modernbert-sentences-3labeled-verbsonly

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: 0.5710
• Accuracy: 0.7945
• F1: 0.7925
• Precision: 0.7928
• Recall: 0.7945
• F1 Class 0: 0.8077
• Precision Class 0: 0.7881
• Recall Class 0: 0.8284
• F1 Class 1: 0.8515
• Precision Class 1: 0.8313
• Recall Class 1: 0.8727
• F1 Class 2: 0.7136
• Precision Class 2: 0.7597
• Recall Class 2: 0.6729

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: 64
• eval_batch_size: 64
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• total_train_batch_size: 512
• total_eval_batch_size: 512
• 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
4.5925	1.0	10072	0.5710	0.7945	0.7925	0.7928	0.7945	0.8077	0.7881	0.8284	0.8515	0.8313	0.8727	0.7136	0.7597	0.6729

Framework versions

• Transformers 4.55.0
• Pytorch 2.8.0+cu128
• Datasets 4.0.0
• Tokenizers 0.21.4