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: 0.4301
Wer: 42.1488

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
22.1208	0.8333	10	25.1031	100.5510
12.838	1.6667	20	10.4898	100.0
4.2236	2.5	30	3.9356	100.0
3.4491	3.3333	40	3.4590	100.0
3.2593	4.1667	50	3.3211	100.0
3.1611	5.0	60	3.1737	100.0
3.1157	5.8333	70	3.1089	100.0
3.0472	6.6667	80	3.0868	100.0
3.0291	7.5	90	3.0445	100.0
2.9996	8.3333	100	3.0058	100.0
2.9187	9.1667	110	2.9600	100.0
2.7708	10.0	120	2.7274	100.0
2.5396	10.8333	130	2.4602	100.0
2.0911	11.6667	140	1.8863	100.0
1.4477	12.5	150	1.2924	95.8678
1.042	13.3333	160	0.9620	80.1653
0.8089	14.1667	170	0.7520	67.4931
0.6621	15.0	180	0.6530	53.7190
0.5476	15.8333	190	0.5838	50.6887
0.4866	16.6667	200	0.5662	50.4132
0.4296	17.5	210	0.5303	49.5868
0.3977	18.3333	220	0.5121	47.9339
0.392	19.1667	230	0.4895	47.3829
0.346	20.0	240	0.4825	44.3526
0.3226	20.8333	250	0.4628	45.1791
0.3145	21.6667	260	0.4662	45.1791
0.2948	22.5	270	0.4492	41.8733
0.2857	23.3333	280	0.4484	43.2507
0.2571	24.1667	290	0.4511	43.2507
0.2706	25.0	300	0.4382	41.8733
0.2404	25.8333	310	0.4528	42.1488
0.2498	26.6667	320	0.4428	41.5978
0.2381	27.5	330	0.4377	40.2204
0.2142	28.3333	340	0.4300	41.0468
0.2236	29.1667	350	0.4305	42.1488
0.2249	30.0	360	0.4253	41.0468
0.209	30.8333	370	0.4272	42.9752
0.2071	31.6667	380	0.4363	43.8017
0.2209	32.5	390	0.4328	44.6281
0.2012	33.3333	400	0.4351	44.0771
0.1895	34.1667	410	0.4362	43.8017
0.1921	35.0	420	0.4383	45.1791
0.1805	35.8333	430	0.4381	45.1791
0.1963	36.6667	440	0.4331	41.3223
0.1829	37.5	450	0.4301	41.5978
0.1927	38.3333	460	0.4290	41.8733
0.1779	39.1667	470	0.4289	42.4242
0.1892	40.0	480	0.4302	42.1488
0.2025	40.8333	490	0.4300	42.4242
0.2105	41.6667	500	0.4301	42.1488

Framework versions

Transformers 4.41.1
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

ivillar/ssw_finetune

ssw_finetune

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ivillar/
ssw_finetune