my_awesome_wnut_model

This model is a fine-tuned version of distilbert/distilbert-base-cased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0770
• Precision: 0.9083
• Recall: 0.8423
• F1: 0.8741
• Accuracy: 0.9790

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 OptimizerNames.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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	118	0.1285	0.8468	0.5851	0.6920	0.9557
No log	2.0	236	0.0904	0.8260	0.7780	0.8013	0.9673
No log	3.0	354	0.0837	0.8756	0.7739	0.8216	0.9714
No log	4.0	472	0.0796	0.8810	0.7988	0.8379	0.9738
0.1058	5.0	590	0.0780	0.9106	0.8029	0.8534	0.9762
0.1058	6.0	708	0.0738	0.9036	0.8361	0.8685	0.9782
0.1058	7.0	826	0.0744	0.9	0.8402	0.8691	0.9784
0.1058	8.0	944	0.0761	0.9002	0.8423	0.8703	0.9782
0.0232	9.0	1062	0.0766	0.9067	0.8465	0.8755	0.9791
0.0232	10.0	1180	0.0770	0.9083	0.8423	0.8741	0.9790

Framework versions

• Transformers 4.47.1
• Pytorch 2.5.1+cpu
• Datasets 3.2.0
• Tokenizers 0.21.0