diff --git a/docs/docs/guides/safety/hugging_face_prompt_injection.ipynb b/docs/docs/guides/safety/hugging_face_prompt_injection.ipynb
index 89c80b6fb8..21224ea0b0 100644
--- a/docs/docs/guides/safety/hugging_face_prompt_injection.ipynb
+++ b/docs/docs/guides/safety/hugging_face_prompt_injection.ipynb
@@ -8,7 +8,7 @@
     "# Hugging Face prompt injection identification\n",
     "\n",
     "This notebook shows how to prevent prompt injection attacks using the text classification model from `HuggingFace`.\n",
-    "It exploits the *deberta* model trained to identify prompt injections: https://huggingface.co/deepset/deberta-v3-base-injection"
+    "By default it uses a *deberta* model trained to identify prompt injections. In this walkthrough we'll use https://huggingface.co/laiyer/deberta-v3-base-prompt-injection."
    ]
   },
   {
@@ -21,19 +21,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "aea25588-3c3f-4506-9094-221b3a0d519b",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58ab3557623a495d8cc3c3e32a61938f",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "'hugging_face_injection_identifier'"
+       "Downloading config.json:   0%|          | 0.00/994 [00:00<?, ?B/s]"
       ]
      },
-     "execution_count": 1,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3bf062f02d304ab5a485a2a228b4cf41",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading model.safetensors:   0%|          | 0.00/738M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -41,7 +59,10 @@
     "    HuggingFaceInjectionIdentifier,\n",
     ")\n",
     "\n",
-    "injection_identifier = HuggingFaceInjectionIdentifier()\n",
+    "# Using https://huggingface.co/laiyer/deberta-v3-base-prompt-injection\n",
+    "injection_identifier = HuggingFaceInjectionIdentifier(\n",
+    "    model=\"laiyer/deberta-v3-base-prompt-injection\"\n",
+    ")\n",
     "injection_identifier.name"
    ]
   },
@@ -299,9 +320,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "poetry-venv",
    "language": "python",
-   "name": "python3"
+   "name": "poetry-venv"
   },
   "language_info": {
    "codemirror_mode": {
@@ -313,7 +334,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,
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 c12c8ca48b..22412a5896 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
@@ -1,16 +1,18 @@
 """Tool for the identification of prompt injection attacks."""
 from __future__ import annotations
 
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Any
 
-from langchain.pydantic_v1 import Field
+from langchain.pydantic_v1 import Field, root_validator
 from langchain.tools.base import BaseTool
 
 if TYPE_CHECKING:
     from transformers import Pipeline
 
 
-def _model_default_factory() -> Pipeline:
+def _model_default_factory(
+    model_name: str = "deepset/deberta-v3-base-injection"
+) -> Pipeline:
     try:
         from transformers import pipeline
     except ImportError as e:
@@ -18,11 +20,11 @@ def _model_default_factory() -> Pipeline:
             "Cannot import transformers, please install with "
             "`pip install transformers`."
         ) from e
-    return pipeline("text-classification", model="deepset/deberta-v3-base-injection")
+    return pipeline("text-classification", model=model_name)
 
 
 class HuggingFaceInjectionIdentifier(BaseTool):
-    """Tool that uses deberta-v3-base-injection to detect prompt injection attacks."""
+    """Tool that uses HF model to detect prompt injection attacks."""
 
     name: str = "hugging_face_injection_identifier"
     description: str = (
@@ -30,7 +32,19 @@ 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: Pipeline = 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
+        model name of a text-classification transformers model. Defaults to 
+        ``deepset/deberta-v3-base-injection`` model.
+    """
+
+    @root_validator(pre=True)
+    def validate_environment(cls, values: dict) -> dict:
+        if isinstance(values.get("model"), str):
+            values["model"] = _model_default_factory(model_name=values["model"])
+        return values
 
     def _run(self, query: str) -> str:
         """Use the tool."""