AllenNLP is a deep semantic natural language processing (NLP) platform designed to facilitate research in NLP using deep learning methods. It addresses common challenges in NLP research, such as the difficulty of tuning models, replicating results, and extending codebases. AllenNLP provides a set of high-level abstractions and modular components that make it easier to write and experiment with NLP models. Key features include: 1. **Easy-to-Use Command-Line Tools**: Simplifies running and debugging experiments. 2. **Declarative Configuration-Driven Experiments**: Allows researchers to define experiments using configuration files, making it easy to change model architectures and hyperparameters. 3. **Modular NLP Abstractions**: Provides abstractions for common NLP tasks, such as text representation, sequence processing, and span extraction. 4. **Live Demos**: Enables sharing and accessibility of complex NLP models through interactive demos. AllenNLP is built on PyTorch and includes reference implementations of state-of-the-art models for tasks like semantic role labeling, machine comprehension, textual entailment, and constituency parsing. These models are designed to be reproducible and shareable, serving as baselines for future research. The platform is widely used internally at the Allen Institute for Artificial Intelligence and is gaining external traction, with a growing open-source community. AllenNLP aims to lower barriers to high-quality NLP research and promote best practices in the field.AllenNLP is a deep semantic natural language processing (NLP) platform designed to facilitate research in NLP using deep learning methods. It addresses common challenges in NLP research, such as the difficulty of tuning models, replicating results, and extending codebases. AllenNLP provides a set of high-level abstractions and modular components that make it easier to write and experiment with NLP models. Key features include: 1. **Easy-to-Use Command-Line Tools**: Simplifies running and debugging experiments. 2. **Declarative Configuration-Driven Experiments**: Allows researchers to define experiments using configuration files, making it easy to change model architectures and hyperparameters. 3. **Modular NLP Abstractions**: Provides abstractions for common NLP tasks, such as text representation, sequence processing, and span extraction. 4. **Live Demos**: Enables sharing and accessibility of complex NLP models through interactive demos. AllenNLP is built on PyTorch and includes reference implementations of state-of-the-art models for tasks like semantic role labeling, machine comprehension, textual entailment, and constituency parsing. These models are designed to be reproducible and shareable, serving as baselines for future research. The platform is widely used internally at the Allen Institute for Artificial Intelligence and is gaining external traction, with a growing open-source community. AllenNLP aims to lower barriers to high-quality NLP research and promote best practices in the field.