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QARiB: QCRI Arabic and Dialectal BERT

About QARiB Farasa

QCRI Arabic and Dialectal BERT (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text. For the tweets, the data was collected using twitter API and using language filter. lang:ar. For the text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS. QARiB: Is the Arabic name for "Boat".

Model and Parameters:

  • Data size: 14B tokens
  • Vocabulary: 64k
  • Iterations: 10M
  • Number of Layers: 12

Training QARiB

See details in Training QARiB

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

This model expects the data to be segmented. You may use Farasa Segmenter API.

How to use

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

>>>from transformers import pipeline
>>>fill_mask = pipeline("fill-mask", model="./models/bert-base-qarib_far")
>>> fill_mask("و+قام ال+مدير [MASK]")

>>> fill_mask("و+قام+ت ال+مدير+ة [MASK]")
>>> fill_mask("قللي وشفيييك يرحم [MASK]")


Experiment mBERT AraBERT0.1 AraBERT1.0 ArabicBERT QARiB
Dialect Identification 6.06% 59.92% 59.85% 61.70% 65.21%
Emotion Detection 27.90% 43.89% 42.37% 41.65% 44.35%
Named-Entity Recognition (NER) 49.38% 64.97% 66.63% 64.04% 61.62%
Offensive Language Detection 83.14% 88.07% 88.97% 88.19% 91.94%
Sentiment Analysis 86.61% 90.80% 93.58% 83.27% 93.31%

Model Weights and Vocab Download

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


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


    title={Pre-Training BERT on Arabic Tweets: Practical Considerations},
    author={Ahmed Abdelali and Sabit Hassan and Hamdy Mubarak and Kareem Darwish and Younes Samih},
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Datasets used to train qarib/bert-base-qarib_far_9920k