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5sents_QoLT_largev2_freeze – AI Model by slplab | AlphaNeural AI
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slplab
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5sents_QoLT_largev2_freeze
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transformers
pytorch
tensorboard
whisper
automatic-speech-recognition
generated_from_trainer
apache-2.0
endpoints_compatible
us
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QoLT_largev2_freeze_2
This model is a fine-tuned version of
openai/whisper-large-v2
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.0155
Wer: 12.6984
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: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
training_steps: 600
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.0003
49.89
100
0.0081
14.2857
0.0
99.89
200
0.0040
21.6931
0.0
149.89
300
0.0036
21.6931
0.0
199.89
400
0.0036
26.9841
0.0
249.89
500
0.0037
27.5132
0.0
299.89
600
0.0036
30.6878
Framework versions
Transformers 4.26.1
Pytorch 1.13.1+cu116
Datasets 2.11.0
Tokenizers 0.13.3
