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checkpoints – AI Model by darinchau | AlphaNeural AI

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darinchau/
checkpoints

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automatic-speech-recognition

generated_from_trainer

darinchau/checkpoints

endpoints_compatible

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checkpoints

This model is a fine-tuned version of darinchau/checkpoints on the None dataset. It achieves the following results on the evaluation set:

eval_loss: 0.8289
eval_cer: 79.9622
eval_runtime: 31.6459
eval_samples_per_second: 3.16
eval_steps_per_second: 0.221
epoch: 35.11
step: 3300

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: 1e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 4000
mixed_precision_training: Native AMP

Framework versions

Transformers 4.36.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0