Fixed broken link in sagemaker/03_distributed_training_data_parallelism

sagemaker/03_distributed_training_data_parallelism/sagemaker-notebook.ipynb

@@ -36,7 +36,7 @@
    "source": [
     "# Introduction\n",
     "\n",
-    "Welcome to our end-to-end `distributed` Question-Answering example. In this demo, we will use the Hugging Face `transformers` and `datasets` library together with a custom Amazon sagemaker-sdk extension to fine-tune a pre-trained transformer for question-answering on multiple-gpus. In particular, the pre-trained model will be fine-tuned using the `squad` dataset. The demo will use the new `smdistributed` library to run training on multiple gpus as training scripting we are going to use one of the `transformers` [example scripts from the repository](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_qa.py).\n",
+    "Welcome to our end-to-end `distributed` Question-Answering example. In this demo, we will use the Hugging Face `transformers` and `datasets` library together with a custom Amazon sagemaker-sdk extension to fine-tune a pre-trained transformer for question-answering on multiple-gpus. In particular, the pre-trained model will be fine-tuned using the `squad` dataset. The demo will use the new `smdistributed` library to run training on multiple gpus as training scripting we are going to use one of the `transformers` [example scripts from the repository](https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering).\n",
     "\n",
     "To get started, we need to set up the environment with a few prerequisite steps, for permissions, configurations, and so on. \n",
     "\n",