Create README.md

README.md

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+# QARiB: QCRI Arabic and Dialectal BERT
+
+## About QARiB
+<img src="./Qarib_logo.png" width="100" align="left"/>
+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/).
+
+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](./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](./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("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'}]
+
+>>> 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'}]
+
+
+```
+
+## Evaluations:
+
+|**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
+
+## Contacts
+
+Ahmed Abdelali, Sabit Hassan, Hamdy Mubarak, Kareem Darwish and Younes Samih
+
+## Reference
+```
+@article{abdelali2020qarib,
+  title={QARiB: QCRI Arabic and Dialectal BERT},
+  author={Ahmed, Abdelali and Sabit, Hassan and Hamdy, Mubarak and Kareem, Darwish and Younes, Samih},
+  link={https://github.com/qcri/QARIB},
+  year={2020}
+}
+```
+