upvantage / modernbert-3pair-adv-3label-clean

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Model's Last Updated: August 12 2025
text-classification

Introduction of modernbert-3pair-adv-3label-clean

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

modernbert-3pair-adv-3label-clean

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: 0.3086
  • Accuracy: 0.9337
  • F1: 0.9336
  • Precision: 0.9336
  • Recall: 0.9337
  • F1 Class 0: 0.9317
  • Precision Class 0: 0.9316
  • Recall Class 0: 0.9319
  • F1 Class 1: 0.9555
  • Precision Class 1: 0.9463
  • Recall Class 1: 0.9649
  • F1 Class 2: 0.9135
  • Precision Class 2: 0.9230
  • Recall Class 2: 0.9042
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: 8e-06
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 6
  • total_train_batch_size: 192
  • total_eval_batch_size: 192
  • optimizer: Use OptimizerNames.ADAMW_TORCH 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: 0.1
  • num_epochs: 1
  • label_smoothing_factor: 0.05
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
1.8369 1.0 9697 0.3086 0.9337 0.9336 0.9336 0.9337 0.9317 0.9316 0.9319 0.9555 0.9463 0.9649 0.9135 0.9230 0.9042
Framework versions
  • Transformers 4.55.0
  • Pytorch 2.8.0+cu128
  • Datasets 4.0.0
  • Tokenizers 0.21.4

Runs of upvantage modernbert-3pair-adv-3label-clean on huggingface.co

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