fasttext-numpy2
fasttext Python bindings, fixed numpy 2 compatibiliy
Decision gist · record as of 2026-08-14
Yes, if you need fastText with numpy 2 support and accept the dormant maintenance status. The package is stable (0.10.4 release), has no known vulnerabilities, and carries a permissive MIT license. However, the repository has not been updated in 644 days and is archived; consider whether you can tolerate lack of future bug fixes or feature updates. For active development or long-term production use, evaluate whether the original fastText package or an actively maintained alternative better fits your risk tolerance.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires numpy and a C++11-capable compiler; text input must be UTF-8 encoded.
- Medium install friction due to compiled C++ dependencies (pybind11, setuptools) and binary wheels for multiple Python versions (3.6–3.13).
- Last release was 644 days ago; repository is archived and dormant, suggesting limited ongoing maintenance.
License · maintenance · safety
MIT (permissive) — MIT license is permissive and poses no restrictions on commercial or private use, modification, or redistribution.
last release 2024-11-08 (644 days) · last repo commit 2024-11-21 · 16 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,046,821 downloads/mo, #4,451 on PyPI
Alternatives
Verify before relying
pip install fasttext-numpy2
import fasttext
# Train unsupervised word vectors
model = fasttext.train_unsupervised('data.txt', model='skipgram')
print(model['king']) # retrieve word vector- Whether numpy 2 compatibility extends to all fastText features or only core functionality.
- Performance or accuracy differences compared to the original fastText package.
- Active maintenance status and responsiveness to bug reports given the dormant repository state.
What it is and what it does
fasttext-numpy2 is a fork of Facebook's fastText library that adds compatibility with numpy 2, addressing breaking changes in the original package. It provides Python bindings for training unsupervised word representation models (skipgram and CBOW) and supervised text classifiers. The package handles UTF-8 encoded text input, tokenizes it, and learns dense vector representations of words or classifies documents based on labeled training data.
The library depends on pybind11 for Python-C++ interop, setuptools for building, and numpy for numerical operations. It ships precompiled wheels for Python 3.6 through 3.13 on Linux x86_64, reducing installation friction for those platforms. Model objects can be saved, loaded, and quantized to reduce file size with minimal accuracy loss.
Use it for
- Train word embeddings on raw text corpora for downstream NLP tasks like similarity search or clustering.
- Build and evaluate text classifiers on labeled datasets with built-in precision/recall metrics.
- Compress trained supervised models using quantization to deploy smaller, faster inference artifacts.
- Migrate existing fastText code to numpy 2 environments without rewriting bindings.
- Extract word vectors for a given vocabulary to use as features in other machine learning pipelines.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need fastText with numpy 2 support and accept the dormant maintenance status.
The package is stable (0.10.4 release), has no known vulnerabilities, and carries a permissive MIT license. However, the repository has not been updated in 644 days and is archived; consider whether you can tolerate lack of future bug fixes or feature updates. For active development or long-term production use, evaluate whether the original fastText package or an actively maintained alternative better fits your risk tolerance.
Install
fasttext-numpy2 on PyPI
Before you install
Medium install friction due to compiled C++ dependencies (pybind11, setuptools) and binary wheels for multiple Python versions (3.6–3.13). Last release was 644 days ago; repository is archived and dormant, suggesting limited ongoing maintenance.
Requires numpy and a C++11-capable compiler; text input must be UTF-8 encoded.
License in practice
MIT license is permissive and poses no restrictions on commercial or private use, modification, or redistribution.
Quickstart
pip install fasttext-numpy2
import fasttext
# Train unsupervised word vectors
model = fasttext.train_unsupervised('data.txt', model='skipgram')
print(model['king']) # retrieve word vectorVerify before relying
- Whether numpy 2 compatibility extends to all fastText features or only core functionality.
- Performance or accuracy differences compared to the original fastText package.
- Active maintenance status and responsiveness to bug reports given the dormant repository state.
Package facts
| License | MIT permissive |
| Python support | Not specified |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 3 packagespybind11setuptoolsnumpy |
| Maintenance | Dormant 644 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,046,821 / month, #4,451 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 :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Software Development |
Evidence: fasttext_numpy2-0.10.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; fasttext_numpy2-0.10.4-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; fasttext_numpy2-0.10.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; fasttext_numpy2-0.10.4-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; fasttext_numpy2-0.10.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; fasttext_numpy2-0.10.4-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; fasttext_numpy2-0.10.4-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; fasttext_numpy2-0.10.4-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; fasttext_numpy2-0.10.4-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; fasttext_numpy2-0.10.4-cp36-cp36m-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; fasttext_numpy2-0.10.4-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; fasttext_numpy2-0.10.4-cp37-cp37m-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; fasttext_numpy2-0.10.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; fasttext_numpy2-0.10.4-cp38-cp38-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; fasttext_numpy2-0.10.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; fasttext_numpy2-0.10.4-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
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See also fasttext · fasttext-wheel · fasttext-predict · floret · fast-langdetect · model2vec · fasttext-langdetect · tensorflow-text · setfit · alt-profanity-check