Initial commit

README.md

@@ -1,6 +1,6 @@
 ---
 title: Petarda
-emoji: 🤯🤯🤯🤯🤯
+emoji: 🤯
 colorFrom: yellow
 colorTo: blue
 sdk: gradio

app.py

@@ -11,27 +11,27 @@ def trait_classifier(text: str) -> Dict[str, float]:
     # Set up text classification pipeline
     agr_classifier = pipeline(task="text-classification", 
                                         # Because our model is on Hugging Face already, we can pass in the model name directly
-                                        model="kar0lina/petarda_xlm-roberta-base_agr", # link to model on HF Hub
+                                        model="kar0lina/petarda_xlm-roberta-base_pandora1000-agr", # link to model on HF Hub
                                         device="cuda" if torch.cuda.is_available() else "cpu",
                                         top_k=None) # return all possible scores (not just top-1)
     con_classifier = pipeline(task="text-classification", 
                                         # Because our model is on Hugging Face already, we can pass in the model name directly
-                                        model="kar0lina/petarda_xlm-roberta-base_con", # link to model on HF Hub
+                                        model="kar0lina/petarda_xlm-roberta-base_pandora1000-con", # link to model on HF Hub
                                         device="cuda" if torch.cuda.is_available() else "cpu",
                                         top_k=None) # return all possible scores (not just top-1)
     ext_classifier = pipeline(task="text-classification", 
                                         # Because our model is on Hugging Face already, we can pass in the model name directly
-                                        model="kar0lina/petarda_xlm-roberta-base_ext", # link to model on HF Hub
+                                        model="kar0lina/petarda_xlm-roberta-base_pandora1000-ext", # link to model on HF Hub
                                         device="cuda" if torch.cuda.is_available() else "cpu",
                                         top_k=None) # return all possible scores (not just top-1)
     neu_classifier = pipeline(task="text-classification", 
                                         # Because our model is on Hugging Face already, we can pass in the model name directly
-                                        model="kar0lina/petarda_xlm-roberta-base_neu", # link to model on HF Hub
+                                        model="kar0lina/petarda_xlm-roberta-base_pandora1000-neu", # link to model on HF Hub
                                         device="cuda" if torch.cuda.is_available() else "cpu",
                                         top_k=None) # return all possible scores (not just top-1)
     ope_classifier = pipeline(task="text-classification", 
                                         # Because our model is on Hugging Face already, we can pass in the model name directly
-                                        model="kar0lina/petarda_xlm-roberta-base_ope", # link to model on HF Hub
+                                        model="kar0lina/petarda_xlm-roberta-base_pandora1000-ope", # link to model on HF Hub
                                         device="cuda" if torch.cuda.is_available() else "cpu",
                                         top_k=None) # return all possible scores (not just top-1)
 
@@ -92,7 +92,7 @@ def trait_classifier(text: str) -> Dict[str, float]:
 description = """
 A text classifier for PErsonality Trait prediction using Ai model Roberta - Demo App. 
 
-Fine-tuned from [xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base)"""
+Fine-tuned from [xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on Pandora dataset"""
 
 
 demo = gr.Interface(fn=trait_classifier,