Researchers from the Allen Institute for AI and University of California, Irvine, have released AllenNLP Interpret, a toolkit for explaining the results from natural-language processing (NLP) models. The extensible toolkit includes several built-in methods for interpretation and visualization components, as well as examples using AllenNLP Interpret to explain the results of state-of-the art NLP models including BERT and RoBERTa. In a paper published on arXiv, the research team described the toolkit in more detail. AllenNLP Interpret uses two gradient-based interpretation methods: saliency maps, which determine how much each word or "token" in the input sentence contributes to the model's prediction, and adversarial attacks, which try to remove or change words in the input while still maintaining the same prediction from the model. These techniques are implemented for a variety of NLP tasks and model architectures.