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	---
	language: ar
	tags:
	- pytorch
	- tf
	- bert-base-qarib60_860k
	- qarib
	datasets:
	- arabic_billion_words
	- open_subtitles
	- twitter
	metrics:
	- f1
	widget:
	- text: " شو عندكم يا [MASK] ."
	---

	# QARiB: QCRI Arabic and Dialectal BERT

	## About QARiB
	QCRI Arabic and Dialectal BERT (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.
	For tweets, the data was collected using twitter API and using language filter. `lang:ar`. For text data, it was a combination from
	[Arabic GigaWord](url), [Abulkhair Arabic Corpus]() and [OPUS](http://opus.nlpl.eu/).

	### bert-base-qarib60_860k
	- Data size: 60Gb
	- Number of Iterations: 860k
	- Loss: 2.2454472

	## Training QARiB
	The training of the model has been performed using Google’s original Tensorflow code on Google Cloud TPU v2.
	We used a Google Cloud Storage bucket, for persistent storage of training data and models.
	See more details in [Training QARiB](https://github.com/qcri/QARiB/blob/main/Training_QARiB.md)

	## Using QARiB

	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. For more details, see [Using QARiB](https://github.com/qcri/QARiB/blob/main/Using_QARiB.md)

	### How to use
	You can use this model directly with a pipeline for masked language modeling:

	```python
	>>>from transformers import pipeline
	>>>fill_mask = pipeline("fill-mask", model="./models/data60gb_86k")

	>>> fill_mask("شو عندكم يا [MASK]")
	[{'sequence': '[CLS] شو عندكم يا عرب [SEP]', 'score': 0.0990147516131401, 'token': 2355, 'token_str': 'عرب'},
	{'sequence': '[CLS] شو عندكم يا جماعة [SEP]', 'score': 0.051633741706609726, 'token': 2308, 'token_str': 'جماعة'},
	{'sequence': '[CLS] شو عندكم يا شباب [SEP]', 'score': 0.046871256083250046, 'token': 939, 'token_str': 'شباب'},
	{'sequence': '[CLS] شو عندكم يا رفاق [SEP]', 'score': 0.03598872944712639, 'token': 7664, 'token_str': 'رفاق'},
	{'sequence': '[CLS] شو عندكم يا ناس [SEP]', 'score': 0.031996358186006546, 'token': 271, 'token_str': 'ناس'}]

	>>> fill_mask("قللي وشفيييك يرحم [MASK]")
	[{'sequence': '[CLS] قللي وشفيييك يرحم والديك [SEP]', 'score': 0.4152909517288208, 'token': 9650, 'token_str': 'والديك'},
	{'sequence': '[CLS] قللي وشفيييك يرحملي [SEP]', 'score': 0.07663793861865997, 'token': 294, 'token_str': '##لي'},
	{'sequence': '[CLS] قللي وشفيييك يرحم حالك [SEP]', 'score': 0.0453166700899601, 'token': 2663, 'token_str': 'حالك'},
	{'sequence': '[CLS] قللي وشفيييك يرحم امك [SEP]', 'score': 0.04390475153923035, 'token': 1942, 'token_str': 'امك'},
	{'sequence': '[CLS] قللي وشفيييك يرحمونك [SEP]', 'score': 0.027349254116415977, 'token': 3283, 'token_str': '##ونك'}]

	>>> fill_mask("وقام المدير [MASK]")
	[
	{'sequence': '[CLS] وقام المدير بالعمل [SEP]', 'score': 0.0678194984793663, 'token': 4230, 'token_str': 'بالعمل'},
	{'sequence': '[CLS] وقام المدير بذلك [SEP]', 'score': 0.05191086605191231, 'token': 984, 'token_str': 'بذلك'},
	{'sequence': '[CLS] وقام المدير بالاتصال [SEP]', 'score': 0.045264165848493576, 'token': 26096, 'token_str': 'بالاتصال'},
	{'sequence': '[CLS] وقام المدير بعمله [SEP]', 'score': 0.03732728958129883, 'token': 40486, 'token_str': 'بعمله'},
	{'sequence': '[CLS] وقام المدير بالامر [SEP]', 'score': 0.0246378555893898, 'token': 29124, 'token_str': 'بالامر'}
	]
	>>> fill_mask("وقامت المديرة [MASK]")

	[{'sequence': '[CLS] وقامت المديرة بذلك [SEP]', 'score': 0.23992691934108734, 'token': 984, 'token_str': 'بذلك'},
	{'sequence': '[CLS] وقامت المديرة بالامر [SEP]', 'score': 0.108805812895298, 'token': 29124, 'token_str': 'بالامر'},
	{'sequence': '[CLS] وقامت المديرة بالعمل [SEP]', 'score': 0.06639821827411652, 'token': 4230, 'token_str': 'بالعمل'},
	{'sequence': '[CLS] وقامت المديرة بالاتصال [SEP]', 'score': 0.05613093823194504, 'token': 26096, 'token_str': 'بالاتصال'},
	{'sequence': '[CLS] وقامت المديرة المديرة [SEP]', 'score': 0.021778125315904617, 'token': 41635, 'token_str': 'المديرة'}]
	```
	## Training procedure

	The training of the model has been performed using Google’s original Tensorflow code on eight core Google Cloud TPU v2.
	We used a Google Cloud Storage bucket, for persistent storage of training data and models.

	## Eval results

	We evaluated QARiB models on five NLP downstream task:
	- Sentiment Analysis
	- Emotion Detection
	- Named-Entity Recognition (NER)
	- Offensive Language Detection
	- Dialect Identification

	The results obtained from QARiB models outperforms multilingual BERT/AraBERT/ArabicBERT.


	## Model Weights and Vocab Download

	From Huggingface site: https://huggingface.co/qarib/bert-base-qarib60_860k

	## Contacts

	Ahmed Abdelali, Sabit Hassan, Hamdy Mubarak, Kareem Darwish and Younes Samih

	## Reference
	```
	@article{abdelali2021pretraining,
	title={Pre-Training BERT on Arabic Tweets: Practical Considerations},
	author={Ahmed Abdelali and Sabit Hassan and Hamdy Mubarak and Kareem Darwish and Younes Samih},
	year={2021},
	eprint={2102.10684},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	```