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ssc-sco-w2vbase-model – AI Model by ctaguchi | AlphaNeural AI
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ssc-sco-w2vbase-model
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transformers
safetensors
wav2vec2
automatic-speech-recognition
generated_from_trainer
endpoints_compatible
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ssc-sco-w2vbase-model
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 3.2065
Cer: 0.9997
Wer: 1.0
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: 0.001
train_batch_size: 2
eval_batch_size: 12
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 4
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: 100
num_epochs: 5
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Cer
Wer
3.002
1.9231
200
3.2523
0.9997
1.0
3.0059
3.8462
400
3.2065
0.9997
1.0
Framework versions
Transformers 4.52.1
Pytorch 2.9.1+cu128
Datasets 3.6.0
Tokenizers 0.21.4
