Salesforce AI Research’s SFR-Embedding

At Salesforce AI Research we understand the significance of having a high performing text-embedding model and the pivotal role it plays in enabling a better representation of textual data, and capturing semantic relationships and nuances effectively. This is why we released SFR-Embedding, the top performing text-embedding model (when compared to other models).

TechBlend

3/6/20232 min read

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Text-embedding models (also known as vector encoders) are like translators for computers—they take words, phrases, or even entire paragraphs of text and convert them into a format that computers can understand and work with more easily.

Think of each word or phrase in a text such as the query above “Will AI ever be able to understand my cat’s meows and translate them to English?” This is unstructured data for the computer - meaning it doesn't have a pre-defined organization or format. A text-embedding model takes this data (each word or phrase) and turns it into a set of numbers (vectors) that capture its meaning and context. These vectors are formats easily interpretable and manipulable by computers. They enable the computer to convert the data into a structured form, wherein information is organized into several vectors for straightforward analysis and interpretation. For example, instead of saying "cat," the model might turn it into a series of numbers that are similar to other numbers representing what a cat looks like, how it behaves, and its relationship with other words around it. It’s a way for computers to have associations and they compare these sets of numbers to understand how similar or different words are in meaning.

Text-embedding models significantly enhance retrieval performance, which measures how well the database can find relevant information based on a user's query. This enhancement is due to the vectors enabling more accurate matching of queries with relevant documents. By comparing the vectors of the query and the documents in the database, systems can quickly identify the most relevant information, making searches faster and more efficient. This process will initially analyze the sentiment of your query, find similar documents that may address the query, and then generate an answer for you.

At Salesforce's AI Research team, we recognize the vital role that text-embedding plays in our artificial intelligence endeavors. CRM systems frequently handle vast amounts of data, encompassing customer emails, feedback, notes, and more. It is essential for us to quickly comprehend this influx of information and seamlessly connect individuals with the data they require. This understanding drives our AI team's development of the cutting-edge text-embedding model, known as SFR- Embedding.