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: 0.3469
allergy Name F1: 0.7059
allergy Name Pres: 0.7326
allergy Name Rec: 0.6811
cancer F1: 0.6499
cancer Pres: 0.6837
cancer Rec: 0.6192
chronic Disease F1: 0.7431
chronic Disease Pres: 0.7462
chronic Disease Rec: 0.7400
treatment F1: 0.7572
treatmen Prest: 0.7680
treatment Rec: 0.7468
Over All Precision: 0.7475
Over All Recall: 0.7237
Over All F1: 0.7354
Over All Accuracy: 0.8824

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
0.5799	1.0	368	0.4111	0.2933	0.825	0.1784	0.5345	0.5010	0.5728	0.6044	0.6269	0.5834	0.6718	0.6294	0.7204	0.6084	0.6379	0.6228	0.8467
0.3846	2.0	736	0.3624	0.6618	0.6054	0.7297	0.6057	0.6025	0.6088	0.6553	0.6925	0.6219	0.7153	0.7450	0.6879	0.7	0.6537	0.6761	0.8642
0.3069	3.0	1104	0.3516	0.6801	0.7284	0.6378	0.6316	0.6489	0.6152	0.6994	0.7227	0.6775	0.7317	0.7368	0.7267	0.7187	0.6906	0.7044	0.8733
0.2571	4.0	1472	0.3492	0.6807	0.7687	0.6108	0.6472	0.6867	0.612	0.7239	0.7276	0.7201	0.7456	0.7548	0.7366	0.7358	0.7092	0.7222	0.8779
0.2276	5.0	1840	0.3469	0.7059	0.7326	0.6811	0.6499	0.6837	0.6192	0.7431	0.7462	0.7400	0.7572	0.7680	0.7468	0.7475	0.7237	0.7354	0.8824

Framework versions

Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1

Polo123/ner_model_ep1

ner_model_ep1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Polo123/
ner_model_ep1