
# Indonesian BERT base model (uncased) 

## Model description
It is BERT-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) objective. This 
model is uncased: it does not make a difference between indonesia and Indonesia.

This is one of several other language models that have been pre-trained with indonesian datasets. More detail about 
its usage on downstream tasks (text classification, text generation, etc) is available at [Transformer based Indonesian Language Models](https://github.com/cahya-wirawan/indonesian-language-models/tree/master/Transformers)

## Intended uses & limitations

### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='cahya/bert-base-indonesian-522M')
>>> unmasker("Ibu ku sedang bekerja [MASK] supermarket")

[{'sequence': '[CLS] ibu ku sedang bekerja di supermarket [SEP]',
  'score': 0.7983310222625732,
  'token': 1495},
 {'sequence': '[CLS] ibu ku sedang bekerja. supermarket [SEP]',
  'score': 0.090003103017807,
  'token': 17},
 {'sequence': '[CLS] ibu ku sedang bekerja sebagai supermarket [SEP]',
  'score': 0.025469014421105385,
  'token': 1600},
 {'sequence': '[CLS] ibu ku sedang bekerja dengan supermarket [SEP]',
  'score': 0.017966199666261673,
  'token': 1555},
 {'sequence': '[CLS] ibu ku sedang bekerja untuk supermarket [SEP]',
  'score': 0.016971781849861145,
  'token': 1572}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import BertTokenizer, BertModel

model_name='cahya/bert-base-indonesian-522M'
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertModel.from_pretrained(model_name)
text = "Silakan diganti dengan text apa saja."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in Tensorflow:
```python
from transformers import BertTokenizer, TFBertModel

model_name='cahya/bert-base-indonesian-522M'
tokenizer = BertTokenizer.from_pretrained(model_name)
model = TFBertModel.from_pretrained(model_name)
text = "Silakan diganti dengan text apa saja."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```

## Training data

This model was pre-trained with 522MB of indonesian Wikipedia.
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are 
then of the form:

```[CLS] Sentence A [SEP] Sentence B [SEP]```

{"language": "id", "license": "mit", "datasets": ["wikipedia"], "widget": [{"text": "Ibu ku sedang bekerja [MASK] sawah."}]}
transformers pytorch tf jax bert fill-mask id dataset:wikipedia license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #id #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 


# Indonesian BERT base model (uncased) 

## Model description
It is BERT-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) objective. This 
model is uncased: it does not make a difference between indonesia and Indonesia.

This is one of several other language models that have been pre-trained with indonesian datasets. More detail about 
its usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models

## Intended uses & limitations

### How to use
You can use this model directly with a pipeline for masked language modeling:

Here is how to use this model to get the features of a given text in PyTorch:

and in Tensorflow:


## Training data

This model was pre-trained with 522MB of indonesian Wikipedia.
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are 
then of the form:



# Indonesian BERT base model (uncased) ## Model description
It is BERT-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) objective. This 
model is uncased: it does not make a difference between indonesia and Indonesia.

This is one of several other language models that have been pre-trained with indonesian datasets. More detail about 
its usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models ## Intended uses & limitations ### How to use
You can use this model directly with a pipeline for masked language modeling:

Here is how to use this model to get the features of a given text in PyTorch:

and in Tensorflow: ## Training data

This model was pre-trained with 522MB of indonesian Wikipedia.
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are 
then of the form:
TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #id #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Indonesian BERT base model (uncased) ## Model description
It is BERT-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) objective. This 
model is uncased: it does not make a difference between indonesia and Indonesia.

This is one of several other language models that have been pre-trained with indonesian datasets. More detail about 
its usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models ## Intended uses & limitations ### How to use
You can use this model directly with a pipeline for masked language modeling:

Here is how to use this model to get the features of a given text in PyTorch:

and in Tensorflow: ## Training data

This model was pre-trained with 522MB of indonesian Wikipedia.
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are 
then of the form:
passage: TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #id #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Indonesian BERT base model (uncased)## Model description
It is BERT-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) objective. This 
model is uncased: it does not make a difference between indonesia and Indonesia.

This is one of several other language models that have been pre-trained with indonesian datasets. More detail about 
its usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models## Intended uses & limitations### How to use
You can use this model directly with a pipeline for masked language modeling:

Here is how to use this model to get the features of a given text in PyTorch:

and in Tensorflow:## Training data

This model was pre-trained with 522MB of indonesian Wikipedia.
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are 
then of the form: