BioLinkBERT-base-finetuned-ner

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

Loss: 0.1226
Precision: 0.8760
Recall: 0.9185
F1: 0.8968
Accuracy: 0.9647

Model description

This model is designed to perform NER function for specific text using BioLink BERT

Intended uses & limitations

The goal was to have a drug tag printed immediately for a particular sentence, but it has the disadvantage of being marked as LABEL

LABEL0 : irrelevant text LABEL1,2 : Drug LABEL3,4 : condition

Training and evaluation data

More information needed

Training procedure

Reference Code: SciBERT Fine-Tuning on Drug/ADE Corpus (https://github.com/jsylee/personal-projects/blob/master/Hugging%20Face%20ADR%20Fine-Tuning/SciBERT%20ADR%20Fine-Tuning.ipynb)

How to use

from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("HMHMlee/BioLinkBERT-base-finetuned-ner")

model = AutoModelForTokenClassification.from_pretrained("HMHMlee/BioLinkBERT-base-finetuned-ner")

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-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	Precision	Recall	F1	Accuracy
0.1099	1.0	201	0.1489	0.8415	0.9032	0.8713	0.9566
0.1716	2.0	402	0.1318	0.8456	0.9135	0.8782	0.9597
0.1068	3.0	603	0.1197	0.8682	0.9110	0.8891	0.9641
0.0161	4.0	804	0.1219	0.8694	0.9157	0.8919	0.9639
0.1499	5.0	1005	0.1226	0.8760	0.9185	0.8968	0.9647

Framework versions

Transformers 4.20.1
Pytorch 1.12.0+cu113
Datasets 2.4.0
Tokenizers 0.12.1