scibert_ner_drugname

This model is a fine-tuned version of allenai/scibert_scivocab_cased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.1243
• Precision: 0.7631
• Recall: 0.8520
• F1: 0.8051
• Accuracy: 0.9722

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: 1e-05
• train_batch_size: 1
• eval_batch_size: 1
• 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	Precision	Recall	F1	Accuracy
0.0733	1.0	120	0.1176	0.6466	0.7713	0.7035	0.9583
0.0069	2.0	240	0.1126	0.6757	0.7848	0.7261	0.9654
0.0521	3.0	360	0.0949	0.7461	0.8565	0.7975	0.9707
0.0217	4.0	480	0.0972	0.7171	0.8296	0.7692	0.9718
0.001	5.0	600	0.1111	0.7422	0.8520	0.7933	0.9707
0.0044	6.0	720	0.1138	0.7664	0.8386	0.8009	0.9715
0.0011	7.0	840	0.1155	0.7449	0.8251	0.7830	0.9699
0.0006	8.0	960	0.1213	0.7344	0.8430	0.7850	0.9716
0.0289	9.0	1080	0.1238	0.7661	0.8520	0.8068	0.9718
0.0096	10.0	1200	0.1243	0.7631	0.8520	0.8051	0.9722

Framework versions

• Transformers 4.38.2
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2