📊 Performance Metrics

Current Model Performance

• F1 Score: 0.53
• Precision: 0.48
• Recall: 0.60
• Accuracy: 0.91

🏆 Comparative Performance on CLL 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-BloodCancerDetect-PubMed-335M
model_name = "OpenMed/OpenMed-NER-BloodCancerDetect-PubMed-335M"

# Create a pipeline
medical_ner_pipeline = pipeline(
    model=model_name,
    aggregation_strategy="simple"
)

# Example usage
text = "The patient presented with chronic lymphocytic leukemia symptoms."
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 presented with chronic lymphocytic leukemia symptoms.",
    "B-cell proliferation was observed in bone marrow samples.",
    "Treatment with ibrutinib showed promising results.",
    "Flow cytometry confirmed the diagnosis of chronic lymphocytic leukemia.",
    "The patient had del(17p), a high-risk feature in CLL.",
]

# 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: CLL
• Description: Clinical Entity Recognition - Clinical entities related to Chronic Lymphocytic Leukemia

Training Details

• Base Model: BiomedNLP-BiomedBERT-large-uncased-abstract
• Training Framework: Hugging Face Transformers
• Optimization: AdamW optimizer with learning rate scheduling
• Validation: Cross-validation on held-out test set

🔬 Model Architecture

• Base Architecture: BiomedNLP-BiomedBERT-large-uncased-abstract
• 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.

Follow OpenMed Org on Hugging Face 🤗 and click "Watch" to stay updated on our latest releases and developments.

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},
}

Proper citation helps support and acknowledge my work. Thank you!