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FastText

27
53
+ 1
1
SpaCy

172
216
+ 1
11
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SpaCy vs FastText: What are the differences?

SpaCy: Industrial-Strength Natural Language Processing in Python. 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; FastText: Library for efficient text classification and representation learning. 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 and FastText can be categorized as "NLP / Sentiment Analysis" tools.

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Pros of FastText
Pros of SpaCy
  • 1
    Simple
  • 10
    Speed
  • 1
    No vendor lock-in

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Cons of FastText
Cons of SpaCy
  • 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
  • 1
    Requires creating a training set and managing training

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What is 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.

What is 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.

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What companies use FastText?
What companies use SpaCy?
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What tools integrate with FastText?
What tools integrate with SpaCy?
What are some alternatives to FastText and SpaCy?
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
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.
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 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
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.
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