bash
pip install transformers torch

python
from transformers import pipeline

Load the model and tokenizer
Model: https://huggingface.co/OpenMed/OpenMed-NER-OrganismDetect-BioMed-109M
model_name = "OpenMed/OpenMed-NER-OrganismDetect-BioMed-109M"

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

Example usage
text = "Caenorhabditis elegans is a model organism for genetic studies."
entities = medical_ner_pipeline(text)

print(entities)

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

python
texts = [
    "Caenorhabditis elegans is a model organism for genetic studies.",
    "The research focused on Drosophila melanogaster development.",
    "Arabidopsis thaliana serves as a model for plant biology.",
    "The zebrafish, Danio rerio, is widely used for studying vertebrate development.",
    "Neurospora crassa is a type of red bread mold used in genetic research.",
]

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},
}