Update README.md
Browse files
README.md
CHANGED
@@ -1,3 +1,4 @@
|
|
|
|
1 |
---
|
2 |
language:
|
3 |
- ar
|
@@ -11,7 +12,7 @@ widget:
|
|
11 |
---
|
12 |
|
13 |
**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).
|
14 |
-
|
15 |
|
16 |
|
17 |
# BibTex
|
|
|
1 |
+
<img src="https://raw.githubusercontent.com/UBC-NLP/marbert/main/ARBERT_MARBERT.jpg" alt="drawing" width="10%" height="10%" align="right"/>
|
2 |
---
|
3 |
language:
|
4 |
- ar
|
|
|
12 |
---
|
13 |
|
14 |
**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).
|
15 |
+
|
16 |
|
17 |
|
18 |
# BibTex
|