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Learn MorePros of SpaCy
Pros of TensorFlow
Pros of SpaCy
- Speed12
- No vendor lock-in2
Pros of TensorFlow
- High Performance32
- Connect Research and Production19
- Deep Flexibility16
- Auto-Differentiation12
- True Portability11
- Easy to use6
- High level abstraction5
- Powerful5
- Is orange2
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Cons of SpaCy
Cons of TensorFlow
Cons of SpaCy
- Requires creating a training set and managing training1
Cons of TensorFlow
- Hard9
- Hard to debug6
- Documentation not very helpful2
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What companies use SpaCy?
What companies use TensorFlow?
What companies use SpaCy?
What companies use TensorFlow?
See which teams inside your own company are using SpaCy or TensorFlow.
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What tools integrate with SpaCy?
What tools integrate with TensorFlow?
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.