From 8711a05a5164195950666a7fe8ad0b62a822a320 Mon Sep 17 00:00:00 2001 From: Ikko Eltociear Ashimine Date: Mon, 1 Apr 2024 23:49:54 +0900 Subject: [PATCH] Update cross_encoder_reranker.ipynb (#19846) HuggingFace -> Hugging Face --- .../document_transformers/cross_encoder_reranker.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/docs/integrations/document_transformers/cross_encoder_reranker.ipynb b/docs/docs/integrations/document_transformers/cross_encoder_reranker.ipynb index 25c87c0ee8aba..4a6a7e6286881 100644 --- a/docs/docs/integrations/document_transformers/cross_encoder_reranker.ipynb +++ b/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`." ] }, {