# Roberta2Roberta_L-24_discofuse EncoderDecoder model

The model was introduced in this paper by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in this repository.

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).

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

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.
`
New

Select AutoNLP in the “Train” menu to fine-tune this model automatically.