Textembedding-gecko@003

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Textembedding-gecko@003

Explore Textembedding-gecko@003 API, a powerful text embedding model by Google, designed for diverse NLP applications and high performance.

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Create an embedding

const { OpenAI } = require('openai');

const main = async () => {
  const api = new OpenAI({ apiKey: '<YOUR_API_KEY>', baseURL: 'https://api.aimlapi.com/v1' });

  const text = 'Your text string goes here';
  const response = await api.embeddings.create({
    input: text,
    model: 'textembedding-gecko@003',
  });
  const embedding = response.data[0].embedding;

  console.log(embedding);
};

main();

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This saves money! If you use chatgpt daily. How come so not many users are knowing this…

Textembedding-gecko@003

Textembedding-gecko@003 is a versatile text embedding model by Google

Textembedding-gecko@003 Description

Basic Information

Model Name: Textembedding-gecko@003
Developer/Creator: Google
Release Date: April 2024
Version: 003
Model Type: Text Embedding

Overview

Textembedding-gecko@003 is a state-of-the-art text embedding model developed by Google, designed to generate high-quality vector representations of text. This model excels in capturing semantic meanings and relationships between textual inputs, making it suitable for various natural language processing tasks.

Key Features

High Dimensionality: Offers 768 embedding dimensions.
Versatility: Competes effectively with larger models while maintaining efficiency.
Performance: Optimized for both accuracy and speed in generating embeddings.

Intended Use

This model is intended for applications, where understanding the contextual meaning of text is crucial.

Semantic search
Text classification
Clustering

Language Support

Textembedding-gecko@003 is primarily designed for English but can be adapted for other languages depending on the training data used.

Technical Details

Architecture

The model is based on a transformer architecture, which allows it to effectively process and understand complex language patterns and relationships.

Training Data

Textembedding-gecko@003 was trained on a diverse dataset comprising over 8 trillion tokens, including web text, books, and other textual sources. This extensive training enables the model to generalize well across various topics.

Data Source and Size

The training data includes a mix of structured and unstructured text, ensuring a broad understanding of language. The model's performance benefits from this vast and varied dataset.

Knowledge Cutoff

The model has a knowledge cutoff date of April 2024.

Diversity and Bias

Efforts were made to include a diverse range of sources to minimize biases. However, like all models, it may still reflect some biases present in the training data.

Performance Metrics

Textembedding-gecko@003, developed by Google, showcases impressive performance across various natural language processing tasks.

Benchmark Performance

Massive Text Embedding Benchmark (MTEB)

Average score of 66.31, outperforming larger models with up to 7 billion parameters while maintaining only 1.2 billion parameters.

Task-Specific Performance

Text Classification: Average score of 81.17.
Semantic Textual Similarity: Average score of 85.06.
Summarization: Average score of 32.63.
Retrieval Tasks: Average score of 55.70.

Zero-Shot Generalization

Textembedding-gecko@003 demonstrates strong zero-shot performance, effectively generalizing to unseen tasks, outperforming several competitive baselines.

Usage

Code Samples

The model is available on the AI/ML API platform as "textembedding-gecko@003".

Create an embedding

const { OpenAI } = require('openai');

const main = async () => {
  const api = new OpenAI({ apiKey: '<YOUR_API_KEY>', baseURL: 'https://api.aimlapi.com/v1' });

  const text = 'Your text string goes here';
  const response = await api.embeddings.create({
    input: text,
    model: 'textembedding-gecko@003',
  });
  const embedding = response.data[0].embedding;

  console.log(embedding);
};

main();

API Documentation

Detailed API Documentation is available on the AI/ML API website, providing comprehensive guidelines for integration.

Ethical Guidelines

The development of Textembedding-gecko@003 adheres to ethical AI principles, focusing on transparency, fairness, and accountability in its use and deployment.