tasks:
- embedding license: Apache License 2.0
gte-base
This repo contains embedding model files for gte-base.
FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search. And it also can be used in vector databases for LLMs.
Information
- dimensions: 768
- max_tokens: 512
- language: en
Usage
Start a local instance of Xinference
xinference -p 9997
Launch and inference
from xinference.client import Client
client = Client("http://localhost:9997")
model_uid = client.launch_model(model_name="gte-base", model_type="embedding")
model = client.get_model(model_uid)
model.create_embedding("write a poem.")