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 21.8K GitHub stars and 3.6K 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. ...

  • rasa NLU

    rasa NLU

    rasa NLU (Natural Language Understanding) is a tool for intent classification and entity extraction. You can think of rasa NLU as a set of high level APIs for building your own language parser using existing NLP and ML libraries. ...

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 · | 5 upvotes · 25.3K 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
            • 16
              Connect Research and Production
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              Deep Flexibility
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              True Portability
            • 9
              Auto-Differentiation
            • 2
              Easy to use
            • 2
              High level abstraction
            • 1
              Powerful
            CONS OF TENSORFLOW
            • 9
              Hard
            • 6
              Hard to debug
            • 1
              Documentation not very helpful

            related TensorFlow posts

            Conor Myhrvold
            Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 1.3M 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

            In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.

            Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.

            !

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            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
                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 · | 5 upvotes · 25.3K 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
                  rasa NLU logo

                  rasa NLU

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                  Conversational AI platform, for personalized conversations at scale
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                  PROS OF RASA NLU
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                    Open Source
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                    Self Hosted
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                    Docker Image
                  • 3
                    Comes with rasa_core
                  • 1
                    Enterprise Ready
                  CONS OF RASA NLU
                  • 3
                    No interface provided
                  • 1
                    Wdfsdf

                  related rasa NLU posts