xty_finetune

This model is a fine-tuned version of Akashpb13/Swahili_xlsr on the ml-superb-subset dataset. It achieves the following results on the evaluation set:

• Loss: 2.0874
• Wer: 89.1263

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 9.6e-05
• train_batch_size: 32
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 25
• training_steps: 500
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
8.5823	1.0526	10	7.7265	100.0629
5.0617	2.1053	20	4.2231	100.0
3.6512	3.1579	30	3.5631	100.0
3.3211	4.2105	40	3.3414	100.0
3.2144	5.2632	50	3.2086	100.0
3.128	6.3158	60	3.1724	100.0
3.0999	7.3684	70	3.1261	100.0
3.0585	8.4211	80	3.1200	100.0
3.0318	9.4737	90	3.1001	100.0
3.0166	10.5263	100	3.0985	100.0
3.0147	11.5789	110	3.0971	100.0
3.0028	12.6316	120	3.0775	100.0
2.991	13.6842	130	3.0619	100.0
2.9692	14.7368	140	3.0477	100.0
2.9355	15.7895	150	3.0081	100.0
2.8754	16.8421	160	2.9190	100.0
2.7087	17.8947	170	2.7367	100.0
2.4346	18.9474	180	2.5043	108.5481
2.3184	20.0	190	2.3709	103.8969
2.0887	21.0526	200	2.2196	103.3941
1.9198	22.1053	210	2.1078	104.7140
1.6995	23.1579	220	2.0556	98.4287
1.6576	24.2105	230	2.0081	100.6914
1.4855	25.2632	240	1.9958	98.0515
1.3788	26.3158	250	1.9729	94.8460
1.3202	27.3684	260	1.9618	98.6172
1.2237	28.4211	270	1.9662	93.6518
1.1389	29.4737	280	1.9882	92.7090
1.0597	30.5263	290	1.9655	92.3947
1.0203	31.5789	300	1.9616	90.1948
0.9778	32.6316	310	1.9585	90.8234
0.9553	33.6842	320	1.9875	90.5091
0.895	34.7368	330	1.9913	91.3891
0.9021	35.7895	340	1.9906	90.2577
0.8105	36.8421	350	2.0182	89.4406
0.8052	37.8947	360	2.0227	89.5663
0.7484	38.9474	370	2.0539	89.0006
0.7886	40.0	380	2.0616	90.6977
0.7348	41.0526	390	2.0590	89.1892
0.7079	42.1053	400	2.0790	89.8806
0.7215	43.1579	410	2.0701	89.3149
0.6997	44.2105	420	2.0832	89.3777
0.721	45.2632	430	2.0798	89.3149
0.6609	46.3158	440	2.0834	88.4349
0.6562	47.3684	450	2.0892	89.0006
0.6418	48.4211	460	2.0878	89.3777
0.677	49.4737	470	2.0874	89.2520
0.6821	50.5263	480	2.0874	89.1263
0.6798	51.5789	490	2.0875	89.0635
0.7188	52.6316	500	2.0874	89.1263

Framework versions

• Transformers 4.41.1
• Pytorch 2.3.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1