Update app.py

app.py

@@ -90,7 +90,7 @@ intf = gr.Interface(fn=apply_ner, inputs="text", outputs="html", title='Slovak N
                               ["Minister financií a líder mandátovo najsilnejšieho hnutia OĽaNO Igor Matovič upozorňuje, že následky tretej vlny budú na Slovensku veľmi veľké."],
                               ["Začiatkom roka 2021 sa objavili nezhody medzi Richardom Sulíkom a šéfom hnutia OĽANO Igorom Matovičom, ktoré v istej miere pretrvávajú aj dodnes."]],
                     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="This model is a fine-tuned version of <u><a href=\"https://huggingface.co/crabz/slovakbert-ner\" target=\"_blank\">gerulata/slovakbert</a></u> on the Slovak wikiann dataset. "
+                    article="This model is a fine-tuned version of <u><a href=\"https://huggingface.co/gerulata/slovakbert\" target=\"_blank\">gerulata/slovakbert</a></u> on the Slovak wikiann dataset. "
                             "It achieves F1 score of 0.9398 on the evaluation set. Note that this also counts background words (no label), so simply labelling all words as background words would yield very high F1. "
                             "The quote requirements comes from pretrained SlovakBERT and was not introduced by me. More details about the model can be found at <u><a href=\"https://huggingface.co/crabz/slovakbert-ner\" target=\"_blank\">crabz/slovakbert-ner</a></u>.<br/>"
                             "Some of the examples where the model makes a mistake are listed below: <ul>"