WAXAL-lug-ful-w2v-bert-2.0 – AI Model by dhasmana | AlphaNeural AI

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dhasmana/
WAXAL-lug-ful-w2v-bert-2.0

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automatic-speech-recognition

generated_from_trainer

facebook/w2v-bert-2.0

endpoints_compatible

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WAXAL-lug-ful-w2v-bert-2.0

This model is a fine-tuned version of facebook/w2v-bert-2.0 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4411
Cer: 0.0793

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: 4
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Use adamw_torch_fused 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: 500
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Cer
4.8496	1.4218	300	0.4804	0.1046
0.8171	2.8436	600	0.4331	0.0942
0.5955	4.2654	900	0.3763	0.0880
0.4671	5.6872	1200	0.3712	0.0844
0.3560	7.1090	1500	0.3939	0.0789
0.2647	8.5308	1800	0.4167	0.0797
0.1797	9.9526	2100	0.4408	0.0794
0.1797	10.0	2110	0.4411	0.0793

Framework versions

Transformers 5.13.0
Pytorch 2.13.0+cu130
Datasets 5.0.0
Tokenizers 0.22.2