AllenNLP is an Apache 2.0 NLP research library, built on PyTorch, for developing state-of-the-art deep learning models on a wide variety of linguistic tasks.
AllenNLP is designed to support researchers who want to build novel language understanding models quickly and easily. It is built on top of PyTorch, allowing for dynamic computation graphs, and provides (1) a flexible data API that handles intelligent batching and padding, (2) highlevel abstractions for common operations in working with text, and (3) a modular and extensible experiment framework that makes doing good science easy.
It also includes reference implementations of high quality approaches for both core semantic problems (e.g. semantic role labeling (Palmer et al., 2005)) and language understanding applications (e.g. machine comprehension (Rajpurkar et al., 2016)).
AllenNLP is an ongoing open-source effort maintained by engineers and researchers at the Allen Institute for Artificial Intelligence. AllenNLP requires Python 3.6.1 or later.
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.
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