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ParsBERT (v2.0)

A Transformer-based Model for Persian Language Understanding

We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT in other scopes! Please follow the ParsBERT repo for the latest information about previous and current models.

Persian Sentiment [Digikala, SnappFood, DeepSentiPers]

It aims to classify text, such as comments, based on their emotional bias. We tested three well-known datasets for this task: Digikala user comments, SnappFood user comments, and DeepSentiPers in two binary-form and multi-form types.


which is a balanced and augmented version of SentiPers, contains 12,138 user opinions about digital products labeled with five different classes; two positives (i.e., happy and delighted), two negatives (i.e., furious and angry) and one neutral class. Therefore, this dataset can be utilized for both multi-class and binary classification. In the case of binary classification, the neutral class and its corresponding sentences are removed from the dataset.


  1. Negative (Furious + Angry)
  2. Positive (Happy + Delighted)


  1. Furious
  2. Angry
  3. Neutral
  4. Happy
  5. Delighted
Label #
Furious 236
Angry 1357
Neutral 2874
Happy 2848
Delighted 2516

Download You can download the dataset from:


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

Dataset ParsBERT v2 ParsBERT v1 mBERT DeepSentiPers
SentiPers (Multi Class) 71.31* 71.11 - 69.33
SentiPers (Binary Class) 92.42* 92.13 - 91.98

How to use :hugs:

Task Notebook
Sentiment Analysis Open In Colab

BibTeX entry and citation info

Please cite in publications as the following:

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


Post a Github issue on the ParsBERT Issues repo.

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