hazm
Persian NLP Toolkit for dependency parsing, embeddings, lemmatization, normalization, POS tagging, and tokenization
GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
Hazm is a Persian NLP Toolkit used for dependency parsing, embeddings, lemmatization, normalization, POS tagging and tokenization in Python.
Good fit when
- When working exclusively with Farsi language texts where comprehensive processing tools like dependency parsing or POS tagging are required.
- For tasks that require integration of multiple NLP functionalities in Persian text analysis workflows.
Avoid when
- If the project involves languages other than Persian, as Hazm lacks capabilities for multilingual support beyond Farsi.
- When advanced machine learning models requiring extensive training data and resources are necessary; Hazm provides utilities but does not include state-of-the-art models.
Observed Jul 15, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Slowing (171d since push)
- As of Sep 20, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 20, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install hazmSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A comprehensive library providing tools to process Persian text including parsers, taggers, and other utilities essential for natural language processing tasks in Farsi.
Capability facts
- Languages
- python
Source: github.language+pyproject.toml · Sep 20, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 20, 2026)
To install the latest version of Hazm (requires Python 3.12+), run:Source link
Tags
README
Installation To install the latest version of Hazm (requires Python 3.12+), run: pip install hazm To use the pretrained models from Hugging Face, ensure you have the package: pip install huggingface hub
For agents
This page has a .md twin and JSON over the API.