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---

```python

%reload_ext autoreload

%autoreload 2

```

```python

# Importation du "contexte", ie la bibliothèque sans installation

import context

```

```python

import spacy

```

```python

from edsnlp.pipelines.sentences import SentenceSegmenter

```

# Sentences

```python

import re

import spacy

from spacy.tokenizer import Tokenizer

from spacy.util import compile_prefix_regex, compile_suffix_regex

# Ajout de règles supplémentaires pour gérer les infix

def custom_tokenizer(nlp):

    infix_re = re.compile(r'''[\,\?\:\;\‘\’\`\“\”\"\'~/\(\)\.\+=(->)\$]''')

    prefix_re = compile_prefix_regex(nlp.Defaults.prefixes + ['-'])

    suffix_re = compile_suffix_regex(nlp.Defaults.suffixes)

    return Tokenizer(

        nlp.vocab,

        prefix_search=prefix_re.search,

        suffix_search=suffix_re.search,

        infix_finditer=infix_re.finditer,

    )

def new_nlp():

    nlp = spacy.blank('fr')

    nlp.tokenizer = custom_tokenizer(nlp)

    return nlp

```

```python

text = (

    "Le patient est admis pour des douleurs dans le bras droit, mais n'a pas de problème de locomotion. "

    "Historique d'AVC dans la famille mais\n"

    "pourrait être un cas de rhume\n"

    "Pourrait aussi être un cas de rhume.\n"

    "Motif :\n"

    "-problème de locomotions\n"

    "Douleurs dans le bras droit.\n\n\n\n"

)

```

```python

nlp = new_nlp()

nlp.add_pipe('sentencizer')

```

```python

doc = nlp(text)

```

```python

for sent in doc.sents:

    print('##', repr(sent.text))

```

```python

nlp = new_nlp()

```

```python

sentencer = SentenceSegmenter()

```

```python

doc = sentencer(nlp(text))

```

```python

for sent in doc.sents:

    print('##', repr(sent.text))

```

Note that the newline character is now linked to the preceding sentence. That is especially relevant if the note ends on a newline.

```python

```