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README.md

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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}
+}
+```