Hey @pierreguillou,
The examples/ scripts are created to have “examples” for NLP, Speech & vision tasks, which work on most available setups, mean SageMaker, Colab, local, VMs, etc. That’s why the script contains the e2e example for pre-processing, training, evaluation, and testing.
I agree with you that moving the CPU tasks (processing) to a CPU makes more sense, but this doesn’t mean you cannot use run_ner.py to get started. The benefit of the examples/ scripts is that we make sure they stay compatible and work with datasets from the datasets library, which can save a lot of time for the community to not rewrite scripts for tasks/benchmarks.
In addition, the processing part for NER for a decently sized dataset (< 50k data points) should take a maximum of a few minutes, which means a few cents.
To answer your two questions:

Did anyone already publish a*HF SageMaker notebook for NER with the associated script ?

Not that i am aware of but you could easily adjust the run_ner.py or you could adjust the train.py to make it compatible fore NER. If you are not sure how you should do this. You can take a look at our course Main NLP tasks - Hugging Face Course 3

In general, Hugging Face plans to publish all these HF SageMaker notebooks for NLP tasks or not?

In quick no, since we have the examples/ scripts, which are supported.