Embed Models in Multiple Languages
At LLMRails, we are passionate about democratizing access to cutting-edge NLP technologies that are driving innovation worldwide
At LLMRails, we are passionate about democratizing access to cutting-edge NLP technologies that are driving innovation worldwide
Our Multilingual Model is the key to unlocking a world of possibilities. By mapping text to a semantic vector space, we enable users to easily identify similar meanings and unlock a range of valuable use cases for multilingual settings. Imagine searching for relevant documents based on meaning rather than just keywords. The technology used in LLMRails makes this possible, delivering search results that are several times better than traditional keyword searches.
It is a game-changer! Unlike our English language embed model, we have trained this one using dot product calculations, resulting in a non-normalized similarity score that reflects the magnitude of the two compared vectors. This approach has proven more effective than standard embeddings, and our multilingual embeddings boast an impressive 768 dimensions. With our technology, you can expect top-notch performance and unmatched accuracy.
Detecting harmful content in online communities can be challenging, especially when users speak many languages. However, our state-of-the-art technology has revolutionized the process. By training our model with just a few English examples, we have created an algorithm that can now detect harmful content across more than 100 different languages. This means that you can rest easy knowing that your online community is protected, no matter what language is being used.
At LLMRails, we strive to remove obstacles and provide widespread access to advanced NLP technologies that drive projects worldwide. Our groundbreaking multilingual language models are accessible to all developers, enabling us to achieve our objective of equipping developers, researchers, and innovators with the latest NLP technologies that challenge the limits of Language
All you need to do is sign up with a company email address. You will then get access to the LLMRails to get started with ingesting documents and testing the search engine.
LLMRails supports PDF, Microsoft Word, Microsoft PowerPoint, Open Office, HTML, JSON, XML, email in RFC822, text, RTF, ePUB, and Common Mark.
You can index data from any text document in a supported file format via LLM Rails APIs.
There's no need for extra or specialized engineering, hardware, or infrastructure resources to implement LLMRails effectively. LLMRails was designed to simplify the process for web and app developers to incorporate semantic search into their sites and applications, eliminating the need for extra training or resources.
Didn’t find the answer you are looking for? Contact our support
With private invitation, join the LLMRails and start your AI advanture!
No ads. No trails. No commitments