
# Indonesian RoBERTa Base
## How to Use

### As Masked Language Model
```python
from transformers import pipeline

pretrained_name = "akahana/roberta-base-indonesia"

fill_mask = pipeline(
    "fill-mask",
    model=pretrained_name,
    tokenizer=pretrained_name
)

fill_mask("Gajah <mask> sedang makan di kebun binatang.")
```

### Feature Extraction in PyTorch
```python
from transformers import RobertaModel, RobertaTokenizerFast

pretrained_name = "akahana/roberta-base-indonesia"
model = RobertaModel.from_pretrained(pretrained_name)
tokenizer = RobertaTokenizerFast.from_pretrained(pretrained_name)

prompt = "Gajah <mask> sedang makan di kebun binatang."
encoded_input = tokenizer(prompt, return_tensors='pt')
output = model(**encoded_input)
```
{"language": "id", "license": "mit", "tags": ["roberta-base-indonesia"], "datasets": ["wikipedia"], "widget": [{"text": "Gajah <mask> sedang makan di kebun binatang."}]}
transformers pytorch tf safetensors roberta feature-extraction roberta-base-indonesia id dataset:wikipedia license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #safetensors #roberta #feature-extraction #roberta-base-indonesia #id #dataset-wikipedia #license-mit #endpoints_compatible #region-us 


# Indonesian RoBERTa Base
## How to Use

### As Masked Language Model


### Feature Extraction in PyTorch

# Indonesian RoBERTa Base ## How to Use ### As Masked Language Model ### Feature Extraction in PyTorch
TAGS
#transformers #pytorch #tf #safetensors #roberta #feature-extraction #roberta-base-indonesia #id #dataset-wikipedia #license-mit #endpoints_compatible #region-us 
 # Indonesian RoBERTa Base ## How to Use ### As Masked Language Model ### Feature Extraction in PyTorch
passage: TAGS
#transformers #pytorch #tf #safetensors #roberta #feature-extraction #roberta-base-indonesia #id #dataset-wikipedia #license-mit #endpoints_compatible #region-us 
# Indonesian RoBERTa Base## How to Use### As Masked Language Model### Feature Extraction in PyTorch