Upload app.py

app.py

@@ -25,7 +25,7 @@ class ProductionTokenizer:
             self.vocab_size = len(self.vocab)
         else:
             # Build vocabulary from the target sentence
-            target_sentence = "This model creates a super relationships between the words to predict what word"
+            target_sentence = "This model create relationships between the words to learn what word is missing!"
             words = target_sentence.lower().split()
             
             # Create vocabulary
@@ -78,7 +78,7 @@ class ProductionMLM(nn.Module):
 # Global variables for the model
 model = None
 tokenizer = None
-TARGET_SENTENCE = "This model creates a super relationships between the words to predict what word"
+TARGET_SENTENCE = "This model create relationships between the words to learn what word is missing!"
 
 def load_model():
     """Load the trained production model."""
@@ -86,7 +86,7 @@ def load_model():
     
     try:
         # Try to load from saved model
-        checkpoint_path = 'encoder_transformer/mlm_production/production_model.pt'
+        checkpoint_path = 'encoder_transformer/mlm/models/production_model.pt'
         if os.path.exists(checkpoint_path):
             checkpoint = torch.load(checkpoint_path, map_location='cpu')
             
@@ -178,7 +178,7 @@ def create_interface():
         # 🔮 Production MLM Word Prediction
         
         This model learns relationships between words in the sentence:
-        > **"This model creates a super relationships between the words to predict what word"**
+        > **"This model create relationships between the words to learn what word is missing!"**
         
         **How it works:**
         1. Mask any word in the sentence by replacing it with `[MASK]`
@@ -200,9 +200,9 @@ def create_interface():
                 gr.Markdown("""
                 **Word positions in the target sentence:**
                 ```
-                0: This     1: model     2: creates    3: a          4: super
-                5: relationships  6: between  7: the      8: words     9: to
-                10: predict  11: what     12: word
+                0: This     1: model     2: create     3: relationships  4: between
+                5: the      6: words     7: to         8: learn         9: what
+                10: word    11: is       12: missing!
                 ```
                 """)
                 
@@ -247,10 +247,10 @@ def create_interface():
         gr.Markdown("### 💡 Example Predictions")
         
         examples = [
-            ["This model [MASK] a super relationships between the words to predict what word", 2],
-            ["This model creates [MASK] super relationships between the words to predict what word", 3],
-            ["This model creates a super [MASK] between the words to predict what word", 5],
-            ["This model creates a super relationships [MASK] the words to predict what word", 6],
+            ["This model [MASK] relationships between the words to learn what word is missing!", 2],
+            ["This model create [MASK] between the words to learn what word is missing!", 3],
+            ["This model create relationships [MASK] the words to learn what word is missing!", 4],
+            ["This model create relationships between the words to [MASK] what word is missing!", 8],
         ]
         
         gr.Examples(