Update README.md

README.md

@@ -10,7 +10,7 @@ metrics:
 - f1
 - accuracy
 model-index:
-- name: output_dir
+- name: slovakbert-ner
   results:
   - task:
       name: Token Classification
@@ -34,7 +34,7 @@ model-index:
       value: 0.9785228256835333
 ---
 
-# output_dir
+# Named Entity Recognition based on SlovakBERT
 
 This model is a fine-tuned version of [gerulata/slovakbert](https://huggingface.co/gerulata/slovakbert) on the wikiann_sk dataset.
 It achieves the following results on the evaluation set:
@@ -44,17 +44,68 @@ It achieves the following results on the evaluation set:
 - F1: 0.9398
 - Accuracy: 0.9785
 
-## Model description
+## Intended uses & limitations
 
-More information needed
+```
+from transformers import pipeline
 
-## Intended uses & limitations
 
-More information needed
+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)
+``` 
+
+with `displaCy`:
+
+```
+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)
+
+```
 
-## Training and evaluation data
+<div class="entities" style="line-height: 2.5; direction: ltr">Minister financií a líder mandátovo najsilnejšieho hnutia
+<mark class="entity" style="background: lightcoral; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;">
+    OĽaNO
+    <span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">ORGANIZÁCIA</span>
+</mark>
 
-More information needed
+<mark class="entity" style="background: lightblue; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;">
+    Igor Matovič
+    <span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">OSOBA</span>
+</mark>
+ upozorňuje, že následky tretej vlny budú na
+<mark class="entity" style="background: lightgreen; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;">
+    Slovensku
+    <span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">LOKALITA</span>
+</mark>
+ veľmi veľké.</div>
 
 ## Training procedure