Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
The Gemini Embedding's inaugural text model, known as gemini-embedding-001, is now officially available through the Gemini API and Gemini Enterprise Agent Platform, having maintained its leading position on the Massive Text Embedding Benchmark Multilingual leaderboard since its experimental introduction in March, attributed to its outstanding capabilities in retrieval, classification, and various embedding tasks, surpassing both traditional Google models and those from external companies. This highly adaptable model accommodates more than 100 languages and has a maximum input capacity of 2,048 tokens, utilizing the innovative Matryoshka Representation Learning (MRL) method, which allows developers to select output dimensions of 3072, 1536, or 768 to ensure the best balance of quality, performance, and storage efficiency. Developers are able to utilize it via the familiar embed_content endpoint in the Gemini API.
Description
Word2Vec is a technique developed by Google researchers that employs a neural network to create word embeddings. This method converts words into continuous vector forms within a multi-dimensional space, effectively capturing semantic relationships derived from context. It primarily operates through two architectures: Skip-gram, which forecasts surrounding words based on a given target word, and Continuous Bag-of-Words (CBOW), which predicts a target word from its context. By utilizing extensive text corpora for training, Word2Vec produces embeddings that position similar words in proximity, facilitating various tasks such as determining semantic similarity, solving analogies, and clustering text. This model significantly contributed to the field of natural language processing by introducing innovative training strategies like hierarchical softmax and negative sampling. Although more advanced embedding models, including BERT and Transformer-based approaches, have since outperformed Word2Vec in terms of complexity and efficacy, it continues to serve as a crucial foundational technique in natural language processing and machine learning research. Its influence on the development of subsequent models cannot be overstated, as it laid the groundwork for understanding word relationships in deeper ways.
API Access
Has API
Yes
API Access
Has API
No
Screenshots View All
No images available
Integrations
Gemini
Yes
Gemini Enterprise
Yes
Gemini Enterprise Agent Platform
Yes
Gensim
No
Google AI Studio
Yes
Mimasa AI
Yes
Python
Yes
Integrations
Gemini
No
Gemini Enterprise
No
Gemini Enterprise Agent Platform
No
Gensim
Yes
Google AI Studio
No
Mimasa AI
No
Python
No
Pricing Details
$0.15 per 1M input tokens
Free Trial
No
Free Version
No
Pricing Details
Free
Open source
Free Trial
No
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
No
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Founded
1998
Country
United States
Website
developers.googleblog.com/en/gemini-embedding-available-gemini-api/
Vendor Details
Company Name
Founded
1998
Country
United States
Website
code.google.com/archive/p/word2vec/