floret
floret Python bindings
Decision gist · record as of 2026-08-14
Yes, if you need compact word embeddings and are willing to work with an aging, Alpha-status package. The MIT license is permissive, install friction is manageable (precompiled wheels available), and there are no known vulnerabilities. However, verify that floret's memory savings and spaCy integration meet your production requirements before committing to it as a core dependency.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires numpy; compiled extension, so platform-specific wheels must match your Python version and architecture.
- Medium install friction: compiled wheels available for Python 3.6–3.12 across macOS, Linux, and Windows, but requires numpy.
- Package is aging (last release 2023-11-04, status: Alpha), though the repository remains active.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you can use, modify, and distribute floret freely in commercial and private projects with minimal restrictions.
last release 2023-11-04 (1014 days) · last repo commit 2025-04-25 · 346 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 191,118 downloads/mo, #9,890 on PyPI
Alternatives
Verify before relying
pip install floret
import floret
model = floret.train_unsupervised(
"data.txt",
model="cbow",
mode="floret",
hashCount=2,
bucket=50000
)
vector = model.get_word_vector("word")- Whether floret's memory savings over fastText are quantified or measurable for typical use cases.
- Current performance or adoption in production NLP pipelines beyond spaCy integration.
- Whether the Alpha status reflects stability concerns or simply incomplete feature coverage.
What it is and what it does
floret is a Python binding for an extended fastText implementation that combines fastText's subword tokenization with Bloom embeddings (the "hashing trick") to produce word vectors in a compact hash table. Instead of storing every word and subword separately, floret uses a smaller, fixed-size table and distributes entries across multiple hash rows, reducing memory overhead while maintaining coverage for out-of-vocabulary words.
You train floret models using unsupervised learning (CBOW or skip-gram), configure the hash table size and row count via parameters like `bucket` and `hashCount`, and export vectors in standard or floret-specific formats. The package integrates directly with spaCy, allowing you to import trained floret vectors as spaCy language models. It retains all fastText functionality, so you can switch between standard fastText and floret modes by changing the `mode` parameter.
Use it for
- Train memory-efficient word embeddings for deployment on resource-constrained devices or large-scale NLP systems.
- Generate embeddings for rare or misspelled words using subword information without storing full fastText vectors.
- Integrate compact pre-trained vectors into spaCy pipelines for production NLP applications.
- Reduce model size in production while maintaining out-of-vocabulary word coverage.
- Experiment with Bloom hashing trade-offs (hash collisions vs. memory) for embedding quality tuning.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need compact word embeddings and are willing to work with an aging, Alpha-status package.
The MIT license is permissive, install friction is manageable (precompiled wheels available), and there are no known vulnerabilities. However, verify that floret's memory savings and spaCy integration meet your production requirements before committing to it as a core dependency.
Install
floret on PyPI
Before you install
Medium install friction: compiled wheels available for Python 3.6–3.12 across macOS, Linux, and Windows, but requires numpy. Package is aging (last release 2023-11-04, status: Alpha), though the repository remains active.
Requires numpy; compiled extension, so platform-specific wheels must match your Python version and architecture.
License in practice
MIT license is permissive; you can use, modify, and distribute floret freely in commercial and private projects with minimal restrictions.
Quickstart
pip install floret
import floret
model = floret.train_unsupervised(
"data.txt",
model="cbow",
mode="floret",
hashCount=2,
bucket=50000
)
vector = model.get_word_vector("word")Verify before relying
- Whether floret's memory savings over fastText are quantified or measurable for typical use cases.
- Current performance or adoption in production NLP pipelines beyond spaCy integration.
- Whether the Alpha status reflects stability concerns or simply incomplete feature coverage.
Package facts
| License | MIT permissive |
| Python support | Not specified |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagenumpy |
| Maintenance | Aging 1,014 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 191,118 / month, #9,890 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 :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Software Development |
Evidence: floret-0.10.5-cp310-cp310-macosx_11_0_arm64.whl; floret-0.10.5-cp310-cp310-macosx_11_0_x86_64.whl; floret-0.10.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; floret-0.10.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; floret-0.10.5-cp310-cp310-win_amd64.whl; floret-0.10.5-cp311-cp311-macosx_11_0_arm64.whl; floret-0.10.5-cp311-cp311-macosx_11_0_x86_64.whl; floret-0.10.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; floret-0.10.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; floret-0.10.5-cp311-cp311-win_amd64.whl; floret-0.10.5-cp312-cp312-macosx_11_0_arm64.whl; floret-0.10.5-cp312-cp312-macosx_11_0_x86_64.whl; floret-0.10.5-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; floret-0.10.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; floret-0.10.5-cp312-cp312-win_amd64.whl; floret-0.10.5-cp36-cp36m-macosx_10_16_x86_64.whl; floret-0.10.5-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; floret-0.10.5-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; floret-0.10.5-cp36-cp36m-win_amd64.whl; floret-0.10.5-cp37-cp37m-macosx_10_16_x86_64.whl
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