Roberta2roberta L 24 Discofuse

🚀 Roberta2Roberta_L-24_discofuse EncoderDecoder model

This is an encoder-decoder model initialized on roberta-large checkpoints and fine-tuned for sentence fusion on the discofuse dataset, offering a solution for sentence combination tasks.

🚀 Quick Start

The model was introduced in this paper by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in this repository. It is an encoder-decoder model initialized on the roberta-large checkpoints for both the encoder and decoder, and fine-tuned on sentence fusion using the discofuse dataset (linked above).

Disclaimer: The model card has been written by the Hugging Face team.

💻 Usage Examples

Basic Usage

# 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).
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.  

Advanced Usage

There is no advanced usage example provided in the original document.

📄 License

The model is under the Apache-2.0 license.

⚠️ Important Note

The model was not trained on the " (double quotation mark) character. So, before tokenizing the text, it is advised to replace all " (double quotation marks) with a single ` (single back tick).