mdeberta-v3-base-finetuned-temporal_focus-classification

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mdeberta-v3-base-finetuned-temporal_focus-classification

This model is a fine-tuned version of microsoft/mdeberta-v3-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.7346
• Accuracy: 0.8510
• Accuracy Balanced: 0.8196
• F1 Macro: 0.8228
• F1 Weighted: 0.8508
• Precision Macro: 0.8262
• Recall Macro: 0.8196
• Precision Weighted: 0.8506
• Recall Weighted: 0.8510
• Mcc: 0.7409
• F1 Class0: 0.7548
• Precision Class0: 0.7695
• Recall Class0: 0.7406
• F1 Class1: 0.8277
• Precision Class1: 0.8249
• Recall Class1: 0.8304
• F1 Class2: 0.8861
• Precision Class2: 0.8843
• Recall Class2: 0.8879
• Roc Auc Ovr Macro: 0.9478

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: 8
• eval_batch_size: 8
• 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_ratio: 0.06
• num_epochs: 4
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.57.6
• Pytorch 2.11.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.2