baoule_stt_2

This model is a fine-tuned version of abdouaziiz/baoule on the abdouaziz/baoule_waxal_concat dataset. It achieves the following results on the evaluation set:

• Loss: 0.2106
• Wer: 0.1831

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: 3e-05
• train_batch_size: 12
• eval_batch_size: 12
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 24
• optimizer: Use 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: 50
• training_steps: 48000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.3006	4.2105	2000	0.2106	0.1831
0.0347	8.4211	4000	0.2498	0.1819
0.0197	12.6316	6000	0.2616	0.1627
0.015	16.8421	8000	0.2623	0.1588
0.0108	21.0526	10000	0.2774	0.1574

Framework versions

• Transformers 4.46.0
• Pytorch 2.9.0+cu128
• Datasets 4.3.0
• Tokenizers 0.20.3