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AraBERTv2 / AraGPT2 / AraELECTRA
<p align="middle"> <img src="https://github.com/aub-mind/arabert/blob/master/arabert_logo.png" width="150" align="left"/> <img src="https://github.com/aub-mind/arabert/blob/master/AraGPT2.png" width="150"/> <img src="https://github.com/aub-mind/arabert/blob/master/AraELECTRA.png" width="150" align="right"/> </p>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...
- AraGPT2: base, medium, large and MEGA. Trained from scratch on Arabic Read More...
- AraELECTRA: Trained from scratch on Arabic Read More...
If you want to clone the old repository:
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
- 02-Apr-2021: AraELECTRA powered Arabic Wikipedia QA system
Installation
Install AraBERT from PyPI:
pip install arabert
then use it as follows:
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 and in the AraBERT Paper
Model | HuggingFace Model Name | Size (MB/Params) | Pre-Segmentation | DataSet (Sentences/Size/nWords) |
---|---|---|---|---|
AraBERTv0.2-Twitter-base | bert-base-arabertv02-twitter | 543MB / 136M | No | Same as v02 + 60M Multi-Dialect Tweets |
AraBERTv0.2-Twitter-large | bert-large-arabertv02-twitter | 1.38G / 371M | No | Same as v02 + 60M Multi-Dialect Tweets |
AraBERTv0.2-base | bert-base-arabertv02 | 543MB / 136M | No | 200M / 77GB / 8.6B |
AraBERTv0.2-large | bert-large-arabertv02 | 1.38G / 371M | No | 200M / 77GB / 8.6B |
AraBERTv2-base | bert-base-arabertv2 | 543MB / 136M | Yes | 200M / 77GB / 8.6B |
AraBERTv2-large | bert-large-arabertv2 | 1.38G / 371M | Yes | 200M / 77GB / 8.6B |
AraBERTv0.1-base | bert-base-arabertv01 | 543MB / 136M | No | 77M / 23GB / 2.7B |
AraBERTv1-base | bert-base-arabert | 543MB / 136M | Yes | 77M / 23GB / 2.7B |
All models are available in the HuggingFace
model page under the 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
Bigger Dataset and More Compute
We used ~3.5 times more data, and trained for longer. For Dataset Sources see the Dataset Section
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
Model
Model | HuggingFace Model Name | Size / Params |
---|---|---|
AraGPT2-base | aragpt2-base | 527MB/135M |
AraGPT2-medium | aragpt2-medium | 1.38G/370M |
AraGPT2-large | aragpt2-large | 2.98GB/792M |
AraGPT2-mega | aragpt2-mega | 5.5GB/1.46B |
AraGPT2-mega-detector-long | aragpt2-mega-detector-long | 516MB/135M |
All models are available in the HuggingFace
model page under the aubmindlab name. Checkpoints are available in PyTorch, TF2 and TF1 formats.
Dataset and Compute
For Dataset Source see the Dataset Section
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
Model
Model | HuggingFace Model Name | Size (MB/Params) |
---|---|---|
AraELECTRA-base-generator | araelectra-base-generator | 227MB/60M |
AraELECTRA-base-discriminator | 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 from 2020/09/01
- The 1.5B words Arabic Corpus
- The OSIAN Corpus
- 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
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.
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:
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 toFalse
): don't remove emojis while preprocessing. -
remove_html_markup(
bool
,optional
, defaults toTrue
): Whether to remove html artfacts, should be set to False when preprocessing TyDi QA. -
replace_urls_emails_mentions(
bool
,optional
, defaults toTrue
): Whether to replace email urls and mentions by special tokens. -
strip_tashkeel(
bool
,optional
, defaults toTrue
): remove diacritics (FATHATAN, DAMMATAN, KASRATAN, FATHA, DAMMA, KASRA, SUKUN, SHADDA). -
strip_tatweel(
bool
,optional
, defaults toTrue
): remove tatweel '\u0640'. -
insert_white_spaces(
bool
,optional
, defaults toTrue
): 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 toTrue
): replace repetition of more than 2 non-digit character with 2 of this character. -
replace_slash_with_dash(
bool
,optional
, defaults toNone
): 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 toNone
): 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 toNone
): 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 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
whereMODEL_NAME
is any model under theaubmindlab
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 Members for the continous support. Also thanks to Yakshof 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 | Twitter | Github | wfa07 (AT) mail (DOT) aub (DOT) edu | wissam.antoun (AT) gmail (DOT) com
Fady Baly: Linkedin | Twitter | Github | fgb06 (AT) mail (DOT) aub (DOT) edu | baly.fady (AT) gmail (DOT) com