---
title: roberta2roberta_L-24_discofuse
canonical_url: "https://www.modelscope.cn/models/google/roberta2roberta_L-24_discofuse"
md_url: "https://www.modelscope.cn/models/google/roberta2roberta_L-24_discofuse.md"
repository: google/roberta2roberta_L-24_discofuse
last_updated: 2025-04-21
license: apache-2.0
pipeline_tag: text2text-generation
tasks:
  - text2text-generation
model_type:
  - encoder-decoder
architectures:
  - EncoderDecoderModel
library_name:
  - transformer
  - pytorch
frameworks:
  - pytorch
language:
  - en
downloads: 415
stars: 0
---

# roberta2roberta_L-24_discofuse

> roberta2roberta_L-24_discofuse - google 在 ModelScope 开源的模型。Roberta2RobertaL-24discofuse EncoderDecoder model

google/roberta2roberta_L-24_discofuse 是 ModelScope 魔搭社区上的text2text-generation模型，采用 apache-2.0 许可。

- **Repository**: google/roberta2roberta_L-24_discofuse
- **License**: apache-2.0
- **Tasks**: text2text-generation
- **Downloads**: 415
- **Stars**: 0
- **Last updated**: 2025-04-21

Source: https://www.modelscope.cn/models/google/roberta2roberta_L-24_discofuse

---

# 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.  
```