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+![Tests](https://img.shields.io/github/actions/workflow/status/aphp/edsnlp/tests.yml?branch=master&label=tests&style=flat-square)
+[![Documentation](https://img.shields.io/github/actions/workflow/status/aphp/edsnlp/documentation.yml?branch=master&label=docs&style=flat-square)](https://aphp.github.io/edsnlp/latest/)
+[![PyPI](https://img.shields.io/pypi/v/edsnlp?color=blue&style=flat-square)](https://pypi.org/project/edsnlp/)
+[![Demo](https://img.shields.io/badge/demo%20%F0%9F%9A%80-streamit-grean?style=flat-square)](https://aphp.github.io/edsnlp/demo/)
+[![Coverage](https://raw.githubusercontent.com/aphp/edsnlp/coverage/coverage.svg)](https://raw.githubusercontent.com/aphp/edsnlp/coverage/coverage.txt)
+[![DOI](https://zenodo.org/badge/467585436.svg)](https://zenodo.org/badge/latestdoi/467585436)
+
+
+EDS-NLP
+=======
+
+EDS-NLP is a collaborative NLP framework that aims primarily at extracting information from French clinical notes.
+At its core, it is a collection of components or pipes, either rule-based functions or
+deep learning modules. These components are organized into a novel efficient and modular pipeline system, built for hybrid and multitask models. We use [spaCy](https://spacy.io) to represent documents and their annotations, and [Pytorch](https://pytorch.org/) as a deep-learning backend for trainable components.
+
+EDS-NLP is versatile and can be used on any textual document. The rule-based components are fully compatible with spaCy's components, and vice versa. This library is a product of collaborative effort, and we encourage further contributions to enhance its capabilities.
+
+Check out our interactive [demo](https://aphp.github.io/edsnlp/demo/) !
+
+## Features
+
+- [Rule-based components](https://aphp.github.io/edsnlp/latest/pipes/) for French clinical notes
+- [Trainable components](https://aphp.github.io/edsnlp/latest/pipes/trainable): NER, Span classification
+- Support for multitask deep-learning models with [weights sharing](https://aphp.github.io/edsnlp/latest/concepts/torch-component/#sharing-subcomponents)
+- [Fast inference](https://aphp.github.io/edsnlp/latest/concepts/inference/), with multi-GPU support out of the box
+- Easy to use, with a spaCy-like API
+- Compatible with rule-based spaCy components
+- Support for various io formats like [BRAT](https://aphp.github.io/edsnlp/latest/data/standoff/), [JSON](https://aphp.github.io/edsnlp/latest/data/json/), [Parquet](https://aphp.github.io/edsnlp/latest/data/parquet/), [Pandas](https://aphp.github.io/edsnlp/latest/data/pandas/) or [Spark](https://aphp.github.io/edsnlp/latest/data/spark/)
+
+## Quick start
+
+### Installation
+
+You can install EDS-NLP via `pip`. We recommend pinning the library version in your projects, or use a strict package manager like [Poetry](https://python-poetry.org/).
+
+```shell
+pip install edsnlp==0.17.0
+```
+
+or if you want to use the trainable components (using pytorch)
+
+```shell
+pip install "edsnlp[ml]==0.17.0"
+```
+
+### A first pipeline
+
+Once you've installed the library, let's begin with a very simple example that extracts mentions of COVID19 in a text, and detects whether they are negated.
+
+```python
+import edsnlp, edsnlp.pipes as eds
+
+nlp = edsnlp.blank("eds")
+
+terms = dict(
+    covid=["covid", "coronavirus"],
+)
+
+# Split the documents into sentences, this isneeded for negation detection
+nlp.add_pipe(eds.sentences())
+# Matcher component
+nlp.add_pipe(eds.matcher(terms=terms))
+# Negation detection (we also support spacy-like API !)
+nlp.add_pipe("eds.negation")
+
+# Process your text in one call !
+doc = nlp("Le patient n'est pas atteint de covid")
+
+doc.ents
+# Out: (covid,)
+
+doc.ents[0]._.negation
+# Out: True
+```
+
+## Documentation & Tutorials
+
+Go to the [documentation](https://aphp.github.io/edsnlp) for more information.
+
+## Disclaimer
+
+The performances of an extraction pipeline may depend on the population and documents that are considered.
+
+## Contributing to EDS-NLP
+
+We welcome contributions ! Fork the project and propose a pull request.
+Take a look at the [dedicated page](https://aphp.github.io/edsnlp/latest/contributing/) for detail.
+
+## Citation
+
+If you use EDS-NLP, please cite us as below.
+
+```bibtex
+@misc{edsnlp,
+  author = {Wajsburt, Perceval and Petit-Jean, Thomas and Dura, Basile and Cohen, Ariel and Jean, Charline and Bey, Romain},
+  doi    = {10.5281/zenodo.6424993},
+  title  = {EDS-NLP: efficient information extraction from French clinical notes},
+  url    = {https://aphp.github.io/edsnlp}
+}
+```
+
+## Acknowledgement
+
+We would like to thank [Assistance Publique – Hôpitaux de Paris](https://www.aphp.fr/), [AP-HP Foundation](https://fondationrechercheaphp.fr/) and [Inria](https://www.inria.fr) for funding this project.