MatricariaV/MMS-3gramm-lm

kbd-asr-colab2

This model is a fine-tuned version of facebook/mms-1b-all on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1829
• Wer: 0.3182

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: 0.001
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use OptimizerNames.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_steps: 100
• num_epochs: 5
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.7968	0.1871	200	0.4521	0.7382
0.6536	0.3742	400	0.3965	0.7499
0.5989	0.5613	600	0.3850	0.7644
0.5212	0.7484	800	0.3473	0.6373
0.5649	0.9355	1000	0.2951	0.5633
0.4996	1.1225	1200	0.2771	0.4806
0.5247	1.3096	1400	0.2553	0.4592
0.4559	1.4967	1600	0.2537	0.4396
0.5153	1.6838	1800	0.2479	0.4364
0.4622	1.8709	2000	0.2363	0.4251
0.4533	2.0580	2200	0.2280	0.4091
0.4529	2.2451	2400	0.2182	0.4066
0.4453	2.4322	2600	0.2191	0.3902
0.4339	2.6193	2800	0.2135	0.3815
0.4179	2.8064	3000	0.2151	0.3906
0.3983	2.9935	3200	0.2059	0.3725
0.3868	3.1805	3400	0.2058	0.3614
0.3804	3.3676	3600	0.1959	0.3558
0.3934	3.5547	3800	0.2015	0.3555
0.3693	3.7418	4000	0.1995	0.3587
0.3678	3.9289	4200	0.2017	0.3876
0.4097	4.1160	4400	0.1893	0.3355
0.34	4.3031	4600	0.1877	0.3291
0.3553	4.4902	4800	0.1857	0.3222
0.3133	4.6773	5000	0.1839	0.3224
0.3077	4.8644	5200	0.1829	0.3182

Framework versions

• Transformers 4.50.0.dev0
• Pytorch 2.5.1+cu121
• Datasets 3.3.2
• Tokenizers 0.21.0