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+[![spaCy](https://img.shields.io/badge/built%20with-spaCy-09a3d5.svg)](https://spacy.io)
+
+# medaCy
+:hospital: Medical Text Mining and Information Extraction with spaCy :hospital:
+
+MedaCy is a text processing and learning framework built over [spaCy](https://spacy.io/) to support the lightning fast 
+prototyping, training, and application of highly predictive medical NLP models. It is designed to streamline researcher 
+workflow by providing utilities for model training, prediction and organization while insuring the replicability of systems.
+
+![alt text](https://nlp.cs.vcu.edu/images/Edit_NanomedicineDatabase.png "Nanoinformatics")
+
+# :star2: Features
+- Highly predictive, shared-task dominating out-of-the-box trained models for medical named entity recognition.
+- Customizable pipelines with detailed development instructions and documentation.
+- Allows the designing of replicable NLP systems for reproducing results and encouraging the distribution of models whilst still allowing for privacy.
+- Active community development spearheaded and maintained by [NLP@VCU](https://nlp.cs.vcu.edu/).
+- Detailed [API](https://medacy.readthedocs.io/en/latest/).
+
+## :thought_balloon: Where to ask questions
+
+MedaCy is actively maintained by a team of researchers at Virginia Commonwealth University. The best way to
+receive immediate responses to any questions is to raise an issue. Make sure to first consult the 
+[API](https://medacy.readthedocs.io/en/latest/).  See how to formulate a good issue or feature request in the [Contribution Guide](CONTRIBUTING.md).
+
+## :computer: Installation Instructions
+MedaCy can be installed for general use or for pipeline development / research purposes.
+
+| Application | Run           |
+| ----------- |:-------------:|
+| Prediction and Model Training (stable) | `pip install git+https://github.com/NLPatVCU/medaCy.git` |
+| Prediction and Model Training (latest) | `pip install git+https://github.com/NLPatVCU/medaCy.git@development` |
+| Pipeline Development and Contribution  | [See Contribution Instructions](/CONTRIBUTING.md) |
+
+
+# :books: Power of medaCy
+After installing medaCy and [medaCy's clinical model](guide/models/clinical_notes_model.md), simply run:
+
+```python
+from medacy.model.model import Model
+
+model = Model.load_external('medacy_model_clinical_notes')
+annotation = model.predict("The patient was prescribed 1 capsule of Advil for 5 days.")
+print(annotation)
+```
+and receive instant predictions:
+```python
+[
+    ('Drug', 40, 45, 'Advil'),
+    ('Dosage', 27, 28, '1'), 
+    ('Form', 29, 36, 'capsule'),
+    ('Duration', 46, 56, 'for 5 days')
+]
+```
+
+MedaCy can also be used through its command line interface, documented [here](./guide/command_line_interface.md)
+
+To explore medaCy's other models or train your own, visit the [examples section](guide).
+
+Reference
+=========
+```bibtex
+@ARTICLE {
+    author  = "Andriy Mulyar, Natassja Lewinski and Bridget McInnes",
+    title   = "TAC SRIE 2018: Extracting Systematic Review Information with MedaCy",
+    journal = "National Institute of Standards and Technology (NIST) 2018 Systematic Review Information Extraction (SRIE) > Text Analysis Conference",
+    year    = "2018",
+    month   = "nov"
+}
+```
+
+License
+=======
+This package is licensed under the GNU General Public License.
+
+Authors
+=======
+Current contributors: Steele Farnsworth, Anna Conte, Gabby Gurdin, Aidan Kierans, Aidan Myers, and Bridget T. McInnes
+
+Former contributors: Andriy Mulyar, Jorge Vargas, Corey Sutphin, and Bobby Best
+
+Acknowledgments
+===============
+- [VCU Natural Language Processing Lab](https://nlp.cs.vcu.edu/) ![alt text](https://nlp.cs.vcu.edu/images/vcu_head_logo "VCU")
+- [Nanoinformatics Vertically Integrated Projects](https://rampages.us/nanoinformatics/)