Instructions to use Naho07/ssahubert_yemba with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Naho07/ssahubert_yemba with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Naho07/ssahubert_yemba")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Naho07/ssahubert_yemba") model = AutoModelForCTC.from_pretrained("Naho07/ssahubert_yemba") - Notebooks
- Google Colab
- Kaggle
ssahubert_yemba
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.7322
- eval_cer: 0.2123
- eval_wer: 0.6381
- eval_runtime: 3.5279
- eval_samples_per_second: 102.611
- eval_steps_per_second: 13.039
- epoch: 11.0
- step: 2002
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: 2
- total_train_batch_size: 16
- 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: 0.1
- num_epochs: 45
- mixed_precision_training: Native AMP
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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