
# 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_gigaword/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.
```

{"language": "en", "license": "apache-2.0", "tags": ["summarization"], "datasets": ["gigaword"]}
transformers pytorch encoder-decoder text2text-generation summarization en dataset:gigaword arxiv:1907.12461 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #summarization #en #dataset-gigaword #arxiv-1907.12461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# Roberta2Roberta_L-24_gigaword 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 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.*



# Roberta2Roberta_L-24_gigaword 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 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.*
TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #summarization #en #dataset-gigaword #arxiv-1907.12461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # Roberta2Roberta_L-24_gigaword 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 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.*
passage: TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #summarization #en #dataset-gigaword #arxiv-1907.12461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# Roberta2Roberta_L-24_gigaword 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 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.*