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https://github.com/aub-mind/arabert

Pre-trained Transformers for Arabic Language Understanding and Generation (Arabic BERT, Arabic GPT2, Arabic ELECTRA)
https://github.com/aub-mind/arabert

arabert arabic arabic-classification arabic-nlp bert electra farasa gpt2 huggingface-transformer

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Pre-trained Transformers for Arabic Language Understanding and Generation (Arabic BERT, Arabic GPT2, Arabic ELECTRA)

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README 

        
# AraBERTv2 / AraGPT2 / AraELECTRA



![visitors](https://visitor-badge.glitch.me/badge?page_id=wissamantoun.arabert)

[![PyPI version](https://badge.fury.io/py/arabert.svg)](https://badge.fury.io/py/arabert)

![PyPI - Downloads](https://img.shields.io/pypi/dm/arabert)





  

  

  




This repository now contains code and implementation for:

- **AraBERT v0.1/v1**: Original

- **AraBERT v0.2/v2**: Base and large versions with better vocabulary, more data, more training [Read More...](#AraBERT)

- **AraGPT2**: base, medium, large and MEGA. Trained from scratch on Arabic [Read More...](#AraGPT2)

- **AraELECTRA**: Trained from scratch on Arabic [Read More...](#AraELECTRA)



If you want to clone the old repository:

```bash

git clone https://github.com/aub-mind/arabert/

cd arabert && git checkout 6a58ca118911ef311cbe8cdcdcc1d03601123291

```

# Update

- **17-jul-2022:** You can now install arabert via `pip install arabert`

- **8-Oct-2021:** New AraBERT models that better supports tweets and emojies.

- **13-Sep-2021:** Arabic NLP Demo Space on HuggingFace [![Open Space](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://huggingface.co/spaces/aubmindlab/Arabic-NLP)

- **02-Apr-2021:** AraELECTRA powered Arabic Wikipedia QA system [![Open in Streamlit](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://share.streamlit.io/wissamantoun/arabic-wikipedia-qa-streamlit/main)



# Installation



Install AraBERT from PyPI:

```bash

pip install arabert

```



then use it as follows:

```python

from arabert import ArabertPreprocessor

from arabert.aragpt2.grover.modeling_gpt2 import GPT2LMHeadModel

```



# AraBERTv2



## What's New!



`AraBERTv0.2-Twitter-base/large` are two new models for Arabic dialects and tweets, trained by continuing the pre-training using the MLM task on ~60M Arabic tweets (filtered from a collection on 100M).



The two new models have had emojies added to their vocabulary in addition to common words that weren't at first present.

The pre-training was done with a max sentence length of 64 only for 1 epoch.



## Models



AraBERT comes in 6 variants:



More Detail in the AraBERT folder and in the [README](https://github.com/aub-mind/arabert/tree/master/arabert) and in the [AraBERT Paper](https://arxiv.org/abs/2003.00104)



 Model | HuggingFace Model Name | Size (MB/Params)| Pre-Segmentation | DataSet (Sentences/Size/nWords) |

 ---|:---:|:---:|:---:|:---:

AraBERTv0.2-Twitter-base| [bert-base-arabertv02-twitter](https://huggingface.co/aubmindlab/bert-base-arabertv02-twitter) | 543MB / 136M | No | Same as v02 + 60M Multi-Dialect Tweets|

AraBERTv0.2-Twitter-large| [bert-large-arabertv02-twitter](https://huggingface.co/aubmindlab/bert-large-arabertv02-twitter) | 1.38G / 371M | No | Same as v02 + 60M Multi-Dialect Tweets|

AraBERTv0.2-base | [bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) | 543MB / 136M | No | 200M / 77GB / 8.6B |

 AraBERTv0.2-large| [bert-large-arabertv02](https://huggingface.co/aubmindlab/bert-large-arabertv02) | 1.38G / 371M | No | 200M / 77GB / 8.6B |

AraBERTv2-base| [bert-base-arabertv2](https://huggingface.co/aubmindlab/bert-base-arabertv2) | 543MB / 136M | Yes | 200M / 77GB / 8.6B |

AraBERTv2-large| [bert-large-arabertv2](https://huggingface.co/aubmindlab/bert-large-arabertv2) | 1.38G / 371M | Yes | 200M / 77GB / 8.6B |

 AraBERTv0.1-base| [bert-base-arabertv01](https://huggingface.co/aubmindlab/bert-base-arabertv01) | 543MB / 136M | No | 77M / 23GB / 2.7B |

AraBERTv1-base| [bert-base-arabert](https://huggingface.co/aubmindlab/bert-base-arabert) | 543MB / 136M | Yes | 77M / 23GB / 2.7B |



All models are available in the `HuggingFace` model page under the [aubmindlab](https://huggingface.co/aubmindlab/) name. Checkpoints are available in PyTorch, TF2 and TF1 formats.



## Better Pre-Processing and New Vocab



We identified an issue with AraBERTv1's wordpiece vocabulary. The issue came from punctuations and numbers that were still attached to words when we trained the wordpiece vocab. We now insert a space between numbers and characters and around punctuation characters.



The new vocabulary was learnt using the `BertWordpieceTokenizer` from the `tokenizers` library, and now supports the Fast tokenizer implementation from the `transformers` library.



**P.S.**: All the old BERT codes should work with the new BERT, just change the model name and check the new preprocessing function



**Please read the section on how to use the [preprocessing function](#Preprocessing)**



## Bigger Dataset and More Compute



We used ~3.5 times more data, and trained for longer.

For Dataset Sources see the [Dataset Section](#Dataset)



Model | Hardware | num of examples with seq len (128 / 512) |128 (Batch Size/ Num of Steps) | 512 (Batch Size/ Num of Steps) | Total Steps | Total Time (in Days) |

 ---|:---:|:---:|:---:|:---:|:---:|:---:

AraBERTv0.2-base | TPUv3-8 | 420M / 207M | 2560 / 1M | 384/ 2M | 3M | 36

AraBERTv0.2-large | TPUv3-128 | 420M / 207M | 13440 / 250K | 2056 / 300K | 550K | 7

AraBERTv2-base | TPUv3-8 | 420M / 207M | 2560 / 1M | 384/ 2M | 3M | 36

AraBERTv2-large | TPUv3-128 | 520M / 245M | 13440 / 250K | 2056 / 300K | 550K | 7

AraBERT-base (v1/v0.1) | TPUv2-8 | - |512 / 900K | 128 / 300K| 1.2M | 4



# AraGPT2



More details and code are available in the AraGPT2 folder and [README](https://github.com/aub-mind/arabert/blob/master/aragpt2/README.md)



## Model



 Model | HuggingFace Model Name | Size / Params|

 ---|:---:|:---:

 AraGPT2-base | [aragpt2-base](https://huggingface.co/aubmindlab/aragpt2-base) | 527MB/135M |

 AraGPT2-medium | [aragpt2-medium](https://huggingface.co/aubmindlab/aragpt2-medium) |  1.38G/370M  |

 AraGPT2-large | [aragpt2-large](https://huggingface.co/aubmindlab/aragpt2-large) |  2.98GB/792M  |

 AraGPT2-mega | [aragpt2-mega](https://huggingface.co/aubmindlab/aragpt2-mega) |  5.5GB/1.46B  |

 AraGPT2-mega-detector-long | [aragpt2-mega-detector-long](https://huggingface.co/aubmindlab/aragpt2-mega-detector-long) | 516MB/135M |



All models are available in the `HuggingFace` model page under the [aubmindlab](https://huggingface.co/aubmindlab/) name. Checkpoints are available in PyTorch, TF2 and TF1 formats.



## Dataset and Compute



For Dataset Source see the [Dataset Section](#Dataset)



Model | Hardware | num of examples (seq len = 1024) | Batch Size | Num of Steps | Time (in days)

 ---|:---:|:---:|:---:|:---:|:---:

AraGPT2-base | TPUv3-128 | 9.7M | 1792 | 125K | 1.5

AraGPT2-medium | TPUv3-8 | 9.7M | 80 | 1M | 15

AraGPT2-large | TPUv3-128 | 9.7M | 256 | 220k | 3

AraGPT2-mega | TPUv3-128 | 9.7M | 256 | 800K | 9



# AraELECTRA



More details and code are available in the AraELECTRA folder and [README](https://github.com/aub-mind/arabert/blob/master/araelectra/README.md)



## Model



Model | HuggingFace Model Name | Size (MB/Params)|

 ---|:---:|:---:

AraELECTRA-base-generator | [araelectra-base-generator](https://huggingface.co/aubmindlab/araelectra-base-generator) |  227MB/60M  |

AraELECTRA-base-discriminator | [araelectra-base-discriminator](https://huggingface.co/aubmindlab/araelectra-base-discriminator) |  516MB/135M  |



## Dataset and Compute

Model | Hardware | num of examples (seq len = 512) | Batch Size | Num of Steps | Time (in days)

 ---|:---:|:---:|:---:|:---:|:---:

ELECTRA-base | TPUv3-8 | - | 256 | 2M | 24



# Dataset



The pretraining data used for the new **AraBERT** model is also used for **AraGPT2 and AraELECTRA**.



The dataset consists of 77GB or 200,095,961 lines or 8,655,948,860 words or 82,232,988,358 chars (before applying Farasa Segmentation)



For the new dataset we added the unshuffled OSCAR corpus, after we thoroughly filter it, to the previous dataset used in AraBERTv1 but with out the websites that we previously crawled:

- OSCAR unshuffled and filtered.

- [Arabic Wikipedia dump](https://archive.org/details/arwiki-20190201) from 2020/09/01

- [The 1.5B words Arabic Corpus](https://www.semanticscholar.org/paper/1.5-billion-words-Arabic-Corpus-El-Khair/f3eeef4afb81223df96575adadf808fe7fe440b4)

- [The OSIAN Corpus](https://www.aclweb.org/anthology/W19-4619)

- Assafir news articles. Huge thank you for Assafir for the data



# Preprocessing



It is recommended to apply our preprocessing function before training/testing on any dataset.

**Install farasapy to segment text for AraBERT v1 & v2 `pip install farasapy`**



```python

from arabert.preprocess import ArabertPreprocessor



model_name = "aubmindlab/bert-base-arabertv2"

arabert_prep = ArabertPreprocessor(model_name=model_name)



text = "ولن نبالغ إذا قلنا: إن 'هاتف' أو 'كمبيوتر المكتب' في زمننا هذا ضروري"

arabert_prep.preprocess(text)

>>>"و+ لن نبالغ إذا قل +نا : إن ' هاتف ' أو ' كمبيوتر ال+ مكتب ' في زمن +نا هذا ضروري"

```



You can also use the `unpreprocess()` function to reverse the preprocessing changes, by fixing the spacing around non alphabetical characters, and also de-segmenting if the model selected need pre-segmentation. We highly recommend unprocessing generated content of `AraGPT2` model, to make it look more natural.

```python

output_text = "و+ لن نبالغ إذا قل +نا : إن ' هاتف ' أو ' كمبيوتر ال+ مكتب ' في زمن +نا هذا ضروري"

arabert_prep.unpreprocess(output_text)

>>>"ولن نبالغ إذا قلنا: إن 'هاتف' أو 'كمبيوتر المكتب' في زمننا هذا ضروري"

```



### The `ArabertPreprocessor` class:



```python

ArabertPreprocessor(

  model_name= "",

  keep_emojis = False,

  remove_html_markup = True,

  replace_urls_emails_mentions = True,

  strip_tashkeel = True,

  strip_tatweel = True,

  insert_white_spaces = True,

  remove_non_digit_repetition = True,

  replace_slash_with_dash = None,

  map_hindi_numbers_to_arabic = None,

  apply_farasa_segmentation = None

)

```



- **model_name** (`str`): model name from the HuggingFace Models page without the aubmindlab tag. Will default to a base Arabic preprocessor if model name was not found.



- **keep_emojis**(`bool`, `optional`, defaults to `False`): don't remove emojis while preprocessing.



- **remove_html_markup**( `bool`, `optional`, defaults to `True`): Whether to remove html artfacts,

  should be set to False when preprocessing TyDi QA.



- **replace_urls_emails_mentions**(`bool`, `optional`, defaults to `True`): Whether to replace email urls

  and mentions by special tokens.



- **strip_tashkeel**(`bool`, `optional`, defaults to `True`): remove diacritics (FATHATAN, DAMMATAN, KASRATAN, FATHA, DAMMA,  KASRA, SUKUN, SHADDA).



- **strip_tatweel**(`bool`, `optional`, defaults to `True`): remove tatweel '\\u0640'.



- **insert_white_spaces**(`bool`, `optional`, defaults to `True`): insert whitespace before and after all non Arabic digits or English digits or Arabic and English Alphabet or the 2 brackets, then inserts whitespace between words and numbers or numbers and words.



- **remove_non_digit_repetition**(`bool`, `optional`, defaults to `True`): replace repetition of more than 2 non-digit character with 2 of this character.



- **replace_slash_with_dash**(`bool`, `optional`, defaults to `None`): Will be automatically set to True in AraBERTv02,  AraELECTRA and AraGPT2.

  - Set to False to force disable, and True to force enable. Replaces the "/"  with "-", since "/" is missing from AraBERTv2, AraELECTRA and ARAGPT2 vocabulary.



- **map_hindi_numbers_to_arabic**(`bool`, `optional`, defaults to `None`): Will be automatically set to True in AraBERTv02, AraELECTRA and AraGPT2.Set to False to force disable, and True to force enable.

  - Replaces hindi numbers with the corresponding Arabic one. ex: "١٩٩٥" --> "1995". This is behavior is present by default in AraBERTv1 and v2 (with pre-segmentation), and fixes the issue of caused by a bug when inserting white spaces.



- **apply_farasa_segmentation**(`bool`, `optional`, defaults to `None`): Will be automatically set to True in

  AraBERTv2, and AraBERTv1. Set to False to force disable, and True to force enable.



# Examples Notebooks



- You can find the old examples that work with AraBERTv1 in the `examples/old` folder

- Check the [Readme.md](https://github.com/aub-mind/arabert/tree/master/examples) file in the examples folder for new links to colab notebooks



# TensorFlow 1.x models



**You can find the PyTorch, TF2 and TF1 models in HuggingFace's Transformer Library under the ```aubmindlab``` username**



- `wget https://huggingface.co/aubmindlab/MODEL_NAME/resolve/main/tf1_model.tar.gz` where `MODEL_NAME` is any model under the `aubmindlab` name



# If you used this model please cite us as :

## AraBERT

Google Scholar has our Bibtex wrong (missing name), use this instead

```

@inproceedings{antoun2020arabert,

  title={AraBERT: Transformer-based Model for Arabic Language Understanding},

  author={Antoun, Wissam and Baly, Fady and Hajj, Hazem},

  booktitle={LREC 2020 Workshop Language Resources and Evaluation Conference 11--16 May 2020},

  pages={9}

}

```

## AraGPT2

```

@inproceedings{antoun-etal-2021-aragpt2,

    title = "{A}ra{GPT}2: Pre-Trained Transformer for {A}rabic Language Generation",

    author = "Antoun, Wissam  and

      Baly, Fady  and

      Hajj, Hazem",

    booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",

    month = apr,

    year = "2021",

    address = "Kyiv, Ukraine (Virtual)",

    publisher = "Association for Computational Linguistics",

    url = "https://www.aclweb.org/anthology/2021.wanlp-1.21",

    pages = "196--207",

}

```



## AraELECTRA

```

@inproceedings{antoun-etal-2021-araelectra,

    title = "{A}ra{ELECTRA}: Pre-Training Text Discriminators for {A}rabic Language Understanding",

    author = "Antoun, Wissam  and

      Baly, Fady  and

      Hajj, Hazem",

    booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",

    month = apr,

    year = "2021",

    address = "Kyiv, Ukraine (Virtual)",

    publisher = "Association for Computational Linguistics",

    url = "https://www.aclweb.org/anthology/2021.wanlp-1.20",

    pages = "191--195",

}

```



# Acknowledgments

Thanks to TensorFlow Research Cloud (TFRC) for the free access to Cloud TPUs, couldn't have done it without this program, and to the [AUB MIND Lab](https://sites.aub.edu.lb/mindlab/) Members for the continous support. Also thanks to [Yakshof](https://www.yakshof.com/#/) and Assafir for data and storage access. Another thanks for Habib Rahal (https://www.behance.net/rahalhabib), for putting a face to AraBERT.



# Contacts

**Wissam Antoun**: [Linkedin](https://www.linkedin.com/in/wissam-antoun-622142b4/) | [Twitter](https://twitter.com/wissam_antoun) | [Github](https://github.com/WissamAntoun) | wfa07 (AT) mail (DOT) aub (DOT) edu | wissam.antoun (AT) gmail (DOT) com



**Fady Baly**: [Linkedin](https://www.linkedin.com/in/fadybaly/) | [Twitter](https://twitter.com/fadybaly) | [Github](https://github.com/fadybaly) | fgb06 (AT) mail (DOT) aub (DOT) edu | baly.fady (AT) gmail (DOT) com