Bert Base Qarib60 860k

🚀 QARiB: QCRI Arabic and Dialectal BERT

QARiB is a model trained on a large - scale Arabic dataset, which can be used for masked language modeling and other NLP tasks, and shows excellent performance in multiple downstream tasks.

🚀 Quick Start

You can quickly start using QARiB by referring to the following steps. For more details, see Using QARiB

✨ Features

• Large - scale Training Data: The model was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.
• Multiple Downstream Tasks: Suitable for multiple NLP downstream tasks such as sentiment analysis, emotion detection, etc., and outperforms multilingual BERT/AraBERT/ArabicBERT.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

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

>>>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'}]

📚 Documentation

About QARiB

The 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 the Twitter API and the language filter lang:ar. For text data, it was a combination from Arabic GigaWord, Abulkhair Arabic Corpus and OPUS.

bert - base - qarib60_860k

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

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.

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 tasks:

• Sentiment Analysis
• Emotion Detection
• Named - Entity Recognition (NER)
• Offensive Language Detection
• Dialect Identification

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

Model Weights and Vocab Download

You can download the model weights and vocab from the 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}
}