updated the readme

README.md

@@ -1,14 +1,63 @@
 ---
 language: id
-license: apache-2.0
-datasets:
-- id_liputan6
 tags:
+- pipeline:summarization
 - summarization
+- bert2bert
+datasets:
+- id_liputan6
+license: apache-2.0
 ---
 
-Bert2Bert Summarization with EncoderDecoder Framework.
-This model is a warm-started *BERT2BERT* model fine-tuned on the *id_liputan6* summarization dataset.
+# Indonesian BERT2BERT Summarization Model
+
+Finetuned BERT-base summarization model for Indonesian.
+
+## Finetuning Corpus
+
+`bert2bert-indonesian-summarization` model is based on `cahya/bert-base-indonesian-1.5G` by [cahya](https://huggingface.co/cahya), finetuned using [id_liputan6](https://huggingface.co/datasets/id_liputan6) dataset.
+
+## Load Finetuned Model
+
+```python
+from transformers import BertTokenizer, EncoderDecoderModel
+
+tokenizer = BertTokenizer.from_pretrained("cahya/bert2bert-indonesian-summarization")
+tokenizer.bos_token = tokenizer.cls_token
+tokenizer.eos_token = tokenizer.sep_token
+model = EncoderDecoderModel.from_pretrained("cahya/bert2bert-indonesian-summarization")
+```
+
+## Code Sample
+
+```python
+from transformers import BertTokenizer, EncoderDecoderModel
+
+tokenizer = BertTokenizer.from_pretrained("cahya/bert2bert-indonesian-summarization")
+tokenizer.bos_token = tokenizer.cls_token
+tokenizer.eos_token = tokenizer.sep_token
+model = EncoderDecoderModel.from_pretrained("cahya/bert2bert-indonesian-summarization")
+
+# 
+ARTICLE_TO_SUMMARIZE = ""
+
+# generate summary
+input_ids = tokenizer.encode(ARTICLE_TO_SUMMARIZE, return_tensors='pt')
+summary_ids = model.generate(input_ids,
+            max_length=100, 
+            num_beams=2,
+            repetition_penalty=2.5, 
+            length_penalty=1.0, 
+            early_stopping=True,
+            no_repeat_ngram_size=2,
+            use_cache=True)
+summary_text = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+print(summary_text)
+```
+
+Output:
+
+```
 
-Detail about this model will be added soon.
+```