SpaCy

spaCy is designed to help you do real work -- to build real products, or gather real insights. The library respects your time, and tries to avoid wasting it. It's easy to install, and its API is simple and productive. We like to think of spaCy as the Ruby on Rails of Natural Language Processing.

spaCy excels at large-scale information extraction tasks. It's written from the ground up in carefully memory-managed Cython. Independent research in 2015 found spaCy to be the fastest in the world. If your application needs to process entire web dumps, spaCy is the library you want to be using.

spaCy is the besta way to prepare text for deep learning. It interoperates seamlessly with TensorFlow, PyTorch, scikit-learn, Gensim and the rest of Python's awesome AI ecosystem. With spaCy, you can easily construct linguistically sophisticated statistical models for a variety of NLP problems.

Learn more from small training corpora by initializing your models with knowledge from raw text. The new pretrain command teaches spaCy's CNN model to predict words based on their context, producing representations of words in contexts. If you've seen Google's BERT system or fast.ai's ULMFiT, spaCy's pretraining is similar - but much more efficient. It's still experimental, but users are already reporting good results, so give it a try!.

spaCy is designed to help you do real work -- to build real products, or gather real insights. The library respects your time, and tries to avoid wasting it. It's easy to install, and its API is simple and productive. We like to think of spaCy as the Ruby on Rails of Natural Language ProcessingNatural Language Processing.

spaCy is designed to help you do real work -- to build real products, or gather real insights. The library respects your time, and tries to avoid wasting it. It's easy to install, and its API is simple and productive. We like to think of spaCy as the RubyRuby on Rails of Natural Language Processing.

spaCy is the best way to prepare text for deep learning. It interoperates seamlessly with TensorFlowTensorFlow, PyTorch, scikit-learn, Gensim and the rest of Python's awesome AI ecosystem. With spaCy, you can easily construct linguistically sophisticated statistical models for a variety of NLP problems.

spaCy is designed to help you do real work -- to build real products, or gather real insights. The library respects your time, and tries to avoid wasting it. It's easy to install, and its API is simple and productive. We like to think of spaCy as the Ruby on Rails of Natural Language Processing.

spaCy excels at large-scale information extraction tasks. It's written from the ground up in carefully memory-managed Cython. Independent research in 2015 found spaCy to be the fastest in the world. If your application needs to process entire web dumps, spaCy is the library you want to be using.

spaCy is the best way to prepare text for deep learning. It interoperates seamlessly with TensorFlow, PyTorch, scikit-learn, Gensim and the rest of Python's awesome AI ecosystem. With spaCy, you can easily construct linguistically sophisticated statistical models for a variety of NLP problems.

Learn more from small training corpora by initializing your models with knowledge from raw text. The new pretrain command teaches spaCy's CNN model to predict words based on their context, producing representations of words in contexts. If you've seen Google's BERT system or fast.ai's ULMFiT, spaCy's pretraining is similar - but much more efficient. It's still experimental, but users are already reporting good results, so give it a try!

In 2015, independent researchers from Emory University and Yahoo! Labs showed that spaCy offered the fastest syntactic parser in the world and that its accuracy was within 1% of the best available (Choi et al., 2015). spaCy v2.0, released in 2017, is more accurate than any of the systems Choi et al. evaluated.