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Embedding
Text-multilingual-embedding-002
Discover Text-multilingual-embedding-002 API, a powerful model for multilingual text embeddings, enhancing NLP applications across languages.
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Multilingual embedding model for diverse NLP applications and languages
Text-multilingual-embedding-002 is a state-of-the-art model designed to convert textual data into numerical vector representations, capturing the semantic meaning and context of the input text. It is particularly focused on supporting multiple languages, making it suitable for global applications.
Text-multilingual-embedding-002 supports a wide range of languages, including but not limited to English, Spanish, French, Chinese, and Arabic, making it suitable for global applications.
The model is also perfect for cross-lingual applications for Clinical Documentation and Research. Learn more about this and other models and their applications in Healthcare here.
The model is based on the Transformer architecture, which utilizes self-attention mechanisms to process and generate embeddings that capture contextual relationships between words in multiple languages.
Text-multilingual-embedding-002 was trained on a diverse and extensive dataset that includes text from books, websites, and other multilingual sources. The training data encompasses approximately 1 billion sentences across various languages, ensuring a broad understanding of linguistic nuances.
The model was trained on a large-scale dataset. The diversity of the data contributes significantly to the model's ability to generalize across different languages and contexts.
The model's knowledge is current as of March 2023.
The training data includes a wide range of sources to minimize bias and improve robustness. However, like all models trained on large datasets, it may still reflect some inherent biases present in the data.
The model's performance on the MTEB benchmark indicates high accuracy across multiple tasks, particularly in retrieval and classification scenarios. These metrics suggest that the model performs well in ranking relevant documents and retrieving information effectively from large datasets.
The model has demonstrated a high level of robustness, effectively handling diverse inputs across different languages. It has been benchmarked against user-generated content (UGC) and has shown resilience in maintaining performance despite variations in language and structure.
Text-multilingual-embedding-002 has shown competitive performance against other leading multilingual embedding models. In the MTEB evaluation, it achieved:
Text-multilingual-embedding-002 outperformed several models in the same category:
The model is available on the AI/ML API platform as "text-multilingual-embedding-002".
Detailed API Documentation is available on the AI/ML API website, providing comprehensive guidelines for integration.
The development of Text-multilingual-embedding-002 adheres to ethical AI practices, focusing on transparency, fairness, and accountability.
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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