lrtc: Low-resource text classification

This crate is a Rust implementation of the low-resource text classification method introduced in Jiang et al. (2023). This implementation allows you to choose from gzip, zstd, zlib, or deflate compression algorithms, at various levels of compression.

```rust use lrtc::{CompressionAlgorithm, classify};

let training = vec!["some normal sentence".tostring(), "godzilla ate mars in June".into(),]; let traininglabels = vec!["normal".tostring(), "godzilla".into(),]; let queries = vec!["another normal sentence".tostring(), "godzilla eats marshes in August".into(),]; // Using a compression level of 3, and 1 nearest neighbor: println!("{:?}", classify(&training, &training_labels, &queries, 3i32, CompressionAlgorithm::Gzip, 1usize)); ```

This method seems to perform decently well for relatively sparse training sets, and does not require the same amount of tuning as neural net methods.

```rust use csv::Reader; use lrtc::{classify, CompressionAlgorithm}; use std::fs::File; let imdb = File::open("./data/imdb.csv").unwrap(); let mut reader = Reader::from_reader(imdb);

let imdb = File::open("./data/imdb.csv").unwrap();
let mut reader = Reader::from_reader(imdb);

let mut content = Vec::with_capacity(50000);
let mut label = Vec::with_capacity(50000);
for record in reader.records() {
    content.push(record.as_ref().unwrap()[0].to_string());
    label.push(record.unwrap()[1].to_string());
}

let predictions = classify( &content[0..1000], &label[0..1000], &content[40000..50000], 3i32, CompressionAlgorithm::Zstd, 5usize, )

let correct = predictions .iter() .zip(label[40000..50000].to_vec().iter()) .filter(|(a, b)| a == b) .count(); println!("{}", correct as f64 / 1000f64) // 0.623 ```

References

Zhiying Jiang, Matthew Yang, Mikhail Tsirlin, Raphael Tang, Yiqin Dai, and Jimmy Lin. 2023. “Low-Resource” Text Classification: A Parameter-Free Classification Method with Compressors. In Findings of the Association for Computational Linguistics: ACL 2023, pages 6810–6828, Toronto, Canada. Association for Computational Linguistics. https://aclanthology.org/2023.findings-acl.426