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Bhojpuri-w2v-bert-2.0 – AI Model by dhasmana | AlphaNeural AI
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dhasmana
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Bhojpuri-w2v-bert-2.0
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transformers
tensorboard
safetensors
wav2vec2-bert
automatic-speech-recognition
generated_from_trainer
facebook/w2v-bert-2.0
finetune
mit
endpoints_compatible
us
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Bhojpuri-w2v-bert-2.0
This model is a fine-tuned version of
facebook/w2v-bert-2.0
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.7012
Cer: 0.1484
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
2.2959
2.0270
300
0.8270
0.1894
0.698
4.0541
600
0.7274
0.1615
0.5202
6.0811
900
0.6847
0.1519
0.378
8.1081
1200
0.7012
0.1484
Framework versions
Transformers 4.57.6
Pytorch 2.9.0+cu126
Datasets 4.0.0
Tokenizers 0.22.2
