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w2v-bert-2.0_swahili_alpha_farmerline – AI Model by ghananlpcommunity | AlphaNeural AI
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w2v-bert-2.0_swahili_alpha_farmerline
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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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w2v-bert-2.0_swahili_alpha
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:
eval_loss: 0.1048
eval_cer: 0.0237
eval_wer: 0.1083
eval_runtime: 174.2236
eval_samples_per_second: 28.567
eval_steps_per_second: 3.576
epoch: 2.1958
step: 2400
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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
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: 800
num_epochs: 16
mixed_precision_training: Native AMP
Framework versions
Transformers 4.52.4
Pytorch 2.6.0+cu124
Datasets 2.14.4
Tokenizers 0.21.1
