fasttext-predict
fasttext with wheels and no external dependency, but only the predict method (<1MB)
Decision gist · record as of 2026-08-14
Yes, if you need lightweight language identification on a pre-trained fastText model and want to avoid NumPy and other heavy dependencies. The dormant maintenance status (629 days since last release) is a concern for long-term support, but the package has no known vulnerabilities and the predict-only scope limits the surface for bugs. Install it for inference-only workloads; do not expect active development or new features.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- You must provide a fastText model file (e.g., a .ftz file); the package only includes the predict method, not model training or other fastText features.
- Medium install friction due to compiled wheels across many architectures, but no runtime dependencies.
- Dormant maintenance (629 days since last release) means no active development or bug fixes.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you may use, modify, and distribute this package freely with minimal restrictions.
last release 2024-11-23 (629 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,531,109 downloads/mo, #3,807 on PyPI
Alternatives
Verify before relying
pip install fasttext-predict
import fasttext
model = fasttext.load_model('lid.176.ftz')
result = model.predict('Fondant au chocolat et tarte aux myrtilles')- Whether the package's predict method is compatible with all fastText model formats or only specific ones.
- Performance characteristics compared to the full fastText library for production language identification workloads.
- Whether dormant status means the package will receive security updates if vulnerabilities are discovered.
What it is and what it does
fasttext-predict is a stripped-down Python wrapper around the fastText library that exposes only the model prediction interface. It is designed for language identification and similar inference tasks where you already have a trained model and only need to run predictions on text. The package removes all training, model manipulation, and other fastText features, keeping the binary under 1MB and eliminating external dependencies like NumPy.
You load a pre-trained fastText model file and call its predict method to classify or identify text. This makes it suitable for embedding language detection into applications where you want minimal dependencies and fast startup. The package provides pre-built wheels for Python 3.9–3.13 across macOS, Windows, and Linux architectures, so installation typically requires no compilation on common platforms.
Use it for
- Detect the language of user-submitted text in a web application without pulling in a heavy ML stack.
- Identify document language in a batch processing pipeline where only inference is needed.
- Embed language detection in a lightweight CLI tool or edge service with minimal footprint.
- Classify short text snippets using a pre-trained fastText model in a real-time API.
- Add language identification to a search engine or content aggregator without external ML dependencies.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need lightweight language identification on a pre-trained fastText model and want to avoid NumPy and other heavy dependencies.
The dormant maintenance status (629 days since last release) is a concern for long-term support, but the package has no known vulnerabilities and the predict-only scope limits the surface for bugs. Install it for inference-only workloads; do not expect active development or new features.
Install
fasttext-predict on PyPI
Before you install
Medium install friction due to compiled wheels across many architectures, but no runtime dependencies. Dormant maintenance (629 days since last release) means no active development or bug fixes.
You must provide a fastText model file (e.g., a .ftz file); the package only includes the predict method, not model training or other fastText features.
License in practice
MIT license is permissive; you may use, modify, and distribute this package freely with minimal restrictions.
Quickstart
pip install fasttext-predict
import fasttext
model = fasttext.load_model('lid.176.ftz')
result = model.predict('Fondant au chocolat et tarte aux myrtilles')Verify before relying
- Whether the package's predict method is compatible with all fastText model formats or only specific ones.
- Performance characteristics compared to the full fastText library for production language identification workloads.
- Whether dormant status means the package will receive security updates if vulnerabilities are discovered.
Package facts
| License | MIT permissive |
| Python support | Not specified |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | None |
| Maintenance | Dormant 629 days since the last release |
| First released | |
| Downloads | 1,531,109 / month, #3,807 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Software Development |
Evidence: fasttext_predict-0.9.2.4-cp310-cp310-macosx_10_9_x86_64.whl; fasttext_predict-0.9.2.4-cp310-cp310-macosx_11_0_arm64.whl; fasttext_predict-0.9.2.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; fasttext_predict-0.9.2.4-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl; fasttext_predict-0.9.2.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; fasttext_predict-0.9.2.4-cp310-cp310-manylinux_2_31_armv7l.whl; fasttext_predict-0.9.2.4-cp310-cp310-musllinux_1_2_aarch64.whl; fasttext_predict-0.9.2.4-cp310-cp310-musllinux_1_2_armv7l.whl; fasttext_predict-0.9.2.4-cp310-cp310-musllinux_1_2_i686.whl; fasttext_predict-0.9.2.4-cp310-cp310-musllinux_1_2_x86_64.whl; fasttext_predict-0.9.2.4-cp310-cp310-win32.whl; fasttext_predict-0.9.2.4-cp310-cp310-win_amd64.whl; fasttext_predict-0.9.2.4-cp311-cp311-macosx_10_9_x86_64.whl; fasttext_predict-0.9.2.4-cp311-cp311-macosx_11_0_arm64.whl; fasttext_predict-0.9.2.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; fasttext_predict-0.9.2.4-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl; fasttext_predict-0.9.2.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; fasttext_predict-0.9.2.4-cp311-cp311-manylinux_2_31_armv7l.whl; fasttext_predict-0.9.2.4-cp311-cp311-musllinux_1_2_aarch64.whl; fasttext_predict-0.9.2.4-cp311-cp311-musllinux_1_2_armv7l.whl
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See also fasttext · fasttext-langdetect · fasttext-wheel · fast-langdetect · fasttext-numpy2 · floret · detoxify · unstructured-inference · ginza · pycld2