Alternatives to Gensim logo

Alternatives to Gensim

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

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.
Gensim is a tool in the NLP / Sentiment Analysis category of a tech stack.
Gensim is an open source tool with 13.4K GitHub stars and 4.3K GitHub forks. Here’s a link to Gensim's open source repository on GitHub

Top Alternatives to Gensim

  • 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. ...

  • Keras
    Keras

    Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/ ...

  • 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. ...

  • 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. ...

  • 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. ...

  • 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. ...

  • 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. ...

Gensim 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
    Be the first to leave a pro
    CONS OF NLTK
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      Keras logo

      Keras

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      Deep Learning library for Theano and TensorFlow
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      PROS OF KERAS
      • 7
        Easy and fast NN prototyping
      • 7
        Quality Documentation
      • 6
        Supports Tensorflow and Theano backends
      CONS OF KERAS
      • 4
        Hard to debug

      related Keras posts

      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 1.4M 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

      I am going to send my website to a Venture Capitalist for inspection. If I succeed, I will get funding for my StartUp! This website is based on Django and Uses Keras and TensorFlow model to predict medical imaging. Should I use Heroku or PythonAnywhere to deploy my website ?? Best Regards, Adarsh.

      See more
      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

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      Biswajit Pathak
      Project Manager at Sony · | 6 upvotes · 112.3K 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
      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

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      TensorFlow logo

      TensorFlow

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      Open Source Software Library for Machine Intelligence
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      PROS OF TENSORFLOW
      • 28
        High Performance
      • 17
        Connect Research and Production
      • 14
        Deep Flexibility
      • 11
        Auto-Differentiation
      • 10
        True Portability
      • 4
        High level abstraction
      • 4
        Easy to use
      • 4
        Powerful
      CONS OF TENSORFLOW
      • 9
        Hard
      • 6
        Hard to debug
      • 1
        Documentation not very helpful

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      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 1.4M 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.

      !

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

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      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

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          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

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