Update app_6.py

app_6.py

@@ -49,14 +49,7 @@ def load_model():
 # Load the model and tokenizer
 tokenizer, model = load_model()
 
-st.write("model_", model)
-st.write("model_weights", model.model)
-st.write("config", model.config)
-st.write("Signal_classifier_weights", model.signal_classifier.weight)
-st.write(model.model.embeddings.LayerNorm.weight)
-#st.write(model.model.encoder.layer.13.attention.self.value.weight)
-roberta_model = AutoModel.from_pretrained("roberta-large")
-st.write(roberta_model.embeddings.LayerNorm.weight)
+
 
 model.eval()  # Set model to evaluation mode
 def extract_arguments(text, tokenizer, model, beam_search=True):
@@ -82,13 +75,6 @@ def extract_arguments(text, tokenizer, model, beam_search=True):
     start_signal_logits = outputs["start_sig_logits"][0]
     end_signal_logits = outputs["end_sig_logits"][0]
 
-    #st.write("start_cause_logits", start_cause_logits)
-    #st.write("end_cause_logits", end_cause_logits)
-    #st.write("start_effect_logits", start_effect_logits)
-    #st.write("end_effect_logits", end_effect_logits)
-    #st.write("start_signal_logits", start_signal_logits)
-    #st.write("end_signal_logits", end_signal_logits)
-
 
     # Set the first and last token logits to a very low value to ignore them
     start_cause_logits[0] = -1e-4