readme: ---
  language: fa
  tags:
  - bert-fa
  - bert-persian
  - persian-lm
  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.
  
  ## 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](https://arxiv.org/abs/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](https://huggingface.co/models?search=bert-fa) to look for
  fine-tuned versions on a task that interests you.
  
  
  ### How to use
  
  #### TensorFlow 2.0
  
  ```python
  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
  
  ```python
  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](https://dumps.wikimedia.org/fawiki/), [MirasText](https://github.com/miras-tech/MirasText)) and six other manually crawled text data from a various type of websites ([BigBang Page](https://bigbangpage.com/) `scientific`, [Chetor](https://www.chetor.com/) `lifestyle`, [Eligasht](https://www.eligasht.com/Blog/) `itinerary`,  [Digikala](https://www.digikala.com/mag/) `digital magazine`, [Ted Talks](https://www.ted.com/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).
  
  ``` bash
  ***** 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](https://huggingface.co/HooshvareLab/bert-fa-base-uncased) 
  
  #### ParsBERT v2.0 Sentiment Analysis
  - [HooshvareLab/bert-fa-base-uncased-sentiment-digikala](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-digikala) 
  - [HooshvareLab/bert-fa-base-uncased-sentiment-snappfood](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-snappfood) 
  - [HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary) 
  - [HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-multi](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-multi) 
  
  #### ParsBERT v2.0 Text Classification
  - [HooshvareLab/bert-fa-base-uncased-clf-digimag](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-clf-digimag) 
  - [HooshvareLab/bert-fa-base-uncased-clf-persiannews](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-clf-persiannews) 
  
  #### ParsBERT v2.0 NER 
  - [HooshvareLab/bert-fa-base-uncased-ner-peyma](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-ner-peyma) 
  - [HooshvareLab/bert-fa-base-uncased-ner-arman](https://huggingface.co/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