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modernbert-Aegis-2.0-Wildguard-Content-Safety
This model is a fine-tuned version of answerdotai/ModernBERT-base on the None dataset.
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: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- total_eval_batch_size: 4
- optimizer: Use adamw_torch_fused with betas=(0.9,0.98) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1
Training results
Framework versions
- PEFT 0.14.0
- Transformers 4.50.3
- Pytorch 2.6.0+cu126
- Datasets 3.3.1
- Tokenizers 0.21.0
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Model tree for AllanK24/modernbert-Aegis-2.0-Wildguard-Content-Safety
Base model
answerdotai/ModernBERT-base