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+## 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!