  Named Entity Recognition (NER) is a foundational NLP task that aims to
provide class labels like Person, Location, Organisation, Time, and Number to
words in free text. Named Entities can also be multi-word expressions where the
additional I-O-B annotation information helps label them during the NER
annotation process. While English and European languages have considerable
annotated data for the NER task, Indian languages lack on that front -- both in
terms of quantity and following annotation standards. This paper releases a
significantly sized standard-abiding Hindi NER dataset containing 109,146
sentences and 2,220,856 tokens, annotated with 11 tags. We discuss the dataset
statistics in all their essential detail and provide an in-depth analysis of
the NER tag-set used with our data. The statistics of tag-set in our dataset
show a healthy per-tag distribution, especially for prominent classes like
Person, Location and Organisation. Since the proof of resource-effectiveness is
in building models with the resource and testing the model on benchmark data
and against the leader-board entries in shared tasks, we do the same with the
aforesaid data. We use different language models to perform the sequence
labelling task for NER and show the efficacy of our data by performing a
comparative evaluation with models trained on another dataset available for the
Hindi NER task. Our dataset helps achieve a weighted F1 score of 88.78 with all
the tags and 92.22 when we collapse the tag-set, as discussed in the paper. To
the best of our knowledge, no available dataset meets the standards of volume
(amount) and variability (diversity), as far as Hindi NER is concerned. We fill
this gap through this work, which we hope will significantly help NLP for
Hindi. We release this dataset with our code and models at
https://github.com/cfiltnlp/HiNER
