heliport

A language identification tool which aims for both speed and accuracy, with support for 220 languages(or add your own languages!).

This tool is an efficient HeLI-OTS port to Rust, achieving 25x speedups while having almost identical output.

Installation

From PyPi

Install it in your environment

pip install heliport

NOTE: Since version 0.8 models do not need to be downloaded anymore.

From source

Install the requirements:

• Python
• PIP
• Rust

Clone the repo, build the package and binarize the model

git clone https://github.com/ZJaume/heliport
cd heliport
pip install .

Usage

CLI

Just run the heliport identify command that reads lines from stdin

cat sentences.txt | heliport identify

eng
cat
rus
...

Identify languages of input text

Usage: heliport identify [OPTIONS] [INPUT_FILE] [OUTPUT_FILE]

Arguments:
  [INPUT_FILE]   Input file, default: stdin
  [OUTPUT_FILE]  Output file, default: stdout

Options:
  -j, --threads <THREADS>                Number of parallel threads to use.
                                         0 means no multi-threading
                                         1 means running the identification in a separated thread
                                         >1 run multithreading [default: 0]
  -b, --batch-size <BATCH_SIZE>          Number of text segments to pre-load for parallel processing [default:
                                         100000]
  -c, --ignore-confidence                Ignore confidence thresholds. Predictions under the thresholds will not be
                                         labeled as 'und'
  -s, --print-scores                     Print confidence score (higher is better) or raw score (lower is better) in
                                         case '-c' is provided
  -n, --not-strict                       Do not be strict when loading confidence thresholds (do not fail if one
                                         language is missing)
  -p, --precision <PRECISION>            Number of decimals precision when printing scores [default: 4]
  -m, --model-dir <MODEL_DIR>            Model directory containing binarized model or plain text model. Default is
                                         Python module path or './LanguageModels' if relevant languages are requested
  -l, --relevant-langs <RELEVANT_LANGS>  Load only relevant languages. Specify a comma-separated list of language
                                         codes. Needs plain text model directory
  -h, --help                             Print help

Python package

>>> from heliport import Identifier
>>> i = Identifier()
>>> i.identify("L'aigua clara")
'cat'

For further information of the avaliable functions and parameters, please take a look at the module docs:

>>> import heliport
>>> help(heliport)

Rust crate

use std::path::PathBuf;
use heliport::identifier::Identifier;
use heliport::lang::Lang;

let identifier = Identifier::load(
    PathBuf::from("/path/to/model_dir",
    None,
    );
let lang, score = identifier.identify("L'aigua clara");
assert_eq!(lang, Lang::cat);

Differences with HeLI-OTS

Although heliport currently uses the same models as HeLI-OTS 2.0 and the identification algorithm is almost the same, there are a few differences (mainly during pre-processing) that may cause different results. However, in most case, these should not deacrease accuracy and should not happen frequently.

Note: Both tools have a pre-processing step for each identified text to remove all non-alphabetic characters.

The implementation differences that can change results are:

• HeLI during preprocessing removes urls and words beginning with @, while heliport does not.
• Since 1.5, during preprocessing, HeLI repeats every word that does not start with capital letter, This is probably to penalize proper nouns. However, in our tests, we have not find a significant improvement with this. Therefore,to avoid multiplying the cost of prediction by almost x2, this has not been implemented. In the future it might end up being implemented if there is need for it and can be implemented efficiently.
• Rust and Java implementations have small precision differences due to Rust accumulating probabilities with double precision floats.

Benchmarks

Speed benchmarks with 100k random sentences from OpenLID, all the tools running single-threaded:

All documents and software contained in this repository reflect only the authors' view. The Innovation and Networks Executive Agency of the European Union is not responsible for any use that may be made of the information it contains.