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+---
+language: en
+license: apache-2.0
+datasets:
+- gigaword
+---
+
+# Roberta2Roberta_L-24_gigaword 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_bbc/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 headline generation using the Gigaword 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 extreme summarization, *e.g.*
+
+```python
+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)[0]
+print(tokenizer.decode(output_ids, skip_special_tokens=True))
+# should output
+# australian shares close down #.# percent.
+```