 b/notebooks/sections/testing.md
+---
+jupyter:
+  jupytext:
+    formats: ipynb,md
+    text_representation:
+      extension: .md
+      format_name: markdown
+      format_version: "1.3"
+      jupytext_version: 1.11.4
+  kernelspec:
+    display_name: Python 3
+    language: python
+    name: python3
+---
+```python
+%reload_ext autoreload
+%autoreload 2
+```
+```python
+import pandas as pd
+```
+```python
+import os
+```
+```python
+import context
+```
+```python
+from edsnlp.utils.brat import BratConnector
+```
+```python
+```
+```python
+import spacy
+```
+# Sections dataset
+We are using [Ivan Lerner's work at EDS](https://gitlab.eds.aphp.fr/IvanL/section_dataset). Make sure you clone the repo.
+```python
+data_dir = '../../data/section_dataset/'
+```
+```python
+brat = BratConnector(data_dir)
+```
+```python
+texts, annotations = brat.get_brat()
+```
+```python
+texts
+```
+```python
+nlp = spacy.blank('fr')
+```
+```python
+nlp.add_pipe('normaliser')
+nlp.add_pipe('sections')
+```
+```python
+df = texts.copy()
+```
+```python
+df['doc'] = df.note_text.apply(nlp)
+```
+```python
+def assign_id(row):
+    row.doc._.note_id = row.note_id
+```
+```python
+df.apply(assign_id, axis=1);
+```
+```python
+df['matches'] = df.doc.apply(lambda d: [dict(
+    lexical_variant=s.text,
+    label=s.label_,
+    start=s.start_char,
+    end=s.end_char
+) for s in d._.section_titles])
+```
+```python
+df = df[['note_text', 'note_id', 'matches']].explode('matches')
+```
+```python
+df = df.dropna()
+```
+```python
+df[['lexical_variant', 'label', 'start', 'end']] = df.matches.apply(pd.Series)
+```
+```python
+df = df.drop('matches', axis=1)
+```
+```python
+df.head(20)
+```
+```python
+df = df.rename(columns={'start': 'offset_begin', 'end': 'offset_end', 'label': 'label_value'})
+```
+```python
+df['label_name'] = df.label_value
+```
+```python
+df['modifier_type'] = ''
+df['modifier_result'] = ''
+```
+```python
+from ipywidgets import Output, Button, VBox, Layout, Text, HTML
+from IPython.display import display
+from labeltool.labelling import GlobalLabels, Labels, Labelling
+out = Output()
+```
+```python
+labels = Labels()
+for label in df.label_value.unique():
+    labels.add(name = label,
+               color = 'green',
+               selection_type = 'button')
+```
+```python
+labeller = Labelling(
+    df,
+    save_path='testing.pickle',
+    labels_dict=labels.dict,
+    from_save=True,
+    out=out,
+    display=display,
+)
+```
+```python
+labeller.run()
+out
+```
+```python
+```