Crate tongrams

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Expand description

`tongrams`: Tons of N-grams

tongrams is a crate to index and query large language models in compressed space, in which the data structures are presented in the following papers:

Giulio Ermanno Pibiri and Rossano Venturini, Efficient Data Structures for Massive N-Gram Datasets. In Proceedings of the 40th ACM Conference on Research and Development in Information Retrieval (SIGIR 2017), pp. 615-624.
Giulio Ermanno Pibiri and Rossano Venturini, Handling Massive N-Gram Datasets Efficiently. ACM Transactions on Information Systems (TOIS), 37.2 (2019): 1-41.

This is a Rust port of tongrams C++ library.

What can do

Store N-gram language models with frequency counts.
Look up N-grams to get the frequency counts.

Features

Compressed language model. tongrams-rs can store large N-gram language models in very compressed space. For example, the word N-gram datasets (N=1..5) in test_data are stored in only 2.6 bytes per gram.
Time and memory efficiency. tongrams-rs employs Elias-Fano Trie, which cleverly encodes a trie data structure consisting of N-grams through Elias-Fano codes, enabling fast lookups in compressed space.
Pure Rust. tongrams-rs is written only in Rust and can be easily pluged into your Rust codes.

Input data format

The file format of N-gram counts files is the same as that used in tongrams, a modified Google format, where

one separate file for each distinct value of N (order) lists one gram per row,
each header row <number_of_grams> indicates the number of N-grams in the file,
tokens in a gram <gram> are sparated by a space (e.g., the same time), and
a gram <gram> and the count <count> are sparated by a horizontal tab.

<number_of_grams>
<gram1><TAB><count1>
<gram2><TAB><count2>
<gram3><TAB><count3>
...

For example,

61516
the // parent       1
the function is     22
the function a      4
the function to     1
the function and    1
...

Examples

The following code uses datasets in test_data at the root of this repository.

use tongrams::EliasFanoTrieCountLm;

// File names of N-grams.
let filenames = vec![
    "../test_data/1-grams.sorted.gz",
    "../test_data/2-grams.sorted.gz",
    "../test_data/3-grams.sorted.gz",
];

// Builds the language model from n-gram counts files.
let lm = EliasFanoTrieCountLm::from_gz_files(&filenames).unwrap();

// Creates the instance for lookup.
let mut lookuper = lm.lookuper();

// Gets the count of a query N-gram written in a space-separated string.
assert_eq!(lookuper.with_str("vector"), Some(182));
assert_eq!(lookuper.with_str("in order"), Some(47));
assert_eq!(lookuper.with_str("the same memory"), Some(8));
assert_eq!(lookuper.with_str("vector is array"), None);

// Gets the count of a query N-gram formed by a string array.
assert_eq!(lookuper.with_tokens(&["vector"]), Some(182));
assert_eq!(lookuper.with_tokens(&["in", "order"]), Some(47));
assert_eq!(lookuper.with_tokens(&["the", "same", "memory"]), Some(8));
assert_eq!(lookuper.with_tokens(&["vector", "is", "array"]), None);

// Serializes the index into a writable stream.
let mut data = vec![];
lm.serialize_into(&mut data).unwrap();

// Deserializes the index from a readable stream.
let other = EliasFanoTrieCountLm::deserialize_from(&data[..]).unwrap();
assert_eq!(lm.num_orders(), other.num_orders());
assert_eq!(lm.num_grams(), other.num_grams());