Biobert Ncbi Disease Ner

🚀 BioBERT NER Model for Disease Extraction

A fine - tuned BioBERT model on the NCBI disease dataset for extracting disease mentions from medical and biological text.

🚀 Quick Start

This model is a fine - tuned version of BioBERT on the NCBI disease dataset, designed for named entity recognition (NER) of diseases. It can effectively extract disease mentions from unstructured text in the medical and biological domains, enhancing information retrieval and knowledge extraction in these fields.

✨ Features

• Disease NER: Specialized in identifying disease mentions in medical and biological text.
• Domain - Specific: Trained on the NCBI disease dataset, making it highly relevant to the medical and biological domains.

📦 Installation

To use this model, you need to have the Hugging Face Transformers library installed. You can install it using the following command:

pip install transformers

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("ugaray96/biobert_ncbi_disease_ner")
model = AutoModelForTokenClassification.from_pretrained(
    "ugaray96/biobert_ncbi_disease_ner"
)

ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer)

text = "The patient was diagnosed with lung cancer and started chemotherapy. They also have a history of diabetes and heart disease."
result = ner_pipeline(text)

diseases = []
for entity in result:
    if entity["entity"] == "Disease":
        diseases.append(entity["word"])
    elif entity["entity"] == "Disease Continuation" and diseases:
        diseases[-1] += f" {entity['word']}"

print(f"Diseases: {', '.join(diseases)}")

This code snippet demonstrates how to load the model and use it to extract disease mentions from a given text. The expected output is: Diseases: lung cancer, diabetes, heart disease

📚 Documentation

Intended Use

This model is intended for use in extracting disease mentions from unstructured text in the medical and biological domains. It can be used to improve information retrieval and knowledge extraction in these fields.

Training Data

This model was trained on the NCBI disease dataset, which consists of 793 PubMed abstracts with 6892 disease mentions.

📄 License

The model is released under the OpenRAIL license.