Hazm
Open-source Persian NLP toolkit for Python, built on NLTK with Hugging Face model loading.
Hazm is a free, open-source choice for Persian text processing, especially for research and prototyping. It's built on NLTK and integrates with Hugging Face Hub, making model management simple. For production-scale multilingual needs, consider Stanza or spaCy with Persian models, but for pure Persian tasks, Hazm's cost and ease of use are hard to beat.
Verified 14d ago · liveness 58/100 · cite: rightaichoice.com/tools/hazm
- 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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Skip Hazm if you need multilingual support, a graphical interface, or high-scale production processing that demands custom tuning beyond its pre-trained models.
While Hazm itself is free, you must download pretrained models (e.g., from Hugging Face Hub), which may require significant disk space and internet bandwidth.
Hazm is free, making it ideal for individual researchers, students, and early-stage projects. Compared to commercial APIs or even open-source alternatives like Stanza (also free), Hazm's cost is unbeatable, though you may need to invest time in model management and potential performance tuning.
In short
Hazm — Open-source Persian NLP toolkit for Python, built on NLTK with Hugging Face model loading. Best for Persian language researchers and computational linguists, Developers building Persian text processing applications, Students learning Persian NLP. Free to use.
What people actually say about Hazm — is it worth it?
We scanned public community sources for Hazm on Aug 26, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Our own analysis of that scan says the posts were off-subject. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
Viability Score
How well maintained and how widely used is Hazm? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 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 free, open-source Python library for Persian natural language processing, built on NLTK and optimized for Persian text. It offers a unified interface for text normalization, sentence and word tokenization, stemming, lemmatization, part-of-speech tagging, chunking, dependency parsing, and loading word and sentence embeddings. Models for these tasks can be automatically downloaded and cached from the Hugging Face Hub, or loaded manually from local files for offline use. Hazm is maintained by the Roshan AI team and is compatible with Python 3.12+. It's designed for researchers, developers, and NLP practitioners who work with Persian text, simplifying workflows like text cleaning, linguistic analysis, and feature extraction.
Behind the Verdict
Hazm offers a comprehensive set of tools for Persian NLP: normalization, tokenization, stemming, lemmatization, POS tagging, chunking, dependency parsing, and embeddings. Its integration with Hugging Face Hub simplifies model distribution and caching, while manual loading supports offline work. As an open-source project under Roshan AI, it's actively maintained and free. Strengths: cost (free), ease of use for Python devs, strong Persian focus. Weaknesses: Persian-only, requires separate model downloads for advanced tasks, no GUI, and may not scale to very large corpora without custom tuning. Fits researchers, students, and developers building Persian applications. Not ideal for multilingual teams or those needing production-grade performance out of the box.
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Real-world workflow fit
Concrete scenarios for the personas Hazm actually fits — and what changes day-one when you adopt it.
You need to normalize and tokenize a corpus of Persian poems and extract noun phrases for thematic analysis.
Outcome: With Hazm, you write a few lines to normalize, tokenize, and chunk text, then run a chunker to extract NPs, saving hours of manual text processing.
You're indexing Persian articles and need to stem or lemmatize terms to improve recall.
Outcome: Using Hazm's Stemmer or Lemmatizer within your pipeline, you transform each token to its base form, enabling more effective search over Persian text.
You have a dataset of Persian customer reviews and need POS tags to build features for an ML model.
Outcome: Hazm's POSTagger quickly annotates your entire dataset, feeding structured linguistic features into your model, accelerating your data prep.
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
- Load pre-trained word and sentence embeddings for similarity tasks
Models Under the Hood
as of 2026-09-13
Limitations
- Hazm is a Python library for Persian NLP that requires pretrained models for advanced tasks such as POS tagging, chunking, and dependency parsing; it is compatible with Python 3.12+ and maintained by the Roshan AI team.
- Models are loaded either automatically from the Hugging Face Hub via a repo_id and model_filename, or manually from a local file path for offline use.
as of 2026-08-26
Verification history
We have re-verified Hazm 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
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Free to cite with attribution — this page re-verifies continuously.
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.
Plans compared
For each published Hazm tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source
$0
Ideal for
Solo developers, researchers, students, and open-source contributors who need free Persian NLP tools and are comfortable managing models via Hugging Face.
What this tier adds
Free, open-source entry point with all core features; requires manual or automatic model downloads.
Where the pricing makes sense
The company stage and team size where Hazm's pricing actually pencils out — and where peers do it cheaper.
Hazm is free, making it ideal for individual researchers, students, and early-stage projects. Compared to commercial APIs or even open-source alternatives like Stanza (also free), Hazm's cost is unbeatable, though you may need to invest time in model management and potential performance tuning.
Setup time & first value
How long it actually takes to get something useful out of Hazm — broken out by persona, not the marketing-page minute.
Most users can install Hazm via pip and run the Quick Start example in under 10 minutes. Downloading pretrained models for POS tagging or dependency parsing may add a few minutes depending on your connection. For the full suite, expect 15-30 minutes to set up and test the first pipeline.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Hazm”, and we withheld 6: 6 could not be judged, because “Hazm” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Hazm.
Official links
Tools that pair well with Hazm
Common stack mates teams adopt alongside Hazm, with the specific reason each pairing earns its keep.
Transformers
The open-source Python library for loading, fine-tuning, and running transformer models across text, vision, audio, and video.
HanLP
Production-grade multilingual NLP toolkit for Chinese and 100+ languages, with deep linguistic analysis.
TextBrewer
Open-source PyTorch toolkit for compressing transformer NLP models via knowledge distillation.
Featured Head-to-Head Comparisons
Hazm vs Surge Ai
Choose Hazm if you need a free, open-source Persian NLP library for research or application development. Choose Surge AI if you need expert human feedback for training or evaluating frontier AI models, especially on complex reasoning tasks. They serve entirely different purposes—Hazm is a tool for processing Persian text programmatically, while Surge AI is a platform for human-in-the-loop alignment of state-of-the-art AI systems.
Hazm vs Praktika
These tools serve completely different needs — Praktika is a mobile app for practicing speaking fluency with AI tutors, while Hazm is a Python library for Persian text processing. If you're a language learner wanting to improve conversation skills, choose Praktika. If you're a developer or researcher working with Persian text, choose Hazm. They are not direct competitors.
Alternatives to Hazm
View all
Transformers
The open-source Python library for loading, fine-tuning, and running transformer models across text, vision, audio, and video.
HanLP
Production-grade multilingual NLP toolkit for Chinese and 100+ languages, with deep linguistic analysis.
TextBrewer
Open-source PyTorch toolkit for compressing transformer NLP models via knowledge distillation.
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