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Text generation is currently possible with GPT-2, OpenAi-GPT, CTRL, XLNet, Transfo-XL and Reformer in
PyTorch and for most models in Tensorflow as well. As can be seen in the example above XLNet and Transfo-XL often
need to be padded to work well. GPT-2 is usually a good choice for open-ended text generation because it was trained
on millions of webpages with a causal language modeling objective.
For more information on how to apply different decoding strategies for text generation, please also refer to our text
generation blog post here.
Named Entity Recognition
Named Entity Recognition (NER) is the task of classifying tokens according to a class, for example, identifying a token
as a person, an organisation or a location. An example of a named entity recognition dataset is the CoNLL-2003 dataset,
