bert-base-uncased-finetuned-ner

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

• Loss: 0.7238
• Precision: 0.3864
• Recall: 0.2742
• F1: 0.3208
• Accuracy: 0.9134

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: 5e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 25

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	16	0.6017	0.4286	0.2258	0.2958	0.9187
No log	2.0	32	0.6134	0.3311	0.2688	0.2967	0.9075
No log	3.0	48	0.5969	0.3525	0.2634	0.3015	0.9096
No log	4.0	64	0.6446	0.3208	0.2742	0.2957	0.9071
No log	5.0	80	0.6219	0.4182	0.2473	0.3108	0.9176
No log	6.0	96	0.6519	0.3401	0.2688	0.3003	0.9103
No log	7.0	112	0.6576	0.3551	0.2634	0.3025	0.9120
No log	8.0	128	0.6534	0.3676	0.2688	0.3106	0.9106
No log	9.0	144	0.6522	0.3732	0.2849	0.3232	0.9124
No log	10.0	160	0.6879	0.3503	0.2957	0.3207	0.9078
No log	11.0	176	0.6825	0.3696	0.2742	0.3148	0.9124
No log	12.0	192	0.7115	0.3732	0.2849	0.3232	0.9120
No log	13.0	208	0.7013	0.3984	0.2742	0.3248	0.9138
No log	14.0	224	0.7016	0.3732	0.2849	0.3232	0.9117
No log	15.0	240	0.7313	0.3643	0.2742	0.3129	0.9110
No log	16.0	256	0.7267	0.3442	0.2849	0.3118	0.9082
No log	17.0	272	0.7159	0.3624	0.2903	0.3224	0.9096
No log	18.0	288	0.6946	0.3542	0.2742	0.3091	0.9099
No log	19.0	304	0.7017	0.3852	0.2796	0.3240	0.9127
No log	20.0	320	0.7229	0.3467	0.2796	0.3095	0.9089
No log	21.0	336	0.7188	0.3817	0.2688	0.3155	0.9124
No log	22.0	352	0.7269	0.3669	0.2742	0.3138	0.9110
No log	23.0	368	0.7248	0.3714	0.2796	0.3190	0.9113
No log	24.0	384	0.7235	0.3835	0.2742	0.3197	0.9131
No log	25.0	400	0.7238	0.3864	0.2742	0.3208	0.9134

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.0+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1