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w2v-bert-2.0-khmer – AI Model by Prakmlis | AlphaNeural AI
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Prakmlis
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w2v-bert-2.0-khmer
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transformers
tensorboard
safetensors
wav2vec2-bert
automatic-speech-recognition
generated_from_trainer
facebook/w2v-bert-2.0
finetune
mit
endpoints_compatible
us
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w2v-bert-2.0-khmer
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: 0.3614
Wer: 0.2559
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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Use OptimizerNames.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_steps: 500
num_epochs: 10
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
9.2596
4.1100
300
0.4418
0.3881
1.0093
8.2199
600
0.3614
0.2559
Framework versions
Transformers 4.47.1
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0
