Create README.md

README.md

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+---
+language: "id"
+license: "mit"
+datasets:
+- Indonesian Wikipedia
+- id_newspapers_2018
+widget:
+- text: "Ibu ku sedang bekerja [MASK] supermarket."
+---
+
+# 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](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-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:
+```python
+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:
+```python
+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](https://huggingface.co/datasets/id_newspapers_2018).
+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]```