fix: fix inference, retrieve synonym map & fix slot names

nlu/data_loaders/jisfdl.py

@@ -116,7 +116,17 @@ class JISFDL(tfbp.DataLoader):
             # Filter out language entities
             slot_entities = filter(
                 lambda e: e["entity"] != "language", entities)
-            slots = {e["entity"]: e["value"] for e in slot_entities}
+            slots = {}
+            for e in slot_entities: 
+            # Create slots with entity values and resolve synonyms
+                if "start" in e and "end" in e and isinstance(e["start"], int) and isinstance(e["end"], int):
+                    original_value = text[e["start"]:e["end"]]
+                    entity_value = e["value"]
+                    if entity_value != original_value:
+                        entity_value = original_value.lower()
+                    slots[e["entity"]] = entity_value
+                else:
+                    continue
             positions = [[e.get("start", -1), e.get("end", -1)]
                          for e in slot_entities]
 

nlu/models/slot_filler.py

@@ -1,6 +1,8 @@
 import os
 import functools
 import json
+import re
+from utils.json_helper import JsonHelper
 from transformers import TFBertModel, AutoTokenizer
 from keras.layers import Dropout, Dense
 from sys import platform
@@ -180,6 +182,19 @@ class SlotFiller(tfbp.Model):
             if input("Try again? (y/n): ").lower() != 'y':
                 break
     
+    def get_synonym_map(self):
+        helper = JsonHelper()
+
+        helper.read_dataset_json_file('train.json')
+        data = helper.read_dataset_json_file('train.json')
+        synonyms = data["entity_synonyms"]
+        synonym_map = {}
+        for entry in data["entity_synonyms"]:
+            value = entry["value"]
+            for synonym in entry["synonyms"]:
+                synonym_map[synonym] = value    
+        return synonym_map 
+
 
     def get_slots_prediction(self, text: str, inputs, slot_probas):
         slot_probas_np = slot_probas.numpy()
@@ -202,6 +217,10 @@ class SlotFiller(tfbp.Model):
             token = tokens[idx]
             slot_id = slot_ids[idx]
 
+            # Skip special tokens
+            # if token in special_tokens:
+            #     idx += 1
+            #     continue
 
             # Get slot name
             slot_name = self.extra_params["slot_names"][slot_id]
@@ -243,13 +262,24 @@ class SlotFiller(tfbp.Model):
 
             # Convert tokens to string
             slot_value = self.tokenizer.convert_tokens_to_string(slot_tokens).strip()
+            # slot_value = re.sub(r'\s+', '', slot_value)            
+
+            # Ensure the slot value exists in the text (avoid -1 for start index)
+            start_idx = text.find(slot_value)
+            if start_idx == -1:
+                print(f"Skipping entity for '{slot_name}' because '{slot_value}' was not found in text.")
+                continue  # Skip this entity if not found in text
+
+            #Post Processing 
+            synonym_map = self.get_synonym_map()
+            final_slot_value = synonym_map.get(slot_value)
 
             # Calculate entity start and end indices
             entity = {
                 "entity": slot_name,
-                "value": slot_value,
+                "value": final_slot_value,
-                "start": text.find(slot_value),
+                "start": start_idx,
-                "end": text.find(slot_value) + len(slot_value),
+                "end": start_idx + len(slot_value),
                 "confidence": 0,
             }