Yet another try

pipeline.py

@@ -1,13 +1,14 @@
 from typing import Dict, List, Any
 from nemo.collections.nlp.models import PunctuationCapitalizationModel
 
-class PreTrainedPipeline():
+
+class PreTrainedPipeline:
     def __init__(self, path=""):
         # IMPLEMENT_THIS
         # Preload all the elements you are going to need at inference.
         # For instance your model, processors, tokenizer that might be needed.
         # This function is only called once, so do all the heavy processing I/O here"""
-        self.model = PunctuationCapitalizationModel.from_pretrained("dchaplinsky/punctuation_uk_bert")
+        self.model = PunctuationCapitalizationModel.restore_from("punctuation_uk_bert.nemo")
 
     def __call__(self, inputs: str) -> List[Dict[str, Any]]:
         """
@@ -31,14 +32,8 @@ class PreTrainedPipeline():
         offset = 0
         for tok, lab in zip(tokens, labels):
             if lab != "OO":
-                res.append({
-                    "entity_group": lab,
-                    "word": tok,
-                    "start": offset,
-                    "end": offset + len(tok),
-                    "score": 1
-                })
+                res.append({"entity_group": lab, "word": tok, "start": offset, "end": offset + len(tok), "score": 0.99})
 
             offset += len(tok) + 1
 
-        return res
+        return res