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275a230
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Add multilingual to the language tag

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

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  1. README.md +3 -2
README.md CHANGED
@@ -2,11 +2,12 @@
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  language:
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  - ar
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  - en
 
 
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  tags:
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  - bert
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  - roberta
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  - exbert
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- license: mit
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  datasets:
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  - arabic_billion_words
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  - cc100
@@ -23,7 +24,7 @@ tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/roberta-large-eng-ara-128k")
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  model = AutoModelForMaskedLM.from_pretrained("jhu-clsp/roberta-large-eng-ara-128k")
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  ```
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- `roberta-large-eng-ara-128k` is an EnglishArabic 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 EnglishArabic joint vocabulary using SentencePiece (Kudo and Richardson, 2018) with size of 128K. We additionally enforce coverage of all Arabic characters after normalization.
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  ## Pretraining Detail
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  language:
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  - ar
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  - en
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+ - multilingual
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+ license: mit
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  tags:
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  - bert
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  - roberta
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  - exbert
 
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  datasets:
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  - arabic_billion_words
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  - cc100
 
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  model = AutoModelForMaskedLM.from_pretrained("jhu-clsp/roberta-large-eng-ara-128k")
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  ```
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+ `roberta-large-eng-ara-128k` is an EnglishArabic 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 EnglishArabic joint vocabulary using SentencePiece (Kudo and Richardson, 2018) with size of 128K. We additionally enforce coverage of all Arabic characters after normalization.
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  ## Pretraining Detail
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