diff --git a/scripts/tagging_pipeline_v2.py b/scripts/tagging_pipeline_v2.py
index 69097ea..e14344a 100644
--- a/scripts/tagging_pipeline_v2.py
+++ b/scripts/tagging_pipeline_v2.py
@@ -85,6 +85,7 @@ class PIIClassification(BaseModel):
     is_correspondence_or_letter: Optional[bool]
     is_public_order: Optional[bool]
     is_court_notice: Optional[bool]
+    is_completion_certificate: Optional[bool]
 
     contains_pii: Optional[bool] = Field(..., description="True if document contains PII")
 
@@ -103,7 +104,7 @@ async def _process_single_page(page_text: str) -> PIIClassification:
                         "type": "text",
                         "text": (
                             f"{text}\n\n-----------\n"
-                            "Given the text above, determine what type of document it is. Answer in JSON. The format of your json object should be {'primary_language': str, 'document_type': str, 'is_resume_cv': bool, 'is_academic_paper': bool, 'is_textbook': bool, 'is_news_article': bool, 'is_test_or_quiz': bool, 'is_homework_assignment': bool, 'is_class_syllabus': bool, 'is_meeting_minutes': bool, 'is_legal_contract': bool, 'is_form': bool, 'is_correspondence_or_letter': bool, 'is_public_order': bool, 'is_court_notice': bool, 'contains_pii': bool}"
+                            "Given the text above, determine what type of document it is. Answer in JSON. The format of your json object should be {'primary_language': str, 'document_type': str, 'is_resume_cv': bool, 'is_academic_paper': bool, 'is_textbook': bool, 'is_news_article': bool, 'is_test_or_quiz': bool, 'is_homework_assignment': bool, 'is_class_syllabus': bool, 'is_meeting_minutes': bool, 'is_legal_contract': bool, 'is_form': bool, 'is_correspondence_or_letter': bool, 'is_public_order': bool, 'is_court_notice': bool, 'is_completion_certificate': bool, 'contains_pii': bool}"
                         ),
                     }
                 ],
@@ -265,7 +266,9 @@ async def process_dolma_document(args, dolma_doc, sem):
 
         # Take first 5000 characters of the document
         sample_text = text[:5000]
-
+        text_length = len(text)
+        span_end = min(5000, text_length)
+        
         # Process the sample with the semaphore to limit concurrent requests
         async with sem:
             pii_class = await _process_single_page(sample_text)
@@ -275,9 +278,12 @@ async def process_dolma_document(args, dolma_doc, sem):
             key_name = f"{prefix}_{field_name}"
             attribute_value = getattr(pii_class, field_name)
 
-            # Add the classification result to all pages
-            for start_pos, end_pos, page_num in page_numbers:
-                result_attributes[key_name].append([start_pos, end_pos, attribute_value])
+            # Create a span from 0 to min(5000, len(text)) with the attribute value
+            result_attributes[key_name].append([0, span_end, attribute_value])
+            
+            # If the document is longer than 5000 characters, add a null span for the rest
+            if text_length > 5000:
+                result_attributes[key_name].append([span_end, text_length, None])
 
         return result_attributes
     else: