m2-bert-80M-retrieval

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This documentation is valid for the following list of our models:

  • togethercomputer/m2-bert-80M-32k-retrieval

Model Overview

The model integrates advanced machine learning techniques to excel in searching and retrieving relevant information from vast datasets. With its 8k parameter design, it balances performance and efficiency, making it suitable for applications requiring high-speed data access and analysis.

Setup your API Key

If you don’t have an API key for the AI/ML API yet, feel free to use our Quickstart guidearrow-up-right.

API Schema

post
Authorizations
AuthorizationstringRequired

Bearer key

Body
modelundefined · enumRequiredPossible values:
inputany ofRequired

Input text to embed, encoded as a string or array of tokens.

string · min: 1Optional
or
string[] · min: 1Optional
Responses
post
/v1/embeddings
200Success

Code Example

This example shows how to set up an API client, send text to the embedding API, and print the response with the embedding vector. See how large a vector response the model generates from just a single short input phrase.

chevron-rightResponsehashtag

You can find a more advanced example of using embedding vectors in our article Find Relevant Answers: Semantic Search with Text Embeddings in the Use Cases section.

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