AllenNLP dev here. We're going to do a "PyTorch 1.0" release of AllenNLP next week, and then after that we're planning to investigate how to incorporate the new "production" aspects.
Could you guys elaborate on the relationship between PyText, torchtext, and AllenNLP? I've briefly used the latter two, but with how quickly things are moving it'd be nice to have a quick answer from the devs themselves.
PyText dev here, Torchtext provides a set of data-abstractions that helps reading and processing raw text data into PyTorch tensors, at the moment we use Torchtext in PyText for training-time data reading and preprocessing.
AllenNLP is a great NLP modeling library that is aimed at providing reference implementations and prebuilt state-of-the-art models, and make it easy to iterate on and research with models for different NLP tasks.
We've built PyText to be a rich NLP modeling library (along the lines of AllenNLP) but with production capabilities baked in the design from day 1.
Examples are:
- We provide interfaces to make sure data preprocessing can be consistent between training and runtime
- The model interfaces are compatible with ONNX and torch.jit
- A core goal for us in the next few month is to be able to run models trained in PyText on mobile.
Among other differences like supporting distributed training and multi-task learning.
That being said, so far our library of models has been mostly influenced by our current production use-cases, we are actively working on enriching this library with more models and tasks while keeping production capabilities and inference speed in mind.
