diff --git a/model_cards/qarib/bert-base-qarib60_1790k/README.md b/model_cards/qarib/bert-base-qarib60_1790k/README.md
new file mode 100644
index 0000000000..ae68b78464
--- /dev/null
+++ b/model_cards/qarib/bert-base-qarib60_1790k/README.md
@@ -0,0 +1,96 @@
+---
+language: ar
+tags:
+- qarib
+
+license: apache-2.0
+datasets:
+- Arabic GigaWord
+- Abulkhair Arabic Corpus
+- opus
+- Twitter data
+---
+
+# 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_1790k
+- Data size: 60Gb
+- Number of Iterations: 1790k
+- Loss: 1.8764963
+
+## 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](../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("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
+TBD
+
+## Contacts
+
+Ahmed Abdelali, Sabit Hassan, Hamdy Mubarak, Kareem Darwish and Younes Samih
+
+
diff --git a/model_cards/qarib/bert-base-qarib60_1970k/README.md b/model_cards/qarib/bert-base-qarib60_1970k/README.md
new file mode 100644
index 0000000000..44c0328896
--- /dev/null
+++ b/model_cards/qarib/bert-base-qarib60_1970k/README.md
@@ -0,0 +1,96 @@
+---
+language: ar
+tags:
+- qarib
+
+license: apache-2.0
+datasets:
+- Arabic GigaWord
+- Abulkhair Arabic Corpus
+- opus
+- Twitter data
+---
+
+# 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_1970k
+- Data size: 60Gb
+- Number of Iterations: 1970k
+- Loss: 1.5708898
+
+## 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](../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("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
+TBD
+
+## Contacts
+
+Ahmed Abdelali, Sabit Hassan, Hamdy Mubarak, Kareem Darwish and Younes Samih
+
+
diff --git a/model_cards/qarib/bert-base-qarib60_860k/README.md b/model_cards/qarib/bert-base-qarib60_860k/README.md
new file mode 100644
index 0000000000..4924489b69
--- /dev/null
+++ b/model_cards/qarib/bert-base-qarib60_860k/README.md
@@ -0,0 +1,96 @@
+---
+language: ar
+tags:
+- qarib
+
+license: apache-2.0
+datasets:
+- Arabic GigaWord
+- Abulkhair Arabic Corpus
+- opus
+- Twitter data
+---
+
+# 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](../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("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
+TBD
+
+## Contacts
+
+Ahmed Abdelali, Sabit Hassan, Hamdy Mubarak, Kareem Darwish and Younes Samih
+
+