Update app.py

app.py

@@ -22,37 +22,37 @@ t2t_example = [["Paraphrase: Bu üründen çok memnun kaldım."]]
 nli_example = [["Bunu çok beğendim. Bunu çok sevdim."]]
 
 
-t2t_gen_model = pipeline("boun-tabi-LMG/TURNA", device=0) 
+t2t_gen_model = pipeline(model="boun-tabi-LMG/TURNA", device=0) 
 # examples =t2t_example,  title="Text-to-Text Generation", description="Please enter an instruction with a prefix to generate.")
-summarization_model = pipeline("boun-tabi-LMG/turna_summarization_mlsum", device=0) 
+summarization_model = pipeline(model="boun-tabi-LMG/turna_summarization_mlsum", device=0) 
 # examples =long_text, title="Summarization", description="TURNA fine-tuned on MLSUM. Enter a text to summarize below.")
-news_sum = pipeline("boun-tabi-LMG/turna_summarization_tr_news", device=0) 
+news_sum = pipeline(model="boun-tabi-LMG/turna_summarization_tr_news", device=0) 
 # examples =long_text, title="News Summarization", description="TURNA fine-tuned on News summarization. Enter a news to summarize.")
-paraphrasing = pipeline("boun-tabi-LMG/turna_paraphrasing_tatoeba", device=0) 
+paraphrasing = pipeline(model="boun-tabi-LMG/turna_paraphrasing_tatoeba", device=0) 
 # examples =long_text,title="Paraphrasing")
-paraphrasing_sub = pipeline("boun-tabi-LMG/turna_paraphrasing_opensubtitles", device=0) 
+paraphrasing_sub = pipeline(model="boun-tabi-LMG/turna_paraphrasing_opensubtitles", device=0) 
 # examples =long_text, title="Paraphrasing on Subtitles")
 
-ttc = pipeline("boun-tabi-LMG/turna_classification_ttc4900", device=0)
+ttc = pipeline(model="boun-tabi-LMG/turna_classification_ttc4900", device=0)
 # examples =long_text, title="Text Categorization")
-product_reviews = pipeline("boun-tabi-LMG/turna_classification_tr_product_reviews", device=0) 
+product_reviews = pipeline(model="boun-tabi-LMG/turna_classification_tr_product_reviews", device=0) 
 # examples=sentiment_example, title="Product Reviews Categorization")
-title_gen = pipeline("boun-tabi-LMG/turna_title_generation_mlsum", device=0)
+title_gen = pipeline(model="boun-tabi-LMG/turna_title_generation_mlsum", device=0)
 # examples =long_text, title="Title Generation", description="Enter a text to generate title to.")
-sentiment_model = pipeline("boun-tabi-LMG/turna_classification_17bintweet_sentiment", device=0) 
+sentiment_model = pipeline(model="boun-tabi-LMG/turna_classification_17bintweet_sentiment", device=0) 
 #examples=sentiment_example, title="Sentiment Analysis", description="Enter a text to generate title to.")
 
-pos_imst = pipeline("boun-tabi-LMG/turna_pos_imst", device=0) 
+pos_imst = pipeline(model="boun-tabi-LMG/turna_pos_imst", device=0) 
 # title="Part of Speech Tagging", examples=ner_example,description="Enter a text to generate title to.")
-nli_model = pipeline("boun-tabi-LMG/turna_nli_nli_tr", device=0) 
+nli_model = pipeline(model="boun-tabi-LMG/turna_nli_nli_tr", device=0) 
 # title="NLI",examples=nli_example, description="Enter two texts to infer entailment.")
-pos_boun = pipeline("boun-tabi-LMG/turna_pos_boun", device=0) 
+pos_boun = pipeline(model="boun-tabi-LMG/turna_pos_boun", device=0) 
 # examples = ner_example, title="Part of Speech Tagging", description="Enter a text to tag parts of speech (POS).")
-stsb_model = pipeline("boun-tabi-LMG/turna_semantic_similarity_stsb_tr", device=0)
+stsb_model = pipeline(model="boun-tabi-LMG/turna_semantic_similarity_stsb_tr", device=0)
 # examples=nli_example, title="Semantic Similarity", description="Enter two texts in the input to assess semantic similarity.")
-ner_model = pipeline("boun-tabi-LMG/turna_ner_milliyet", device=0) 
+ner_model = pipeline(model="boun-tabi-LMG/turna_ner_milliyet", device=0) 
 # title="NER WikiANN", examples=ner_example, description="Enter a text for NER.")
-ner_wikiann = pipeline("boun-tabi-LMG/turna_ner_wikiann", device=0) 
+ner_wikiann = pipeline(model="boun-tabi-LMG/turna_ner_wikiann", device=0) 
 #title="NER",examples=ner_example, description="Enter a text for NER.")