
## ParsBERT: Transformer-based Model for Persian Language Understanding

ParsBERT is a monolingual language model based on Google’s BERT architecture with the same configurations as BERT-Base. 

Paper presenting ParsBERT: [arXiv:2005.12515](https://arxiv.org/abs/2005.12515)

All the models (downstream tasks) are uncased and trained with whole word masking. (coming soon stay tuned)


## Persian NER [ARMAN, PEYMA, ARMAN+PEYMA]

This task aims to extract named entities in the text, such as names and label with appropriate `NER` classes such as locations, organizations, etc. The datasets used for this task contain sentences that are marked with `IOB` format. In this format, tokens that are not part of an entity are tagged as `”O”` the `”B”`tag corresponds to the first word of an object, and the `”I”` tag corresponds to the rest of the terms of the same entity. Both `”B”` and `”I”` tags are followed by a hyphen (or underscore), followed by the entity category. Therefore, the NER task is a multi-class token classification problem that labels the tokens upon being fed a raw text. There are two primary datasets used in Persian NER, `ARMAN`, and `PEYMA`. In ParsBERT, we prepared ner for both datasets as well as a combination of both datasets.



### PEYMA

PEYMA dataset includes 7,145 sentences with a total of 302,530 tokens from which 41,148 tokens are tagged with seven different classes.

1. Organization
2. Money
3. Location
4. Date
5. Time
6. Person
7. Percent


|     Label    |   #   |
|:------------:|:-----:|
| Organization | 16964 |
|     Money    |  2037 |
|   Location   |  8782 |
|     Date     |  4259 |
|     Time     |  732  |
|    Person    |  7675 |
|    Percent   |  699  |



**Download**
You can download the dataset from [here](http://nsurl.org/tasks/task-7-named-entity-recognition-ner-for-farsi/)

---

### ARMAN

ARMAN dataset holds 7,682 sentences with 250,015 sentences tagged over six different classes.

1. Organization
2. Location
3. Facility
4. Event
5. Product
6. Person


|     Label    |   #   |
|:------------:|:-----:|
| Organization | 30108 |
|   Location   | 12924 |
|   Facility   |  4458 |
|     Event    |  7557 |
|    Product   |  4389 |
|    Person    | 15645 |



**Download**
You can download the dataset from [here](https://github.com/HaniehP/PersianNER)



## Results

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

| Dataset         | ParsBERT | MorphoBERT |  Beheshti-NER  |  LSTM-CRF  |  Rule-Based CRF  |  BiLSTM-CRF  |
|:---------------:|:--------:|:----------:|:--------------:|:----------:|:----------------:|:------------:|
|  ARMAN + PEYMA  |   95.13* |      -     |        -       |      -     |         -        |       -      |
|  PEYMA          |   98.79* |      -     |      90.59     |      -     |       84.00      |       -      |
|  ARMAN          |   93.10* |    89.9    |      84.03     |    86.55   |         -        |     77.45    |


## How to use :hugs:
| Notebook     |      Description      |   |
|:----------|:-------------|------:|
| [How to use Pipelines](https://github.com/hooshvare/parsbert-ner/blob/master/persian-ner-pipeline.ipynb)  | Simple and efficient way to use State-of-the-Art models on downstream tasks through transformers | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/hooshvare/parsbert-ner/blob/master/persian-ner-pipeline.ipynb) |


## Cite 

Please cite the following paper in your publication if you are using [ParsBERT](https://arxiv.org/abs/2005.12515) in your research:

```markdown
@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}
}
```


## Acknowledgments

We hereby, express our gratitude to the [Tensorflow Research Cloud (TFRC) program](https://tensorflow.org/tfrc) for providing us with the necessary computation resources. We also thank [Hooshvare](https://hooshvare.com) Research Group for facilitating dataset gathering and scraping online text resources.


## Contributors

- Mehrdad Farahani: [Linkedin](https://www.linkedin.com/in/m3hrdadfi/), [Twitter](https://twitter.com/m3hrdadfi), [Github](https://github.com/m3hrdadfi)
- Mohammad Gharachorloo:  [Linkedin](https://www.linkedin.com/in/mohammad-gharachorloo/), [Twitter](https://twitter.com/MGharachorloo), [Github](https://github.com/baarsaam)
- Marzieh Farahani:  [Linkedin](https://www.linkedin.com/in/marziehphi/), [Twitter](https://twitter.com/marziehphi), [Github](https://github.com/marziehphi)
- Mohammad Manthouri:  [Linkedin](https://www.linkedin.com/in/mohammad-manthouri-aka-mansouri-07030766/), [Twitter](https://twitter.com/mmanthouri), [Github](https://github.com/mmanthouri)
- Hooshvare Team:  [Official Website](https://hooshvare.com/), [Linkedin](https://www.linkedin.com/company/hooshvare), [Twitter](https://twitter.com/hooshvare), [Github](https://github.com/hooshvare), [Instagram](https://www.instagram.com/hooshvare/)

+ And a special thanks to Sara Tabrizi for her fantastic poster design. Follow her on: [Linkedin](https://www.linkedin.com/in/sara-tabrizi-64548b79/), [Behance](https://www.behance.net/saratabrizi), [Instagram](https://www.instagram.com/sara_b_tabrizi/)

## Releases

### Release v0.1 (May 29, 2019)
This is the first version of our ParsBERT NER!

{"language": "fa", "license": "apache-2.0"}
transformers pytorch tf jax bert token-classification fa arxiv:2005.12515 license:apache-2.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tf #jax #bert #token-classification #fa #arxiv-2005.12515 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 

ParsBERT: Transformer-based Model for Persian Language Understanding
--------------------------------------------------------------------


ParsBERT is a monolingual language model based on Google’s BERT architecture with the same configurations as BERT-Base.


Paper presenting ParsBERT: arXiv:2005.12515


All the models (downstream tasks) are uncased and trained with whole word masking. (coming soon stay tuned)


Persian NER [ARMAN, PEYMA, ARMAN+PEYMA]
---------------------------------------


This task aims to extract named entities in the text, such as names and label with appropriate 'NER' classes such as locations, organizations, etc. The datasets used for this task contain sentences that are marked with 'IOB' format. In this format, tokens that are not part of an entity are tagged as '”O”' the '”B”'tag corresponds to the first word of an object, and the '”I”' tag corresponds to the rest of the terms of the same entity. Both '”B”' and '”I”' tags are followed by a hyphen (or underscore), followed by the entity category. Therefore, the NER task is a multi-class token classification problem that labels the tokens upon being fed a raw text. There are two primary datasets used in Persian NER, 'ARMAN', and 'PEYMA'. In ParsBERT, we prepared ner for both datasets as well as a combination of both datasets.


### PEYMA


PEYMA dataset includes 7,145 sentences with a total of 302,530 tokens from which 41,148 tokens are tagged with seven different classes.


1. Organization
2. Money
3. Location
4. Date
5. Time
6. Person
7. Percent



Download
You can download the dataset from here




---


### ARMAN


ARMAN dataset holds 7,682 sentences with 250,015 sentences tagged over six different classes.


1. Organization
2. Location
3. Facility
4. Event
5. Product
6. Person



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:
-----------------



Cite
----


Please cite the following paper in your publication if you are using ParsBERT in your research:


Acknowledgments
---------------


We hereby, express our gratitude to the Tensorflow Research Cloud (TFRC) program for providing us with the necessary computation resources. We also thank Hooshvare Research Group for facilitating dataset gathering and scraping online text resources.


Contributors
------------


* Mehrdad Farahani: Linkedin, Twitter, Github
* Mohammad Gharachorloo: Linkedin, Twitter, Github
* Marzieh Farahani: Linkedin, Twitter, Github
* Mohammad Manthouri: Linkedin, Twitter, Github
* Hooshvare Team: Official Website, Linkedin, Twitter, Github, Instagram


* And a special thanks to Sara Tabrizi for her fantastic poster design. Follow her on: Linkedin, Behance, Instagram


Releases
--------


### Release v0.1 (May 29, 2019)


This is the first version of our ParsBERT NER!



### PEYMA


PEYMA dataset includes 7,145 sentences with a total of 302,530 tokens from which 41,148 tokens are tagged with seven different classes.


1. Organization
2. Money
3. Location
4. Date
5. Time
6. Person
7. Percent



Download
You can download the dataset from here




--- ### ARMAN


ARMAN dataset holds 7,682 sentences with 250,015 sentences tagged over six different classes.


1. Organization
2. Location
3. Facility
4. Event
5. Product
6. Person



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:
-----------------



Cite
----


Please cite the following paper in your publication if you are using ParsBERT in your research:


Acknowledgments
---------------


We hereby, express our gratitude to the Tensorflow Research Cloud (TFRC) program for providing us with the necessary computation resources. We also thank Hooshvare Research Group for facilitating dataset gathering and scraping online text resources.


Contributors
------------


* Mehrdad Farahani: Linkedin, Twitter, Github
* Mohammad Gharachorloo: Linkedin, Twitter, Github
* Marzieh Farahani: Linkedin, Twitter, Github
* Mohammad Manthouri: Linkedin, Twitter, Github
* Hooshvare Team: Official Website, Linkedin, Twitter, Github, Instagram


* And a special thanks to Sara Tabrizi for her fantastic poster design. Follow her on: Linkedin, Behance, Instagram


Releases
-------- ### Release v0.1 (May 29, 2019)


This is the first version of our ParsBERT NER!
TAGS
#transformers #pytorch #tf #jax #bert #token-classification #fa #arxiv-2005.12515 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### PEYMA


PEYMA dataset includes 7,145 sentences with a total of 302,530 tokens from which 41,148 tokens are tagged with seven different classes.


1. Organization
2. Money
3. Location
4. Date
5. Time
6. Person
7. Percent



Download
You can download the dataset from here




--- ### ARMAN


ARMAN dataset holds 7,682 sentences with 250,015 sentences tagged over six different classes.


1. Organization
2. Location
3. Facility
4. Event
5. Product
6. Person



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:
-----------------



Cite
----


Please cite the following paper in your publication if you are using ParsBERT in your research:


Acknowledgments
---------------


We hereby, express our gratitude to the Tensorflow Research Cloud (TFRC) program for providing us with the necessary computation resources. We also thank Hooshvare Research Group for facilitating dataset gathering and scraping online text resources.


Contributors
------------


* Mehrdad Farahani: Linkedin, Twitter, Github
* Mohammad Gharachorloo: Linkedin, Twitter, Github
* Marzieh Farahani: Linkedin, Twitter, Github
* Mohammad Manthouri: Linkedin, Twitter, Github
* Hooshvare Team: Official Website, Linkedin, Twitter, Github, Instagram


* And a special thanks to Sara Tabrizi for her fantastic poster design. Follow her on: Linkedin, Behance, Instagram


Releases
-------- ### Release v0.1 (May 29, 2019)


This is the first version of our ParsBERT NER!
passage: TAGS
#transformers #pytorch #tf #jax #bert #token-classification #fa #arxiv-2005.12515 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
### PEYMA


PEYMA dataset includes 7,145 sentences with a total of 302,530 tokens from which 41,148 tokens are tagged with seven different classes.


1. Organization
2. Money
3. Location
4. Date
5. Time
6. Person
7. Percent



Download
You can download the dataset from here




---### ARMAN


ARMAN dataset holds 7,682 sentences with 250,015 sentences tagged over six different classes.


1. Organization
2. Location
3. Facility
4. Event
5. Product
6. Person



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:
-----------------



Cite
----


Please cite the following paper in your publication if you are using ParsBERT in your research:


Acknowledgments
---------------


We hereby, express our gratitude to the Tensorflow Research Cloud (TFRC) program for providing us with the necessary computation resources. We also thank Hooshvare Research Group for facilitating dataset gathering and scraping online text resources.


Contributors
------------


* Mehrdad Farahani: Linkedin, Twitter, Github
* Mohammad Gharachorloo: Linkedin, Twitter, Github
* Marzieh Farahani: Linkedin, Twitter, Github
* Mohammad Manthouri: Linkedin, Twitter, Github
* Hooshvare Team: Official Website, Linkedin, Twitter, Github, Instagram


* And a special thanks to Sara Tabrizi for her fantastic poster design. Follow her on: Linkedin, Behance, Instagram


Releases
--------### Release v0.1 (May 29, 2019)


This is the first version of our ParsBERT NER!