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.
You can use this model for extreme summarization, e.g.
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) print(tokenizer.decode(output_ids, skip_special_tokens=True)) # should output # australian shares close down #.# percent.
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