readme: ---
  language: id
  tags:
  - pipeline:summarization
  - summarization
  - t5
  datasets:
  - id_liputan6
  ---
  
  # Indonesian T5 Summarization Base Model
  
  Finetuned T5 base summarization model for Indonesian. 
  
  ## Finetuning Corpus
  
  `t5-base-indonesian-summarization-cased` model is based on `t5-base-bahasa-summarization-cased` by [huseinzol05](https://huggingface.co/huseinzol05), finetuned using [id_liputan6](https://huggingface.co/datasets/id_liputan6) dataset.
  
  ## Load Finetuned Model
  
  ```python
  from transformers import T5Tokenizer, T5Model, T5ForConditionalGeneration
  
  tokenizer = T5Tokenizer.from_pretrained("cahya/t5-base-indonesian-summarization-cased")
  model = T5ForConditionalGeneration.from_pretrained("cahya/t5-base-indonesian-summarization-cased")
  ```
  
  ## Code Sample
  
  ```python
  from transformers import T5Tokenizer, T5ForConditionalGeneration
  
  tokenizer = T5Tokenizer.from_pretrained("cahya/t5-base-indonesian-summarization-cased")
  model = T5ForConditionalGeneration.from_pretrained("cahya/t5-base-indonesian-summarization-cased")
  
  # 
  ARTICLE_TO_SUMMARIZE = ""
  
  # generate summary
  input_ids = tokenizer.encode(ARTICLE_TO_SUMMARIZE, return_tensors='pt')
  summary_ids = model.generate(input_ids,
              min_length=20,
              max_length=80,
              num_beams=10,
              repetition_penalty=2.5,
              length_penalty=1.0,
              early_stopping=True,
              no_repeat_ngram_size=2,
              use_cache=True,
              do_sample = True,
              temperature = 0.8,
              top_k = 50,
              top_p = 0.95)
  
  summary_text = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
  print(summary_text)
  ```
  
  Output:
  
  ```
  
  ```
  
  