# `glowrs`
The `glowrs` library provides an easy and familiar interface to use pre-trained models for embeddings and sentence similarity.
## Example
```rust
use glowrs::SentenceTransformer;
fn main() {
let encoder = SentenceTransformer::from_repo_string("sentence-transformers/all-MiniLM-L6-v2").unwrap();
let sentences = vec![
"Hello, how are you?",
"Hey, how are you doing?"
];
let embeddings = encoder.encode_batch(sentences, true).unwrap();
println!("{:?}", embeddings);
}
```
## Features
- Load models from Hugging Face Hub
- 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.