upvantage / modernbert-7m-sentences-continue-e2

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Total runs: 13
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Model's Last Updated: August 05 2025
text-classification

Introduction of modernbert-7m-sentences-continue-e2

Model Details of modernbert-7m-sentences-continue-e2

modernbert-7m-sentences-continue-e2

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: 0.3017
  • Accuracy: 0.8752
  • F1: 0.8745
  • Precision: 0.8777
  • Recall: 0.8752
  • F1 Class 0: 0.8880
  • Precision Class 0: 0.8526
  • Recall Class 0: 0.9264
  • F1 Class 1: 0.8591
  • Precision Class 1: 0.9064
  • Recall Class 1: 0.8164
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: 6e-06
  • train_batch_size: 1000
  • eval_batch_size: 1000
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • total_train_batch_size: 8000
  • total_eval_batch_size: 8000
  • 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: 2
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
2.4896 1.0 902 0.3084 0.8717 0.8711 0.8739 0.8717 0.8846 0.8511 0.9210 0.8556 0.9000 0.8153
2.3794 2.0 1804 0.3017 0.8752 0.8745 0.8777 0.8752 0.8880 0.8526 0.9264 0.8591 0.9064 0.8164
Framework versions
  • Transformers 4.54.1
  • Pytorch 2.7.1+cu126
  • Datasets 4.0.0
  • Tokenizers 0.21.4

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