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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: 'text-multilingual-embedding-002',
});
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="text-multilingual-embedding-002")
embedding = response.data[0].embedding
print(json.dumps(embedding, indent=2))
main()
-
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Product Detail
Introducing Text-multilingual-embedding-002
The Text-multilingual-embedding-002 model from Google Cloud represents a significant advancement in Natural Language Processing (NLP). Released in March 2023, this state-of-the-art text embedding model is engineered to transform textual data into high-quality numerical vector representations, expertly capturing the semantic meaning and contextual nuances across a multitude of languages.
Its core strength lies in its unparalleled multilingual support, making it an indispensable tool for global applications requiring sophisticated language understanding.
⭐ Key Model Details
- Model Name: Text-multilingual-embedding-002
- Developer: Google Cloud
- Release Date: March 2023
- Version: 002
- Model Type: Text Embedding
🚀 Core Capabilities
- Supports over 100 languages, enabling truly global reach.
- Generates high-quality semantic embeddings that accurately reflect text meaning.
- Fine-tuned for various NLP tasks, ensuring versatility and performance.
- Offers efficient inference speed, crucial for real-time applications.
- Demonstrates robustness against diverse linguistic structures.
🎯 Intended Applications
This powerful model is ideally suited for a broad spectrum of multilingual and cross-lingual applications, including:
- Cross-lingual search engines for accurate global information retrieval.
- Multilingual chatbots that can communicate effectively across language barriers.
- Sentiment analysis to understand public opinion in different languages.
- Enhanced language translation services with better contextual understanding.
- Sophisticated content recommendation systems tailored for diverse audiences.
Notably, Text-multilingual-embedding-002 excels in cross-lingual applications for Clinical Documentation and Research. For more insights into this and other AI models in healthcare, you can learn more here (referencing the original content's section on "Clinical Documentation and Research" from the article titled "AI in Healthcare: Generative AI Uses & Examples").
⚙️ Technical Specifications
Architecture
The model's foundation is the highly effective Transformer architecture. This design leverages self-attention mechanisms to efficiently process and generate embeddings that adeptly capture intricate contextual relationships between words across multiple languages.
Training Data & Diversity
Text-multilingual-embedding-002 was trained on an extensive and diverse dataset, comprising approximately 1 billion sentences gathered from books, websites, and various other multilingual sources. This vast training corpus ensures a comprehensive understanding of linguistic nuances and aids in robust generalization across different languages and contexts.
The model's knowledge is current as of March 2023. While efforts were made to minimize bias through diverse data sources, it's important to acknowledge that, like all large language models, some inherent biases present in the training data may still be reflected.
📊 Performance Benchmarks
Massive Text Embedding Benchmark (MTEB)
Performance on the MTEB benchmark underscores the model's high accuracy, particularly in retrieval and classification scenarios. Key metrics include:
- ✅ nDCG@10: 60.8
- ✅ Recall@100: 92.4
These results confirm the model's proficiency in ranking relevant documents and efficiently retrieving information from large, complex datasets. It has also demonstrated exceptional robustness, consistently performing well even with diverse user-generated content (UGC) across various languages and structures.
Comparative Analysis
Text-multilingual-embedding-002 showcases highly competitive performance against other leading multilingual embedding models. In MTEB evaluations, it achieved an average Accuracy of 64.0 across various tasks, highlighting its strength in handling multilingual queries.
The model notably outperformed several established models in its category:
- Text-multilingual-embedding-002: 64.0 (Average Accuracy)
- LaBSE (Language-agnostic BERT Sentence Embedding): 45.2
- Cohere: 64.0
- BGE (Best Generative Embedding): 64.2
💡 Usage & Integration
API Access & Code Samples
The Text-multilingual-embedding-002 model is readily available on the AI/ML API platform, identifiable as "text-multilingual-embedding-002". Practical code samples are provided within the platform to facilitate quick integration.
(Reference: AI/ML API Platform, section "Code Samples")
API Documentation
For comprehensive guidance on integration and detailed usage instructions, refer to the API Documentation available on the AI/ML API website.
⚖️ Ethical AI & Licensing
The development of Text-multilingual-embedding-002 adheres to rigorous ethical AI practices, emphasizing transparency, fairness, and accountability in its design and application.
The model is available under commercial licensing, permitting both commercial and non-commercial usage, subject to Google Cloud's established terms of service.
Frequently Asked Questions (FAQs)
Q1: What is Text-multilingual-embedding-002?
A: It's a cutting-edge text embedding model from Google Cloud, released in March 2023, designed to convert text into numerical vector representations that capture semantic meaning and context across over 100 languages.
Q2: How many languages does it support?
A: The model supports over 100 languages, including widely used ones like English, Spanish, French, Chinese, and Arabic, making it highly versatile for global applications.
Q3: What are the primary use cases for this model?
A: It's ideal for cross-lingual search engines, multilingual chatbots, sentiment analysis, language translation services, and content recommendation systems. It also has specific applications in clinical documentation and research.
Q4: How does its performance compare to other models?
A: Text-multilingual-embedding-002 demonstrates competitive performance, achieving an average accuracy of 64.0 on the MTEB benchmark, matching or outperforming models like LaBSE and Cohere in various tasks.
Q5: Is it available for commercial use?
A: Yes, Text-multilingual-embedding-002 is available under commercial licensing, allowing for both commercial and non-commercial usage, subject to Google Cloud's terms of service.
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