embc25_finetuned_30000_es-ipa

• Loss: 0.6341
• Accuracy: 0.7457
• Precision: 0.7343
• Recall: 0.77
• F1: 0.7517

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• 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: 500
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.48.1
• Pytorch 2.3.1
• Datasets 3.2.0
• Tokenizers 0.21.0