upvantage / modernbert-20m-sentences-combined-fixed

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Total runs: 51
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Model's Last Updated: August 05 2025
text-classification

Introduction of modernbert-20m-sentences-combined-fixed

Model Details of modernbert-20m-sentences-combined-fixed

modernbert-20m-sentences-combined-fixed

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: 0.2021
  • Accuracy: 0.9189
  • F1: 0.9188
  • Precision: 0.9191
  • Recall: 0.9189
  • F1 Class 0: 0.9213
  • Precision Class 0: 0.9105
  • Recall Class 0: 0.9323
  • F1 Class 1: 0.9163
  • Precision Class 1: 0.9280
  • Recall Class 1: 0.9050
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: 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
1.707 1.0 2739 0.2117 0.9149 0.9149 0.9149 0.9149 0.9162 0.9180 0.9145 0.9134 0.9117 0.9152
1.5938 2.0 5478 0.2021 0.9189 0.9188 0.9191 0.9189 0.9213 0.9105 0.9323 0.9163 0.9280 0.9050
Framework versions
  • Transformers 4.54.1
  • Pytorch 2.7.1+cu126
  • Datasets 4.0.0
  • Tokenizers 0.21.4

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