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The Embedding model can be used to generate embeddings from text. Embeddings can be used for estimating semantic similarity between two sentences, choosing a sentence which is most likely to follow another sentence, or categorizing user feedback.
The multilingual representation model embeddings have 768 dimensions.
Multilingual Sentence Embeddings
Most word and sentence embeddings are dependent on the language that the model is trained on. If you were to try to fit the French sentence “Bonjour, comment ça va?” (meaning: hello, how are you?) in the embedding from the previous section, it will struggle to understand that it should be close to the sentence “Hello, how are you?” in English. For the purpose of unifying many languages into one, and being able to understand text in all these languages, Cohere has trained a large multilingual model, that has showed wonderful results with more than 100 languages. Here is a small example, with the following sentences in English, French, and Spanish.
- The bear lives in the woods
- El oso vive en el bosque
- L’ours vit dans la foret
- The world cup is in Qatar
- El mundial es en Qatar
- La coupe du monde est au Qatar
- An apple is a fruit
- Una manzana es una fruta
- Une pomme est un fruit
- El cielo es azul
- The sky is blue
- Le ciel est bleu
The model returned the following embedding. Notice that the model managed to identify the sentences about the bear, soccer, an apple, and the sky, even if they are in different languages.
The multilingual embedding model supports over 100 languages, including Chinese, Spanish, and French. For a full list of languages it support, please reference this page.
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- Model Type IDText Embedder
- DescriptionCohere's Multilingual embedding model empowers language generation in LLM, capturing semantic relationships for coherent and contextually relevant text. It enhances generative power, improving the quality of generated content.
- Last UpdatedNov 29, 2023
- Use Case