This model is pre-trained on a large Persian corpus with various writing styles from numerous subjects (e.g., scientific, novels, news) with more than 2M documents. A large subset of this corpus was crawled manually.

As a part of ParsBERT methodology, an extensive pre-processing combining POS tagging and WordPiece segmentation was carried out to bring the corpus into a proper format. This process produces more than 40M true sentences.

Evaluation

ParsBERT is evaluated on three NLP downstream tasks: Sentiment Analysis (SA), Text Classification, and Named Entity Recognition (NER). For this matter and due to insufficient resources, two large datasets for SA and two for text classification were manually composed, which are available for public use and benchmarking. ParsBERT outperformed all other language models, including multilingual BERT and other hybrid deep learning models for all tasks, improving the state-of-the-art performance in Persian language modeling.

Results

The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.

Sentiment Analysis (SA) task

Dataset	ParsBERT	mBERT	DeepSentiPers
Digikala User Comments	81.74*	80.74	-
SnappFood User Comments	88.12*	87.87	-
SentiPers (Multi Class)	71.11*	-	69.33
SentiPers (Binary Class)	92.13*	-	91.98

Text Classification (TC) task

Dataset	ParsBERT	mBERT
Digikala Magazine	93.59*	90.72
Persian News	97.19*	95.79

Named Entity Recognition (NER) task

Dataset	ParsBERT	mBERT	MorphoBERT	Beheshti-NER	LSTM-CRF	Rule-Based CRF	BiLSTM-CRF
PEYMA	93.10*	86.64	-	90.59	-	84.00	-
ARMAN	98.79*	95.89	89.9	84.03	86.55	-	77.45

If you tested ParsBERT on a public dataset and you want to add your results to the table above, open a pull request or contact us. Also make sure to have your code available online so we can add it as a reference

How to use

TensorFlow 2.0

python

from transformers import AutoConfig, AutoTokenizer, TFAutoModel

config = AutoConfig.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
model = AutoModel.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")

text = "ما در هوشواره معتقدیم با انتقال صحیح دانش و آگاهی، همه افراد می‌توانند از ابزارهای هوشمند استفاده کنند. شعار ما هوش مصنوعی برای همه است."
tokenizer.tokenize(text)

>>> ['ما', 'در', 'هوش', '##واره', 'معتقدیم', 'با', 'انتقال', 'صحیح', 'دانش', 'و', 'اگاهی', '،', 'همه', 'افراد', 'میتوانند', 'از', 'ابزارهای', 'هوشمند', 'استفاده', 'کنند', '.', 'شعار', 'ما', 'هوش', 'مصنوعی', 'برای', 'همه', 'است', '.']

Pytorch

python

from transformers import AutoConfig, AutoTokenizer, AutoModel

config = AutoConfig.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
model = AutoModel.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")

NLP Tasks Tutorial

Coming soon stay tuned

Cite

Please cite the following paper in your publication if you are using ParsBERT 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 for providing us with the necessary computation resources. We also thank Hooshvare Research Group for facilitating dataset gathering and scraping online text resources.

Contributors

Mehrdad Farahani: Linkedin, Twitter, Github
Mohammad Gharachorloo: Linkedin, Twitter, Github
Marzieh Farahani: Linkedin, Twitter, Github
Mohammad Manthouri: Linkedin, Twitter, Github
Hooshvare Team: Official Website, Linkedin, Twitter, Github, Instagram

Releases

Release v0.1 (May 27, 2019)

This is the first version of our ParsBERT based on BERT_BASE