embedding
a set of numbers that a computer uses to represent the meaning or features of something
A numerical representation, usually a vector, of text, images, users, or other data, designed to capture important features or relationships for machine learning.
putting something inside another thing so that it stays there
The act or process of putting or fixing something firmly inside a surrounding material, object, or place.
putting online content, such as a video or map, inside another page
The act or method of inserting media, code, or content from one source so that it appears inside another web page, app, or document.
making something become a normal part of a larger system or group
The process of making someone or something part of a larger group, system, routine, culture, or context.
a mathematical way of putting one structure inside another while keeping its important properties
In mathematics, a map from one structure into another that preserves specified relations, operations, or properties.
- Base: embedding
- Plural: embedding, embeddings
embedding dimension
1number of vector values
how many numbers are in each embedding
- We set the embedding dimension to 384 to reduce storage costs.
- A larger embedding dimension may capture more information but uses more memory.
- The database requires all vectors to have the same embedding dimension.
- The embedding dimension is 128.
- A bigger embedding dimension needs more memory.
embedding layer
1neural layer producing vectors
a part of a neural network that changes tokens or IDs into number vectors
- The first part of the network is an embedding layer that turns token IDs into vectors.
- We trained the embedding layer together with the rest of the model.
- A larger embedding layer can improve accuracy but also increases memory use.
- The embedding layer turns words into vectors.
- The model has an embedding layer at the start.
embedding model
1model that creates embeddings
a model that turns information into number vectors
- We used an embedding model to convert customer questions into vectors.
- The new embedding model performs better on multilingual search.
- Choose an embedding model that matches the type of data you need to compare.
- The embedding model changes text into numbers.
- Our app uses an embedding model for search.
embedding space
1space containing embeddings
the space where embeddings are placed so a computer can compare them
- In the embedding space, articles about the same topic appear close to each other.
- The system searches for nearby items in the embedding space.
- Visualizing the embedding space can reveal clusters of related products.
- Similar words are close in embedding space.
- The app finds nearby points in the embedding space.
graph embedding
1vector representation of graphs
numbers that represent a network or parts of a network
- Graph embeddings can help predict links in a social network.
- The researchers used a graph embedding to compare molecular structures.
- Each node has a graph embedding that reflects its position in the network.
- A graph embedding can represent a social network.
- The model creates graph embeddings for the nodes.
text embedding
1vector representation of text
numbers that represent a piece of writing for a computer
- The app creates a text embedding for each article and stores it in a database.
- Semantic search works by comparing the text embedding of a query with the embeddings of documents.
- A good text embedding can capture similarity even when two sentences use different words.
- The system makes a text embedding for each question.
- Similar texts have similar text embeddings.
video embedding
1placing video in a page
showing a video inside a page instead of only linking to it
- The course website supports video embedding from several platforms.
- Video embedding was turned off, so the clip could only be watched on the original site.
- The editor makes video embedding easy for nontechnical users.
- Video embedding lets students watch the clip on the lesson page.
- This blog allows video embedding.
word embedding
1vector representation of a word
numbers that represent a word so a computer can compare it with other words
- The model learned word embeddings from millions of sentences.
- Words with similar meanings often have word embeddings that are close together.
- Early word embedding methods helped improve many natural-language applications.
- The word embedding for cat is close to the word embedding for dog.
- We used word embeddings in the language model.