UBC-NLP
/

MARBERT

@@ -8,8 +8,11 @@ tags:
   - Masked Langauge Model
 widget:
   - text: "اللغة العربية هي لغة [MASK]."
 ---
 <img src="https://raw.githubusercontent.com/UBC-NLP/marbert/main/ARBERT_MARBERT.jpg" alt="drawing" width="200" height="200" align="right"/>
 **MARBERT** is one of three models described in our **ACL 2021 paper** **["ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic"](https://aclanthology.org/2021.acl-long.551.pdf)**. MARBERT is a large-scale pre-trained masked language model focused on both Dialectal Arabic (DA) and MSA. Arabic has multiple varieties. To train MARBERT, we randomly sample 1B Arabic tweets from a large in-house dataset of about 6B tweets. We only include tweets with at least 3 Arabic words, based on character string matching, regardless whether the tweet has non-Arabic string or not. That is, we do not remove non-Arabic so long as the tweet meets the 3 Arabic word criterion. The dataset makes up **128GB of text** (**15.6B tokens**). We use the same network architecture as ARBERT (BERT-base), but without the next sentence prediction (NSP) objective since tweets are short. See our [repo](https://github.com/UBC-NLP/LMBERT) for modifying BERT code to remove NSP. For more information about MARBERT, please visit our own GitHub [repo](https://github.com/UBC-NLP/marbert).

   - Masked Langauge Model
 widget:
   - text: "اللغة العربية هي لغة [MASK]."
 ---
 <img src="https://raw.githubusercontent.com/UBC-NLP/marbert/main/ARBERT_MARBERT.jpg" alt="drawing" width="200" height="200" align="right"/>
 **MARBERT** is one of three models described in our **ACL 2021 paper** **["ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic"](https://aclanthology.org/2021.acl-long.551.pdf)**. MARBERT is a large-scale pre-trained masked language model focused on both Dialectal Arabic (DA) and MSA. Arabic has multiple varieties. To train MARBERT, we randomly sample 1B Arabic tweets from a large in-house dataset of about 6B tweets. We only include tweets with at least 3 Arabic words, based on character string matching, regardless whether the tweet has non-Arabic string or not. That is, we do not remove non-Arabic so long as the tweet meets the 3 Arabic word criterion. The dataset makes up **128GB of text** (**15.6B tokens**). We use the same network architecture as ARBERT (BERT-base), but without the next sentence prediction (NSP) objective since tweets are short. See our [repo](https://github.com/UBC-NLP/LMBERT) for modifying BERT code to remove NSP. For more information about MARBERT, please visit our own GitHub [repo](https://github.com/UBC-NLP/marbert).