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--- |
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language: "id" |
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license: "mit" |
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datasets: |
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- wikipedia |
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- id_newspapers_2018 |
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widget: |
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- text: "Ibu ku sedang bekerja [MASK] sawah." |
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--- |
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# Indonesian BERT base model (uncased) |
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## Model description |
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It is BERT-base model pre-trained with indonesian Wikipedia and indonesian newspapers using a masked language modeling (MLM) objective. This |
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model is uncased. |
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This is one of several other language models that have been pre-trained with indonesian datasets. More detail about |
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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) |
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## Intended uses & limitations |
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### How to use |
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You can use this model directly with a pipeline for masked language modeling: |
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```python |
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>>> from transformers import pipeline |
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>>> unmasker = pipeline('fill-mask', model='cahya/bert-base-indonesian-1.5G') |
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>>> unmasker("Ibu ku sedang bekerja [MASK] supermarket") |
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[{'sequence': '[CLS] ibu ku sedang bekerja di supermarket [SEP]', |
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'score': 0.7983310222625732, |
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'token': 1495}, |
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{'sequence': '[CLS] ibu ku sedang bekerja. supermarket [SEP]', |
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'score': 0.090003103017807, |
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'token': 17}, |
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{'sequence': '[CLS] ibu ku sedang bekerja sebagai supermarket [SEP]', |
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'score': 0.025469014421105385, |
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'token': 1600}, |
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{'sequence': '[CLS] ibu ku sedang bekerja dengan supermarket [SEP]', |
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'score': 0.017966199666261673, |
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'token': 1555}, |
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{'sequence': '[CLS] ibu ku sedang bekerja untuk supermarket [SEP]', |
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'score': 0.016971781849861145, |
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'token': 1572}] |
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``` |
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Here is how to use this model to get the features of a given text in PyTorch: |
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```python |
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from transformers import BertTokenizer, BertModel |
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model_name='cahya/bert-base-indonesian-1.5G' |
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tokenizer = BertTokenizer.from_pretrained(model_name) |
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model = BertModel.from_pretrained(model_name) |
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text = "Silakan diganti dengan text apa saja." |
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encoded_input = tokenizer(text, return_tensors='pt') |
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output = model(**encoded_input) |
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``` |
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and in Tensorflow: |
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```python |
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from transformers import BertTokenizer, TFBertModel |
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model_name='cahya/bert-base-indonesian-1.5G' |
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tokenizer = BertTokenizer.from_pretrained(model_name) |
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model = TFBertModel.from_pretrained(model_name) |
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text = "Silakan diganti dengan text apa saja." |
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encoded_input = tokenizer(text, return_tensors='tf') |
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output = model(encoded_input) |
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``` |
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## Training data |
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This model was pre-trained with 522MB of indonesian Wikipedia and 1GB of |
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[indonesian newspapers](https://huggingface.co/datasets/id_newspapers_2018). |
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The texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are |
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then of the form: |
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```[CLS] Sentence A [SEP] Sentence B [SEP]``` |
