bash
pip install transformers torch

python
from transformers import pipeline

Load the model and tokenizer
Model: https://huggingface.co/OpenMed/OpenMed-NER-OncologyDetect-MultiMed-568M
model_name = "OpenMed/OpenMed-NER-OncologyDetect-MultiMed-568M"

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

Example usage
text = "Mutations in KRAS gene drive oncogenic transformation."
entities = medical_ner_pipeline(text)

print(entities)

token = entities[0]
print(text[token["start"] : token["end"]])

python
texts = [
    "Mutations in KRAS gene drive oncogenic transformation.",
    "The tumor suppressor p53 pathway was disrupted.",
    "EGFR amplification promotes cancer cell proliferation.",
    "Loss of function of the PTEN gene is common in many cancers.",
    "The PI3K/AKT/mTOR pathway is a critical regulator of cell growth.",
]

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}")

python
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")

python
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)

latex
@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},
}