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ALBERT Persian

A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language

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ALBERT-Persian is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Version 2.0 over various writing styles from numerous subjects (e.g., scientific, novels, news) with more than 3.9M documents, 73M sentences, and 1.3B words, like the way we did for ParsBERT.

Please follow the ALBERT-Persian repo for the latest information about previous and current models.

Persian Text Classification [DigiMag, Persian News]

The task target is labeling texts in a supervised manner in both existing datasets DigiMag and Persian News.


A total of 8,515 articles scraped from Digikala Online Magazine. This dataset includes seven different classes.

  1. Video Games
  2. Shopping Guide
  3. Health Beauty
  4. Science Technology
  5. General
  6. Art Cinema
  7. Books Literature
Label #
Video Games 1967
Shopping Guide 125
Health Beauty 1610
Science Technology 2772
General 120
Art Cinema 1667
Books Literature 254

Download You can download the dataset from here


The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.

Dataset ALBERT-fa-base-v2 ParsBERT-v1 mBERT
Digikala Magazine 92.33 93.59 90.72

BibTeX entry and citation info

Please cite in publications as the following:

  author = {Mehrdad Farahani},
  title = {ALBERT-Persian: A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/m3hrdadfi/albert-persian}},

    title={ParsBERT: Transformer-based Model for Persian Language Understanding},
    author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},


Post a Github issue on the ALBERT-Persian repo.

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