diff --git a/docs/docs/guides/productionization/safety/hugging_face_prompt_injection.ipynb b/docs/docs/guides/productionization/safety/hugging_face_prompt_injection.ipynb
index f4b306f4553..2bafd7a95b7 100644
--- a/docs/docs/guides/productionization/safety/hugging_face_prompt_injection.ipynb
+++ b/docs/docs/guides/productionization/safety/hugging_face_prompt_injection.ipynb
@@ -50,7 +50,7 @@
     "from transformers import AutoTokenizer, pipeline\n",
     "\n",
     "# Using https://huggingface.co/protectai/deberta-v3-base-prompt-injection-v2\n",
-    "model_path = \"laiyer/deberta-v3-base-prompt-injection-v2\"\n",
+    "model_path = \"protectai/deberta-v3-base-prompt-injection-v2\"\n",
     "revision = None  # We recommend specifiying the revision to avoid breaking changes or supply chain attacks\n",
     "tokenizer = AutoTokenizer.from_pretrained(\n",
     "    model_path, revision=revision, model_input_names=[\"input_ids\", \"attention_mask\"]\n",
diff --git a/libs/experimental/langchain_experimental/prompt_injection_identifier/hugging_face_identifier.py b/libs/experimental/langchain_experimental/prompt_injection_identifier/hugging_face_identifier.py
index ecd5f7f6822..519ee54c85f 100644
--- a/libs/experimental/langchain_experimental/prompt_injection_identifier/hugging_face_identifier.py
+++ b/libs/experimental/langchain_experimental/prompt_injection_identifier/hugging_face_identifier.py
@@ -1,7 +1,7 @@
 """Tool for the identification of prompt injection attacks."""
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Union
+from typing import TYPE_CHECKING, Any
 
 from langchain.pydantic_v1 import Field, root_validator
 from langchain.tools.base import BaseTool
@@ -59,7 +59,7 @@ class HuggingFaceInjectionIdentifier(BaseTool):
         "Useful for when you need to ensure that prompt is free of injection attacks. "
         "Input should be any message from the user."
     )
-    model: Union[Pipeline, str, None] = Field(default_factory=_model_default_factory)
+    model: Any = Field(default_factory=_model_default_factory)
     """Model to use for prompt injection detection. 
     
     Can be specified as transformers Pipeline or string. String should correspond to the
@@ -98,3 +98,6 @@ class HuggingFaceInjectionIdentifier(BaseTool):
             raise PromptInjectionException("Prompt injection attack detected", score)
 
         return query
+
+
+HuggingFaceInjectionIdentifier.update_forward_refs()