diff --git a/README.md b/README.md
index 67f02d3..07599be 100644
--- a/README.md
+++ b/README.md
@@ -4,3 +4,38 @@ PyEuroVoc is a tool for legal document classification with [EuroVoc](https://eur
 
 The tool uses BERT at its core. The list of BERT variant for each language can be found [here]().
 
+## Installation
+
+Make sure you have Python3 installed and a suitable package for PyTorch. Then, install `pyeurovoc` with pip:
+
+```
+pip install pyeurovoc
+```
+
+## Usage
+
+Import the `EuroVocBERT` class from `pyeurovoc`. Instantiate the class with the desired langauge (default is "en") and then simply pass a document text to the model.
+
+``` python
+from pyeurovoc import EuroVocBERT
+
+model = EuroVocBERT(lang="en")
+prediction = model("Commission Decision on a modification of the system of aid applied in Italy in respect of shipbuilding")
+```
+
+The prediction of the model is a dictionary that contains the predicted ID descriptors as keys together with their confidence score as values.
+
+``` python
+{'155': 0.9995473027229309, '230': 0.9377984404563904, '889': 0.9193254113197327, '1519': 0.714003324508667, '5020': 0.5, '5541': 0.5}
+```
+
+The number of most probable labels returned by the model is controlled by the `num_labels` parameter (default is 6).
+
+``` python
+prediction = model("Commission Decision on a modification of the system of aid applied in Italy in respect of shipbuilding", num_labels=4)
+```
+
+Which outputs:
+``` python
+{'155': 0.9995473027229309, '230': 0.9377984404563904, '889': 0.9193254113197327, '1519': 0.714003324508667}
+```