Update Curator links

README.md

@@ -38,7 +38,7 @@ The Internal State of an LLM Knows When It's Lying: https://arxiv.org/pdf/2304.1
 
 ## Software Integration:
 **Runtime Engine(s):** 
-* NeMo Curator: https://github.com/NVIDIA/NeMo-Curator <br>
+* NeMo Curator: https://github.com/NVIDIA-NeMo/Curator <br>
 * Aegis: https://huggingface.co/nvidia/Aegis-AI-Content-Safety-LlamaGuard-Defensive-1.0 <br>
 
 **Supported Hardware Microarchitecture Compatibility:** <br>
@@ -74,8 +74,8 @@ Success is defined as having an acceptable catch rate (recall scores for each at
 * A100 80GB GPU <br>
 
 ## How to Use in NeMo Curator:
-The inference code is available on [NeMo Curator's GitHub repository](https://github.com/NVIDIA/NeMo-Curator). <br>
-Check out [this example notebook](https://github.com/NVIDIA/NeMo-Curator/blob/main/tutorials/distributed_data_classification/instruction-data-guard-classification.ipynb) to get started.
+The inference code is available on [NeMo Curator's GitHub repository](https://github.com/NVIDIA-NeMo/Curator). <br>
+Check out [this example notebook](https://github.com/NVIDIA-NeMo/Curator/blob/main/tutorials/text/distributed-data-classification/instruction-data-guard-classification.ipynb) to get started.
 
 ## How to Use in Transformers:
 To use this AEGIS classifiers, you must get access to Llama Guard on Hugging Face here: https://huggingface.co/meta-llama/LlamaGuard-7b. Afterwards, you should set up a [user access token](https://huggingface.co/docs/hub/en/security-tokens) and pass that token into the constructor of this classifier.