urduhack
Natural Language Processing (NLP) library for Urdu language.
Decision gist · record as of 2026-08-14
Yes, if you are working specifically with Urdu text and can tolerate dormant maintenance. The library offers genuine value for NLP tasks on Urdu data and has low install friction. However, verify that its TensorFlow dependencies and pre-trained models work in your environment—no active maintenance means you may need to patch compatibility issues yourself or pin older dependency versions. Not suitable if you need ongoing support or expect regular updates.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires TensorFlow (cpu or gpu variant) and model download via urduhack.download() before first use.
- Low install friction with a pure-wheel distribution.
- Maintenance is dormant—the last release was in 2020 and the last commit in January 2024—so expect no active bug fixes or updates, though the repository remains open.
License · maintenance · safety
MIT License (permissive) — MIT License permits commercial and private use with minimal restrictions, making it safe to adopt from a licensing standpoint.
last release 2020-07-06 (2230 days) · last repo commit 2024-01-04 · 310 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 118,230 downloads/mo, #12,129 on PyPI
Alternatives
Verify before relying
pip install urduhack[tf]
import urduhack
urduhack.download()
nlp = urduhack.Pipeline()
doc = nlp("your urdu text here")
for sentence in doc.sentences:
for token in sentence.tokens:
print(token.text, token.ner)- Whether TensorFlow dependency versions are compatible with Python 3.8+ environments
- Current status of pre-trained models and whether they remain downloadable and functional
- Compatibility of tf2crf and tensorflow-datasets with recent TensorFlow releases
What it is and what it does
Urduhack is a NLP library tailored for the Urdu language, offering a pipeline-based approach to common text processing tasks. It wraps TensorFlow models for part-of-speech tagging and named entity recognition, alongside utilities for normalization, preprocessing, and tokenization. The library is designed to lower the barrier to entry for academic researchers, NLP beginners, and developers building production applications with Urdu text.
The package depends on tf2crf, tensorflow-datasets, Click, and regex. It ships as a pure Python wheel with low install friction. However, the project is dormant—no releases since 2020 and commits only through early 2024—meaning you should expect no active maintenance, bug fixes, or compatibility updates for newer Python or TensorFlow versions.
Use it for
- Extract part-of-speech tags and named entities from Urdu text documents for linguistic analysis or information extraction tasks.
- Preprocess and normalize Urdu text at scale before feeding it into downstream machine learning models.
- Build a rapid prototype of an Urdu NLP application using pre-trained models without implementing tokenization and tagging from scratch.
- Conduct academic research on Urdu language processing with production-quality code structure and pre-built datasets.
- Tokenize Urdu sentences and words for text analysis, search indexing, or language study applications.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are working specifically with Urdu text and can tolerate dormant maintenance.
The library offers genuine value for NLP tasks on Urdu data and has low install friction. However, verify that its TensorFlow dependencies and pre-trained models work in your environment—no active maintenance means you may need to patch compatibility issues yourself or pin older dependency versions. Not suitable if you need ongoing support or expect regular updates.
Install
urduhack on PyPI
Before you install
Low install friction with a pure-wheel distribution. Maintenance is dormant—the last release was in 2020 and the last commit in January 2024—so expect no active bug fixes or updates, though the repository remains open.
Requires TensorFlow (cpu or gpu variant) and model download via urduhack.download() before first use.
License in practice
MIT License permits commercial and private use with minimal restrictions, making it safe to adopt from a licensing standpoint.
Quickstart
pip install urduhack[tf]
import urduhack
urduhack.download()
nlp = urduhack.Pipeline()
doc = nlp("your urdu text here")
for sentence in doc.sentences:
for token in sentence.tokens:
print(token.text, token.ner)Verify before relying
- Whether TensorFlow dependency versions are compatible with Python 3.8+ environments
- Current status of pre-trained models and whether they remain downloadable and functional
- Compatibility of tf2crf and tensorflow-datasets with recent TensorFlow releases
Package facts
| License | MIT License permissive |
| Python support | Supports the current Python release >= 3.6 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagestf2crftensorflow-datasetsClickregex |
| Maintenance | Dormant 2,230 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 118,230 / month, #12,129 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseNatural Language :: UrduOperating System :: MacOS :: MacOS XOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Topic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python ModulesTopic :: Text Processing :: Linguistic |
Evidence: urduhack-1.1.1-py3-none-any.whl
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