word2vec

word2vec

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About

The Universal Sentence Encoder (USE) encodes text into high-dimensional vectors that can be utilized for tasks such as text classification, semantic similarity, and clustering. It offers two model variants: one based on the Transformer architecture and another on Deep Averaging Network (DAN), allowing a balance between accuracy and computational efficiency. The Transformer-based model captures context-sensitive embeddings by processing the entire input sequence simultaneously, while the DAN-based model computes embeddings by averaging word embeddings, followed by a feedforward neural network. These embeddings facilitate efficient semantic similarity calculations and enhance performance on downstream tasks with minimal supervised training data. The USE is accessible via TensorFlow Hub, enabling seamless integration into various applications.

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

Data scientists and machine learning engineers seeking a tool to optimize their natural language processing models with robust sentence embeddings

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

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Pricing

No information available.
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

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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

Tensorflow
Founded: 2015
United States
www.tensorflow.org/hub/tutorials/semantic_similarity_with_tf_hub_universal_encoder

Company Information

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

Alternatives

word2vec

word2vec

Google

Alternatives

Gensim

Gensim

Radim Řehůřek
Exa

Exa

Exa.ai
GloVe

GloVe

Stanford NLP
txtai

txtai

NeuML
LexVec

LexVec

Alexandre Salle

Categories

Categories

Integrations

Gensim
Google Colab
TensorFlow

Integrations

Gensim
Google Colab
TensorFlow
Claim Universal Sentence Encoder and update features and information
Claim Universal Sentence Encoder and update features and information
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Claim word2vec and update features and information