Embeddinghub

Embeddinghub

Featureform
GloVe

GloVe

Stanford NLP
+
+

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About

Operationalize your embeddings with one simple tool. Experience a comprehensive database designed to provide embedding functionality that, until now, required multiple platforms. Elevate your machine learning quickly and painlessly through Embeddinghub. Embeddings are dense, numerical representations of real-world objects and relationships, expressed as vectors. They are often created by first defining a supervised machine learning problem, known as a "surrogate problem." Embeddings intend to capture the semantics of the inputs they were derived from, subsequently getting shared and reused for improved learning across machine learning models. Embeddinghub lets you achieve this in a streamlined, intuitive way.

About

GloVe (Global Vectors for Word Representation) is an unsupervised learning algorithm developed by the Stanford NLP Group to obtain vector representations for words. It constructs word embeddings by analyzing global word-word co-occurrence statistics from a given corpus, resulting in vector spaces where the geometric relationships reflect semantic similarities and differences among words. A notable feature of GloVe is its ability to capture linear substructures within the word vector space, enabling vector arithmetic to express relationships. The model is trained on the non-zero entries of a global word-word co-occurrence matrix, which records how frequently pairs of words appear together in a corpus. This approach efficiently leverages statistical information by focusing on significant co-occurrences, leading to meaningful word representations. Pre-trained word vectors are available for various corpora, including Wikipedia 2014.

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

Machine learning developers interested in a powerful vector/embeddings database

Audience

Data scientists in search of a solution to enhance their natural language processing models with word embeddings that capture global statistical information from large text corpora

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

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

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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

Featureform
Founded: 2019
United States
www.featureform.com/embeddinghub

Company Information

Stanford NLP
United States
nlp.stanford.edu/projects/glove/

Alternatives

Alternatives

Gensim

Gensim

Radim Řehůřek
LexVec

LexVec

Alexandre Salle
word2vec

word2vec

Google
txtai

txtai

NeuML

Categories

Categories

Integrations

No info available.

Integrations

No info available.
Claim Embeddinghub and update features and information
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