|
--- |
|
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`. |
|
|
|
|
|
### 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 v2 | ParsBERT v1 | mBERT | MorphoBERT | Beheshti-NER | LSTM-CRF | Rule-Based CRF | BiLSTM-CRF | |
|
|---------|-------------|-------------|-------|------------|--------------|----------|----------------|------------| |
|
| ARMAN | 99.84* | 98.79 | 95.89 | 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) | |
|
|
|
|
|
### 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. |