ALBERT Persian

A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language

میتونی بهش بگی برت_کوچولو

ALBERT-Persian is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Version 2.0 over various writing styles from numerous subjects (e.g., scientific, novels, news) with more than 3.9M documents, 73M sentences, and 1.3B words, like the way we did for ParsBERT.

Please follow the ALBERT-Persian repo for the latest information about previous and current models.

Persian Sentiment [Digikala, SnappFood, DeepSentiPers]

It aims to classify text, such as comments, based on their emotional bias. We tested three well-known datasets for this task: Digikala user comments, SnappFood user comments, and DeepSentiPers in two binary-form and multi-form types.

SnappFood

Snappfood (an online food delivery company) user comments containing 70,000 comments with two labels (i.e. polarity classification):

Happy
Sad

Label	#
Negative	35000
Positive	35000

Download You can download the dataset from here

Results

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

Dataset	ALBERT-fa-base-v2	ParsBERT-v1	mBERT	DeepSentiPers
SnappFood User Comments	85.79	88.12	87.87	-

BibTeX entry and citation info

Please cite in publications as the following:

@misc{ALBERTPersian,
  author = {Mehrdad Farahani},
  title = {ALBERT-Persian: A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/m3hrdadfi/albert-persian}},
}

@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 ALBERT-Persian repo.