Model Description: french document embedding is an embedding model for documents in the French English language, with a context length of up to 8096 tokens. This model is a specialized text embedding model trained specifically for the French English language. It is built upon gte multilingual and trained using the [SimilarityLoss], Multi Negative Ranking Loss, Matryoshka2dLoss and GISTEmbedLoss using guide model. This model embeds and converts long texts or documents into vectors with 786 dimensions, making it useful for vector databases serving semantic search or RAG (Retrieval Augmented Generation). Full Model Architecture Usage: Using this model becomes easy when you have sentence transformers installed: Then you can use the model like this: Evaluation Citation @article{reimers2019sentence, title={Sentence BERT: Sentence Embeddings using Siamese BERT Networks}, author={Nils Reimers, Iryna Gurevych}, journal={https://arxiv.org/abs/1908.10084}, year={2019} } @article{zhang2024mgte, title={mGTE: Generalized Long Context Text Representation and Reranking Models for Multilingual Text Retrieval}, author={Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Wen and Dai, Ziqi and Ta…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy