DadmaTools: A Python NLP Library for Persian

DadmaTools

DadmaTools is a repository for Natural Language Processing resources for the Persian Language. The aim is to make it easier and more applicable to practitioners in the industry to use Persian NLP, and hence this project is licensed to allow commercial use. The project features code examples on how to use the models in popular NLP frameworks such as spaCy and Transformers, as well as Deep Learning frameworks such as PyTorch. Furthermore, DadmaTools support common Persian embedding and Persian datasets. for more details about how to use this tool read the instruction below.

Contents:

• Installation
• NLP Models
  • Normalizer
  • Pipline (tok,lem,dep,pos,cons,chunk,kasreh,spellchecker)
• Datasets
• Embeddings
• Evaluation
• How to use in colab
• Cite us

Installation

To get started using DadmaTools, install the project with pip, it supports python 10, 11 and 12 :

• Full Version
  Includes all features, including transformers and trainable modules:

  pip install dadmatools[full]
  

• Colab Version
  For installing in colab and kaggle environment:

  pip install dadmatools[colab]
  

NLP Models

Natural Language Processing is an active area of research, and it consists of many different tasks. The DadmaTools repository provides an overview of Persian models for some of the most basic NLP tasks (and is continuously evolving).

Here is the list of NLP tasks we currently cover in the repository. These NLP tasks are defined as pipelines. Therefore, a pipeline list must be created and passed through the model. This will allow the user to choose the only task needed without loading others. Each task has its abbreviation as follows:

• Named Entity Recognition: ner
• Part of speech tagging: pos
• Dependency parsing: dep
• Constituency parsing: cons
• Kasreh Ezafe Detection: kasreh
• Chunking: chunk
• Lemmatizing: lem
• Tokenizing: tok
• Spellchecker: spellchecker
• Normalizing
• informal2formal: itf
• Sentiment analysis: sent

Note that the normalizer can be used outside of the pipeline as there are several configs (the default config is in the pipeline with the name of def-norm). Note that if no pipeline is passed to the model, the tokenizer will be loaded as default.

Normalizer

cleaning text and unify characters.

Note: None means no action!

from dadmatools.normalizer import Normalizer

normalizer = Normalizer(
    full_cleaning=False,
    unify_chars=True,
    refine_punc_spacing=True,
    remove_extra_space=True,
    remove_puncs=False,
    remove_html=False,
    remove_stop_word=False,
    replace_email_with="<EMAIL>",
    replace_number_with=None,
    replace_url_with="",
    replace_mobile_number_with=None,
    replace_emoji_with=None,
    replace_home_number_with=None
)

text = """
<p>
دادماتولز اولین نسخش سال ۱۴۰۰ منتشر شده. 
امیدواریم که این تولز بتونه کار با متن رو براتون شیرین‌تر و راحت‌تر کنه
لطفا با ایمیل dadmatools@dadmatech.ir با ما در ارتباط باشید
آدرس گیت‌هاب هم که خب معرف حضور مبارک هست:
 https://github.com/Dadmatech/DadmaTools
</p>
"""
normalized_text = normalizer.normalize(text)
# <p> دادماتولز اولین نسخش سال 1400 منتشر شده. امیدواریم که این تولز بتونه کار با متن رو براتون شیرین‌تر و راحت‌تر کنه لطفا با ایمیل <EMAIL> با ما در ارتباط باشید آدرس گیت‌هاب هم که خب معرف حضور مبارک هست: </p>

# full cleaning
normalizer = Normalizer(full_cleaning=True)
normalized_text = normalizer.normalize(text)
# دادماتولز نسخش سال منتشر تولز بتونه کار متن براتون شیرین‌تر راحت‌تر کنه ایمیل ارتباط آدرس گیت‌هاب معرف حضور مبارک

Pipeline

Containing Tokenizer, Lemmatizer, POS Tagger, Dependancy Parser, Constituency Parser, Kasreh, Spellcheker, Infromal To Formal, Name Entity Recognation.

import dadmatools.pipeline.language as language

# here lemmatizer and pos tagger will be loaded
# as tokenizer is the default tool, it will be loaded as well even without calling
pips = 'tok,lem,pos,dep,chunk,cons,spellchecker,kasreh,itf,ner,sent'
nlp = language.Pipeline(pips)
# doc is now a proper spaCy Doc object
doc = nlp('کشور بزرگ ایران توانسته در طی سال‌ها اقشار مختلفی از قومیت‌های گوناگون را به خوبی در خودش جا بده')

The pipeline now returns a proper spaCy Doc object that conforms to spaCy's Doc type. This provides full compatibility with spaCy's ecosystem and allows you to use all spaCy features and utilities.

Key Features of the spaCy Doc Object:

• Standard spaCy attributes: doc.text, doc.ents, doc.sents, etc.
• Token-level annotations: token.pos_, token.lemma_, token.dep_, token.head, etc.
• Custom extensions: token._.kasreh, doc._.lang, doc._.sentiment, doc._.spellchecker
• Entity recognition: Named entities are properly converted to spaCy's entity format
• Sentence boundaries: Sentence segmentation is preserved

Example Usage:

# Access token information
for token in doc:
    print(f"Token: {token.text}, POS: {token.pos_}, Lemma: {token.lemma_}, Dep: {token.dep_}")

# Access entities
for ent in doc.ents:
    print(f"Entity: {ent.text}, Type: {ent.label_}")

# Access custom attributes
print(f"Language: {doc._.lang}")
print(f"Sentiment: {doc._.sentiment}")

# Use spaCy utilities
from spacy import displacy
displacy.serve(doc, style="dep")  # Visualize dependency tree

Backward Compatibility:

You can still get the original dictionary format by setting return_spacy_doc=False:

# Get original dictionary format
doc_dict = nlp(text, return_spacy_doc=False)
print(type(doc_dict))  # <class 'dict'>

# Get spaCy Doc object (default)
doc = nlp(text)  # or nlp(text, return_spacy_doc=True)
print(type(doc))  # <class 'spacy.tokens.Doc'>

{'spellchecker': {'orginal': 'کشور بزرگ ایران توانسته در طی سال\u200cها اغشار مختلفی از قومیت\u200cهای گوناگون رو به خوبی تو خودش  جا بده', 'corrected': 'کشور بزرگ ایران توانسته در طی سال\u200cها اقشار مختلفی از قومیت\u200cهای گوناگون رو به خوبی تو خودش جا بده', 'checked_words': [('اغشار', 'اقشار')]}, 'itf': ' کشور بزرگ ایران توانسته در طی سال\u200cها اغشار مختلفی از قومیت های گوناگون را به خوبی در خودش جا بده', 'sentences': [{'id': 1, 'tokens': [{'id': 1, 'text': 'کشور', 'upos': 'NOUN', 'xpos': 'N_PL', 'feats': 'Number=Plur|Person=2|Polarity=Neg|Tense=Pres', 'head': 14, 'deprel': 'nsubj', 'lemma': 'کشور', 'ner': 'O', 'kasreh': 'S-kasreh'}, {'id': 2, 'text': 'بزرگ', 'upos': 'ADJ', 'xpos': 'ADJ', 'feats': 'Degree=Pos', 'head': 8, 'deprel': 'amod', 'lemma': 'بزرگ', 'ner': 'O', 'kasreh': 'S-kasreh'}, {'id': 3, 'text': 'ایران', 'upos': 'SCONJ', 'xpos': 'CON', 'feats': 'Number=Plur|Person=3|PronType=Prs', 'head': 2, 'deprel': 'nmod:poss', 'lemma': 'ایران', 'ner': 'S-loc', 'kasreh': 'O'}, {'id': 4, 'text': 'توانسته', 'upos': 'VERB', 'xpos': 'V_PP', 'feats': 'Number=Sing|Person=3|VerbForm=Part', 'head': 14, 'deprel': 'aux', 'lemma': 'توانست#توان', 'ner': 'O', 'kasreh': 'O'}, {'id': 5, 'text': 'در', 'upos': 'ADP', 'xpos': 'P', 'head': 14, 'deprel': 'case', 'lemma': 'در', 'ner': 'O', 'kasreh': 'O'}, {'id': 6, 'text': 'طی', 'upos': 'ADP', 'xpos': 'P', 'head': 5, 'deprel': 'fixed', 'lemma': 'طی', 'ner': 'O', 'kasreh': 'S-kasreh'}, {'id': 7, 'text': 'سال\u200cها', 'upos': 'AUX', 'xpos': 'V_PRS', 'feats': 'Number=Sing|Person=3|Tense=Pres', 'head': 14, 'deprel': 'fixed', 'lemma': 'سال', 'ner': 'O', 'kasreh': 'O'}, {'id': 8, 'text': 'اغشار', 'upos': 'NOUN', 'xpos': 'N_PL', 'feats': 'Number=Plur', 'head': 19, 'deprel': 'nsubj', 'lemma': 'اغشار', 'ner': 'O', 'kasreh': 'S-kasreh'}, {'id': 9, 'text': 'مختلفی', 'upos': 'ADJ', 'xpos': 'ADJ', 'feats': 'Degree=Pos', 'head': 8, 'deprel': 'amod', 'lemma': 'مختلفی', 'ner': 'O', 'kasreh': 'O'}, {'id': 10, 'text': 'از', 'upos': 'ADP', 'xpos': 'P', 'head': 15, 'deprel': 'case', 'lemma': 'از', 'ner': 'O', 'kasreh': 'O'}, {'id': 11, 'text': 'قومیت\u200cهای', 'upos': 'NOUN', 'xpos': 'N_PL', 'feats': 'Number=Plur', 'head': 8, 'deprel': 'nmod:poss', 'lemma': 'قومیت', 'ner': 'O', 'kasreh': 'S-kasreh'}, {'id': 12, 'text': 'گوناگون', 'upos': 'ADJ', 'xpos': 'ADJ', 'feats': 'Degree=Pos', 'head': 11, 'deprel': 'amod', 'lemma': 'گوناگون', 'ner': 'O', 'kasreh': 'O'}, {'id': 13, 'text': 'رو', 'upos': 'PART', 'xpos': 'CLITIC', 'head': 8, 'deprel': 'case', 'lemma': 'رو', 'ner': 'O', 'kasreh': 'O'}, {'id': 14, 'text': 'به', 'upos': 'ADP', 'xpos': 'P', 'head': 19, 'deprel': 'case', 'lemma': 'به', 'ner': 'O', 'kasreh': 'O'}, {'id': 15, 'text': 'خوبی', 'upos': 'ADJ', 'xpos': 'ADJ', 'feats': 'Degree=Pos', 'head': 14, 'deprel': 'advcl', 'lemma': 'خوب', 'ner': 'O', 'kasreh': 'O'}, {'id': 16, 'text': 'تو', 'upos': 'ADP', 'xpos': 'P', 'feats': 'Number=Sing|Person=2|PronType=Prs', 'head': 19, 'deprel': 'case', 'lemma': 'تو', 'ner': 'O', 'kasreh': 'O'}, {'id': 17, 'text': 'خودش', 'upos': 'PRON', 'xpos': 'PRO', 'feats': 'Number=Sing|Person=3|PronType=Prs|Reflex=Yes', 'head': 19, 'deprel': 'obl', 'lemma': 'خودش', 'ner': 'O', 'kasreh': 'O'}, {'id': 18, 'text': 'جا', 'upos': 'VERB', 'xpos': 'PREV', 'feats': 'Number=Sing|Person=3|Tense=Pres', 'head': 19, 'deprel': 'compound:lvc', 'lemma': 'جا', 'ner': 'O', 'kasreh': 'O'}, {'id': 19, 'text': 'بده', 'upos': 'VERB', 'xpos': 'V_SUB', 'feats': 'Mood=Sub', 'head': 0, 'deprel': 'root', 'lemma': 'داد#ده', 'ner': 'O', 'kasreh': 'O'}]}], 'lang': 'persian', 'sentiment': [{'label': 'positive', 'score': 0.7366364598274231}]}

Loading Persian NLP Datasets

We provide an easy-to-use way to load some popular Persian NLP datasets

Here is the list of supported datasets.

all datasets are iterator and can be used like below:

from dadmatools.datasets import FarsTail
from dadmatools.datasets import SnappfoodSentiment
from dadmatools.datasets import Peyma
from dadmatools.datasets import PerUDT
from dadmatools.datasets import PersianTweets
from dadmatools.datasets import PnSummary


farstail = FarsTail()
#len of dataset
print(len(farstail.train))

#like a generator
print(next(farstail.train))

#dataset details
pn_summary = PnSummary()
print('PnSummary dataset information: ', pn_summary.info)

#loop over dataset
snpfood_sa = SnappfoodSentiment()
for i, item in enumerate(snpfood_sa.test):
    print(item['comment'], item['label'])

#get first tokens' lemma of all dev items
perudt = PerUDT()
for token_list in perudt.dev:
    print(token_list[0]['lemma'])

#get NER tag of first Peyma's data
peyma = Peyma()
print(next(peyma.data)[0]['tag'])

#corpus 
tweets = PersianTweets()
print('tweets count : ', len(tweets.data))
print('sample tweet: ', next(tweets.data))

get dataset info:

from dadmatools.datasets import get_all_datasets_info

get_all_datasets_info().keys()
#dict_keys(['Persian-NEWS', 'fa-wiki', 'faspell', 'PnSummary', 'TEP', 'PerUDT', 'FarsTail', 'Peyma', 'snappfoodSentiment', 'Persian-NER', 'Arman', 'PerSent'])

#specify task
get_all_datasets_info(tasks=['NER', 'Sentiment-Analysis'])

the output will be:

{"ARMAN": {"description": "ARMAN dataset holds 7,682 sentences with 250,015 sentences tagged over six different classes.\n\nOrganization\nLocation\nFacility\nEvent\nProduct\nPerson",
  "filenames": ["train_fold1.txt",
   "train_fold2.txt",
   "train_fold3.txt",
   "test_fold1.txt",
   "test_fold2.txt",
   "test_fold3.txt"],
  "name": "ARMAN",
  "size": {"test": 7680, "train": 15361},
  "splits": ["train", "test"],
  "task": "NER",
  "version": "1.0.0"},
 "PersianNer": {"description": "source: https://github.com/Text-Mining/Persian-NER",
  "filenames": ["Persian-NER-part1.txt",
   "Persian-NER-part2.txt",
   "Persian-NER-part3.txt",
   "Persian-NER-part4.txt",
   "Persian-NER-part5.txt"],
  "name": "PersianNer",
  "size": 976599,
  "splits": [],
  "task": "NER",
  "version": "1.0.0"},
 "Peyma": {"description": "source: http://nsurl.org/2019-2/tasks/task-7-named-entity-recognition-ner-for-farsi/",
  "filenames": ["peyma/600K", "peyma/300K"],
  "name": "Peyma",
  "size": 10016,
  "splits": [],
  "task": "NER",
  "version": "1.0.0"},
 "snappfoodSentiment": {"description": "source: https://huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-snappfood",
  "filenames": ["snappfood/train.csv",
   "snappfood/test.csv",
   "snappfood/dev.csv"],
  "name": "snappfoodSentiment",
  "size": {"dev": 6274, "test": 6972, "train": 56516},
  "splits": ["train", "test", "dev"],
  "task": "Sentiment-Analysis",
  "version": "1.0.0"}}

Loading Persian Word Embeddings

To start using embedding please install fasttext:

pip install fasttext

download, load and use some pre-trained Persian word embeddings.

dadmatools supports all glove, fasttext, and word2vec formats.

from dadmatools.embeddings import get_embedding, get_all_embeddings_info, get_embedding_info
from pprint import pprint

pprint(get_all_embeddings_info())

#get embedding information of specific embedding
embedding_info = get_embedding_info('glove-wiki')

#### load embedding ####
word_embedding = get_embedding('glove-wiki')

#get vector of the word
print(word_embedding['سلام'])

#vocab
vocab = word_embedding.get_vocab()

### some useful functions ###
print(word_embedding.top_nearest("زمستان", 10))
print(word_embedding.similarity('کتب', 'کتاب'))
print(word_embedding.embedding_text('امروز هوای خوبی بود'))

The following word embeddings are currently supported:

Evaluation

We have compared our pos tagging, dependancy parsing, and lemmatization models to stanza and hazm.

How to use

You can see the codes and the output in colab.

Cite

@inproceedings{jafari2025dadmatools,
  title={DadmaTools V2: an Adapter-Based Natural Language Processing Toolkit for the Persian Language},
  author={Jafari, Sadegh and Farsi, Farhan and Ebrahimi, Navid and Sajadi, Mohamad Bagher and Eetemadi, Sauleh},
  booktitle={Proceedings of the 1st Workshop on NLP for Languages Using Arabic Script},
  pages={37--43},
  year={2025}
}