This model is a fine-tuned version of ylacombe/w2v-bert-2.0 on the common_voice_16_0 dataset. It achieves the following results on the evaluation set:

Loss: 0.5032
Wer: 0.3251

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: 5e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3.7516	0.79	100	2.4041	1.0089
1.0185	1.58	200	0.7642	0.6153
0.5366	2.37	300	0.6518	0.5328
0.4153	3.16	400	0.6116	0.4811
0.353	3.95	500	0.6357	0.4806
0.2876	4.74	600	0.6213	0.4434
0.2389	5.53	700	0.5103	0.4243
0.1735	6.32	800	0.5079	0.3753
0.1419	7.11	900	0.5264	0.3638
0.1031	7.91	1000	0.5454	0.3466
0.0743	8.7	1100	0.5286	0.3337
0.054	9.49	1200	0.5032	0.3251

Framework versions

Transformers 4.37.0.dev0
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0

hf-audio/w2v-bert-2.0-mongolian-colab-CV16.0

w2v-bert-2.0-mongolian-colab-CV16.0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

hf-audio/
w2v-bert-2.0-mongolian-colab-CV16.0