Added conf params to POS

app.py

@@ -74,13 +74,13 @@ def sentiment_analysis(input, model_choice="turna_classification_17bintweet_sent
     return sentiment_model(input, max_new_tokens = 4)[0]["generated_text"]
 
 @spaces.GPU
-def pos(input, model_choice="turna_pos_imst"): 
+def pos(input, model_choice, max_new_tokens, length_penalty, no_repeat_ngram_size): 
     if model_choice=="turna_pos_imst":
         pos_imst = pipeline(model="boun-tabi-LMG/turna_pos_imst", device=0) 
-        return pos_imst(input)[0]["generated_text"]
+        return pos_imst(input, max_new_tokens = max_new_tokens, length_penalty=length_penalty, no_repeat_ngram_size=no_repeat_ngram_size)[0]["generated_text"]
     else:
         pos_boun = pipeline(model="boun-tabi-LMG/turna_pos_boun", device=0)
-        return pos_boun(input)[0]["generated_text"]
+        return pos_boun(input, max_new_tokens = max_new_tokens, length_penalty=length_penalty, no_repeat_ngram_size=no_repeat_ngram_size)[0]["generated_text"]
 
 @spaces.GPU
 def ner(input, model_choice, max_new_tokens, length_penalty, no_repeat_ngram_size):
@@ -188,12 +188,22 @@ with gr.Blocks(theme="abidlabs/Lime") as demo:
             with gr.Row():
                 with gr.Column():
                     pos_choice = gr.Radio(choices = ["turna_pos_imst", "turna_pos_boun"], label ="Model", value="turna_pos_imst")
+                    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():    
                     pos_input = gr.Textbox(label="POS Input")
-                
                     pos_submit = gr.Button()
                 pos_output = gr.Textbox(label="POS Output")
-                pos_submit.click(pos, inputs=[pos_input, pos_choice], outputs=pos_output)
-            pos_examples = gr.Examples(examples = ner_example, inputs = [pos_input, pos_choice], outputs=pos_output, fn=pos)
+                pos_submit.click(pos, inputs=[pos_input, pos_choice, max_new_tokens, length_penalty, no_repeat_ngram_size], outputs=pos_output)
+            pos_examples = gr.Examples(examples = ner_example, inputs = [pos_input, pos_choice, max_new_tokens, length_penalty, no_repeat_ngram_size], outputs=pos_output, fn=pos)
     
     with gr.Tab("NER"):
         gr.Markdown("TURNA fine-tuned on named entity recognition. Enter text to parse named entities and pick the model.")