readme: ---
  language: fa
  license: apache-2.0
  ---
  
  ## 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.
  
  
  ### 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   | 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!
  