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

Loss: 0.3739
allergy Name F1: 0.7755
allergy Name Pres: 0.76
allergy Name Rec: 0.7917
cancer F1: 0.7389
cancer Pres: 0.7283
cancer Rec: 0.7497
chronic Disease F1: 0.7778
chronic Disease Pres: 0.7676
chronic Disease Rec: 0.7882
treatment F1: 0.7918
treatmen Prest: 0.7837
treatment Rec: 0.7999
Over All Precision: 0.7698
Over All Recall: 0.7887
Over All F1: 0.7792
Over All Accuracy: 0.8803

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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	allergy Name F1	allergy Name Pres	allergy Name Rec	cancer F1	cancer Pres	cancer Rec	chronic Disease F1	chronic Disease Pres	chronic Disease Rec	treatment F1	treatmen Prest	treatment Rec	Over All Precision	Over All Recall	Over All F1	Over All Accuracy
0.5174	1.0	1005	0.3949	0.7230	0.7710	0.6806	0.6254	0.6354	0.6158	0.6958	0.6914	0.7003	0.7376	0.7683	0.7093	0.7218	0.6925	0.7068	0.8570
0.3297	2.0	2010	0.3664	0.7746	0.7857	0.7639	0.7133	0.7171	0.7095	0.7509	0.7746	0.7287	0.7738	0.7834	0.7643	0.7711	0.7444	0.7576	0.8732
0.2691	3.0	3015	0.3585	0.7589	0.8364	0.6944	0.7415	0.7417	0.7412	0.7674	0.7754	0.7596	0.7819	0.7652	0.7994	0.7670	0.7748	0.7709	0.8780
0.2278	4.0	4020	0.3686	0.7717	0.7878	0.7562	0.7400	0.7170	0.7645	0.7762	0.7717	0.7807	0.7885	0.7604	0.8188	0.7588	0.7965	0.7772	0.8795
0.2038	5.0	5025	0.3739	0.7755	0.76	0.7917	0.7389	0.7283	0.7497	0.7778	0.7676	0.7882	0.7918	0.7837	0.7999	0.7698	0.7887	0.7792	0.8803

Framework versions

Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1

Polo123/ner_model_ep_all

ner_model_ep_all

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Polo123/
ner_model_ep_all