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WAXAL-nyn_asr-w2v-bert-2.0 – AI Model by dhasmana | AlphaNeural AI
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dhasmana
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WAXAL-nyn_asr-w2v-bert-2.0
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transformers
safetensors
wav2vec2-bert
automatic-speech-recognition
generated_from_trainer
facebook/w2v-bert-2.0
finetune
mit
endpoints_compatible
us
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WAXAL-nyn_asr-w2v-bert-2.0
This model is a fine-tuned version of
facebook/w2v-bert-2.0
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.9253
Cer: 0.1853
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 OptimizerNames.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
6.1867
2.0692
300
0.9197
0.2310
0.9965
4.1384
600
0.8067
0.1973
0.6612
6.2076
900
0.8198
0.1893
0.4300
8.2768
1200
0.8920
0.1871
0.4300
10.0
1450
0.9253
0.1853
Framework versions
Transformers 5.12.1
Pytorch 2.11.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2
