AllenNLP is a deep learning platform for natural language processing (NLP) research, designed to simplify the process of developing, testing, and sharing NLP models. It addresses challenges in NLP research by providing easy-to-use command-line tools, declarative configuration-driven experiments, and modular NLP abstractions. The platform has already improved research efficiency and component sharing at the Allen Institute for Artificial Intelligence, and aims to have a broader impact on the field. AllenNLP is built on PyTorch and provides a flexible data API, high-level abstractions for common NLP operations, and a modular, extensible experiment framework. It ensures high test coverage and is designed to be easy to use, with a permissive Apache 2.0 license. The platform includes reference implementations of state-of-the-art models, such as semantic role labeling, machine comprehension, textual entailment, and constituency parsing, which demonstrate how to use the framework and serve as baselines for future research. AllenNLP's design allows researchers to focus on high-level model summaries rather than implementation details, enabling reproducible and careful research. The platform includes abstractions for text data processing, NLP-focused model building, and an experimental framework that supports controlled experiments. It also provides interactive online demos and visualizations to help interpret model decisions. AllenNLP is an ongoing open-source project maintained by researchers and engineers at the Allen Institute for Artificial Intelligence, as well as contributors from the broader NLP community. It is widely used internally for research on various NLP tasks and is gaining traction externally. The platform is designed to be a standard for advancing NLP research using PyTorch.AllenNLP is a deep learning platform for natural language processing (NLP) research, designed to simplify the process of developing, testing, and sharing NLP models. It addresses challenges in NLP research by providing easy-to-use command-line tools, declarative configuration-driven experiments, and modular NLP abstractions. The platform has already improved research efficiency and component sharing at the Allen Institute for Artificial Intelligence, and aims to have a broader impact on the field. AllenNLP is built on PyTorch and provides a flexible data API, high-level abstractions for common NLP operations, and a modular, extensible experiment framework. It ensures high test coverage and is designed to be easy to use, with a permissive Apache 2.0 license. The platform includes reference implementations of state-of-the-art models, such as semantic role labeling, machine comprehension, textual entailment, and constituency parsing, which demonstrate how to use the framework and serve as baselines for future research. AllenNLP's design allows researchers to focus on high-level model summaries rather than implementation details, enabling reproducible and careful research. The platform includes abstractions for text data processing, NLP-focused model building, and an experimental framework that supports controlled experiments. It also provides interactive online demos and visualizations to help interpret model decisions. AllenNLP is an ongoing open-source project maintained by researchers and engineers at the Allen Institute for Artificial Intelligence, as well as contributors from the broader NLP community. It is widely used internally for research on various NLP tasks and is gaining traction externally. The platform is designed to be a standard for advancing NLP research using PyTorch.