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 repo for the latest information about previous and current models.

Introduction

ParsBERT is a monolingual language model based on Google’s BERT architecture. This model is pre-trained on large Persian corpora with various writing styles from numerous subjects (e.g., scientific, novels, news) with more than 3.9M documents, 73M sentences, and 1.3B words.

Paper presenting ParsBERT: arXiv:2005.12515

Intended uses & limitations

You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you.

How to use

TensorFlow 2.0

from transformers import AutoConfig, AutoTokenizer, TFAutoModel

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

from transformers import AutoConfig, AutoTokenizer, AutoModel

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

Training

ParsBERT trained on a massive amount of public corpora ( Persian Wikidumps , MirasText ) and six other manually crawled text data from a various type of websites ( BigBang Page scientific , Chetor lifestyle , Eligasht itinerary , Digikala digital magazine , Ted Talks general conversational , Books novels, storybooks, short stories from old to the contemporary era ).

As a part of ParsBERT methodology, an extensive pre-processing combining POS tagging and WordPiece segmentation was carried out to bring the corpora into a proper format.

Goals

Objective goals during training are as below (after 300k steps).

***** Eval results *****
global_step = 300000
loss = 1.4392426
masked_lm_accuracy = 0.6865794
masked_lm_loss = 1.4469004
next_sentence_accuracy = 1.0
next_sentence_loss = 6.534152e-05

Derivative models

Base Config

ParsBERT v2.0 Model

• HooshvareLab/bert-fa-base-uncased

ParsBERT v2.0 Sentiment Analysis

• HooshvareLab/bert-fa-base-uncased-sentiment-digikala
• HooshvareLab/bert-fa-base-uncased-sentiment-snappfood
• HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary
• HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-multi

ParsBERT v2.0 Text Classification

• HooshvareLab/bert-fa-base-uncased-clf-digimag
• HooshvareLab/bert-fa-base-uncased-clf-persiannews

ParsBERT v2.0 NER

• HooshvareLab/bert-fa-base-uncased-ner-peyma
• HooshvareLab/bert-fa-base-uncased-ner-arman

Eval results

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.

Sentiment Analysis (SA) Task

Dataset	ParsBERT v2	ParsBERT v1	mBERT	DeepSentiPers
Digikala User Comments	81.72	81.74*	80.74	-
SnappFood User Comments	87.98	88.12*	87.87	-
SentiPers (Multi Class)	71.31*	71.11	-	69.33
SentiPers (Binary Class)	92.42*	92.13	-	91.98

Text Classification (TC) Task

Dataset	ParsBERT v2	ParsBERT v1	mBERT
Digikala Magazine	93.65*	93.59	90.72
Persian News	97.44*	97.19	95.79

Named Entity Recognition (NER) Task

Dataset	ParsBERT v2	ParsBERT v1	mBERT	MorphoBERT	Beheshti-NER	LSTM-CRF	Rule-Based CRF	BiLSTM-CRF
PEYMA	93.40*	93.10	86.64	-	90.59	-	84.00	-
ARMAN	99.84*	98.79	95.89	89.9	84.03	86.55	-	77.45

BibTeX entry and citation info

Please cite in publications as the following:

@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 repo.