
# Roberta2Roberta_L-24_discofuse 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_discofuse/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 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).

```python
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)[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.  
```

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


# 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).



# 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).
TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #en #dataset-discofuse #arxiv-1907.12461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # 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).
passage: TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #en #dataset-discofuse #arxiv-1907.12461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
# 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).