upvantage / modernbert-ready-labeled-91m-adv

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Total runs: 7
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30-day runs: 2
Model's Last Updated: August 31 2025
text-classification

Introduction of modernbert-ready-labeled-91m-adv

Model Details of modernbert-ready-labeled-91m-adv

modernbert-ready-labeled-91m-adv

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: 0.0812
  • Accuracy: 0.9687
  • F1: 0.9687
  • Precision: 0.9687
  • Recall: 0.9687
  • F1 Class 0: 0.9687
  • Precision Class 0: 0.9689
  • Recall Class 0: 0.9685
  • F1 Class 1: 0.9687
  • Precision Class 1: 0.9686
  • Recall Class 1: 0.9689
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: 600
  • eval_batch_size: 600
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 7
  • total_train_batch_size: 4200
  • total_eval_batch_size: 4200
  • 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
0.5713 1.0 21450 0.0812 0.9687 0.9687 0.9687 0.9687 0.9687 0.9689 0.9685 0.9687 0.9686 0.9689
Framework versions
  • Transformers 4.56.0
  • Pytorch 2.8.0+cu128
  • Datasets 4.0.0
  • Tokenizers 0.22.0

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