pretrained_result

• Loss: 0.6537
• Accuracy: 0.7843

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• learning_rate: 3e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 256
• 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: cosine
• lr_scheduler_warmup_steps: 1000
• num_epochs: 10

Training results

Framework versions

• Transformers 4.48.2
• Pytorch 2.6.0+cu126
• Datasets 3.2.0
• Tokenizers 0.21.0