upvantage / modernbert-e1-large-non-adv

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Model's Last Updated: Julho 25 2025
text-classification

Introduction of modernbert-e1-large-non-adv

Model Details of modernbert-e1-large-non-adv

modernbert-e1-large-non-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: 0.0805
  • Accuracy: 0.9686
  • F1: 0.9686
  • Precision: 0.9686
  • Recall: 0.9686
  • F1 Class 0: 0.9704
  • Precision Class 0: 0.9680
  • Recall Class 0: 0.9729
  • F1 Class 1: 0.9666
  • Precision Class 1: 0.9694
  • Recall Class 1: 0.9639
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: 2
  • total_train_batch_size: 400
  • total_eval_batch_size: 400
  • 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
0.1018 0.2500 4117 0.1098 0.9565 0.9565 0.9573 0.9565 0.9581 0.9764 0.9405 0.9547 0.9358 0.9745
0.0878 0.5001 8234 0.0878 0.9657 0.9657 0.9657 0.9657 0.9675 0.9702 0.9648 0.9637 0.9607 0.9667
0.0822 0.7501 12351 0.0805 0.9686 0.9686 0.9686 0.9686 0.9704 0.9680 0.9729 0.9666 0.9694 0.9639
Framework versions
  • Transformers 4.53.3
  • Pytorch 2.8.0.dev20250319+cu128
  • Datasets 4.0.0
  • Tokenizers 0.21.2

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