New: July 20, 2020: The tutorial now uses AllenNLP 1.0, and so should be up-to-date with the latest AllenNLP.

This tutorial is meant to teach you both how to use AllenNLP and a principled approach to doing deep learning research in NLP. The content is mirrored (and updated) on my personal site: jbarrow.ai. If you're interested in reading the latest version, you can find it there. But the code will always be stored in this repository. It consists of 10 sections, and I recommend you do them in order:

0. Setup
1. Building a Dataset Reader
2. Building a Baseline Model
3. Configuring Experiments
4. Tackling Your Own Experiments
5. Predictors
6. Debugging [WIP]
7. Advanced Modeling: Hierarchical LSTMs, CRF Decoding, and BERT [WIP]
8. Digging Into the Documentation [WIP]
9. Hyperparameter Search: AllenTune [WIP]
10. Appendix: Migrating from AllenNLP 0.9 to 1.0 [WIP]

The tutorial makes no assumptions about familiarity with AllenNLP, and goes through using it as an experimental platform, using JSON configurations.