Add multilingual to the language tag

Hi! A PR to add multilingual to the language tag to improve the referencing.

README.md

@@ -2,11 +2,12 @@
 language:
 - ar
 - en
+- multilingual
+license: mit
 tags:
 - bert
 - roberta
 - exbert
-license: mit
 datasets:
 - arabic_billion_words
 - cc100
@@ -23,7 +24,7 @@ tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/roberta-large-eng-ara-128k")
 model = AutoModelForMaskedLM.from_pretrained("jhu-clsp/roberta-large-eng-ara-128k")
 ```
 
-`roberta-large-eng-ara-128k` is an English–Arabic bilingual encoders of 24-layer Transformers (d\_model= 1024), the same size as XLM-R large. We use the same Common Crawl corpus as XLM-R for pretraining. Additionally, we also use English and Arabic Wikipedia, Arabic Gigaword (Parker et al., 2011), Arabic OSCAR (Ortiz Suárez et al., 2020), Arabic News Corpus (El-Khair, 2016), and Arabic OSIAN (Zeroual et al.,2019). In total, we train with 9.2B words of Arabic text and 26.8B words of English text, more than either XLM-R (2.9B words/23.6B words) or GigaBERT v4 (Lan et al., 2020) (4.3B words/6.1B words). We build an English–Arabic joint vocabulary using SentencePiece (Kudo and Richardson, 2018) with size of 128K. We additionally enforce coverage of all Arabic characters after normalization.
+`roberta-large-eng-ara-128k` is an English�Arabic bilingual encoders of 24-layer Transformers (d\_model= 1024), the same size as XLM-R large. We use the same Common Crawl corpus as XLM-R for pretraining. Additionally, we also use English and Arabic Wikipedia, Arabic Gigaword (Parker et al., 2011), Arabic OSCAR (Ortiz Su�rez et al., 2020), Arabic News Corpus (El-Khair, 2016), and Arabic OSIAN (Zeroual et al.,2019). In total, we train with 9.2B words of Arabic text and 26.8B words of English text, more than either XLM-R (2.9B words/23.6B words) or GigaBERT v4 (Lan et al., 2020) (4.3B words/6.1B words). We build an English�Arabic joint vocabulary using SentencePiece (Kudo and Richardson, 2018) with size of 128K. We additionally enforce coverage of all Arabic characters after normalization.
 
 ## Pretraining Detail