readme: ---
  language: fa
  license: apache-2.0
  ---
  
  # 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`.
  
  
  ### 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/)
  
  ## 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 |
  |---------|-------------|-------------|-------|------------|--------------|----------|----------------|------------|
  | PEYMA   | 93.40*      | 93.10       | 86.64 | -          | 90.59        | -        | 84.00          | -          |
  
  
  ## 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.