968	0.1692
235.4528	19.0	21888	579.6030	0.3945	0.1693
241.0213	20.0	23040	593.2683	0.3965	0.1691
239.1832	21.0	24192	598.4885	0.3933	0.1690
235.8785	22.0	25344	598.2966	0.3937	0.1698
226.0029	23.0	26496	601.1956	0.3919	0.1689
224.7058	24.0	27648	609.4273	0.3937	0.1694
224.8545	25.0	28800	621.3796	0.3937	0.1695
225.248	26.0	29952	623.1083	0.3916	0.1694
221.3672	27.0	31104	625.9757	0.3909	0.1692
223.3846	28.0	32256	629.9022	0.3901	0.1694
221.9715	29.0	33408	629.5982	0.3903	0.1692
217.2283	30.0	34560	630.4043	0.3892	0.1693