[model_cards] sarahlintang/IndoBERT (#7748)

* Create README.md

* Update model_cards/sarahlintang/IndoBERT/README.md

Co-authored-by: Julien Chaumond <chaumond@gmail.com>

model_cards/sarahlintang/IndoBERT/README.md

@@ -0,0 +1,43 @@
+---
+language: id
+datasets:
+- oscar
+---
+# IndoBERT (Indonesian BERT Model)
+
+## Model description
+IndoBERT is a pre-trained language model based on BERT architecture for the Indonesian Language. 
+
+This model is base-uncased version which use bert-base config.
+
+## Intended uses & limitations
+
+#### How to use
+
+```python
+from transformers import AutoTokenizer, AutoModel
+tokenizer = AutoTokenizer.from_pretrained("sarahlintang/IndoBERT")
+model = AutoModel.from_pretrained("sarahlintang/IndoBERT")
+tokenizer.encode("hai aku mau makan.")
+[2, 8078, 1785, 2318, 1946, 18, 4]
+```
+
+
+## Training data
+
+This model was pre-trained on 16 GB of raw text ~2 B words from Oscar Corpus (https://oscar-corpus.com/). 
+
+This model is equal to bert-base model which has 32,000 vocabulary size. 
+
+## Training procedure
+
+The training of the model has been performed using Google’s original Tensorflow code on eight core Google Cloud TPU v2.
+We used a Google Cloud Storage bucket, for persistent storage of training data and models.
+
+## Eval results
+
+We evaluate this model on three Indonesian NLP downstream task:
+- some extractive summarization model
+- sentiment analysis
+- Part-of-Speech Tagger
+it was proven that this model outperforms multilingual BERT for all downstream tasks.