schmmd

AllenNLP started before transformers, and so it provided high level abstractions to experiment with model architectures, which is where much of NLP research was happening at the time. Transformers definitely changed the playing field, as it became the basis for most models!

When we started AllenNLP, PyTorch was just starting to emerge as a competitor to Tensorflow and we made the difficult decision to support PyTorch. In hindsight this was a great decision as the majority of top research is done in PyTorch today.

Tango primarily supports PyTorch, but unlike AllenNLP, is flexible enough to support other deep learning libraries as well. For example, we're adding support for JAX so we can easily leverage TPUs.

Thanks for sharing! If your content (such as a thesis) is out of domain (not a news article) all bets are off on how the model will perform.

You could try yourself using the interactive demo, but it was trained on news text so it might struggle.

You can generate your own examples with the "Generate" button. It will fill in that field based on generated text using the other fields as input.

For example, you could clear all the fields, enter a headline, and generate an article for that headline.