add short article

app.py

@@ -68,16 +68,16 @@ def postprocess(classifications):
 def set_entities(sentence, entities):
     doc = nlp(sentence)
     ents = []
-    for ee in entities:
-        ents.append(doc.char_span(ee[1], ee[2], ee[0]))
+    for label, start, end in entities:
+        ents.append(doc.char_span(start, end, label))
     doc.ents = ents
     return doc
 
 
-def apply_ner(sentence: str):
-    classifications = ner_pipeline(sentence)
+def apply_ner(Sentence: str):
+    classifications = ner_pipeline(Sentence)
     entities = postprocess(classifications)
-    doc = set_entities(sentence, entities)
+    doc = set_entities(Sentence, entities)
     displacy_html = displacy.render(doc, style="ent", options=options)
     return displacy_html
 
@@ -99,6 +99,9 @@ intf = gr.Interface(fn=apply_ner, inputs="text", outputs="html", title='Slovak N
                     description="Named-entity recognition (NER) labels named-entities in unstructured text. This "
                                 "implementation supports three labels: person (OSOBA), organization (ORGANIZÁCIA) and "
                                 "location (LOKALITA). You can try out one of the examples below or type your own "
-                                "sentence. Don't forget to use double quotes (\" \") instead of curved quotes („ “)",
-                    article="")
+                                "sentence. Don't forget to use double quotes (\" \") instead of curved quotes („ “).",
+                    article="This model is a fine-tuned version of [gerulata/slovakbert]"
+                            "(https://huggingface.co/gerulata/slovakbert) on the Slovak wikiann dataset. It achieves "
+                            "F1 score of 0.9398 on the evaluation set. The quote requirements comes from pretrained "
+                            "SlovakBERT and was not introduced by me.")
 intf.launch()