update

app.py

@@ -11,7 +11,7 @@ with gr.Blocks() as demo:
     gr.Markdown(
     """
     # ParaPLUIE (Paraphrase Generation Evaluation Powered by an LLM)
-    Is a metric for evaluating the semantic proximity of two sentences. 
+    ParaPLUIE is a metric for evaluating the semantic proximity of two sentences. 
     ParaPLUIE use the perplexity of an LLM to compute a confidence score.
     It has shown the highest correlation with human judgement on paraphrase classification meanwhile reamin the computional cost low as it roughtly equal to one token generation cost.
     """)

code/PPLUIE/config.py

@@ -1,3 +1,8 @@
+def show_available_models():
+    print("LLM tested with PPLUIE: ")
+    for k in (model_dict.keys()):
+        print(k)
+
 model_dict = {
     #small
     "HuggingFaceTB/SmolLM2-135M-Instruct" : {

code/PPLUIE/ppluie.py

@@ -45,11 +45,6 @@ class ppluie:
     def show_templates(self):
         print_templates()
 
-    def show_available_models(self):
-        print("LLM tested with PPLUIE: ")
-        for k in (model_dict.keys()):
-            print(k)
-
     def setTemplate(self, template: str):
         check_template_name(template)
         self.template = template

code/example.py

@@ -1,11 +1,15 @@
 # install this lib : pip install {PATH}/ParaPLUIE
 
 from PPLUIE import ppluie
+from PPLUIE.config import show_available_models
 
 template = "FS-DIRECT"
 
 device = "cuda:1"
 
+# to show LLM already tested with ParaPLUIE
+show_available_models()
+
 scorer = ppluie("mistralai/Mistral-7B-Instruct-v0.2", device)
 scorer.setTemplate(template)
 
@@ -14,8 +18,6 @@ scorer.show_templates()
 # to show how is the prompt encoded, to ensure that the correct numbers of special tokens are removed
 # and Yes / No words fit on one token
 scorer.chech_end_tokens_tmpl()
-# to show LLM already tested with ParaPLUIE
-scorer.show_available_models()
 
 S = "Have you ever seen a tsunami ?"   	
 H = "Have you ever seen a tiramisu ?"