bert-finetuned-typetag

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

• Loss: 0.0116
• Precision: 0.9893
• Recall: 0.9886
• F1: 0.9889
• Accuracy: 0.9966

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	59	0.0362	0.9714	0.9521	0.9617	0.9891
No log	2.0	118	0.0169	0.9858	0.9832	0.9845	0.9951
No log	3.0	177	0.0145	0.9825	0.9867	0.9846	0.9954
No log	4.0	236	0.0126	0.9865	0.9868	0.9867	0.9959
No log	5.0	295	0.0115	0.9889	0.9876	0.9882	0.9964
No log	6.0	354	0.0113	0.9905	0.9865	0.9885	0.9965
No log	7.0	413	0.0119	0.9890	0.9891	0.9890	0.9966
No log	8.0	472	0.0114	0.9898	0.9884	0.9891	0.9966
0.0506	9.0	531	0.0115	0.9896	0.9886	0.9891	0.9966
0.0506	10.0	590	0.0116	0.9893	0.9886	0.9889	0.9966

Framework versions

• Transformers 4.38.2
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2