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 30.3K GitHub stars and 4.4K 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. ...

  • Postman
    Postman

    It is the only complete API development environment, used by nearly five million developers and more than 100,000 companies worldwide. ...

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 · 854.4K 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
            • 2
              Multi-lingual

            related Amazon Comprehend posts

            TensorFlow logo

            TensorFlow

            3.8K
            3.5K
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            Open Source Software Library for Machine Intelligence
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            PROS OF TENSORFLOW
            • 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
            CONS OF TENSORFLOW
            • 9
              Hard
            • 6
              Hard to debug
            • 2
              Documentation not very helpful

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            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
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              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
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                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
                  • 1
                    Simple
                  CONS OF FASTTEXT
                  • 1
                    No step by step API support
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                    No in-built performance plotting facility or to get it
                  • 1
                    No step by step API access

                  related FastText posts

                  Biswajit Pathak
                  Project Manager at Sony · | 6 upvotes · 854.4K 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
                  Postman logo

                  Postman

                  94.5K
                  81K
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                  Only complete API development environment
                  94.5K
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                    Makes developing rest api's easy peasy
                  • 156
                    Easy setup, looks good
                  • 144
                    The best api workflow out there
                  • 53
                    It's the best
                  • 53
                    History feature
                  • 44
                    Adds real value to my workflow
                  • 43
                    Great interface that magically predicts your needs
                  • 35
                    The best in class app
                  • 12
                    Can save and share script
                  • 10
                    Fully featured without looking cluttered
                  • 8
                    Collections
                  • 8
                    Option to run scrips
                  • 8
                    Global/Environment Variables
                  • 7
                    Shareable Collections
                  • 7
                    Dead simple and useful. Excellent
                  • 7
                    Dark theme easy on the eyes
                  • 6
                    Awesome customer support
                  • 6
                    Great integration with newman
                  • 5
                    Documentation
                  • 5
                    Simple
                  • 5
                    The test script is useful
                  • 4
                    Saves responses
                  • 4
                    This has simplified my testing significantly
                  • 4
                    Makes testing API's as easy as 1,2,3
                  • 4
                    Easy as pie
                  • 3
                    API-network
                  • 3
                    I'd recommend it to everyone who works with apis
                  • 3
                    Mocking API calls with predefined response
                  • 2
                    Now supports GraphQL
                  • 2
                    Postman Runner CI Integration
                  • 2
                    Easy to setup, test and provides test storage
                  • 2
                    Continuous integration using newman
                  • 2
                    Pre-request Script and Test attributes are invaluable
                  • 2
                    Runner
                  • 2
                    Graph
                  • 1
                    <a href="http://fixbit.com/">useful tool</a>
                  CONS OF POSTMAN
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                    Stores credentials in HTTP
                  • 9
                    Bloated features and UI
                  • 8
                    Cumbersome to switch authentication tokens
                  • 7
                    Poor GraphQL support
                  • 5
                    Expensive
                  • 3
                    Not free after 5 users
                  • 3
                    Can't prompt for per-request variables
                  • 1
                    Import swagger
                  • 1
                    Support websocket
                  • 1
                    Import curl

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                  Noah Zoschke
                  Engineering Manager at Segment · | 30 upvotes · 3M views

                  We just launched the Segment Config API (try it out for yourself here) — a set of public REST APIs that enable you to manage your Segment configuration. A public API is only as good as its #documentation. For the API reference doc we are using Postman.

                  Postman is an “API development environment”. You download the desktop app, and build API requests by URL and payload. Over time you can build up a set of requests and organize them into a “Postman Collection”. You can generalize a collection with “collection variables”. This allows you to parameterize things like username, password and workspace_name so a user can fill their own values in before making an API call. This makes it possible to use Postman for one-off API tasks instead of writing code.

                  Then you can add Markdown content to the entire collection, a folder of related methods, and/or every API method to explain how the APIs work. You can publish a collection and easily share it with a URL.

                  This turns Postman from a personal #API utility to full-blown public interactive API documentation. The result is a great looking web page with all the API calls, docs and sample requests and responses in one place. Check out the results here.

                  Postman’s powers don’t end here. You can automate Postman with “test scripts” and have it periodically run a collection scripts as “monitors”. We now have #QA around all the APIs in public docs to make sure they are always correct

                  Along the way we tried other techniques for documenting APIs like ReadMe.io or Swagger UI. These required a lot of effort to customize.

                  Writing and maintaining a Postman collection takes some work, but the resulting documentation site, interactivity and API testing tools are well worth it.

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                  Simon Reymann
                  Senior Fullstack Developer at QUANTUSflow Software GmbH · | 27 upvotes · 5.1M views

                  Our whole Node.js backend stack consists of the following tools:

                  • Lerna as a tool for multi package and multi repository management
                  • npm as package manager
                  • NestJS as Node.js framework
                  • TypeScript as programming language
                  • ExpressJS as web server
                  • Swagger UI for visualizing and interacting with the API’s resources
                  • Postman as a tool for API development
                  • TypeORM as object relational mapping layer
                  • JSON Web Token for access token management

                  The main reason we have chosen Node.js over PHP is related to the following artifacts:

                  • Made for the web and widely in use: Node.js is a software platform for developing server-side network services. Well-known projects that rely on Node.js include the blogging software Ghost, the project management tool Trello and the operating system WebOS. Node.js requires the JavaScript runtime environment V8, which was specially developed by Google for the popular Chrome browser. This guarantees a very resource-saving architecture, which qualifies Node.js especially for the operation of a web server. Ryan Dahl, the developer of Node.js, released the first stable version on May 27, 2009. He developed Node.js out of dissatisfaction with the possibilities that JavaScript offered at the time. The basic functionality of Node.js has been mapped with JavaScript since the first version, which can be expanded with a large number of different modules. The current package managers (npm or Yarn) for Node.js know more than 1,000,000 of these modules.
                  • Fast server-side solutions: Node.js adopts the JavaScript "event-loop" to create non-blocking I/O applications that conveniently serve simultaneous events. With the standard available asynchronous processing within JavaScript/TypeScript, highly scalable, server-side solutions can be realized. The efficient use of the CPU and the RAM is maximized and more simultaneous requests can be processed than with conventional multi-thread servers.
                  • A language along the entire stack: Widely used frameworks such as React or AngularJS or Vue.js, which we prefer, are written in JavaScript/TypeScript. If Node.js is now used on the server side, you can use all the advantages of a uniform script language throughout the entire application development. The same language in the back- and frontend simplifies the maintenance of the application and also the coordination within the development team.
                  • Flexibility: Node.js sets very few strict dependencies, rules and guidelines and thus grants a high degree of flexibility in application development. There are no strict conventions so that the appropriate architecture, design structures, modules and features can be freely selected for the development.
                  See more