bert-base-cased-finetuned-ner

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

• Loss: 0.0948
• Precision: 0.8623
• Recall: 0.9148
• F1: 0.8878
• Accuracy: 0.9787

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	88	0.3132	0.5506	0.4970	0.5224	0.9194
No log	2.0	176	0.1509	0.7119	0.7667	0.7383	0.9591
No log	3.0	264	0.1047	0.7981	0.8499	0.8232	0.9705
No log	4.0	352	0.0787	0.8314	0.8905	0.8599	0.9770
No log	5.0	440	0.0802	0.9033	0.8905	0.8968	0.9806
0.2051	6.0	528	0.0752	0.8420	0.9189	0.8788	0.9770
0.2051	7.0	616	0.0704	0.8854	0.9087	0.8969	0.9823
0.2051	8.0	704	0.0732	0.8939	0.9229	0.9082	0.9814
0.2051	9.0	792	0.0801	0.8656	0.9148	0.8895	0.9792
0.2051	10.0	880	0.0669	0.9192	0.9229	0.9211	0.9850
0.2051	11.0	968	0.0783	0.8851	0.9067	0.8958	0.9801
0.0035	12.0	1056	0.0914	0.8542	0.9148	0.8834	0.9780
0.0035	13.0	1144	0.1002	0.8414	0.9148	0.8766	0.9770
0.0035	14.0	1232	0.0978	0.8442	0.9229	0.8818	0.9777
0.0035	15.0	1320	0.0748	0.8830	0.9189	0.9006	0.9816
0.0035	16.0	1408	0.0830	0.8674	0.9026	0.8847	0.9787
0.0035	17.0	1496	0.0938	0.8596	0.9189	0.8882	0.9792
0.0013	18.0	1584	0.0919	0.8651	0.9108	0.8874	0.9792
0.0013	19.0	1672	0.0873	0.8656	0.9148	0.8895	0.9792
0.0013	20.0	1760	0.0888	0.8656	0.9148	0.8895	0.9792
0.0013	21.0	1848	0.0851	0.8685	0.9108	0.8891	0.9792
0.0013	22.0	1936	0.0940	0.8623	0.9148	0.8878	0.9787
0.0005	23.0	2024	0.0845	0.8826	0.9148	0.8984	0.9811
0.0005	24.0	2112	0.0911	0.8690	0.9148	0.8913	0.9792
0.0005	25.0	2200	0.0915	0.8787	0.9108	0.8944	0.9806
0.0005	26.0	2288	0.0951	0.8651	0.9108	0.8874	0.9787
0.0005	27.0	2376	0.0949	0.8585	0.9108	0.8839	0.9782
0.0005	28.0	2464	0.0949	0.8623	0.9148	0.8878	0.9787
0.0005	29.0	2552	0.0946	0.8623	0.9148	0.8878	0.9787
0.0005	30.0	2640	0.0948	0.8623	0.9148	0.8878	0.9787

Framework versions

• Transformers 4.36.2
• Pytorch 2.1.2+cu118
• Datasets 2.16.0
• Tokenizers 0.15.0