Controlled Text Reduction
Paper • 2210.13449 • Published
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Check out the documentation for more information.
Controlled Text Reduction Aviv Slobodkin, Paul Roit, Eran Hirsch, Ori Ernst, Ido Dagan, 2022. PDF
How to use?
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("biu-nlp/led-base-controlled-text-reduction")
model = AutoModelForSeq2SeqLM.from_pretrained("biu-nlp/led-base-controlled-text-reduction")
The original repo is here.
If you find our work useful, please cite the paper as:
@misc{https://doi.org/10.48550/arxiv.2210.13449,
doi = {10.48550/ARXIV.2210.13449},
url = {https://arxiv.org/abs/2210.13449},
author = {Slobodkin, Aviv and Roit, Paul and Hirsch, Eran and Ernst, Ori and Dagan, Ido},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Controlled Text Reduction},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Zero v1.0 Universal}
}
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# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("biu-nlp/led-base-controlled-text-reduction") model = AutoModelForSeq2SeqLM.from_pretrained("biu-nlp/led-base-controlled-text-reduction", device_map="auto")