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---
language: en
tags:
- medical
- ner
- named-entity-recognition
- healthcare
- i2b2
license: apache-2.0
datasets:
- i2b2-2018
metrics:
- f1
- precision
- recall
---
# i2b2 2018 Medical NER Model
This model is fine-tuned for medical Named Entity Recognition (NER) using the i2b2 2018 dataset.
## Model Details
- **Task**: Named Entity Recognition (NER)
- **Domain**: Medical/Healthcare
- **Dataset**: i2b2 2018
- **Model Type**: Token Classification
## Usage
```python
from transformers import pipeline
# Load the model
ner_pipeline = pipeline(
"ner",
model="prakharsinghAI/i2b2-ner-model",
aggregation_strategy="simple"
)
# Example usage
text = "Patient was prescribed aspirin 100mg twice daily for headache."
results = ner_pipeline(text)
print(results)
```
## Training Details
- **Dataset**: i2b2 2018 Medical NER
- **Task**: Token Classification
- **Labels**: Medical entities (Drug, Dosage, Route, etc.)
## Performance
This model was trained on the i2b2 2018 dataset for medical named entity recognition tasks.
## Citation
If you use this model, please cite the i2b2 2018 dataset:
```bibtex
@article{krallinger2015chemdner,
title={The CHEMDNER corpus of chemicals and drugs and its annotation principles},
author={Krallinger, Martin and Rabal, Obdulia and Leitner, Florian and Vazquez, Miguel and Salgado, David and Lu, Zhiyong and Leaman, Robert and Lu, Yanan and Ji, Donghong and Lowe, Daniel M and others},
journal={Journal of cheminformatics},
volume={7},
number={1},
pages={1--17},
year={2015},
publisher={BioMed Central}
}
```
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