readme: ---
  language: en
  license: apache-2.0
  datasets:
  - gigaword
  tags:
  - summarization
  ---
  
  # Roberta2Roberta_L-24_gigaword EncoderDecoder model
  
  The model was introduced in 
  [this paper](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in [this repository](https://tfhub.dev/google/bertseq2seq/roberta24_gigaword/1). 
  
  The model is an encoder-decoder model that was initialized on the `roberta-large` checkpoints for both the encoder 
  and decoder and fine-tuned on headline generation using the Gigaword dataset, which is linked above.
  
  Disclaimer: The model card has been written by the Hugging Face team.
  
  ## How to use
  
  You can use this model for extreme summarization, *e.g.*
  
  ```python
  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
  
  tokenizer = AutoTokenizer.from_pretrained("google/roberta2roberta_L-24_gigaword")
  model = AutoModelForSeq2SeqLM.from_pretrained("google/roberta2roberta_L-24_gigaword")
  
  article = """australian shares closed down #.# percent monday
  following a weak lead from the united states and
  lower commodity prices , dealers said ."""
  
  input_ids = tokenizer(article, return_tensors="pt").input_ids
  output_ids = model.generate(input_ids)[0]
  print(tokenizer.decode(output_ids, skip_special_tokens=True))
  # should output
  # australian shares close down #.# percent.
  ```
  