outputs

This model is a fine-tuned version of distilbert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0122
• F1 Micro: 0.6194
• F1 Macro: 0.0119
• Precision Micro: 0.7934
• Recall Micro: 0.5080
• Subset Accuracy: 0.3677

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: 0.0001
• train_batch_size: 32
• eval_batch_size: 8
• seed: 42
• 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: cosine
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Micro	F1 Macro	Precision Micro	Recall Micro	Subset Accuracy
0.0129	1.0	1025	0.0122	0.6194	0.0119	0.7934	0.5080	0.3677

Framework versions

• Transformers 4.50.3
• Pytorch 2.6.0+cu124
• Tokenizers 0.21.1