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---

```python

%load_ext autoreload

%autoreload 2

```

```python

import spacy

```

```python

from edsnlp.pipelines.endlines.endlinesmodel import EndLinesModel

```

```python

import pandas as pd

```

```python

from spacy import displacy

```

# Train

```python

nlp = spacy.blank("fr")

```

```python

text =  r"""Le patient est arrivé hier soir.

Il est accompagné par son fils

ANTECEDENTS

Il a fait une TS en 2010;

Fumeur, il est arreté il a 5 mois

Chirurgie de coeur en 2011

CONCLUSION

Il doit prendre

le medicament indiqué 3 fois par jour. Revoir médecin

dans 1 mois.

DIAGNOSTIC :

Antecedents Familiaux:

- 1. Père avec diabete

"""

```

```python

doc = nlp(text)

```

```python

text2 = """J'aime le \nfromage...\n"""

doc2 = nlp(text2)

```

```python

text3 = '\nIntervention(s) - acte(s) réalisé(s) :\nParathyroïdectomie élective le [DATE]'

doc3 = nlp(text3)

```

```python

corpus = [doc,doc2, doc3]

```

```python

endlines = EndLinesModel(nlp = nlp)

```

```python

df = endlines.fit_and_predict(corpus)

df.head()

```

```python

pd.set_option("max_columns",None)

```

```python

# Save model

PATH= "/path_to_model"

endlines.save()

```

# Predict

```python

df2 = pd.DataFrame({"A1":[12646014,4191891561709484510 , 1668228190683662995],

                   "A2":[12646065887601541794,4191891561709484510 , 1668228190683662995],

                   "A3": ["UPPER","DIGIT","sdf"],

                   "A4": ["DIGIT","ENUMERATION","STRONG_PUNCT"],

                   "B1": [.5,.7,10.2],

                   "B2": [.0,.2,-10.2],

                  "BLANK_LINE":[False,True,False]})

df2 = endlines.predict(df2)

df2

```

# Set spans in training data (for viz)

```python

set_spans = endlines.set_spans

```

```python

set_spans(corpus, df)

```

```python

df.loc[df.DOC_ID==1]

```

```python

doc_exemple = corpus[1]

```

```python

doc_exemple.spans

```

```python

doc_exemple.ents = tuple(doc_exemple.spans['new_lines'])

```

```python

displacy.render(doc_exemple, style="ent", options={"colors":{"end_line":"green","space":"red"}})

```

# Pipe spacy (inference)

```python

```

```python

nlp = spacy.blank("fr")

```

```python

nlp.add_pipe("endlines", config=dict(model_path = PATH))

```

```python

docs2 = list(nlp.pipe([text,text2,text3]))

```

```python

doc_exemple = docs2[1]

```

```python

doc_exemple

```

```python

from edsnlp.utils.filter import filter_spans

spaces = tuple(s for s in doc_exemple.spans['new_lines'] if s.label_=="space")

ents = doc_exemple.ents + spaces

ents_f = filter_spans(ents)

doc_exemple.ents = ents_f

```

```python

displacy.render(doc_exemple, style="ent", options={"colors":{"space":"red"}})

```

```python

```