Interested in fine-tuning on your own custom datasets but unsure how to get going? I just added a tutorial to the docs with several examples that each walk you through downloading a dataset, preprocessing & tokenizing, and training with either Trainer, native PyTorch, or native TensorFlow 2.
Examples include:

Sequence classification (sentiment) – IMDb 68

Token classification (NER) – W-NUT Emerging and Rare entities 892

Question answering (span selection) – SQuAD 2.0 91


Click the Open in Colab button at the top to open a colab notebook in either TF or PT. This tutorial demonstrates one workflow for working with custom datasets, but there are many valid ways to accomplish the same thing. The intention is to be demonstrative rather than definitive. Also, we highly recommend you check out and contribute to our NLP datasets & metrics library 27 for easy access 150+ datasets.
Tutorial: https://huggingface.co/transformers/master/custom_datasets.html 778
Feedback and questions welcome!