metadata

annotations_creators:
  - crowdsourced
language_creators:
  - crowdsourced
language:
  - en
license:
  - apache-2.0
task_categories:
  - text-generation
pretty_name: task1198_atomic_classification_owant
dataset_info:
  config_name: plain_text
  features:
    - name: input
      dtype: string
    - name: output
      dtype: string
    - name: id
      dtype: string
  splits:
    - name: train
      num_examples: 5194
    - name: valid
      num_examples: 649
    - name: test
      num_examples: 650

natural-instructions) Task: task1198_atomic_classification_owant

Dataset Description

Homepage: https://github.com/allenai/natural-instructions
Paper: https://arxiv.org/abs/2204.07705
Paper: https://arxiv.org/abs/2407.00066
Point of Contact: Rickard Brüel Gabrielsson

Additional Information

Citation Information

The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:

@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
    title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks}, 
    author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi},
    year={2022},
    eprint={2204.07705},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    url={https://arxiv.org/abs/2204.07705}, 
}

More details can also be found in the following paper:

@misc{brüelgabrielsson2024compressserveservingthousands,
    title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead}, 
    author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon},
    year={2024},
    eprint={2407.00066},
    archivePrefix={arXiv},
    primaryClass={cs.DC},
    url={https://arxiv.org/abs/2407.00066}, 
}

Contact Information

For any comments or questions, please email Rickard Brüel Gabrielsson