Hazm
Open-source Persian NLP library for Python, built on NLTK.
An essential library for Persian NLP, but limited to Persian language processing. Ideal for researchers and developers who need a ready-to-use toolkit with Hugging Face integration.
- Persian language researchers and computational linguists
- Developers building Persian text processing applications
- Students learning Persian NLP
- Data scientists working on Persian datasets
- Non-Persian language processing (English, Arabic, etc.)
- Production-ready large-scale deployment without custom models
- Users needing a graphical user interface
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In short
Hazm — Open-source Persian NLP library for Python, built on NLTK. Best for Persian language researchers and computational linguists, Developers building Persian text processing applications, Students learning Persian NLP. Free to use.
What independent users actually report about Hazm
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
72 mentions across 6 sources (Hacker News, YouTube, Bluesky, Stack Overflow, GitHub, Lemmy).
- +Comprehensive Persian NLP pipeline in one library for free.
- +Includes normalization, tokenization, stemming, lemmatization, POS tagging.
- +Supports fastText and sent2vec embeddings.
- +Integrates with Hugging Face Hub for easy model loading.
- +Compatible with Python 3.12+ and pip installation.
- −Installation often fails on Windows due to missing dependencies.
- −API breaks between versions break existing code without migration docs.
- −Inconsistent POS tagging after library update (abstract class errors).
- −Minimal community support; few tutorials or forums.
- −Slow issue resolution on GitHub — some bugs open for years.
- • Potential time cost debugging installation and API changes.
- • No premium support; community help is minimal.
Viability Score
How likely is Hazm to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Text normalization for Persian characters and diacritics
- Sentence tokenization
- Word tokenization
- Stemming for Persian words
- Lemmatization
- Part-of-speech tagging
- Chunking (phrase parsing)
- Syntactic dependency parsing
- Word embedding loading (fastText)
- Sentence embedding loading (sent2vec)
- Automatic model loading from Hugging Face Hub
- Manual model loading for offline use
- Compatible with Python 3.12+
- Installable via pip
About Hazm
Hazm is a comprehensive Python library for processing the Persian language, built on top of NLTK and optimized for Persian text. It provides tools for text normalization, sentence and word tokenization, stemming, lemmatization, part-of-speech tagging, chunking, and dependency parsing. Designed for researchers, developers, and NLP practitioners, Hazm simplifies Persian NLP tasks by offering a unified interface and seamless integration with Hugging Face Hub for model loading. It supports automatic model downloading and caching, as well as manual loading for offline use. What sets Hazm apart is its focus on Persian-specific linguistic features and its maintenance by the Roshan AI team, ensuring ongoing development and support. The library is fully compatible with Python 3.12+ and is available via pip, making it easy to integrate into any Python project. Unlike general-purpose NLP libraries, Hazm addresses Persian-specific challenges such as character normalization, semi-space handling, and stemming tailored to Persian morphology, making it a specialized tool for anyone working with Persian text.
Behind the Verdict
Hazm is a specialized library that fills a gap in Persian NLP. Its deep Hugging Face Hub integration simplifies model management, allowing users to load pretrained models with minimal code. The library covers the full pipeline from tokenization to dependency parsing, and includes word and sentence embeddings. However, its reliance on NLTK may limit performance on very large datasets compared to more modern, performant frameworks. If your sole need is Persian text processing, Hazm is a solid choice. For multilingual projects, you would pair it with other tools. The offline model loading capability is a plus for secure environments. Overall, it's a practical, well-maintained library for research and development.
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Use Cases
- Normalize and clean Persian text from user input or web scraping.
- Tokenize Persian texts into sentences and words for further analysis.
- Perform part-of-speech tagging to identify grammatical roles in Persian sentences.
- Stem or lemmatize Persian words to reduce inflectional forms to base forms.
- Extract noun phrases and verb phrases from Persian text using chunking.
- Parse syntactic dependencies in Persian sentences for deeper linguistic analysis.
Models Under the Hood
Limitations
- Hazm is designed for Persian NLP only and does not support other languages.
- For advanced tasks like dependency parsing, pretrained models must be downloaded, which may require an internet connection.
- The library is built on NLTK, which may not be optimized for high-throughput production environments.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Tools that pair well with Hazm
Common stack mates teams adopt alongside Hazm, with the specific reason each pairing earns its keep.
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