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SpaCy

209
277
+ 1
14
TensorFlow

3.3K
3.3K
+ 1
108
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Pros of SpaCy
Pros of TensorFlow
  • 12
    Speed
  • 2
    No vendor lock-in
  • 32
    High Performance
  • 19
    Connect Research and Production
  • 16
    Deep Flexibility
  • 12
    Auto-Differentiation
  • 11
    True Portability
  • 6
    Easy to use
  • 5
    High level abstraction
  • 5
    Powerful
  • 2
    Is orange

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Cons of SpaCy
Cons of TensorFlow
  • 1
    Requires creating a training set and managing training
  • 9
    Hard
  • 6
    Hard to debug
  • 2
    Documentation not very helpful

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What companies use SpaCy?
What companies use TensorFlow?
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What tools integrate with SpaCy?
What tools integrate with TensorFlow?

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What are some alternatives to SpaCy and TensorFlow?
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.
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.
Stanza
It is a Python natural language analysis package. It contains tools, which can be used in a pipeline, to convert a string containing human language text into lists of sentences and words, to generate base forms of those words, their parts of speech and morphological features, to give a syntactic structure dependency parse, and to recognize named entities. The toolkit is designed to be parallel among more than 70 languages, using the Universal Dependencies formalism.
See all alternatives