Update app.py

app.py

@@ -21,23 +21,23 @@ ner_example = [["Benim adım Turna."]]
 t2t_example = [["Paraphrase: Bu üründen çok memnun kaldım."]]
 nli_example = [["Bunu çok beğendim. Bunu çok sevdim."]]
 
-t2t_gen = gr.load("boun-tabi-LMG/TURNA", examples =t2t_example,  title="Text-to-Text Generation", description="Please enter an instruction with a prefix to generate.")
-summarization = gr.load("boun-tabi-LMG/turna_summarization_mlsum",examples =long_text, title="Summarization", description="TURNA fine-tuned on MLSUM. Enter a text to summarize below.")
-news_sum =  gr.load("boun-tabi-LMG/turna_summarization_tr_news",examples =long_text, title="News Summarization", description="TURNA fine-tuned on News summarization. Enter a news to summarize.")
-paraphrase = gr.load("boun-tabi-LMG/turna_paraphrasing_tatoeba", examples =long_text,title="Paraphrasing")
-paraphrasing_sub = gr.load("boun-tabi-LMG/turna_paraphrasing_opensubtitles",examples =long_text, title="Paraphrasing on Subtitles")
-
-ttc = gr.load("boun-tabi-LMG/turna_classification_ttc4900", examples =long_text, title="Text Categorization")
-product_reviews = gr.load("boun-tabi-LMG/turna_classification_tr_product_reviews", examples=sentiment_example, title="Product Reviews Categorization")
-title_gen = gr.load("boun-tabi-LMG/turna_title_generation_mlsum", examples =long_text, title="Title Generation", description="Enter a text to generate title to.")
-sentiment = gr.load("boun-tabi-LMG/turna_classification_17bintweet_sentiment",examples=sentiment_example, title="Sentiment Analysis", description="Enter a text to generate title to.")
-
-pos = gr.load("boun-tabi-LMG/turna_pos_imst", title="Part of Speech Tagging", examples=ner_example,description="Enter a text to generate title to.")
-nli = gr.load("boun-tabi-LMG/turna_nli_nli_tr", title="NLI",examples=nli_example, description="Enter two texts to infer entailment.")
-pos_boun = gr.load("boun-tabi-LMG/turna_pos_boun", examples = ner_example, title="Part of Speech Tagging", description="Enter a text to tag parts of speech (POS).")
-stsb = gr.load("boun-tabi-LMG/turna_semantic_similarity_stsb_tr", examples=nli_example, title="Semantic Similarity", description="Enter two texts in the input to assess semantic similarity.")
-ner = gr.load("boun-tabi-LMG/turna_ner_milliyet", title="NER WikiANN", examples=ner_example, description="Enter a text for NER.")
-ner_wikiann = gr.load("boun-tabi-LMG/turna_ner_wikiann", title="NER",examples=ner_example, description="Enter a text for NER.")
+t2t_gen = gr.load("huggingface/boun-tabi-LMG/TURNA", examples =t2t_example,  title="Text-to-Text Generation", description="Please enter an instruction with a prefix to generate.")
+summarization = gr.load("huggingface/boun-tabi-LMG/turna_summarization_mlsum",examples =long_text, title="Summarization", description="TURNA fine-tuned on MLSUM. Enter a text to summarize below.")
+news_sum =  gr.load("huggingface/boun-tabi-LMG/turna_summarization_tr_news",examples =long_text, title="News Summarization", description="TURNA fine-tuned on News summarization. Enter a news to summarize.")
+paraphrase = gr.load("huggingface/boun-tabi-LMG/turna_paraphrasing_tatoeba", examples =long_text,title="Paraphrasing")
+paraphrasing_sub = gr.load("huggingface/boun-tabi-LMG/turna_paraphrasing_opensubtitles",examples =long_text, title="Paraphrasing on Subtitles")
+
+ttc = gr.load("huggingface/boun-tabi-LMG/turna_classification_ttc4900", examples =long_text, title="Text Categorization")
+product_reviews = gr.load("huggingface/boun-tabi-LMG/turna_classification_tr_product_reviews", examples=sentiment_example, title="Product Reviews Categorization")
+title_gen = gr.load("huggingface/boun-tabi-LMG/turna_title_generation_mlsum", examples =long_text, title="Title Generation", description="Enter a text to generate title to.")
+sentiment = gr.load("huggingface/boun-tabi-LMG/turna_classification_17bintweet_sentiment",examples=sentiment_example, title="Sentiment Analysis", description="Enter a text to generate title to.")
+
+pos = gr.load("huggingface/boun-tabi-LMG/turna_pos_imst", title="Part of Speech Tagging", examples=ner_example,description="Enter a text to generate title to.")
+nli = gr.load("huggingface/boun-tabi-LMG/turna_nli_nli_tr", title="NLI",examples=nli_example, description="Enter two texts to infer entailment.")
+pos_boun = gr.load("huggingface/boun-tabi-LMG/turna_pos_boun", examples = ner_example, title="Part of Speech Tagging", description="Enter a text to tag parts of speech (POS).")
+stsb = gr.load("huggingface/boun-tabi-LMG/turna_semantic_similarity_stsb_tr", examples=nli_example, title="Semantic Similarity", description="Enter two texts in the input to assess semantic similarity.")
+ner = gr.load("huggingface/boun-tabi-LMG/turna_ner_milliyet", title="NER WikiANN", examples=ner_example, description="Enter a text for NER.")
+ner_wikiann = gr.load("huggingface/boun-tabi-LMG/turna_ner_wikiann", title="NER",examples=ner_example, description="Enter a text for NER.")
 
 
 interface_list = ["t2t_gen","summarization", "news_sum", "paraphrase", "paraphrasing_sub", "ttc",