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+# Getting started
+
+EDS-NLP is a collaborative NLP framework that aims 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 pipelines, 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/) !
+
+## 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/).
+
+```{: data-md-color-scheme="slate" }
+pip install edsnlp==0.17.0
+```
+
+or if you want to use the trainable components (using pytorch)
+
+```{: data-md-color-scheme="slate" }
+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")  # (1)
+
+terms = dict(
+    covid=["covid", "coronavirus"],  # (2)
+)
+
+# Sentencizer component, needed for negation detection
+nlp.add_pipe(eds.sentences())  # (3)
+# Matcher component
+nlp.add_pipe(eds.matcher(terms=terms))  # (4)
+# Negation detection
+nlp.add_pipe(eds.negation())
+
+# Process your text in one call !
+doc = nlp("Le patient n'est pas atteint de covid")
+
+doc.ents  # (5)
+# Out: (covid,)
+
+doc.ents[0]._.negation  # (6)
+# Out: True
+```
+
+1. 'eds' is the name of the language, which defines the [tokenizer](/tokenizers).
+2. This example terminology provides a very simple, and by no means exhaustive, list of synonyms for COVID19.
+3. Similarly to spaCy, pipes are added via the [`nlp.add_pipe` method](https://spacy.io/api/language#add_pipe).
+4. See the [matching tutorial](tutorials/matching-a-terminology.md) for mode details.
+5. spaCy stores extracted entities in the [`Doc.ents` attribute](https://spacy.io/api/doc#ents).
+6. The `eds.negation` component has adds a `negation` custom attribute.
+
+This example is complete, it should run as-is.
+
+## Tutorials
+
+To learn more about EDS-NLP, we have prepared a series of tutorials that should cover the main features of the library.
+
+--8<-- "docs/tutorials/index.md:tutorials"
+
+## Available pipeline components
+
+--8<-- "docs/pipes/index.md:components"
+
+## 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}
+}
+```