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w2v-bert-2.0-mongolian-colab-CV16.0 – AI Model by mhlongoke91 | AlphaNeural AI
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w2v-bert-2.0-mongolian-colab-CV16.0
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transformers
tensorboard
safetensors
wav2vec2-bert
automatic-speech-recognition
generated_from_trainer
common_voice_16_0
facebook/w2v-bert-2.0
finetune
mit
model-index
endpoints_compatible
us
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w2v-bert-2.0-mongolian-colab-CV16.0
This model is a fine-tuned version of
facebook/w2v-bert-2.0
on the common_voice_16_0 dataset. It achieves the following results on the evaluation set:
Loss: 0.6984
Wer: 0.9657
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: 2
total_train_batch_size: 16
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
3.4128
1.9763
500
3.2537
1.0
1.7038
3.9526
1000
1.5989
1.0
0.722
5.9289
1500
0.9174
0.9878
0.4558
7.9051
2000
0.7443
0.9746
0.3257
9.8814
2500
0.6984
0.9657
Framework versions
Transformers 4.51.2
Pytorch 2.1.0+cu121
Datasets 2.14.6
Tokenizers 0.21.1
