from transformers import pipeline


ner_pipeline = pipeline(task='ner', model='crabz/slovakbert-ner')
input_sentence = "Minister financií a líder mandátovo najsilnejšieho hnutia OĽaNO Igor Matovič upozorňuje, že následky tretej vlny budú na Slovensku veľmi veľké."
classifications = ner_pipeline(input_sentence)

import spacy
from spacy import displacy


ner_map = {0: '0', 1: 'B-OSOBA', 2: 'I-OSOBA', 3: 'B-ORGANIZÁCIA', 4: 'I-ORGANIZÁCIA', 5: 'B-LOKALITA', 6: 'I-LOKALITA'}

entities = []
for i in range(len(classifications)):
    if classifications[i]['entity'] != 0:
        if ner_map[classifications[i]['entity']][0] == 'B':
            j = i + 1
            while j < len(classifications) and ner_map[classifications[j]['entity']][0] == 'I':
                j += 1
            entities.append((ner_map[classifications[i]['entity']].split('-')[1], classifications[i]['start'],
                             classifications[j - 1]['end']))

nlp = spacy.blank("en")  # it should work with any language

doc = nlp(input_sentence)

ents = []
for ee in entities:
    ents.append(doc.char_span(ee[1], ee[2], ee[0]))

doc.ents = ents

options = {"ents": ["OSOBA", "ORGANIZÁCIA", "LOKALITA"],
           "colors": {"OSOBA": "lightblue", "ORGANIZÁCIA": "lightcoral", "LOKALITA": "lightgreen"}}
displacy_html = displacy.render(doc, style="ent", options=options)