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ParsBERT (v2.0)

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A Transformer-based Model for Persian Language Understanding

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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.

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Persian Sentiment [Digikala, SnappFood, DeepSentiPers]

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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.

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Digikala

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Digikala user comments provided by Open Data Mining Program (ODMP). This dataset contains 62,321 user comments with three labels:

-Label -# -no_idea -10394 -not_recommended -15885 -recommended -36042 -

Download -You can download the dataset from here

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Results

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

-Dataset -ParsBERT v2 -ParsBERT v1 -mBERT -DeepSentiPers -Digikala User Comments -81.72 -81.74* -80.74 -- -

How to use :hugs:

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TaskNotebook
Sentiment AnalysisOpen In Colab
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BibTeX entry and citation info

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Please cite in publications as the following:

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@article{ParsBERT,
-    title={ParsBERT: Transformer-based Model for Persian Language Understanding},
-    author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
-    journal={ArXiv},
-    year={2020},
-    volume={abs/2005.12515}
-}
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Questions?

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Post a Github issue on the ParsBERT Issues repo.

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