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API StatusChangelog
FastText
ByFasttextFasttext

FastText

#15in Text & Language Models
Discussions2
Followers65
OverviewDiscussions2

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.

FastText is a tool in the Text & Language Models category of a tech stack.

Key Features

Train supervised and unsupervised representations of words and sentencesWritten in C++

FastText Pros & Cons

Pros of FastText

  • ✓Simple

Cons of FastText

  • ✗No in-built performance plotting facility or to get it
  • ✗No step by step API access
  • ✗No step by step API support

FastText Alternatives & Comparisons

What are some alternatives to FastText?

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.

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.

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.

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.

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.

FastText Integrations

Python, C++, macOS, C#, GPTCache are some of the popular tools that integrate with FastText. Here's a list of all 5 tools that integrate with FastText.

Python
Python
C++
C++
macOS
macOS
C#
C#
GPTCache
GPTCache

FastText Discussions

Discover why developers choose FastText. Read real-world technical decisions and stack choices from the StackShare community.

Sonali Ajankar
Sonali Ajankar

Mar 22, 2022

Needs adviceonFastTextFastText

I want to encode the news article which has many named entities like person names, organization names, etc. means many vocabulary words are out of a dictionary. My dataset is having around 3 million articles and the average length of an article is 650. What are the benefits or drawbacks if I used FastText word embedding?

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Biswajit Pathak
Biswajit Pathak

Project Manager

Sep 13, 2021

Needs adviceonFastTextFastTextGensimGensimMLflowMLflow

Can you please advise which one to choose FastText Or Gensim, in terms of:

  1. Operability with ML Ops tools such as @{MLflow}|tool:9078|, @{Kubeflow}|tool:8052|, 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.

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