Specialized model for Disease Entity Recognition - Disease entities from the BC5CDR dataset
Model Overview
This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for disease entity recognition - disease entities from the bc5cdr dataset. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as drug interaction detection, medication extraction from patient records, adverse event monitoring, literature mining for drug discovery, and biomedical knowledge graph construction with production-ready reliability for clinical and research applications.
Key Features
- High Precision: Optimized for biomedical entity recognition
- Domain-Specific: Trained on curated BC5CDR_DISEASE dataset
- Production-Ready: Validated on clinical benchmarks
- Easy Integration: Compatible with Hugging Face Transformers ecosystem
Supported Entity Types
This model can identify and classify the following biomedical entities:
Dataset
BC5CDR-Disease targets disease entity recognition from the BioCreative V Chemical-Disease Relation extraction corpus.
The BC5CDR-Disease corpus is the disease-focused component of the BioCreative V Chemical-Disease Relation (CDR) task, containing 1,500 PubMed abstracts with 5,818 annotated disease entities. This manually curated dataset is designed to advance automated disease name recognition for medical diagnosis, pathology research, and clinical decision support systems. The corpus includes annotations for various disease types, medical conditions, and pathological states mentioned in biomedical literature. It serves as a benchmark for evaluating NER models in clinical and biomedical applications where accurate disease entity identification is crucial for medical informatics and healthcare analytics.
Performance Metrics
Current Model Performance
- F1 Score:
0.87
- Precision:
0.85
- Recall:
0.88
- Accuracy:
0.97
Comparative Performance on BC5CDR_DISEASE Dataset
Rankings based on F1-score performance across all models trained on this dataset.
Figure: OpenMed (Open-Source) vs. Latest SOTA (Closed-Source) performance comparison across biomedical NER datasets.
Quick Start
Installation
pip install transformers torch
Usage
from transformers import pipeline
# Load the model and tokenizer
# Model: https://huggingface.co/OpenMed/OpenMed-NER-DiseaseDetect-ElectraMed-109M
model_name = "OpenMed/OpenMed-NER-DiseaseDetect-ElectraMed-109M"
# Create a pipeline
medical_ner_pipeline = pipeline(
model=model_name,
aggregation_strategy="simple"
)
# Example usage
text = "The patient was diagnosed with diabetes mellitus type 2."
entities = medical_ner_pipeline(text)
print(entities)
token = entities[0]
print(text[token["start"] : token["end"]])
NOTE: The aggregation_strategy parameter defines how token predictions are grouped into entities. For a detailed explanation, please refer to the Hugging Face documentation.
Here is a summary of the available strategies:
- none: Returns raw token predictions without any aggregation.
- simple: Groups adjacent tokens with the same entity type (e.g., B-LOC followed by I-LOC).
- first: For word-based models, if tokens within a word have different entity tags, the tag of the first token is assigned to the entire word.
- average: For word-based models, this strategy averages the scores of tokens within a word and applies the label with the highest resulting score.
- max: For word-based models, the entity label from the token with the highest score within a word is assigned to the entire word.
Batch Processing
For efficient processing of large datasets, use proper batching with the batch_size parameter:
texts = [
"The patient was diagnosed with diabetes mellitus type 2.",
"Symptoms of Alzheimer's disease became apparent over several months.",
"Treatment for hypertension was initiated immediately.",
"A possible link between Crohn's disease and gut microbiota is being investigated.",
"The patient has a family history of cystic fibrosis.",
]
# Efficient batch processing with optimized batch size
# Adjust batch_size based on your GPU memory (typically 8, 16, 32, or 64)
results = medical_ner_pipeline(texts, batch_size=8)
for i, entities in enumerate(results):
print(f"Text {i+1} entities:")
for entity in entities:
print(f" - {entity['word']} ({entity['entity_group']}): {entity['score']:.4f}")
Large Dataset Processing
For processing large datasets efficiently:
from transformers.pipelines.pt_utils import KeyDataset
from datasets import Dataset
import pandas as pd
# Load your data
# Load a medical dataset from Hugging Face
from datasets import load_dataset
# Load a public medical dataset (using a subset for testing)
medical_dataset = load_dataset("BI55/MedText", split="train[:100]") # Load first 100 examples
data = pd.DataFrame({"text": medical_dataset["Completion"]})
dataset = Dataset.from_pandas(data)
# Process with optimal batching for your hardware
batch_size = 16 # Tune this based on your GPU memory
results = []
for out in medical_ner_pipeline(KeyDataset(dataset, "text"), batch_size=batch_size):
results.extend(out)
print(f"Processed {len(results)} texts with batching")
Performance Optimization
Batch Size Guidelines:
- CPU: Start with batch_size=1-4
- Single GPU: Try batch_size=8-32 depending on GPU memory
- High-end GPU: Can handle batch_size=64 or higher
- Monitor GPU utilization to find the optimal batch size for your hardware
Memory Considerations:
# For limited GPU memory, use smaller batches
medical_ner_pipeline = pipeline(
model=model_name,
aggregation_strategy="simple",
device=0 # Specify GPU device
)
# Process with memory-efficient batching
for batch_start in range(0, len(texts), batch_size):
batch = texts[batch_start:batch_start + batch_size]
batch_results = medical_ner_pipeline(batch, batch_size=len(batch))
results.extend(batch_results)
Dataset Information
- Dataset: BC5CDR_DISEASE
- Description: Disease Entity Recognition - Disease entities from the BC5CDR dataset
Training Details
- Base Model: e5-base-v2
- Training Framework: Hugging Face Transformers
- Optimization: AdamW optimizer with learning rate scheduling
- Validation: Cross-validation on held-out test set
Model Architecture
- Base Architecture: e5-base-v2
- Task: Token Classification (Named Entity Recognition)
- Labels: Dataset-specific entity types
- Input: Tokenized biomedical text
- Output: BIO-tagged entity predictions
Use Cases
This model is particularly useful for:
- Clinical Text Mining: Extracting entities from medical records
- Biomedical Research: Processing scientific literature
- Drug Discovery: Identifying chemical compounds and drugs
- Healthcare Analytics: Analyzing patient data and outcomes
- Academic Research: Supporting biomedical NLP research
License
Licensed under the Apache License 2.0. See LICENSE for details.
Contributing
We welcome contributions of all kinds! Whether you have ideas, feature requests, or want to join our mission to advance open-source Healthcare AI, we'd love to hear from you.
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Citation
If you use this model in your research or applications, please cite the following paper:
@misc{panahi2025openmedneropensourcedomainadapted,
title={OpenMed NER: Open-Source, Domain-Adapted State-of-the-Art Transformers for Biomedical NER Across 12 Public Datasets},
author={Maziyar Panahi},
year={2025},
eprint={2508.01630},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2508.01630},
}
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