bert_sentiment_analysis

• Loss: 1.0592
• Accuracy: 0.4444
• F1 Weighted: 0.3556

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 32
• seed: 42
• 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: 50
• num_epochs: 8
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 5.10.2
• Pytorch 2.12.0+cu130
• Datasets 5.0.0
• Tokenizers 0.22.2