What is SpaCy?
It is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. It comes with pre-trained statistical models and word vectors, and currently supports tokenization for 49+ languages.
SpaCy is a tool in the NLP / Sentiment Analysis category of a tech stack.
SpaCy is an open source tool with 30.1K GitHub stars and 4.4K GitHub forks. Here’s a link to SpaCy's open source repository on GitHub
Who uses SpaCy?
Companies
51 companies reportedly use SpaCy in their tech stacks, including Shelf, kraken, and Compile Inc.
Developers
160 developers on StackShare have stated that they use SpaCy.
SpaCy Integrations
Pros of SpaCy
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SpaCy Alternatives & Comparisons
What are some alternatives to SpaCy?
NLTK
It is a suite of libraries and programs for symbolic and statistical natural language processing for English written in the Python programming language.
Gensim
It is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.
Amazon Comprehend
Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to discover insights from text. Amazon Comprehend provides Keyphrase Extraction, Sentiment Analysis, Entity Recognition, Topic Modeling, and Language Detection APIs so you can easily integrate natural language processing into your applications.
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
Flair
Flair allows you to apply our state-of-the-art natural language processing (NLP) models to your text, such as named entity recognition (NER), part-of-speech tagging (PoS), sense disambiguation and classification.
