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@@ -46,6 +46,9 @@ with torch.no_grad():
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  ## Cookbook on Model Usage as a Guardrail
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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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  ## Performance Comparison with Other Models
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  The model outperforms most popular models with significantly lower inference latency. If a better F1 score is required, please refer to IBM's 12-layer model [here](https://huggingface.co/ibm-granite/granite-guardian-hap-125m).
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  ## Cookbook on Model Usage as a Guardrail
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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 Annotation of Documents
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+ This recipe illustrates the use of the model for HAP annotation of documents. The recipe reads documents from a parquet file, checks every sentence of the document for HAP and then decides on a HAP score for the document. This is then added to 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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  The model outperforms most popular models with significantly lower inference latency. If a better F1 score is required, please refer to IBM's 12-layer model [here](https://huggingface.co/ibm-granite/granite-guardian-hap-125m).
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