Indonesian BERT base model (uncased)

Model description

It is BERT-base model pre-trained with indonesian Wikipedia and indonesian newspapers using a masked language modeling (MLM) objective. This model is uncased.

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:

>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='cahya/bert-base-indonesian-1.5G')
>>> 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:

from transformers import BertTokenizer, BertModel

model_name='cahya/bert-base-indonesian-1.5G'
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:

from transformers import BertTokenizer, TFBertModel

model_name='cahya/bert-base-indonesian-1.5G'
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 and 1GB of indonesian newspapers . 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]