deberta-v3-base_fulldataset_nli_classifier_mnli_anli_fevernli_xnli

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.4254
• F1 Macro: 0.8118
• F1 Micro: 0.8346
• Accuracy Balanced: 0.8071
• Accuracy: 0.8346
• Precision Macro: 0.8175
• Recall Macro: 0.8071
• Precision Micro: 0.8346
• Recall Micro: 0.8346

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: 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

Training Loss	Epoch	Step	Accuracy	Accuracy Balanced	F1 Macro	F1 Micro	Validation Loss	Precision Macro	Precision Micro	Recall Macro	Recall Micro
0.1959	1.0	12340	0.8333	0.7971	0.8067	0.8333	0.3943	0.8209	0.8333	0.7971	0.8333
0.1375	2.0	24680	0.4254	0.8118	0.8346	0.8071	0.8346	0.8175	0.8071	0.8346	0.8346

Framework versions

• Transformers 4.52.4
• Pytorch 2.6.0+cu124
• Datasets 3.6.0
• Tokenizers 0.21.1