Built on PyTorch, AllenNLP makes it easy to design and evaluate new deep learning models for nearly any NLP problem, along with the infrastructure to easily run them in the cloud or on your laptop. AllenNLP was designed with the following principles:

  • Hyper-modular and lightweight. Use the parts which you like seamlessly with PyTorch.

  • Extensively tested and easy to extend. Test coverage is above 90% and the example models provide a template for contributions.

  • Take padding and masking seriously, making it easy to implement correct models without the pain.

  • Experiment friendly. Run reproducible experiments from a json specification with comprehensive logging.

AllenNLP includes reference implementations of high quality models for Semantic Role Labelling, Question and Answering (BiDAF), Entailment (decomposable attention), and more (see https://allennlp.org/models).

AllenNLP is built and maintained by the Allen Institute for Artificial Intelligence, in close collaboration with researchers at the University of Washington and elsewhere. With a dedicated team of best-in-field researchers and software engineers, the AllenNLP project is uniquely positioned to provide state of the art models with high quality engineering.

Package Reference

Indices and tables