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w2v2-dbart-5k-1e-4 – AI Model by sanchit-gandhi | AlphaNeural AI
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w2v2-dbart-5k-1e-4
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transformers
pytorch
speech-encoder-decoder
automatic-speech-recognition
generated_from_trainer
endpoints_compatible
us
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w2v2-dbart-5k-1e-4
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.3370
Wer: 15.0932
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: 0.0001
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 250
training_steps: 5000
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
2.0771
0.2
1000
1.8878
64.0932
0.7272
0.4
2000
0.7003
23.8557
0.5948
0.6
3000
0.4765
14.4223
0.4597
0.8
4000
0.3761
14.1429
0.3704
1.0
5000
0.3370
15.0932
Framework versions
Transformers 4.25.0.dev0
Pytorch 1.11.0
Datasets 2.6.1
Tokenizers 0.13.2
