Update cross_encoder_reranker.ipynb (langchain-ai#19846)

HuggingFace -> Hugging Face

docs/docs/integrations/document_transformers/cross_encoder_reranker.ipynb

@@ -7,11 +7,11 @@
    "source": [
     "# Cross Encoder Reranker\n",
     "\n",
-    "This notebook shows how to implement reranker in a retriever with your own cross encoder from [HuggingFace cross encoder models](https://huggingface.co/cross-encoder) or HuggingFace models that implements cross encoder function ([example: BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base)). `SagemakerEndpointCrossEncoder` enables you to use these HuggingFace models loaded on Sagemaker.\n",
+    "This notebook shows how to implement reranker in a retriever with your own cross encoder from [Hugging Face cross encoder models](https://huggingface.co/cross-encoder) or Hugging Face models that implements cross encoder function ([example: BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base)). `SagemakerEndpointCrossEncoder` enables you to use these HuggingFace models loaded on Sagemaker.\n",
     "\n",
     "This builds on top of ideas in the [ContextualCompressionRetriever](/docs/modules/data_connection/retrievers/contextual_compression/). Overall structure of this document came from [Cohere Reranker documentation](/docs/integrations/retrievers/cohere-reranker.ipynb).\n",
     "\n",
-    "For more about why cross encoder can be used as reranking mechanism in conjunction with embeddings for better retrieval, refer to [HuggingFace Cross-Encoders documentation](https://www.sbert.net/examples/applications/cross-encoder/README.html)."
+    "For more about why cross encoder can be used as reranking mechanism in conjunction with embeddings for better retrieval, refer to [Hugging Face Cross-Encoders documentation](https://www.sbert.net/examples/applications/cross-encoder/README.html)."
    ]
   },
   {
@@ -173,11 +173,11 @@
    "id": "419a2bf3-de4b-4c4d-9a40-4336552f604c",
    "metadata": {},
    "source": [
-    "## Uploading HuggingFace model to SageMaker endpoint\n",
+    "## Uploading Hugging Face model to SageMaker endpoint\n",
     "\n",
     "Refer to [this article](https://www.philschmid.de/custom-inference-huggingface-sagemaker) for general guideline. Here is a simple `inference.py` for creating an endpoint that works with `SagemakerEndpointCrossEncoder`.\n",
     "\n",
-    "It downloads HuggingFace model on the fly, so you do not need to keep the model artifacts such as `pytorch_model.bin` in your `model.tar.gz`."
+    "It downloads Hugging Face model on the fly, so you do not need to keep the model artifacts such as `pytorch_model.bin` in your `model.tar.gz`."
    ]
   },
   {