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Magadhi-Kumaoni-w2v-bert-2.0 – AI Model by dhasmana | AlphaNeural AI
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dhasmana
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Magadhi-Kumaoni-w2v-bert-2.0
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transformers
safetensors
wav2vec2-bert
automatic-speech-recognition
generated_from_trainer
facebook/w2v-bert-2.0
finetune
mit
endpoints_compatible
us
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Magadhi-Kumaoni-w2v-bert-2.0
This model is a fine-tuned version of
facebook/w2v-bert-2.0
on the None dataset. It achieves the following results on the evaluation set:
Loss: 1.3501
Cer: 0.1843
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: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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
Cer
4.9714
1.5
300
0.9350
0.2305
1.6338
3.0
600
0.8575
0.2244
1.1425
4.5
900
0.8715
0.1880
0.7653
6.0
1200
0.9303
0.1830
0.4113
7.5
1500
1.1443
0.1876
0.1864
9.0
1800
1.3501
0.1843
Framework versions
Transformers 5.0.0
Pytorch 2.9.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2
