Added conf params to NER

app.py

@@ -83,13 +83,13 @@ def pos(input, model_choice="turna_pos_imst"):
         return pos_boun(input)[0]["generated_text"]
 
 @spaces.GPU
-def ner(input, model_choice="turna_ner_wikiann"):
+def ner(input, model_choice, max_new_tokens, length_penalty, no_repeat_ngram_size):
     if model_choice=="turna_ner_wikiann":
         ner_wikiann = pipeline(model="boun-tabi-LMG/turna_ner_wikiann", device=0) 
-        return ner_wikiann(input)[0]["generated_text"]
+        return ner_wikiann(input, max_new_tokens = max_new_tokens, length_penalty=length_penalty, no_repeat_ngram_size=no_repeat_ngram_size)[0]["generated_text"]
     else:
         ner_model = pipeline(model="boun-tabi-LMG/turna_ner_milliyet", device=0) 
-        return ner_model(input)[0]["generated_text"]
+        return ner_model(input, max_new_tokens = max_new_tokens, length_penalty=length_penalty, no_repeat_ngram_size=no_repeat_ngram_size)[0]["generated_text"]
 
 
 @spaces.GPU
@@ -121,7 +121,7 @@ def generate_title(input, model_choice, max_new_tokens, length_penalty, no_repea
 def categorize(input):
     ttc = pipeline(model="boun-tabi-LMG/turna_classification_ttc4900", device=0) 
 
-    return ttc(input)[0]["generated_text"]
+    return ttc(input, max_new_tokens = 8)[0]["generated_text"]
 
 @spaces.GPU
 def turna(input, max_new_tokens, length_penalty,
@@ -201,12 +201,23 @@ with gr.Blocks(theme="abidlabs/Lime") as demo:
             with gr.Row():
                 with gr.Column():
                     ner_choice = gr.Radio(choices = ["turna_ner_wikiann", "turna_ner_milliyet"], label ="Model", value="turna_ner_wikiann")
+                    with gr.Accordion("Advanced Generation Parameters"):
+                        max_new_tokens = gr.Slider(label = "Maximum length",
+                        		minimum = 0,
+                        		maximum = 64,
+                        		value = 64)
+                        length_penalty = gr.Slider(label = "Length penalty",
+                                    minimum = -10,
+                        		    maximum = 10,
+                            		value=2.0)
+                        no_repeat_ngram_size =gr.Slider(label="No Repeat N-Gram Size", minimum=0,value=3,)
+                with gr.Column():    
                     ner_input = gr.Textbox(label="NER Input")
                     ner_submit = gr.Button()
                 ner_output = gr.Textbox(label="NER Output")
                 
-                ner_submit.click(ner, inputs=[ner_input, ner_choice], outputs=ner_output)
-            ner_examples = gr.Examples(examples = ner_example, inputs = [ner_input, ner_choice], outputs=ner_output, fn=ner)
+                ner_submit.click(ner, inputs=[ner_input, ner_choice, max_new_tokens, length_penalty, no_repeat_ngram_size], outputs=ner_output)
+            ner_examples = gr.Examples(examples = ner_example, inputs = [ner_input, ner_choice, max_new_tokens, length_penalty, no_repeat_ngram_size], outputs=ner_output, fn=ner)
     with gr.Tab("Paraphrase"):
         gr.Markdown("TURNA fine-tuned on paraphrasing. Enter text to paraphrase and pick the model.")
         with gr.Column():