Test-demo-colab

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.9479
• Wer: 0.6856

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: 0.0001
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1000
• num_epochs: 30
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
4.2676	1.0	500	2.2725	1.0013
2.0086	2.01	1000	1.2788	0.8053
1.6389	3.01	1500	1.1333	0.7458
1.4908	4.02	2000	1.0369	0.7356
1.4137	5.02	2500	0.9894	0.7111
1.3507	6.02	3000	0.9394	0.7098
1.3101	7.03	3500	0.9531	0.6966
1.2682	8.03	4000	0.9255	0.6892
1.239	9.04	4500	0.9222	0.6818
1.2161	10.04	5000	0.9079	0.6911
1.1871	11.04	5500	0.9100	0.7033
1.1688	12.05	6000	0.9080	0.6924
1.1383	13.05	6500	0.9097	0.6910
1.1304	14.06	7000	0.9052	0.6810
1.1181	15.06	7500	0.9025	0.6847
1.0905	16.06	8000	0.9296	0.6832
1.0744	17.07	8500	0.9120	0.6912
1.0675	18.07	9000	0.9039	0.6864
1.0511	19.08	9500	0.9157	0.7004
1.0401	20.08	10000	0.9259	0.6792
1.0319	21.08	10500	0.9478	0.6976
1.0194	22.09	11000	0.9438	0.6820
1.0117	23.09	11500	0.9577	0.6891
1.0038	24.1	12000	0.9670	0.6918
0.9882	25.1	12500	0.9579	0.6884
0.9979	26.1	13000	0.9502	0.6869
0.9767	27.11	13500	0.9537	0.6833
0.964	28.11	14000	0.9525	0.6880
0.9867	29.12	14500	0.9479	0.6856

Framework versions

• Transformers 4.17.0
• Pytorch 1.11.0+cu113
• Datasets 1.18.3
• Tokenizers 0.12.1