finetuned_deberta-v3-base_fulldataset_nli_classifier_mnli_anli_fevernli

• Loss: 0.4236
• F1 Macro: 0.8831
• F1 Micro: 0.8889
• Accuracy Balanced: 0.8912
• Accuracy: 0.8889
• Precision Macro: 0.8778
• Recall Macro: 0.8912
• Precision Micro: 0.8889
• Recall Micro: 0.8889

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 128
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• 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: linear
• lr_scheduler_warmup_ratio: 0.06
• num_epochs: 2
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.52.3
• Pytorch 2.6.0+cu124
• Datasets 3.6.0
• Tokenizers 0.21.1