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71 lines
3.9 KiB
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71 lines
3.9 KiB
Plaintext
//- 💫 DOCS > USAGE > FACTS & FIGURES > OTHER LIBRARIES
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p
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| Data scientists, researchers and machine learning engineers have
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| converged on Python as the language for AI. This gives developers a rich
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| ecosystem of NLP libraries to work with. Here's how we think the pieces
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| fit together.
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//-+aside("Using spaCy with other libraries")
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| For details on how to use spaCy together with popular machine learning
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| libraries like TensorFlow, Keras or PyTorch, see the
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| #[+a("/usage/deep-learning") usage guide on deep learning].
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+infobox
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+infobox-logos(["nltk", 80, 25, "http://nltk.org"])
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| #[+label-inline NLTK] offers some of the same functionality as spaCy.
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| Although originally developed for teaching and research, its longevity
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| and stability has resulted in a large number of industrial users. It's
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| the main alternative to spaCy for tokenization and sentence segmentation.
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| In comparison to spaCy, NLTK takes a much more "broad church" approach –
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| so it has some functions that spaCy doesn't provide, at the expense of a
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| bit more clutter to sift through. spaCy is also much more
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| performance-focussed than NLTK: where the two libraries provide the same
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| functionality, spaCy's implementation will usually be faster and more
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| accurate.
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+infobox
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+infobox-logos(["gensim", 40, 40, "https://radimrehurek.com/gensim/"])
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| #[+label-inline Gensim] provides unsupervised text modelling algorithms.
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| Although Gensim isn't a runtime dependency of spaCy, we use it to train
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| word vectors. There's almost no overlap between the libraries – the two
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| work together.
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+infobox
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+infobox-logos(["tensorflow", 35, 42, "https://www.tensorflow.org"], ["keras", 45, 45, "https://www.keras.io"])
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| #[+label-inline Tensorflow / Keras] is the most popular deep learning library.
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| spaCy provides efficient and powerful feature extraction functionality,
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| that can be used as a pre-process to any deep learning library. You can
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| also use Tensorflow and Keras to create spaCy pipeline components, to add
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| annotations to the #[code Doc] object.
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+infobox
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+infobox-logos(["scikitlearn", 90, 44, "http://scikit-learn.org"])
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| #[+label-inline scikit-learn] features a number of useful NLP functions,
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| especially for solving text classification problems using linear models
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| with bag-of-words features. If you know you need exactly that, it might
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| be better to use scikit-learn's built-in pipeline directly. However, if
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| you want to extract more detailed features, using part-of-speech tags,
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| named entity labels, or string transformations, you can use spaCy as a
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| pre-process in your classification system. scikit-learn also provides a
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| lot of experiment management and evaluation utilities that people use
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| alongside spaCy.
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+infobox
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+infobox-logos(["pytorch", 100, 48, "http://pytorch.org"], ["dynet", 80, 34, "http://dynet.readthedocs.io/"], ["chainer", 80, 43, "http://chainer.org"])
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| #[+label-inline PyTorch, DyNet and Chainer] are dynamic neural network
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| libraries, which can be much easier to work with for NLP. Outside of
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| Google, there's a general shift among NLP researchers to both DyNet and
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| Pytorch. spaCy is the front-end of choice for PyTorch's
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| #[code torch.text] extension. You can use any of these libraries to
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| create spaCy pipeline components, to add annotations to the #[code Doc]
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| object.
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+infobox
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+infobox-logos(["allennlp", 124, 22, "http://allennlp.org"])
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| #[+label-inline AllenNLP] is a new library designed to accelerate NLP
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| research, by providing a framework that supports modern deep learning
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| workflows for cutting-edge language understanding problems. AllenNLP uses
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| spaCy as a preprocessing component. You can also use AllenNLP to develop
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| spaCy pipeline components, to add annotations to the #[code Doc] object.
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