Fix typo in `examples/vertex-ai/notebooks/deploy-gemma-*/vertex-noteb…

…ook.ipynb`
huggingface · Sep 9, 2024 · 3a8b15a · 3a8b15a
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diff --git a/examples/vertex-ai/notebooks/deploy-gemma-from-gcs-on-vertex-ai/vertex-notebook.ipynb b/examples/vertex-ai/notebooks/deploy-gemma-from-gcs-on-vertex-ai/vertex-notebook.ipynb
@@ -582,7 +582,7 @@
    "source": [
     "If the Vertex AI Endpoint was deployed in a different session and you want to use it but don't have access to the `deployed_model` variable returned by the `aiplatform.Model.deploy` method as in the previous section; you can also run the following snippet to instantiate the deployed `aiplatform.Endpoint` via its resource name as `projects/{PROJECT_ID}/locations/{LOCATION}/endpoints/{ENDPOINT_ID}`.\n",
     "\n",
-    "> Note that you will need to either retrieve the resource name i.e. the `projects/{PROJECT_ID}/locations/{LOCATION}/endpoints/{ENDPOINT_ID}` URL youself via the Google Cloud Console, or just replace the `ENDPOINT_ID` below that can either be found via the previously instantiated `endpoint` as `endpoint.id` or via the Google Cloud Console under the Online predictions where the endpoint is listed."
+    "> Note that you will need to either retrieve the resource name i.e. the `projects/{PROJECT_ID}/locations/{LOCATION}/endpoints/{ENDPOINT_ID}` URL yourself via the Google Cloud Console, or just replace the `ENDPOINT_ID` below that can either be found via the previously instantiated `endpoint` as `endpoint.id` or via the Google Cloud Console under the Online predictions where the endpoint is listed."
    ]
   },
   {

diff --git a/examples/vertex-ai/notebooks/deploy-gemma-on-vertex-ai/vertex-notebook.ipynb b/examples/vertex-ai/notebooks/deploy-gemma-on-vertex-ai/vertex-notebook.ipynb
@@ -149,7 +149,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install --upgrade --quiet huggingface_hub"
+    "!pip install --upgrade --quiet huggingface_hub "
    ]
   },
   {
@@ -437,7 +437,7 @@
    "source": [
     "If the Vertex AI Endpoint was deployed in a different session and you want to use it but don't have access to the `deployed_model` variable returned by the `aiplatform.Model.deploy` method as in the previous section; you can also run the following snippet to instantiate the deployed `aiplatform.Endpoint` via its resource name as `projects/{PROJECT_ID}/locations/{LOCATION}/endpoints/{ENDPOINT_ID}`.\n",
     "\n",
-    "> Note that you will need to either retrieve the resource name i.e. the `projects/{PROJECT_ID}/locations/{LOCATION}/endpoints/{ENDPOINT_ID}` URL youself via the Google Cloud Console, or just replace the `ENDPOINT_ID` below that can either be found via the previously instantiated `endpoint` as `endpoint.id` or via the Google Cloud Console under the Online predictions where the endpoint is listed."
+    "> Note that you will need to either retrieve the resource name i.e. the `projects/{PROJECT_ID}/locations/{LOCATION}/endpoints/{ENDPOINT_ID}` URL yourself via the Google Cloud Console, or just replace the `ENDPOINT_ID` below that can either be found via the previously instantiated `endpoint` as `endpoint.id` or via the Google Cloud Console under the Online predictions where the endpoint is listed."
    ]
   },
   {