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language: fa |
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license: apache-2.0 |
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## ParsBERT: Transformer-based Model for Persian Language Understanding |
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ParsBERT is a monolingual language model based on Google’s BERT architecture with the same configurations as BERT-Base. |
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Paper presenting ParsBERT: [arXiv:2005.12515](https://arxiv.org/abs/2005.12515) |
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All the models (downstream tasks) are uncased and trained with whole word masking. (coming soon stay tuned) |
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## Persian NER [ARMAN, PEYMA, ARMAN+PEYMA] |
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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. |
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### PEYMA |
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PEYMA dataset includes 7,145 sentences with a total of 302,530 tokens from which 41,148 tokens are tagged with seven different classes. |
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1. Organization |
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2. Money |
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3. Location |
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4. Date |
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5. Time |
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6. Person |
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7. Percent |
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| Label | # | |
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|:------------:|:-----:| |
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| Organization | 16964 | |
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| Money | 2037 | |
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| Location | 8782 | |
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| Date | 4259 | |
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| Time | 732 | |
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| Person | 7675 | |
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| Percent | 699 | |
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**Download** |
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You can download the dataset from [here](http://nsurl.org/tasks/task-7-named-entity-recognition-ner-for-farsi/) |
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--- |
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### ARMAN |
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ARMAN dataset holds 7,682 sentences with 250,015 sentences tagged over six different classes. |
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1. Organization |
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2. Location |
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3. Facility |
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4. Event |
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5. Product |
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6. Person |
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| Label | # | |
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|:------------:|:-----:| |
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| Organization | 30108 | |
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| Location | 12924 | |
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| Facility | 4458 | |
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| Event | 7557 | |
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| Product | 4389 | |
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| Person | 15645 | |
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**Download** |
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You can download the dataset from [here](https://github.com/HaniehP/PersianNER) |
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## Results |
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The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures. |
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| Dataset | ParsBERT | MorphoBERT | Beheshti-NER | LSTM-CRF | Rule-Based CRF | BiLSTM-CRF | |
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|:---------------:|:--------:|:----------:|:--------------:|:----------:|:----------------:|:------------:| |
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| ARMAN + PEYMA | 95.13* | - | - | - | - | - | |
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| PEYMA | 98.79* | - | 90.59 | - | 84.00 | - | |
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| ARMAN | 93.10* | 89.9 | 84.03 | 86.55 | - | 77.45 | |
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## How to use :hugs: |
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| Notebook | Description | | |
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|:----------|:-------------|------:| |
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| [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 | [](https://colab.research.google.com/github/hooshvare/parsbert-ner/blob/master/persian-ner-pipeline.ipynb) | |
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## Cite |
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Please cite the following paper in your publication if you are using [ParsBERT](https://arxiv.org/abs/2005.12515) in your research: |
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```markdown |
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@article{ParsBERT, |
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title={ParsBERT: Transformer-based Model for Persian Language Understanding}, |
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author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri}, |
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journal={ArXiv}, |
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year={2020}, |
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volume={abs/2005.12515} |
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} |
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``` |
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## Acknowledgments |
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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. |
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## Contributors |
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- Mehrdad Farahani: [Linkedin](https://www.linkedin.com/in/m3hrdadfi/), [Twitter](https://twitter.com/m3hrdadfi), [Github](https://github.com/m3hrdadfi) |
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- Mohammad Gharachorloo: [Linkedin](https://www.linkedin.com/in/mohammad-gharachorloo/), [Twitter](https://twitter.com/MGharachorloo), [Github](https://github.com/baarsaam) |
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- Marzieh Farahani: [Linkedin](https://www.linkedin.com/in/marziehphi/), [Twitter](https://twitter.com/marziehphi), [Github](https://github.com/marziehphi) |
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- Mohammad Manthouri: [Linkedin](https://www.linkedin.com/in/mohammad-manthouri-aka-mansouri-07030766/), [Twitter](https://twitter.com/mmanthouri), [Github](https://github.com/mmanthouri) |
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- 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/) |
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+ 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/) |
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## Releases |
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### Release v0.1 (May 29, 2019) |
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This is the first version of our ParsBERT NER! |
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