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Thenlper · Embedding model
GTE-Base
thenlper/gte-base- Embedding
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.
Specifications
- Input
- text
- Output
- vector per input (float array; base64 on request)
- Batch
- up to 512 inputs per call
- Billing
- per input token
Use it
POST /api/v1/embeddings · MCP embed · full reference
curl https://omnirail.org/api/v1/embeddings \
-H "Authorization: Bearer $OMNIRAIL_KEY" -H "Content-Type: application/json" \
-d '{"model":"thenlper/gte-base","input":["brass lever espresso machine","coffee grinder"]}'