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---
datasets:
- fajrikoto/id_liputan6
language:
- id
base_model:
- cahya/bert2bert-indonesian-summarization
---
# Fine-Tuned BERT2BERT Summarization Model
This model is fine-tuned based on the original [BERT2BERT Indonesian Summarization](https://huggingface.co./cahya/bert2bert-indonesian-summarization) model.
### Fine-Tuned Dataset:
- **Dataset**: [Liputan6_ID](https://huggingface.co./datasets/fajrikoto/id_liputan6)
- **Task**: Summarization
This model was fine-tuned using the [Liputan6_ID](https://huggingface.co./datasets/fajrikoto/id_liputan6) dataset, which contains Indonesian news articles. The model is optimized for summarizing domain-specific texts from the Liputan6 dataset.
## Code Sample
```python
from transformers import BertTokenizer, EncoderDecoderModel
tokenizer = BertTokenizer.from_pretrained("rowjak/bert-indonesian-news-summarization")
tokenizer.bos_token = tokenizer.cls_token
tokenizer.eos_token = tokenizer.sep_token
model = EncoderDecoderModel.from_pretrained("rowjak/bert-indonesian-news-summarization")
#
ARTICLE = ""
# generate summary
input_ids = tokenizer.encode(ARTICLE, 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:
```
---
```
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