modernbert-7m-sentences-continue

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: 0.3219
• Accuracy: 0.8652
• F1: 0.8644
• Precision: 0.8678
• Recall: 0.8652
• F1 Class 0: 0.8793
• Precision Class 0: 0.8425
• Recall Class 0: 0.9194
• F1 Class 1: 0.8473
• Precision Class 1: 0.8968
• Recall Class 1: 0.8030

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: 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
2.598	1.0	902	0.3219	0.8652	0.8644	0.8678	0.8652	0.8793	0.8425	0.9194	0.8473	0.8968	0.8030

Framework versions

• Transformers 4.54.1
• Pytorch 2.7.1+cu126
• Datasets 4.0.0
• Tokenizers 0.21.4