
# 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 NER [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`.


### 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 v2 | ParsBERT v1 | mBERT | MorphoBERT | Beheshti-NER | LSTM-CRF | Rule-Based CRF | BiLSTM-CRF |
|---------|-------------|-------------|-------|------------|--------------|----------|----------------|------------|
| ARMAN   | 99.84*      | 98.79       | 95.89 | 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) |


### 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 token-classification fa license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #jax #bert #token-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 NER [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'.


### 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:
-----------------



### BibTeX entry and citation info


Please cite in publications as the following:


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


Post a Github issue on the ParsBERT Issues repo.



### 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:
----------------- ### 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 #token-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### 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:
----------------- ### 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 #token-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### 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:
-----------------### BibTeX entry and citation info


Please cite in publications as the following:


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


Post a Github issue on the ParsBERT Issues repo.