readme: ---
  language: en
  license: apache-2.0
  datasets:
  - discofuse
  ---
  
  # Roberta2Roberta_L-24_discofuse 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_discofuse/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 sentencefusion on the discofuse 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 sentence fusion, *e.g.*
  
  IMPORTANT: The model was not trained on the `"` (double quotation mark) character -> so the before tokenizing the text, it is advised to replace all `"` (double quotation marks) with a single `` ` `` (single back tick).
  
  ```python
  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
  
  tokenizer = AutoTokenizer.from_pretrained("google/roberta2roberta_L-24_discofuse")
  model = AutoModelForSeq2SeqLM.from_pretrained("google/roberta2roberta_L-24_discofuse")
  
  discofuse = """As a run-blocker, Zeitler moves relatively well. Zeitler often struggles at the point of contact in space."""
  
  input_ids = tokenizer(discofuse, return_tensors="pt").input_ids
  output_ids = model.generate(input_ids)[0]
  print(tokenizer.decode(output_ids, skip_special_tokens=True))
  # should output
  # As a run-blocker, Zeitler moves relatively well. However, Zeitler often struggles at the point of contact in space.  
  ```
  