Crate glowrs

glowrs

The glowrs library provides an easy and familiar interface to use pre-trained models for embeddings and sentence similarity.

Example

use glowrs::{SentenceTransformer, Device, PoolingStrategy, Error};

fn main() -> Result<(), Error> {
    let encoder = SentenceTransformer::from_repo_string("sentence-transformers/all-MiniLM-L6-v2", &Device::Cpu)?;

    let sentences = vec![
        "Hello, how are you?",
        "Hey, how are you doing?"
    ];

    let embeddings = encoder.encode_batch(sentences, true, PoolingStrategy::Mean)?;

    println!("{:?}", embeddings);
    
    Ok(())
}

Features

• Load models from Hugging Face Hub
• Use hardware acceleration (Metal, CUDA)
• More to come!

Build features

• metal: Compile with Metal acceleration
• cuda: Compile with CUDA acceleration
• accelerate: Compile with Accelerate framework acceleration (CPU)

Disclaimer

This is still a work-in-progress. The embedding performance is decent but can probably do with some benchmarking.

Do not use this in a production environment.

Re-exports

pub use model::pooling::PoolingStrategy;

pub use model::sentence_transformer::SentenceTransformer;

Modules

model

Structs

Usage

Enums

Device

Error

Type Aliases

Result