Alternatives to Google Cloud Natural Language API logo

Alternatives to Google Cloud Natural Language API

SpaCy, rasa NLU, Gensim, Transformers, and Amazon Comprehend are the most popular alternatives and competitors to Google Cloud Natural Language API.
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What is Google Cloud Natural Language API and what are its top alternatives?

You can use it to extract information about people, places, events and much more, mentioned in text documents, news articles or blog posts. You can use it to understand sentiment about your product on social media or parse intent from customer conversations happening in a call center or a messaging app. You can analyze text uploaded in your request or integrate with your document storage on Google Cloud Storage.
Google Cloud Natural Language API is a tool in the NLP / Sentiment Analysis category of a tech stack.

Top Alternatives to Google Cloud Natural Language API

  • SpaCy
    SpaCy

    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. ...

  • 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. ...

  • 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. ...

  • Transformers
    Transformers

    It provides general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch. ...

  • 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. ...

  • 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. ...

  • Spark NLP
    Spark NLP

    It is a Natural Language Processing library built on top of Apache Spark ML. It provides simple, performant & accurate NLP annotations for machine learning pipelines that scale easily in a distributed environment. It comes with 160+ pretrained pipelines and models in more than 20+ languages. ...

  • AlchemyAPI
    AlchemyAPI

    AlchemyLanguageTM is the world’s most popular natural language processing service. AlchemyVisionTM is the world’s first computer vision service for understanding complex scenes. AlchemyAPI is used by more than 40,000 developers across 36 countries and a wide variety of industries to process over 3 billion texts and images every month. ...

Google Cloud Natural Language API alternatives & related posts

SpaCy logo

SpaCy

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Industrial-Strength Natural Language Processing in Python
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PROS OF SPACY
  • 11
    Speed
  • 2
    No vendor lock-in
CONS OF SPACY
  • 1
    Requires creating a training set and managing training

related SpaCy posts

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
  • 6
    Self Hosted
  • 5
    Docker Image
  • 3
    Comes with rasa_core
  • 1
    Enterprise Ready
CONS OF RASA NLU
  • 4
    No interface provided
  • 1
    Wdfsdf

related rasa NLU posts

Gensim logo

Gensim

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A python library for Topic Modelling
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PROS OF GENSIM
    Be the first to leave a pro
    CONS OF GENSIM
      Be the first to leave a con

      related Gensim posts

      Biswajit Pathak
      Project Manager at Sony · | 6 upvotes · 83.1K 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
      Transformers logo

      Transformers

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      State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0
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      PROS OF TRANSFORMERS
        Be the first to leave a pro
        CONS OF TRANSFORMERS
          Be the first to leave a con

          related Transformers posts

          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

            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
            • 1
              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 · 83.1K 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
            Spark NLP logo

            Spark NLP

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            State of the Art Natural Language Processing
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            PROS OF SPARK NLP
              Be the first to leave a pro
              CONS OF SPARK NLP
                Be the first to leave a con

                related Spark NLP posts

                AlchemyAPI logo

                AlchemyAPI

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                Text and image analysis APIs for processing all unstructured data
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                PROS OF ALCHEMYAPI
                  Be the first to leave a pro
                  CONS OF ALCHEMYAPI
                    Be the first to leave a con

                    related AlchemyAPI posts