Alternatives to FastText logo

Alternatives to FastText

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

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.
FastText is a tool in the NLP / Sentiment Analysis category of a tech stack.
FastText is an open source tool with 23.2K GitHub stars and 4.4K GitHub forks. Here’s a link to FastText's open source repository on GitHub

Top Alternatives to FastText

  • 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. ...

  • 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. ...

  • 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. ...

  • 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. ...

  • 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. ...

  • Google Cloud Natural Language API

    Google Cloud Natural Language API

    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. ...

  • 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. ...

FastText alternatives & related 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
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    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

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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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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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      Biswajit Pathak
      Project Manager at Sony · | 5 upvotes · 29.4K 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
      SpaCy logo

      SpaCy

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      Industrial-Strength Natural Language Processing in Python
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      PROS OF SPACY
      • 10
        Speed
      • 1
        No vendor lock-in
      CONS OF SPACY
      • 1
        Requires creating a training set and managing training

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      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
      • 8
        Open Source
      • 6
        Self Hosted
      • 5
        Docker Image
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        Comes with rasa_core
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        Enterprise Ready
      CONS OF RASA NLU
      • 3
        No interface provided
      • 1
        Wdfsdf

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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
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        CONS OF AMAZON COMPREHEND
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          Multi-lingual

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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
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          CONS OF TRANSFORMERS
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            Google Cloud Natural Language API logo

            Google Cloud Natural Language API

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            Derive insights from unstructured text using Google machine learning
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            PROS OF GOOGLE CLOUD NATURAL LANGUAGE API
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              CONS OF GOOGLE CLOUD NATURAL LANGUAGE API
              • 2
                Multi-lingual

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              Spark NLP logo

              Spark NLP

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              State of the Art Natural Language Processing
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              PROS OF SPARK NLP
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                CONS OF SPARK NLP
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