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Update README.md

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changed wording for the bulk processing cookbook

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@@ -47,8 +47,8 @@ with torch.no_grad():
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  This recipe illustrates the use of the model either in a prompt, the output, or both. This is an example of a “guard rail” typically used in generative AI applications for safety.
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  [Guardrail Cookbook](https://github.com/ibm-granite-community/granite-code-cookbook/blob/main/recipes/Guard-Rails/HAP.ipynb)
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- ## Cookbook on Model Usage for Bulk HAP Annotations of Documents
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- This recipe illustrates the use of the model for bulk HAP annotation of documents. The documents are read from a parquet file. It is then fed to the model sentence by sentence for a document and a HAP score for the document is decided. This is then stored back in the parquet file. [Document Annotation Cookbook](https://github.com/IBM/data-prep-kit/tree/dev/transforms/universal/hap/python)
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  ## Performance Comparison with Other Models
 
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  This recipe illustrates the use of the model either in a prompt, the output, or both. This is an example of a “guard rail” typically used in generative AI applications for safety.
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  [Guardrail Cookbook](https://github.com/ibm-granite-community/granite-code-cookbook/blob/main/recipes/Guard-Rails/HAP.ipynb)
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+ ## Cookbook on Model Usage for HAP Annotation on Documents in Bulk using [Data Prep Kit] (https://github.com/IBM/data-prep-kit)
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+ This recipe illustrates the use of the model for HAP annotation of documents in bulk. The documents are read from a parquet file. It is then fed to the model sentence by sentence for a document and a HAP score for the document is decided. This is then stored back in the parquet file. Here is the sample cookbook for it [Data Prep Kit Cookbook](https://github.com/IBM/data-prep-kit/tree/dev/transforms/universal/hap/python)
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  ## Performance Comparison with Other Models