my_awesome_wnut_model_2

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

• Loss: 0.0982
• Precision: 0.0
• Recall: 0.0
• F1: 0.0
• Accuracy: 0.9814

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 13

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	118	0.0915	0.0	0.0	0.0	0.9808
No log	2.0	236	0.0942	0.0	0.0	0.0	0.9812
No log	3.0	354	0.1112	0.0	0.0	0.0	0.9786
No log	4.0	472	0.0931	0.0	0.0	0.0	0.9806
0.0017	5.0	590	0.1000	0.0	0.0	0.0	0.9810
0.0017	6.0	708	0.0925	0.0	0.0	0.0	0.9810
0.0017	7.0	826	0.0976	0.0	0.0	0.0	0.9815
0.0017	8.0	944	0.0930	0.0	0.0	0.0	0.9815
0.0012	9.0	1062	0.1012	0.0	0.0	0.0	0.9810
0.0012	10.0	1180	0.0993	0.0	0.0	0.0	0.9814
0.0012	11.0	1298	0.0995	0.0	0.0	0.0	0.9812
0.0012	12.0	1416	0.0975	0.0	0.0	0.0	0.9814
0.0006	13.0	1534	0.0982	0.0	0.0	0.0	0.9814

Framework versions

• Transformers 4.47.1
• Pytorch 2.5.1+cu121
• Tokenizers 0.21.0