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.
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).
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) 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.
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