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Embedding

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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Textembedding-gecko@003

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

<pre><code data-snippet="embedding" data-model="textembedding-gecko@003"></code></pre>

Model Overview Card for Textembedding-gecko@003

Basic Information

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

Description

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".

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.

Licensing

Textembedding-gecko@003 is available under a permissive license, allowing both commercial and non-commercial usage rights.

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