metadata
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
license: mit
language:
- en
Infinity Embedding Model
More details please refer to the Github: Infinity.
Usage
Usage for Embedding Model via infinity
Its also possible to deploy files with the infinity_emb pip package.
Recommended is device="cuda", engine="torch"
with flash attention on gpu, and device="cpu", engine="optimum"
for onnx inference.
import asyncio
from infinity_emb import AsyncEmbeddingEngine, EngineArgs
sentences = ["Embed this is sentence via Infinity.", "Paris is in France."]
engine = AsyncEmbeddingEngine.from_args(
EngineArgs(model_name_or_path = "BAAI/bge-small-en-v1.5", device="cpu", engine="optimum" # or engine="torch"
))
async def main():
async with engine:
embeddings, usage = await engine.embed(sentences=sentences)
asyncio.run(main())
Contact
If you have any question or suggestion related to this project, feel free to open an issue or pull request. You also can email Michael Feil (infinity at michaelfeil.eu).
Citation
If you find this repository useful, please consider giving a star :star: and citation
@software{Feil_Infinity_2023,
author = {Feil, Michael},
month = oct,
title = {{Infinity - To Embeddings and Beyond}},
url = {https://github.com/michaelfeil/infinity},
year = {2023}
}
License
Infinity is licensed under the MIT License.