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Update README.md

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@@ -9,27 +9,32 @@ widget:
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  - text: "Budi adalah anak yang pintar karena ia suka <mask>."
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  ---
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- ## Indonesian RoBERTa Base
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  ## How to Use
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  ### As Masked Language Model
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  ```python
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  from transformers import pipeline
 
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  pretrained_name = "akahana/roberta-base-indonesia"
 
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  fill_mask = pipeline(
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  "fill-mask",
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  model=pretrained_name,
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  tokenizer=pretrained_name
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  )
 
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  fill_mask("Budi adalah anak yang pintar karena ia suka <mask>.")
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  ```
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  ### Feature Extraction in PyTorch
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  ```python
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  from transformers import RobertaModel, RobertaTokenizerFast
 
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  pretrained_name = "akahana/roberta-base-indonesia"
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  model = RobertaModel.from_pretrained(pretrained_name)
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  tokenizer = RobertaTokenizerFast.from_pretrained(pretrained_name)
 
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  prompt = "Budi adalah anak yang pintar karena ia suka belajar."
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  encoded_input = tokenizer(prompt, return_tensors='pt')
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  output = model(**encoded_input)
 
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  - text: "Budi adalah anak yang pintar karena ia suka <mask>."
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  ---
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+ # Indonesian RoBERTa Base
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  ## How to Use
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  ### As Masked Language Model
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  ```python
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  from transformers import pipeline
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+
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  pretrained_name = "akahana/roberta-base-indonesia"
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+
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  fill_mask = pipeline(
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  "fill-mask",
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  model=pretrained_name,
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  tokenizer=pretrained_name
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  )
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+
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  fill_mask("Budi adalah anak yang pintar karena ia suka <mask>.")
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  ```
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  ### Feature Extraction in PyTorch
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  ```python
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  from transformers import RobertaModel, RobertaTokenizerFast
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+
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  pretrained_name = "akahana/roberta-base-indonesia"
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  model = RobertaModel.from_pretrained(pretrained_name)
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  tokenizer = RobertaTokenizerFast.from_pretrained(pretrained_name)
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+
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  prompt = "Budi adalah anak yang pintar karena ia suka belajar."
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  encoded_input = tokenizer(prompt, return_tensors='pt')
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  output = model(**encoded_input)