Alternatives to SpaCy logo

Alternatives to SpaCy

NLTK, Gensim, Amazon Comprehend, TensorFlow, and Flair are the most popular alternatives and competitors to SpaCy.
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What is SpaCy and what are its top alternatives?

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 29K GitHub stars and 4.3K GitHub forks. Here’s a link to SpaCy's open source repository on GitHub

Top Alternatives to SpaCy

  • NLTK
    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
    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

    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

    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

    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
    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. ...

  • FastText
    FastText

    It is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices. ...

  • JavaScript
    JavaScript

    JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles. ...

SpaCy alternatives & related posts

NLTK logo

NLTK

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It is a leading platform for building Python programs to work with human language data
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PROS OF NLTK
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    CONS OF NLTK
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      related NLTK posts

      Gensim logo

      Gensim

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      A python library for Topic Modelling
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      PROS OF GENSIM
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        CONS OF GENSIM
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          related Gensim posts

          Biswajit Pathak
          Project Manager at Sony · | 6 upvotes · 846K views

          Can you please advise which one to choose FastText Or Gensim, in terms of:

          1. Operability with ML Ops tools such as MLflow, Kubeflow, etc.
          2. Performance
          3. Customization of Intermediate steps
          4. FastText and Gensim both have the same underlying libraries
          5. Use cases each one tries to solve
          6. Unsupervised Vs Supervised dimensions
          7. Ease of Use.

          Please mention any other points that I may have missed here.

          See more
          Amazon Comprehend logo

          Amazon Comprehend

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          Discover insights and relationships in text
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          PROS OF AMAZON COMPREHEND
            Be the first to leave a pro
            CONS OF AMAZON COMPREHEND
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              Multi-lingual

            related Amazon Comprehend posts

            TensorFlow logo

            TensorFlow

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            Open Source Software Library for Machine Intelligence
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            PROS OF TENSORFLOW
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              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
            CONS OF TENSORFLOW
            • 9
              Hard
            • 6
              Hard to debug
            • 2
              Documentation not very helpful

            related TensorFlow posts

            Tom Klein

            Google Analytics is a great tool to analyze your traffic. To debug our software and ask questions, we love to use Postman and Stack Overflow. Google Drive helps our team to share documents. We're able to build our great products through the APIs by Google Maps, CloudFlare, Stripe, PayPal, Twilio, Let's Encrypt, and TensorFlow.

            See more
            Conor Myhrvold
            Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 2.8M views

            Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

            At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

            TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

            Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

            https://eng.uber.com/horovod/

            (Direct GitHub repo: https://github.com/uber/horovod)

            See more
            Flair logo

            Flair

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            A simple framework for natural language processing
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            PROS OF FLAIR
            • 1
              Open Source
            CONS OF FLAIR
              Be the first to leave a con

              related Flair posts

              Stanza logo

              Stanza

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              A Python NLP Library for Many Human Languages
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              PROS OF STANZA
                Be the first to leave a pro
                CONS OF STANZA
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                  related Stanza posts

                  FastText logo

                  FastText

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                  Library for efficient text classification and representation learning
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                  PROS OF FASTTEXT
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                    Simple
                  CONS OF FASTTEXT
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                    No step by step API support
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                    No in-built performance plotting facility or to get it
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                    No step by step API access

                  related FastText posts

                  Biswajit Pathak
                  Project Manager at Sony · | 6 upvotes · 846K views

                  Can you please advise which one to choose FastText Or Gensim, in terms of:

                  1. Operability with ML Ops tools such as MLflow, Kubeflow, etc.
                  2. Performance
                  3. Customization of Intermediate steps
                  4. FastText and Gensim both have the same underlying libraries
                  5. Use cases each one tries to solve
                  6. Unsupervised Vs Supervised dimensions
                  7. Ease of Use.

                  Please mention any other points that I may have missed here.

                  See more
                  JavaScript logo

                  JavaScript

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                  PROS OF JAVASCRIPT
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                    Can be used on frontend/backend
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                    It's everywhere
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                    Lots of great frameworks
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                    Fast
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                    Light weight
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                    Flexible
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                    You can't get a device today that doesn't run js
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                    Non-blocking i/o
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                    Ubiquitousness
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                    Expressive
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                    Extended functionality to web pages
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                    Relatively easy language
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                    Executed on the client side
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                    Relatively fast to the end user
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                    Pure Javascript
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                    Functional programming
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                    Async
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                    Full-stack
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                    Setup is easy
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                    Future Language of The Web
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                    Its everywhere
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                    Because I love functions
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                    JavaScript is the New PHP
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                    Like it or not, JS is part of the web standard
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                    Expansive community
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                    Everyone use it
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                    Can be used in backend, frontend and DB
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                    Easy
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                    Most Popular Language in the World
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                    Powerful
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                    Can be used both as frontend and backend as well
                  • 8
                    For the good parts
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                    No need to use PHP
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                    Easy to hire developers
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                    Agile, packages simple to use
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                    Love-hate relationship
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                    Photoshop has 3 JS runtimes built in
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                    Evolution of C
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                    It's fun
                  • 7
                    Hard not to use
                  • 7
                    Versitile
                  • 7
                    Its fun and fast
                  • 7
                    Nice
                  • 7
                    Popularized Class-Less Architecture & Lambdas
                  • 7
                    Supports lambdas and closures
                  • 6
                    It let's me use Babel & Typescript
                  • 6
                    Can be used on frontend/backend/Mobile/create PRO Ui
                  • 6
                    1.6K Can be used on frontend/backend
                  • 6
                    Client side JS uses the visitors CPU to save Server Res
                  • 6
                    Easy to make something
                  • 5
                    Clojurescript
                  • 5
                    Promise relationship
                  • 5
                    Stockholm Syndrome
                  • 5
                    Function expressions are useful for callbacks
                  • 5
                    Scope manipulation
                  • 5
                    Everywhere
                  • 5
                    Client processing
                  • 5
                    What to add
                  • 4
                    Because it is so simple and lightweight
                  • 4
                    Only Programming language on browser
                  • 1
                    Test
                  • 1
                    Hard to learn
                  • 1
                    Test2
                  • 1
                    Not the best
                  • 1
                    Easy to understand
                  • 1
                    Subskill #4
                  • 1
                    Easy to learn
                  • 0
                    Hard 彤
                  CONS OF JAVASCRIPT
                  • 22
                    A constant moving target, too much churn
                  • 20
                    Horribly inconsistent
                  • 15
                    Javascript is the New PHP
                  • 9
                    No ability to monitor memory utilitization
                  • 8
                    Shows Zero output in case of ANY error
                  • 7
                    Thinks strange results are better than errors
                  • 6
                    Can be ugly
                  • 3
                    No GitHub
                  • 2
                    Slow

                  related JavaScript posts

                  Zach Holman

                  Oof. I have truly hated JavaScript for a long time. Like, for over twenty years now. Like, since the Clinton administration. It's always been a nightmare to deal with all of the aspects of that silly language.

                  But wowza, things have changed. Tooling is just way, way better. I'm primarily web-oriented, and using React and Apollo together the past few years really opened my eyes to building rich apps. And I deeply apologize for using the phrase rich apps; I don't think I've ever said such Enterprisey words before.

                  But yeah, things are different now. I still love Rails, and still use it for a lot of apps I build. But it's that silly rich apps phrase that's the problem. Users have way more comprehensive expectations than they did even five years ago, and the JS community does a good job at building tools and tech that tackle the problems of making heavy, complicated UI and frontend work.

                  Obviously there's a lot of things happening here, so just saying "JavaScript isn't terrible" might encompass a huge amount of libraries and frameworks. But if you're like me, yeah, give things another shot- I'm somehow not hating on JavaScript anymore and... gulp... I kinda love it.

                  See more
                  Conor Myhrvold
                  Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 10.8M views

                  How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

                  Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

                  Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

                  https://eng.uber.com/distributed-tracing/

                  (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

                  Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

                  See more