B-DRUG: beginning of a drug entity I-DRUG: within a drug entity B-EFFECT: beginning of an AE entity I-EFFECT: within an AE entity O: outside either of the above entities

BioBERT-finetuned-ner

This model is a fine-tuned version of Dinithi/BioBERT on ade_corpus_v2. It achieves the following results on the evaluation set:

Loss: 0.1602
Precision: 0.8136
Recall: 0.8961
F1: 0.8528
Accuracy: 0.9524

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: 16
eval_batch_size: 16
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1673	1.0	113	0.2197	0.7545	0.8573	0.8027	0.9334
0.174	2.0	226	0.1691	0.7820	0.8870	0.8312	0.9472
0.1832	3.0	339	0.1596	0.8043	0.8915	0.8457	0.9506
0.0327	4.0	452	0.1591	0.8068	0.8980	0.8500	0.9526
0.036	5.0	565	0.1602	0.8136	0.8961	0.8528	0.9524

Framework versions

Transformers 4.46.2
Pytorch 2.5.1+cu121
Tokenizers 0.20.3

Dataset used

Dataset: https://huggingface.co/datasets/ade_corpus_v2

Lediona/BioBERT-finetuned-ner

BioBERT-finetuned-ner

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Dataset used

Lediona/
BioBERT-finetuned-ner