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Update cross_encoder_reranker.ipynb #19846

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8 changes: 4 additions & 4 deletions docs/docs/integrations/document_transformers/cross_encoder_reranker.ipynb
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"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)."
]
},
{
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"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`."
]
},
{
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