← All models
thenlper
Thenlper: GTE-Base
The gte-base embedding model encodes English sentences and paragraphs into a 768-dimensional dense vector space, delivering efficient and effective semantic embeddings optimized for textual similarity, semantic search, and clustering applications.
512 context
Modalities:text->embeddings
Released:11/17/2025
The gte-base embedding model encodes English sentences and paragraphs into a 768-dimensional dense vector space, delivering efficient and effective semantic embeddings optimized for textual similarity, semantic search, and clustering applications.
Weekly tokens
279.3M
Tokens generated this week (network-wide)
Usage by period
No ranking data yet for this model.