1 --- 2 language: en 3 license: apache-2.0 4 datasets: 5 - discofuse 6 --- 7 8 # Roberta2Roberta_L-24_discofuse EncoderDecoder model 9 10 The model was introduced in  11 [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).  12 13 The model is an encoder-decoder model that was initialized on the roberta-large checkpoints for both the encoder  14 and decoder and fine-tuned on sentencefusion on the discofuse dataset, which is linked above. 15 16 Disclaimer: The model card has been written by the Hugging Face team. 17 18 ## How to use 19 20 You can use this model for sentence fusion, *e.g.* 21 22 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). 23 24 python 25 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM 26 27 tokenizer = AutoTokenizer.from_pretrained("google/roberta2roberta_L-24_discofuse") 28 model = AutoModelForSeq2SeqLM.from_pretrained("google/roberta2roberta_L-24_discofuse") 29 30 discofuse = """As a run-blocker, Zeitler moves relatively well. Zeitler often struggles at the point of contact in space.""" 31 32 input_ids = tokenizer(discofuse, return_tensors="pt").input_ids 33 output_ids = model.generate(input_ids)[0] 34 print(tokenizer.decode(output_ids, skip_special_tokens=True)) 35 # should output 36 # As a run-blocker, Zeitler moves relatively well. However, Zeitler often struggles at the point of contact in space.  37  38`