BioBERT-base-cased-v1.2-bc2gm-ner Open-source Model - Empowering Precise Identification of Biomedical Named Entities

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Biobert Base Cased V1.2 Bc2gm Ner

Developed by chintagunta85

Biomedical named entity recognition model fine-tuned on the bc2gm_corpus dataset based on BioBERT

Sequence Labeling

Transformers

#Biomedical NER #High-precision entity recognition #BioBERT fine-tuning

Downloads 26

Release Time : 10/26/2022

Model Overview

This model is a BERT-based model optimized for biomedical named entity recognition tasks, capable of identifying biomedical entities such as genes and proteins

Model Features

Biomedical domain optimization

Based on BioBERT architecture, specifically pre-trained and fine-tuned for biomedical texts

High-performance entity recognition

Achieves 81.14% F1 score on the bc2gm_corpus dataset, demonstrating excellent performance

Precise gene/protein recognition

Specifically optimized for recognizing gene and protein names

Model Capabilities

Biomedical text analysis

Named entity recognition

Gene/protein name extraction

Use Cases

Biomedical research

Literature mining

Automatically extract gene and protein names from biomedical literature

Helps researchers quickly identify key biological entities

Knowledge graph construction

Automatically annotate entities for biomedical knowledge graphs

Improves knowledge graph construction efficiency

Clinical text processing

Electronic medical record analysis

Identify gene-related entities from clinical records

Assists clinical decision support

🚀 biobert-base-cased-v1.2-bc2gm-ner

This model is a fine - tuned version of [dmis - lab/biobert - base - cased - v1.2](https://huggingface.co/dmis - lab/biobert - base - cased - v1.2) on the bc2gm_corpus dataset, achieving high performance in token classification tasks.

🚀 Quick Start

This model is a fine - tuned version of [dmis - lab/biobert - base - cased - v1.2](https://huggingface.co/dmis - lab/biobert - base - cased - v1.2) on the bc2gm_corpus dataset. It achieves the following results on the evaluation set:

Loss: 0.1528
Precision: 0.7988
Recall: 0.8243
F1: 0.8114
Accuracy: 0.9772

📚 Documentation

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.057	1.0	782	0.0670	0.7446	0.8051	0.7736	0.9738
0.0586	2.0	1564	0.0689	0.7689	0.8106	0.7892	0.9755
0.0123	3.0	2346	0.0715	0.7846	0.8076	0.7959	0.9750
0.0002	4.0	3128	0.0896	0.7942	0.8199	0.8068	0.9767
0.0004	5.0	3910	0.1119	0.7971	0.8201	0.8084	0.9765
0.0004	6.0	4692	0.1192	0.7966	0.8337	0.8147	0.9768
0.013	7.0	5474	0.1274	0.7932	0.8266	0.8095	0.9773
0.0236	8.0	6256	0.1419	0.7976	0.8213	0.8093	0.9771
0.0004	9.0	7038	0.1519	0.8004	0.8261	0.8130	0.9772
0.0	10.0	7820	0.1528	0.7988	0.8243	0.8114	0.9772

Framework versions

Transformers 4.23.1
Pytorch 1.12.1+cu113
Datasets 2.6.1
Tokenizers 0.13.1

Property	Details
Model Type	Fine - tuned version of [dmis - lab/biobert - base - cased - v1.2](https://huggingface.co/dmis - lab/biobert - base - cased - v1.2)
Training Data	bc2gm_corpus dataset
Metrics	Precision, Recall, F1, Accuracy

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