This model is a fine-tuned version of facebook/w2v-bert-2.0 on the CLEAR-GLOBAL/LUO_19H - NA dataset. It achieves the following results on the evaluation set:

Loss: 0.2682
Wer: 0.2998
Cer: 0.0930

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: 3e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 64
optimizer: Use 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_ratio: 0.1
training_steps: 100000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.698	6.4935	1000	0.7171	0.5988	0.1884
0.2666	12.9870	2000	0.3521	0.3862	0.1107
0.1497	19.4805	3000	0.2914	0.3351	0.0979
0.0802	25.9740	4000	0.2682	0.2976	0.0931
0.053	32.4675	5000	0.3036	0.3060	0.0913
0.0309	38.9610	6000	0.3689	0.2906	0.0939
0.0245	45.4545	7000	0.4164	0.3792	0.1007
0.0122	51.9481	8000	0.3996	0.3166	0.0964
0.0088	58.4416	9000	0.4323	0.3056	0.0952

Framework versions

Transformers 4.48.1
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

CLEAR-Global/w2v-bert-2.0-luo_cv_fleurs_19h

w2v-bert-2.0-luo_cv_fleurs_19h

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

CLEAR-Global/
w2v-bert-2.0-luo_cv_fleurs_19h