fast-langdetect 🚀

Overview

fast-langdetect is an ultra-fast and highly accurate language detection library based on FastText, a library developed by Facebook. Its incredible speed and accuracy make it 80x faster than conventional methods and deliver up to 95% accuracy.

• Supported Python 3.9 to 3.12.
• Works offline in low memory mode
• No numpy required (thanks to @dalf).

Background

This project builds upon zafercavdar/fasttext-langdetect with enhancements in packaging. For more information about the underlying model, see the official FastText documentation: Language Identification.

Possible memory usage

This library requires at least 200MB memory in low-memory mode.

Installation 💻

To install fast-langdetect, you can use either pip or pdm:

Using pip

pip install fast-langdetect

Using pdm

pdm add fast-langdetect

Usage 🖥️

In scenarios where accuracy is important, you should not rely on the detection results of small models, use low_memory=False to download larger models!

Prerequisites

• The “/n” character in the argument string must be removed before calling the function.
• If the sample is too long or too short, the accuracy will be reduced (e.g. if it is too short, Chinese will be predicted as Japanese).
• The model will be downloaded to the /tmp/fasttext-langdetect directory upon first use.

Native API (Recommended)

from fast_langdetect import detect, detect_multilingual

# Single language detection
print(detect("Hello, world!"))
# Output: {'lang': 'en', 'score': 0.12450417876243591}

# `use_strict_mode` determines whether the model loading process should enforce strict conditions before using fallback options.
# If `use_strict_mode` is set to True, we will load only the selected model, not the fallback model.
print(detect("Hello, world!", low_memory=False, use_strict_mode=True))

# How to deal with multiline text
multiline_text = """
Hello, world!
This is a multiline text.
But we need remove `\n` characters or it will raise an ValueError.
REMOVE \n
"""
multiline_text = multiline_text.replace("\n", "")  
print(detect(multiline_text))
# Output: {'lang': 'en', 'score': 0.8509423136711121}

print(detect("Привет, мир!")["lang"])
# Output: ru

# Multi-language detection with low memory mode enabled
# The accuracy is not as good as it should be
print(detect_multilingual("Hello, world!你好世界!Привет, мир!"))
# Output: [{'lang': 'ja', 'score': 0.32009604573249817}, {'lang': 'uk', 'score': 0.27781224250793457}, {'lang': 'zh', 'score': 0.17542070150375366}, {'lang': 'sr', 'score': 0.08751443773508072}, {'lang': 'bg', 'score': 0.05222449079155922}]

# Multi-language detection with low memory mode disabled
print(detect_multilingual("Hello, world!你好世界!Привет, мир!", low_memory=False))
# Output: [{'lang': 'ru', 'score': 0.39008623361587524}, {'lang': 'zh', 'score': 0.18235979974269867}, {'lang': 'ja', 'score': 0.08473210036754608}, {'lang': 'sr', 'score': 0.057975586503744125}, {'lang': 'en', 'score': 0.05422825738787651}]

Fallbacks

We provide a fallback mechanism: when use_strict_mode=False, if the program fails to load the large model (low_memory=False), it will fall back to the offline small model to complete the prediction task.

Convenient detect_language Function

from fast_langdetect import detect_language

# Single language detection
print(detect_language("Hello, world!"))
# Output: EN

print(detect_language("Привет, мир!"))
# Output: RU

print(detect_language("你好，世界！"))
# Output: ZH

Splitting Text by Language 🌐

For text splitting based on language, please refer to the split-lang repository.

Benchmark 📊

For detailed benchmark results, refer to zafercavdar/fasttext-langdetect#benchmark.

References 📚

[1] A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, Bag of Tricks for Efficient Text Classification

@article{joulin2016bag,
  title={Bag of Tricks for Efficient Text Classification},
  author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Mikolov, Tomas},
  journal={arXiv preprint arXiv:1607.01759},
  year={2016}
}

[2] A. Joulin, E. Grave, P. Bojanowski, M. Douze, H. Jégou, T. Mikolov, FastText.zip: Compressing text classification models

@article{joulin2016fasttext,
  title={FastText.zip: Compressing text classification models},
  author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Douze, Matthijs and J{\'e}gou, H{\'e}rve and Mikolov, Tomas},
  journal={arXiv preprint arXiv:1612.03651},
  year={2016}
}