word2vec

word2vec

Google
+
+

Related Products

  • Vertex AI
    961 Ratings
    Visit Website
  • NINJIO
    415 Ratings
    Visit Website
  • Expedience Software
    33 Ratings
    Visit Website
  • Google Cloud Speech-to-Text
    355 Ratings
    Visit Website
  • Adaptive Security
    87 Ratings
    Visit Website
  • FrontFace
    49 Ratings
    Visit Website
  • Docmosis
    48 Ratings
    Visit Website
  • Bluehost
    29,377 Ratings
    Visit Website
  • LM-Kit.NET
    26 Ratings
    Visit Website
  • ClickLearn
    67 Ratings
    Visit Website

About

fastText is an open source, free, and lightweight library developed by Facebook's AI Research (FAIR) lab for efficient learning of word representations and text classification. It supports both unsupervised learning of word vectors and supervised learning for text classification tasks. A key feature of fastText is its ability to capture subword information by representing words as bags of character n-grams, which enhances the handling of morphologically rich languages and out-of-vocabulary words. The library is optimized for performance and capable of training on large datasets quickly, and the resulting models can be reduced in size for deployment on mobile devices. Pre-trained word vectors are available for 157 languages, trained on Common Crawl and Wikipedia data, and can be downloaded for immediate use. fastText also offers aligned word vectors for 44 languages, facilitating cross-lingual natural language processing tasks.

About

Word2Vec is a neural network-based technique for learning word embeddings, developed by researchers at Google. It transforms words into continuous vector representations in a multi-dimensional space, capturing semantic relationships based on context. Word2Vec uses two main architectures: Skip-gram, which predicts surrounding words given a target word, and Continuous Bag-of-Words (CBOW), which predicts a target word based on surrounding words. By training on large text corpora, Word2Vec generates word embeddings where similar words are positioned closely, enabling tasks like semantic similarity, analogy solving, and text clustering. The model was influential in advancing NLP by introducing efficient training techniques such as hierarchical softmax and negative sampling. Though newer embedding models like BERT and Transformer-based methods have surpassed it in complexity and performance, Word2Vec remains a foundational method in natural language processing and machine learning research.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Language processing practitioners and researchers requiring a tool for learning word embeddings and building text classifiers

Audience

Researchers, data scientists, and developers working in natural language processing (NLP) and machine learning who need efficient word embeddings for text analysis and semantic understanding

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

No images available

Pricing

Free
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

fastText
fasttext.cc/

Company Information

Google
Founded: 1998
United States
code.google.com/archive/p/word2vec/

Alternatives

Gensim

Gensim

Radim Řehůřek

Alternatives

GloVe

GloVe

Stanford NLP
Gensim

Gensim

Radim Řehůřek
word2vec

word2vec

Google
GloVe

GloVe

Stanford NLP
LexVec

LexVec

Alexandre Salle

Categories

Categories

Integrations

Gensim
JavaScript
Python
WebAssembly

Integrations

Gensim
JavaScript
Python
WebAssembly
Claim fastText and update features and information
Claim fastText and update features and information
Claim word2vec and update features and information
Claim word2vec and update features and information