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language: fa |
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license: apache-2.0 |
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--- |
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# ParsBERT (v2.0) |
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A Transformer-based Model for Persian Language Understanding |
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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! |
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Please follow the [ParsBERT](https://github.com/hooshvare/parsbert) repo for the latest information about previous and current models. |
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## Persian NER [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`. |
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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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## 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 v2 | ParsBERT v1 | mBERT | MorphoBERT | Beheshti-NER | LSTM-CRF | Rule-Based CRF | BiLSTM-CRF | |
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|---------|-------------|-------------|-------|------------|--------------|----------|----------------|------------| |
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| PEYMA | 93.40* | 93.10 | 86.64 | - | 90.59 | - | 84.00 | - | |
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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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### BibTeX entry and citation info |
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Please cite in publications as the following: |
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```bibtex |
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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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## Questions? |
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Post a Github issue on the [ParsBERT Issues](https://github.com/hooshvare/parsbert/issues) repo. |
