upvantage / modernbert-3pair-adv-3label

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Model's Last Updated: 8월 12 2025
text-classification

Introduction of modernbert-3pair-adv-3label

Model Details of modernbert-3pair-adv-3label

modernbert-3pair-adv-3label

This model is a fine-tuned version of answerdotai/ModernBERT-large on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2386
  • Accuracy: 0.9065
  • F1: 0.9063
  • Precision: 0.9063
  • Recall: 0.9065
  • F1 Class 0: 0.9070
  • Precision Class 0: 0.9057
  • Recall Class 0: 0.9084
  • F1 Class 1: 0.9297
  • Precision Class 1: 0.9176
  • Recall Class 1: 0.9421
  • F1 Class 2: 0.8824
  • Precision Class 2: 0.8958
  • Recall Class 2: 0.8694
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: 1e-05
  • train_batch_size: 200
  • eval_batch_size: 200
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 6
  • total_train_batch_size: 1200
  • total_eval_batch_size: 1200
  • 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: cosine
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 1
Training results
Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall F1 Class 0 Precision Class 0 Recall Class 0 F1 Class 1 Precision Class 1 Recall Class 1 F1 Class 2 Precision Class 2 Recall Class 2
1.4646 1.0 2177 0.2386 0.9065 0.9063 0.9063 0.9065 0.9070 0.9057 0.9084 0.9297 0.9176 0.9421 0.8824 0.8958 0.8694
Framework versions
  • Transformers 4.55.0
  • Pytorch 2.8.0+cu128
  • Datasets 4.0.0
  • Tokenizers 0.21.4

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