
# 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](https://github.com/hooshvare/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.



### SnappFood

[Snappfood](https://snappfood.ir/) (an online food delivery company) user comments containing 70,000 comments with two labels (i.e. polarity classification): 

1. Happy
2. Sad

|   Label  |   #   |
|:--------:|:-----:|
| Negative | 35000 |
| Positive | 35000 |

**Download**
You can download the dataset from [here](https://drive.google.com/uc?id=15J4zPN1BD7Q_ZIQ39VeFquwSoW8qTxgu)

## Results

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

|          Dataset         | ParsBERT v2 | ParsBERT v1 | mBERT | DeepSentiPers |
|:------------------------:|:-----------:|:-----------:|:-----:|:-------------:|
|  SnappFood User Comments |    87.98    |    88.12*   | 87.87 |       -       |


## How to use :hugs:

| Task                | Notebook                                                                                                                                                                                          |
|---------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Sentiment Analysis | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/hooshvare/parsbert/blob/master/notebooks/Taaghche_Sentiment_Analysis.ipynb) |


### BibTeX entry and citation info

Please cite in publications as the following:

```bibtex
@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}
}
```

## Questions?
Post a Github issue on the [ParsBERT Issues](https://github.com/hooshvare/parsbert/issues) repo.
{"language": "fa", "license": "apache-2.0"}
transformers pytorch tf jax bert text-classification fa license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #jax #bert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

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.


### SnappFood


Snappfood (an online food delivery company) user comments containing 70,000 comments with two labels (i.e. polarity classification):


1. Happy
2. Sad



Download
You can download the dataset from here


Results
-------


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



How to use :hugs:
-----------------



### BibTeX entry and citation info


Please cite in publications as the following:


Questions?
----------


Post a Github issue on the ParsBERT Issues repo.



### SnappFood


Snappfood (an online food delivery company) user comments containing 70,000 comments with two labels (i.e. polarity classification):


1. Happy
2. Sad



Download
You can download the dataset from here


Results
-------


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



How to use :hugs:
----------------- ### BibTeX entry and citation info


Please cite in publications as the following:


Questions?
----------


Post a Github issue on the ParsBERT Issues repo.
TAGS
#transformers #pytorch #tf #jax #bert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### SnappFood


Snappfood (an online food delivery company) user comments containing 70,000 comments with two labels (i.e. polarity classification):


1. Happy
2. Sad



Download
You can download the dataset from here


Results
-------


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



How to use :hugs:
----------------- ### BibTeX entry and citation info


Please cite in publications as the following:


Questions?
----------


Post a Github issue on the ParsBERT Issues repo.
passage: TAGS
#transformers #pytorch #tf #jax #bert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### SnappFood


Snappfood (an online food delivery company) user comments containing 70,000 comments with two labels (i.e. polarity classification):


1. Happy
2. Sad



Download
You can download the dataset from here


Results
-------


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



How to use :hugs:
-----------------### BibTeX entry and citation info


Please cite in publications as the following:


Questions?
----------


Post a Github issue on the ParsBERT Issues repo.