
# 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.



### DeepSentiPers

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.

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

**Multi**
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:

- [SentiPers](https://github.com/phosseini/sentipers)
- [DeepSentiPers](https://github.com/JoyeBright/DeepSentiPers)

## 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 |
|:------------------------:|:-----------:|:-----------:|:-----:|:-------------:|
|  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](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.


### DeepSentiPers


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.


Binary:


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


Multi


1. Furious
2. Angry
3. Neutral
4. Happy
5. Delighted



Download
You can download the dataset from:


* SentiPers
* DeepSentiPers


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.



### DeepSentiPers


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.


Binary:


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


Multi


1. Furious
2. Angry
3. Neutral
4. Happy
5. Delighted



Download
You can download the dataset from:


* SentiPers
* DeepSentiPers


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 
 ### DeepSentiPers


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.


Binary:


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


Multi


1. Furious
2. Angry
3. Neutral
4. Happy
5. Delighted



Download
You can download the dataset from:


* SentiPers
* DeepSentiPers


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 
### DeepSentiPers


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.


Binary:


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


Multi


1. Furious
2. Angry
3. Neutral
4. Happy
5. Delighted



Download
You can download the dataset from:


* SentiPers
* DeepSentiPers


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.