BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-NDD-NER

This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the ManpreetK/NDD_NER dataset. It achieves the following results on the evaluation set:

• Overall Precision: 0.6297

• Overall Recall: 0.7068

• Overall F1: 0.6660

• Overall Accuracy: 0.9044

• Loss: 0.3763

• Associated_Problem Precision/Recall/F1: 0.6316/0.5294/0.576

• Associated_Problem Number: 68

• Condition Precision/Recall/F1: 0.8052/0.8921/0.8464

• Condition Number: 139

• Intervention Precision/Recall/F1: 0.5159/0.6633/0.5804

• Intervention Number: 98

• Patient_Group Precision/Recall/F1: 0.5512/0.8046/0.6542

• Patient_Group Number: 87

• Test Precision/Recall/F1: 0.5882/0.4878/0.5333

• Test Number: 82

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Associated Problem Precision	Associated Problem Recall	Associated Problem F1	Associated Problem Number	Condition Precision	Condition Recall	Condition F1	Condition Number	Intervention Precision	Intervention Recall	Intervention F1	Intervention Number	Patient Group Precision	Patient Group Recall	Patient Group F1	Patient Group Number	Test Precision	Test Recall	Test F1	Test Number	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.4014	1.0	11	0.7804	0.0	0.0	0.0	68	0.0	0.0	0.0	139	0.0	0.0	0.0	98	0.0	0.0	0.0	87	0.0	0.0	0.0	82	0.0	0.0	0.0	0.7808
0.7625	2.0	22	0.5575	0.0	0.0	0.0	68	0.7468	0.8273	0.7850	139	0.3333	0.1429	0.2	98	0.7627	0.5172	0.6164	87	0.4286	0.1098	0.1748	82	0.6630	0.3861	0.488	0.8546
0.5152	3.0	33	0.4489	0.2222	0.0588	0.0930	68	0.7011	0.9281	0.7988	139	0.4674	0.4388	0.4526	98	0.5528	0.7816	0.6476	87	0.5758	0.4634	0.5135	82	0.5839	0.5949	0.5893	0.8820
0.3621	4.0	44	0.4020	0.2727	0.1324	0.1782	68	0.7716	0.8993	0.8306	139	0.4538	0.5510	0.4977	98	0.5752	0.7471	0.65	87	0.7059	0.4390	0.5414	82	0.6046	0.6097	0.6071	0.8900
0.252	5.0	55	0.3764	0.5	0.5588	0.5278	68	0.8219	0.8633	0.8421	139	0.5426	0.5204	0.5312	98	0.5610	0.7931	0.6571	87	0.5641	0.5366	0.55	82	0.6228	0.6793	0.6498	0.9014
0.1988	6.0	66	0.3839	0.4918	0.4412	0.4651	68	0.7590	0.9065	0.8262	139	0.4161	0.6327	0.5020	98	0.552	0.7931	0.6509	87	0.5606	0.4512	0.5	82	0.5714	0.6835	0.6225	0.8961
0.1623	7.0	77	0.3669	0.4941	0.6176	0.5490	68	0.8105	0.8921	0.8493	139	0.4667	0.6429	0.5408	98	0.5702	0.7931	0.6635	87	0.5634	0.4878	0.5229	82	0.5982	0.7131	0.6506	0.9020
0.1319	8.0	88	0.3763	0.6316	0.5294	0.576	68	0.8052	0.8921	0.8464	139	0.5159	0.6633	0.5804	98	0.5512	0.8046	0.6542	87	0.5882	0.4878	0.5333	82	0.6297	0.7068	0.6660	0.9044
0.117	9.0	99	0.3834	0.6481	0.5147	0.5738	68	0.8158	0.8921	0.8522	139	0.4923	0.6531	0.5614	98	0.5738	0.8046	0.6699	87	0.5909	0.4756	0.5270	82	0.6336	0.7004	0.6653	0.9030
0.1125	10.0	110	0.3854	0.5441	0.5441	0.5441	68	0.8170	0.8993	0.8562	139	0.4737	0.6429	0.5455	98	0.5635	0.8161	0.6667	87	0.5882	0.4878	0.5333	82	0.6131	0.7089	0.6575	0.9028

Framework versions

• Transformers 4.25.1
• Pytorch 1.12.0+cu116
• Datasets 2.8.0
• Tokenizers 0.12.1