Compact Language Detector v3 (CLD3) — Python bindings
Modernised Python bindings for Google's CLD3 neural language detector, distributed on PyPI as
cld3-py. Prebuilt wheels are published for:
| Platform | Architectures |
|---|---|
| Linux (manylinux_2_28) | x86_64, aarch64 |
| Linux (musllinux_1_2) | x86_64, aarch64 |
| macOS | x86_64 (Intel), arm64 (Apple Silicon) |
| Windows | AMD64 |
Supported Python versions: 3.10, 3.11, 3.12, 3.13, 3.14 (including free-threaded cp313t / cp314t builds).
Install
pip install cld3-py
Quick start
The import name is gcld3, so code written against the original Google gcld3 package works unchanged:
import gcld3
detector = gcld3.NNetLanguageIdentifier(min_num_bytes=0, max_num_bytes=1000)
result = detector.FindLanguage(text="This text is written in English.")
print(result.language) # "en"
print(result.probability) # ~0.999
print(result.is_reliable) # True
print(result.proportion) # 1.0
# Multiple languages in the same document:
sample = "This piece of text is in English. Този текст е на Български."
for r in detector.FindTopNMostFreqLangs(text=sample, num_langs=2):
print(r.language, r.probability, r.proportion)
Drop-in replacement for gcld3
If you are migrating from the old PyPI gcld3 package:
pip uninstall gcld3
pip install cld3-py
# No code changes required — `import gcld3` still works.
Table of contents
- Model
- Supported Languages
- Building from source
- Verification / parity
- Bugs and Feature Requests
- Credits
Model
CLD3 is a neural network model for language identification. This package contains the inference code and a trained model. The inference code extracts character ngrams from the input text and computes the fraction of times each of them appears. For example, as shown in the figure below, if the input text is "banana", then one of the extracted trigrams is "ana" and the corresponding fraction is 2/4. The ngrams are hashed down to an id within a small range, and each id is represented by a dense embedding vector estimated during training.
The model averages the embeddings corresponding to each ngram type according to the fractions, and the averaged embeddings are concatenated to produce the embedding layer. The remaining components of the network are a hidden (Rectified linear) layer and a softmax layer.
To get a language prediction for the input text, we simply perform a forward pass through the network.
Supported Languages
The model outputs BCP-47-style language codes, shown in the table below. For some languages, output is differentiated by script. Language and script names from Unicode CLDR.
| Output Code | Language Name | Script Name |
|---|---|---|
| af | Afrikaans | Latin |
| am | Amharic | Ethiopic |
| ar | Arabic | Arabic |
| bg | Bulgarian | Cyrillic |
| bg-Latn | Bulgarian | Latin |
| bn | Bangla | Bangla |
| bs | Bosnian | Latin |
| ca | Catalan | Latin |
| ceb | Cebuano | Latin |
| co | Corsican | Latin |
| cs | Czech | Latin |
| cy | Welsh | Latin |
| da | Danish | Latin |
| de | German | Latin |
| el | Greek | Greek |
| el-Latn | Greek | Latin |
| en | English | Latin |
| eo | Esperanto | Latin |
| es | Spanish | Latin |
| et | Estonian | Latin |
| eu | Basque | Latin |
| fa | Persian | Arabic |
| fi | Finnish | Latin |
| fil | Filipino | Latin |
| fr | French | Latin |
| fy | Western Frisian | Latin |
| ga | Irish | Latin |
| gd | Scottish Gaelic | Latin |
| gl | Galician | Latin |
| gu | Gujarati | Gujarati |
| ha | Hausa | Latin |
| haw | Hawaiian | Latin |
| hi | Hindi | Devanagari |
| hi-Latn | Hindi | Latin |
| hmn | Hmong | Latin |
| hr | Croatian | Latin |
| ht | Haitian Creole | Latin |
| hu | Hungarian | Latin |
| hy | Armenian | Armenian |
| id | Indonesian | Latin |
| ig | Igbo | Latin |
| is | Icelandic | Latin |
| it | Italian | Latin |
| iw | Hebrew | Hebrew |
| ja | Japanese | Japanese |
| ja-Latn | Japanese | Latin |
| jv | Javanese | Latin |
| ka | Georgian | Georgian |
| kk | Kazakh | Cyrillic |
| km | Khmer | Khmer |
| kn | Kannada | Kannada |
| ko | Korean | Korean |
| ku | Kurdish | Latin |
| ky | Kyrgyz | Cyrillic |
| la | Latin | Latin |
| lb | Luxembourgish | Latin |
| lo | Lao | Lao |
| lt | Lithuanian | Latin |
| lv | Latvian | Latin |
| mg | Malagasy | Latin |
| mi | Maori | Latin |
| mk | Macedonian | Cyrillic |
| ml | Malayalam | Malayalam |
| mn | Mongolian | Cyrillic |
| mr | Marathi | Devanagari |
| ms | Malay | Latin |
| mt | Maltese | Latin |
| my | Burmese | Myanmar |
| ne | Nepali | Devanagari |
| nl | Dutch | Latin |
| no | Norwegian | Latin |
| ny | Nyanja | Latin |
| pa | Punjabi | Gurmukhi |
| pl | Polish | Latin |
| ps | Pashto | Arabic |
| pt | Portuguese | Latin |
| ro | Romanian | Latin |
| ru | Russian | Cyrillic |
| ru-Latn | Russian | English |
| sd | Sindhi | Arabic |
| si | Sinhala | Sinhala |
| sk | Slovak | Latin |
| sl | Slovenian | Latin |
| sm | Samoan | Latin |
| sn | Shona | Latin |
| so | Somali | Latin |
| sq | Albanian | Latin |
| sr | Serbian | Cyrillic |
| st | Southern Sotho | Latin |
| su | Sundanese | Latin |
| sv | Swedish | Latin |
| sw | Swahili | Latin |
| ta | Tamil | Tamil |
| te | Telugu | Telugu |
| tg | Tajik | Cyrillic |
| th | Thai | Thai |
| tr | Turkish | Latin |
| uk | Ukrainian | Cyrillic |
| ur | Urdu | Arabic |
| uz | Uzbek | Latin |
| vi | Vietnamese | Latin |
| xh | Xhosa | Latin |
| yi | Yiddish | Hebrew |
| yo | Yoruba | Latin |
| zh | Chinese | Han (including Simplified and Traditional) |
| zh-Latn | Chinese | Latin |
| zu | Zulu | Latin |
Building from source
Most users should just pip install cld3-py. To build from source (e.g. for an unreleased Python version or
to work on the bindings):
git clone https://github.com/malteos/cld3
cd cld3
pip install -v .
The build uses scikit-build-core + CMake. Protobuf v3.21.12 is fetched and statically linked via CMake
FetchContent, so no system protoc / libprotobuf is required.
To additionally build the reference C++ CLI binaries:
cmake -S . -B build -DCLD3_BUILD_TOOLS=ON
cmake --build build -j
./build/language_identifier_main
Verification / parity
A parity test (tests/test_parity.py) verifies that the Python bindings produce the same predictions as the
original C++ reference for ~75 multilingual samples. Run it locally with:
cmake -S . -B build -DCLD3_BUILD_TOOLS=ON && cmake --build build -j
CLD3_PARITY_BINARY=$PWD/build/parity_runner pytest tests/test_parity.py -v
The same test runs in CI after every wheel build.
Bugs and Feature Requests
Open a GitHub issue for this repository to file bugs and feature requests.
Announcements and Discussion
For announcements regarding major updates as well as general discussion list, please subscribe to: cld3-users@googlegroups.com
Credits
Original authors of the code in this package include (in alphabetical order):
- Alex Salcianu
- Andy Golding
- Anton Bakalov
- Chris Alberti
- Daniel Andor
- David Weiss
- Emily Pitler
- Greg Coppola
- Jason Riesa
- Kuzman Ganchev
- Michael Ringgaard
- Nan Hua
- Ryan McDonald
- Slav Petrov
- Stefan Istrate
- Terry Koo
1 maintainer
Metadata
1 maintainer
Release files for cld3-py 3.1.0
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| Tags | CPython 3.10 macOS 11.0+ ARM64 |
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| Download URL | cld3_py-3.1.0-cp310-cp310-macosx_10_15_x86_64.whl |
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| Size | 652.9 kB |
| Tags | CPython 3.10 macOS 10.15+ x86-64 |
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SHA-256 checksum How to use checksums |
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