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const { OpenAI } = require('openai');
const main = async () => {
const api = new OpenAI({ apiKey: '', baseURL: 'https://api.ai.cc/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();
import json
from openai import OpenAI
def main():
client = OpenAI(
base_url="https://api.ai.cc/v1",
api_key="",
)
text = "Your text string goes here"
response = client.embeddings.create(input=text, model="textembedding-gecko@003")
embedding = response.data[0].embedding
print(json.dumps(embedding, indent=2))
main()
-
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Product Detail
Textembedding-gecko@003: Google's Advanced Text Embedding Model
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 Google's cutting-edge text embedding model, engineered to generate high-quality vector representations of textual data. It excels in capturing intricate semantic meanings and relationships, making it an ideal solution for a diverse range of natural language processing (NLP) applications.
Key Features
- 🚀 High Dimensionality: Outputs embeddings with 768 dimensions, allowing for rich semantic representation.
- ✅ Versatility: Demonstrates strong performance comparable to much larger models, while maintaining impressive efficiency.
- ⚡ Optimized Performance: Engineered for superior accuracy and speed in generating text embeddings.
Intended Use Cases
This model is primarily designed for applications where a deep understanding of contextual text meaning is paramount. Key applications include:
- Semantic search and information retrieval
- Text classification and categorization
- Document clustering and organization
Language Support
Textembedding-gecko@003 is primarily optimized for English language processing. However, its adaptability allows for potential use with other languages, depending on the specifics of the training data utilized for fine-tuning.
Technical Specifications
Architecture
The model leverages a sophisticated transformer architecture. This design enables it to efficiently process complex language patterns and discern intricate relationships within textual data, forming the backbone of its high-performance capabilities.
Training Data
Textembedding-gecko@003 was rigorously trained on an expansive and diverse dataset, encompassing over 8 trillion tokens. This includes a wide array of sources such as web text, digital books, and other textual corpora, ensuring robust generalization across numerous topics and domains.
Data Source and Size
The training data mix incorporates both structured and unstructured text, which contributes to the model's comprehensive understanding of language nuances. This vast and varied dataset is a critical factor in the model's exceptional performance.
Knowledge Cutoff
The model's knowledge base is current up to April 2024. Information or events occurring after this date may not be reflected in its understanding.
Diversity and Bias
Significant efforts were made during development to incorporate a diverse range of sources, aiming to mitigate potential biases. However, consistent with all AI models, Textembedding-gecko@003 may still inadvertently reflect some biases inherent in its extensive training data.
Performance Benchmarks
Developed by Google, Textembedding-gecko@003 consistently delivers impressive performance across a spectrum of natural language processing tasks.
Massive Text Embedding Benchmark (MTEB)
- 📊 Average Score: 66.31
- 🏆 Key Achievement: Outperforms larger models with up to 7 billion parameters, despite having only 1.2 billion parameters itself. This highlights its exceptional efficiency and compact design.
Task-Specific Performance (Average Scores)
- Text Classification: 81.17
- Semantic Textual Similarity: 85.06
- Summarization: 32.63
- Retrieval Tasks: 55.70
Zero-Shot Generalization
Textembedding-gecko@003 exhibits robust zero-shot performance, effectively generalizing to tasks it hasn't been explicitly trained on. This capability allows it to outperform several established competitive baselines in unseen scenarios.
Getting Started & Usage
Code Samples & API Access
The model is readily available for integration on the AI/ML API platform under the identifier "textembedding-gecko@003". For direct access and code examples, please visit the platform: 🔗 AI/ML API Platform (Sign-up)
API Documentation
Comprehensive API Documentation is provided on the AI/ML API website, offering detailed guidelines and examples for seamless integration into your applications. 📖 API Documentation Portal
Ethical AI & Licensing
Ethical Guidelines
The development and deployment of Textembedding-gecko@003 strictly adhere to core ethical AI principles. Our focus is on ensuring transparency, fairness, and accountability throughout its lifecycle and application.
Licensing
Textembedding-gecko@003 is made available under a permissive license, granting users extensive rights for both commercial and non-commercial utilization.
Frequently Asked Questions (FAQ)
❓ What is Textembedding-gecko@003?
Textembedding-gecko@003 is a state-of-the-art text embedding model developed by Google. It generates high-quality vector representations of text, capturing semantic meanings and relationships for various NLP tasks.
❓ What are its key features?
Key features include high dimensionality (768 embedding dimensions), versatility to compete with larger models efficiently, and optimized performance for both accuracy and speed in generating embeddings.
❓ What is the knowledge cutoff date for this model?
The model has a knowledge cutoff date of April 2024. This means its understanding is based on data available up to that time.
❓ Where can I find API documentation and code samples?
Detailed API documentation and code samples are available on the API Documentation Portal and the AI/ML API Platform, respectively.
❓ Is Textembedding-gecko@003 suitable for commercial use?
Yes, Textembedding-gecko@003 is available under a permissive license that allows for both commercial and non-commercial usage.
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