upvantage / modernbert-large-adv-sample-3

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Model's Last Updated: July 29 2025
text-classification

Introduction of modernbert-large-adv-sample-3

Model Details of modernbert-large-adv-sample-3

modernbert-large-adv-sample-3

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: 0.1586
  • Accuracy: 0.9342
  • F1: 0.9342
  • Precision: 0.9342
  • Recall: 0.9342
  • F1 Class 0: 0.9344
  • Precision Class 0: 0.9349
  • Recall Class 0: 0.9340
  • F1 Class 1: 0.9339
  • Precision Class 1: 0.9334
  • Recall Class 1: 0.9344
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: 4
  • total_train_batch_size: 800
  • total_eval_batch_size: 800
  • 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
0.5638 1.0 1163 0.1586 0.9342 0.9342 0.9342 0.9342 0.9344 0.9349 0.9340 0.9339 0.9334 0.9344
Framework versions
  • Transformers 4.54.0
  • Pytorch 2.7.1+cu126
  • Datasets 4.0.0
  • Tokenizers 0.21.4

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