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Word2Vec

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**Word2Vec**

What is Word2Vec?

Word2Vec is a group of related models used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. Word2Vec takes a large corpus of text as input and produces a vector space, typically of several hundred dimensions, with each unique word in the corpus being assigned a corresponding vector in the space. Word vectors are positioned in the vector space such that words that share common contexts in the corpus are located in close proximity to one another in the space.

Find 328 organizations using Word2Vec on Sumble →

What other technologies are related to Word2Vec?

Word2Vec Competitor Technologies

GloVe
GloVe
GloVe (Global Vectors for Word Representation) is an unsupervised learning algorithm for obtaining vector representations for words. It is a competitor to Word2Vec as both are used for generating word embeddings.
FastText
FastText
FastText is a library for efficient learning of word representations and sentence classification. It is a competitor to Word2Vec as both provide word embeddings, but FastText also handles subword information.
BERT
BERT
BERT (Bidirectional Encoder Representations from Transformers) is a transformer-based model that produces contextualized word embeddings. While more advanced, it competes with Word2Vec in providing word representations.
TF-IDF
TF-IDF (Term Frequency-Inverse Document Frequency) is a technique for weighting terms in a document. It is an alternative way to represent text and can be seen as a competitor to Word2Vec in some simpler NLP tasks.
T5
T5
T5 (Text-to-Text Transfer Transformer) is a transformer-based model that can be used for various NLP tasks, including generating word representations. It is a competitor to Word2Vec in providing contextualized embeddings.
ELMo
ELMo
ELMo (Embeddings from Language Models) is a deep contextualized word representation model. It is a competitor to Word2Vec as both are used for generating word embeddings.
FLAIR
FLAIR
FLAIR is a framework for NLP that includes contextual string embeddings. It's a competitor as it offers alternative word embedding techniques.
Number of organizations that mention technology
328
Word2Vec
104
GloVe
110
FastText
3,157
BERT
110
TF-IDF
574
T5
321
ELMo
151
FLAIR
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Word2Vec Complementary Technologies

Doc2Vec
No summary available
Doc2Vec (also known as Paragraph Vector) is an extension of Word2Vec that learns vector representations for entire documents. It builds upon the principles of Word2Vec, making it complementary.
Gensim
Gensim
Gensim is a Python library for topic modeling, document indexing and similarity retrieval with large corpora. It provides an implementation of Word2Vec, making it complementary.
Number of organizations that mention technology
328
Word2Vec
39
Doc2Vec
650
Gensim
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Which job functions commonly mention Word2Vec?

Machine Learning
0.3% of all Machine Learning jobs mention Word2Vec
View 292 jobs on Sumble
Data Scientist
0.2% of all Data Scientist jobs mention Word2Vec
View 417 jobs on Sumble
AI Engineer
0.1% of all AI Engineer jobs mention Word2Vec
View 57 jobs on Sumble
Research Scientist
22 Research Scientist jobs mention Word2Vec
View 22 jobs on Sumble
Data, Analytics & Machine Learning
778 Data, Analytics & Machine Learning jobs mention Word2Vec
View 778 jobs on Sumble
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Which organizations are mentioning Word2Vec?

Word2Vec
TikTok
Information 
10 team
mention Word2Vec
↗
 1 person
use Word2Vec
↗
Word2Vec
JPMorgan Chase
Finance and Insurance 
6 team
mention Word2Vec
↗
 2 people
use Word2Vec
↗
Word2Vec
Roche
Health Care and Social Assistance 
6 team
mention Word2Vec
↗
 2 people
use Word2Vec
↗
Word2Vec
Walmart
Retail Trade 
5 team
mention Word2Vec
↗
 1 person
use Word2Vec
↗
Word2Vec
PwC
Professional Services 
4 team
mention Word2Vec
↗
 1 person
use Word2Vec
↗
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Whether you're looking to get your foot in the door, find the right person to talk to, or close the deal — accurate, detailed, trustworthy, and timely information about the organization you're selling to is invaluable.

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