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Lots-of-LoRAs/task860_prost_mcq_generation

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task860_prost_mcq_generation dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 163 - name: valid num_examples: 20 - name: test num_examples: 21 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task860_prost_mcq_generation ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task342_winomt_classification_profession_pro

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task342_winomt_classification_profession_pro dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 1261 - name: valid num_examples: 158 - name: test num_examples: 158 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task342_winomt_classification_profession_pro ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task1677_xquad-ca_translation

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task1677_xquad-ca_translation dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 944 - name: valid num_examples: 118 - name: test num_examples: 118 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1677_xquad-ca_translation ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task1340_msr_text_compression_compression

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task1340_msr_text_compression_compression dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 3947 - name: valid num_examples: 493 - name: test num_examples: 494 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1340_msr_text_compression_compression ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task559_alt_translation_en_fi

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task559_alt_translation_en_fi dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 480 - name: valid num_examples: 60 - name: test num_examples: 60 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task559_alt_translation_en_fi ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task1193_food_course_classification

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task1193_food_course_classification dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 176 - name: valid num_examples: 22 - name: test num_examples: 22 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1193_food_course_classification ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task483_cls_french_dvd_classification

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task483_cls_french_dvd_classification dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 1574 - name: valid num_examples: 197 - name: test num_examples: 197 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task483_cls_french_dvd_classification ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task1619_menyo20k-mt_en_yo_translation

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task1619_menyo20k-mt_en_yo_translation dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 3180 - name: valid num_examples: 397 - name: test num_examples: 398 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1619_menyo20k-mt_en_yo_translation ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task658_tep_en_fa_translation

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task658_tep_en_fa_translation dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 5195 - name: valid num_examples: 649 - name: test num_examples: 650 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task658_tep_en_fa_translation ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task1360_numer_sense_multiple_choice_qa_generation

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task1360_numer_sense_multiple_choice_qa_generation dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 4985 - name: valid num_examples: 623 - name: test num_examples: 624 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1360_numer_sense_multiple_choice_qa_generation ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task1144_xcsr_sw_commonsense_mc_classification

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task1144_xcsr_sw_commonsense_mc_classification dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 794 - name: valid num_examples: 99 - name: test num_examples: 100 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1144_xcsr_sw_commonsense_mc_classification ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task1371_newscomm_translation

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task1371_newscomm_translation dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 1596 - name: valid num_examples: 200 - name: test num_examples: 200 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1371_newscomm_translation ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task156_codah_classification_adversarial

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task156_codah_classification_adversarial dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 2216 - name: valid num_examples: 277 - name: test num_examples: 278 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task156_codah_classification_adversarial ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task315_europarl_sv-en_language_identification

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task315_europarl_sv-en_language_identification dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 5199 - name: valid num_examples: 650 - name: test num_examples: 650 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task315_europarl_sv-en_language_identification ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task798_pawsx_spanish_german_translation

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task798_pawsx_spanish_german_translation dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 199 - name: valid num_examples: 25 - name: test num_examples: 25 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task798_pawsx_spanish_german_translation ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task111_asset_sentence_simplification

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task111_asset_sentence_simplification dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 1600 - name: valid num_examples: 200 - name: test num_examples: 200 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task111_asset_sentence_simplification ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task227_clariq_classification

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task227_clariq_classification dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 5200 - name: valid num_examples: 650 - name: test num_examples: 650 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task227_clariq_classification ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task1395_europa_ecdc_tm_en_sv_translation

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task1395_europa_ecdc_tm_en_sv_translation dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 1938 - name: valid num_examples: 242 - name: test num_examples: 243 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1395_europa_ecdc_tm_en_sv_translation ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task1488_sarcasmdetection_headline_classification

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task1488_sarcasmdetection_headline_classification dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 403 - name: valid num_examples: 50 - name: test num_examples: 51 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1488_sarcasmdetection_headline_classification ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task392_inverse_causal_relationship

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task392_inverse_causal_relationship dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 2082 - name: valid num_examples: 260 - name: test num_examples: 261 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task392_inverse_causal_relationship ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task1119_alt_fil_ja_translation

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task1119_alt_fil_ja_translation dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 5197 - name: valid num_examples: 650 - name: test num_examples: 650 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1119_alt_fil_ja_translation ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task1079_pib_translation_english_gujarati

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task1079_pib_translation_english_gujarati dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 1196 - name: valid num_examples: 150 - name: test num_examples: 150 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1079_pib_translation_english_gujarati ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task521_trivia_question_classification

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task521_trivia_question_classification dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 5193 - name: valid num_examples: 649 - name: test num_examples: 650 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task521_trivia_question_classification ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

paulsxb/luxun-llama3-tt

paulsxb

--- license: apache-2.0 ---

penfever/allenai_WildChat-1M-Full-Qwen_Qwen2.5-72B-Instruct-lc

penfever

--- dataset_info: features: - name: conversation_hash dtype: string - name: conversation list: - name: content dtype: string - name: content_token_ids dtype: 'null' - name: country dtype: 'null' - name: cumulative_logprob dtype: 'null' - name: finish_reason dtype: string - name: hashed_ip dtype: string - name: header dtype: 'null' - name: language dtype: 'null' - name: redacted dtype: 'null' - name: role dtype: string - name: state dtype: 'null' - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: turn_identifier dtype: 'null' - name: model dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 6694409099 num_examples: 806219 download_size: 3282606144 dataset_size: 6694409099 configs: - config_name: default data_files: - split: train path: data/train-* ---

yobro4619/sample_dataset

yobro4619

--- dataset_info: features: [] splits: - name: train num_bytes: 0 num_examples: 0 download_size: 324 dataset_size: 0 configs: - config_name: default data_files: - split: train path: data/train-* ---

LynxLegion/gzoalaidataset

LynxLegion

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: response dtype: string splits: - name: train num_bytes: 2245.25 num_examples: 7 - name: test num_bytes: 352 num_examples: 1 download_size: 9151 dataset_size: 2597.25 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

RyanYr/reflect_llama8b-t0_llama33-t12_om2-300to500k_llama_reflection

RyanYr

--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: response@0_correctness dtype: bool - name: response@2_correctness dtype: bool splits: - name: train num_bytes: 419889919 num_examples: 93382 download_size: 150620611 dataset_size: 419889919 configs: - config_name: default data_files: - split: train path: data/train-* ---

RyanYr/reflect_llama8b-t0_llama33-t12_om2-300to500k_llama_correction

RyanYr

--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: response@0_correctness dtype: bool - name: response@2_correctness dtype: bool splits: - name: train num_bytes: 669669286 num_examples: 93382 download_size: 247345097 dataset_size: 669669286 configs: - config_name: default data_files: - split: train path: data/train-* ---

Nash-pAnDiTa/Moamn-x9U57UjWLFQ

Nash-pAnDiTa

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string splits: - name: train num_bytes: 481502450.0 num_examples: 47 download_size: 481518029 dataset_size: 481502450.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

Raghava1401/surg_mainv1

Raghava1401

--- license: mit ---

ThatsGroes/wiki_views

ThatsGroes

--- dataset_info: features: - name: article dtype: string - name: rank dtype: int64 - name: timestamp dtype: int64 - name: total.total dtype: int64 - name: month dtype: string - name: timeRange.start dtype: string - name: timeRange.end dtype: string splits: - name: train num_bytes: 1785781 num_examples: 13882 download_size: 391994 dataset_size: 1785781 configs: - config_name: default data_files: - split: train path: data/train-* ---

AhmedCodes64/FinTech_Datasets

AhmedCodes64

--- license: mit ---

AlirezaF138/FAspell

AlirezaF138

--- license: cc-by-4.0 language: - fa pretty_name: 'FAspell: Naturally-occurring Persian (Farsi) spelling mistakes' size_categories: - 1K<n<10K configs: - config_name: default data_files: - split: main path: data/main-* - split: ocr path: data/ocr-* dataset_info: features: - name: '#misspelt' dtype: string - name: corrected dtype: string - name: error-category dtype: int64 splits: - name: main num_bytes: 192826 num_examples: 4858 - name: ocr num_bytes: 26547 num_examples: 800 download_size: 102727 dataset_size: 219373 --- # FASpell Dataset ## Context The FASpell dataset was developed to evaluate spell-checking algorithms. It consists of pairs of misspelled Persian (Farsi) words and their corresponding corrected forms, similar to the ASpell dataset used for English. ## Content The dataset is divided into two parts: 1. **faspell_main**: A list of 5050 pairs collected from errors made by elementary school pupils and professional typists. 2. **faspell_ocr**: A list of 800 pairs collected from the output of a Farsi OCR system. ## Acknowledgements The dataset is based on a work from [http://pars.ie/lr/FAspell_Dataset](http://pars.ie/lr/FAspell_Dataset). Please acknowledge the use of this dataset by referencing one of the following papers: - Barari, L., & QasemiZadeh, B. (2005). CloniZER spell checker adaptive language independent spell checker. In AIML 2005 Conference CICC, Cairo, Egypt (pp. 65-71). - QasemiZadeh, B., Ilkhani, A., & Ganjeii, A. (2006, June). Adaptive language independent spell checking using intelligent traverse on a tree. In Cybernetics and Intelligent Systems, 2006 IEEE Conference on (pp. 1-6). IEEE. ## License FASpell by Behrang QasemiZadeh is licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). This work is based on [http://pars.ie/lr/FAspell_Dataset](http://pars.ie/lr/FAspell_Dataset). ## Inspiration The dataset can be used to explore various questions, including: - Which kinds of misspellings occur more often? - Are certain characters more likely to be misspelled? Certain words? - Can you construct a finite state automaton spell checker for Persian based on this data?

penfever/tulu-3-code

penfever

--- dataset_info: features: - name: id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: source dtype: string splits: - name: train num_bytes: 441399414.4280896 num_examples: 142275 download_size: 148861735 dataset_size: 441399414.4280896 configs: - config_name: default data_files: - split: train path: data/train-* ---

Greenbe/greenbe

Greenbe

--- license: bigscience-openrail-m ---

prakash-sumit/ultravox-json2

prakash-sumit

--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: transcription dtype: string - name: audio struct: - name: array sequence: float64 - name: path dtype: string - name: sampling_rate dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 79151421 num_examples: 40 - name: validation num_bytes: 9009283 num_examples: 5 download_size: 65337532 dataset_size: 88160704 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

amuvarma/amu-zucktts-with-qaudio

amuvarma

--- dataset_info: features: - name: split_name dtype: string - name: index dtype: string - name: round dtype: string - name: question dtype: string - name: question_audio struct: - name: array sequence: float64 - name: path dtype: string - name: sampling_rate dtype: int64 - name: answer dtype: string - name: answer_snac dtype: string splits: - name: train num_bytes: 1909977535 num_examples: 2000 download_size: 1455327345 dataset_size: 1909977535 configs: - config_name: default data_files: - split: train path: data/train-* ---

amuvarma/amu-zucktts-with-qaudio-total

amuvarma

--- dataset_info: features: - name: split_name dtype: string - name: index dtype: string - name: round dtype: string - name: question dtype: string - name: question_audio dtype: audio - name: answer dtype: string - name: answer_snac dtype: string - name: answer_audio struct: - name: array sequence: float64 - name: path dtype: 'null' - name: sampling_rate dtype: int64 splits: - name: train num_bytes: 881164361.0 num_examples: 100 download_size: 686959233 dataset_size: 881164361.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

Altariste/apeironnft_library

Altariste

--- license: llama3.3 ---

antitheft159/_600_soil_classification

antitheft159

--- license: apache-2.0 ---

ninetyone/so100_training_20250102_001

ninetyone

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so100 configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.0", "robot_type": "so100", "total_episodes": 2, "total_frames": 2388, "total_tasks": 1, "total_videos": 2, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:2" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.phone": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```

Reaperxxxx/Vv

Reaperxxxx

--- task_categories: - text-classification language: - an pretty_name: Reiker size_categories: - 1K<n<10K ---

DT4LM/t5v1-1ba_mr_faster-alzantot_differential_old5

DT4LM

--- dataset_info: features: - name: text dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 60209 num_examples: 457 download_size: 42627 dataset_size: 60209 --- # Dataset Card for "t5v1-1ba_mr_faster-alzantot_differential_old5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

DT4LM/t5v1-1ba_mr_faster-alzantot_differential_original_old5

DT4LM

--- dataset_info: features: - name: text dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 59461 num_examples: 457 download_size: 41370 dataset_size: 59461 --- # Dataset Card for "t5v1-1ba_mr_faster-alzantot_differential_original_old5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

nnheui/orig_feynman

nnheui

--- dataset_info: features: - name: name dtype: string - name: symbols sequence: string - name: symbol_descs sequence: string - name: symbol_properties sequence: string - name: expression dtype: string - name: symbol_ranges sequence: sequence: float64 splits: - name: train num_bytes: 25386 num_examples: 100 download_size: 9349 dataset_size: 25386 configs: - config_name: default data_files: - split: train path: data/train-* ---

shin020810/LLM_23_121_1

shin020810

--- dataset_info: - config_name: 공학 features: - name: add_info dtype: string - name: question dtype: string - name: contents dtype: string splits: - name: train num_bytes: 1286078 num_examples: 705 - name: validation num_bytes: 433218 num_examples: 235 - name: test num_bytes: 437278 num_examples: 235 download_size: 7164738 dataset_size: 2156574 - config_name: 기타 features: - name: add_info dtype: string - name: question dtype: string - name: contents dtype: string splits: - name: train num_bytes: 1129311 num_examples: 755 - name: validation num_bytes: 377102 num_examples: 251 - name: test num_bytes: 364546 num_examples: 251 download_size: 6605576 dataset_size: 1870959 - config_name: 명칭 features: - name: add_info dtype: string - name: question dtype: string - name: contents dtype: string splits: - name: train num_bytes: 361032 num_examples: 268 - name: validation num_bytes: 118551 num_examples: 89 - name: test num_bytes: 124867 num_examples: 89 download_size: 2219772 dataset_size: 604450 - config_name: 보건 features: - name: add_info dtype: string - name: question dtype: string - name: contents dtype: string splits: - name: train num_bytes: 991754 num_examples: 591 - name: validation num_bytes: 329490 num_examples: 197 - name: test num_bytes: 332551 num_examples: 196 download_size: 6558690 dataset_size: 1653795 - config_name: 사회 features: - name: add_info dtype: string - name: question dtype: string - name: contents dtype: string splits: - name: train num_bytes: 1563926 num_examples: 902 - name: validation num_bytes: 498755 num_examples: 300 - name: test num_bytes: 516368 num_examples: 300 download_size: 8552226 dataset_size: 2579049 - config_name: 산업 features: - name: add_info dtype: string - name: question dtype: string - name: contents dtype: string splits: - name: train num_bytes: 1106103 num_examples: 608 - name: validation num_bytes: 360722 num_examples: 202 - name: test num_bytes: 366224 num_examples: 202 download_size: 6154650 dataset_size: 1833049 - config_name: 예체능 features: - name: add_info dtype: string - name: question dtype: string - name: contents dtype: string splits: - name: train num_bytes: 1584020 num_examples: 959 - name: validation num_bytes: 513639 num_examples: 319 - name: test num_bytes: 531817 num_examples: 319 download_size: 8962981 dataset_size: 2629476 - config_name: 인문 features: - name: add_info dtype: string - name: question dtype: string - name: contents dtype: string splits: - name: train num_bytes: 1246574 num_examples: 716 - name: validation num_bytes: 421538 num_examples: 238 - name: test num_bytes: 423206 num_examples: 238 download_size: 7143675 dataset_size: 2091318 - config_name: 자연 features: - name: add_info dtype: string - name: question dtype: string - name: contents dtype: string splits: - name: train num_bytes: 1996295 num_examples: 1252 - name: validation num_bytes: 666300 num_examples: 417 - name: test num_bytes: 684297 num_examples: 417 download_size: 11130635 dataset_size: 3346892 - config_name: 종교 features: - name: add_info dtype: string - name: question dtype: string - name: contents dtype: string splits: - name: train num_bytes: 518461 num_examples: 309 - name: validation num_bytes: 172873 num_examples: 103 - name: test num_bytes: 177792 num_examples: 103 download_size: 3040989 dataset_size: 869126 configs: - config_name: 공학 data_files: - split: train path: 공학/train-* - split: validation path: 공학/validation-* - split: test path: 공학/test-* - config_name: 기타 data_files: - split: train path: 기타/train-* - split: validation path: 기타/validation-* - split: test path: 기타/test-* - config_name: 명칭 data_files: - split: train path: 명칭/train-* - split: validation path: 명칭/validation-* - split: test path: 명칭/test-* - config_name: 보건 data_files: - split: train path: 보건/train-* - split: validation path: 보건/validation-* - split: test path: 보건/test-* - config_name: 사회 data_files: - split: train path: 사회/train-* - split: validation path: 사회/validation-* - split: test path: 사회/test-* - config_name: 산업 data_files: - split: train path: 산업/train-* - split: validation path: 산업/validation-* - split: test path: 산업/test-* - config_name: 예체능 data_files: - split: train path: 예체능/train-* - split: validation path: 예체능/validation-* - split: test path: 예체능/test-* - config_name: 인문 data_files: - split: train path: 인문/train-* - split: validation path: 인문/validation-* - split: test path: 인문/test-* - config_name: 자연 data_files: - split: train path: 자연/train-* - split: validation path: 자연/validation-* - split: test path: 자연/test-* - config_name: 종교 data_files: - split: train path: 종교/train-* - split: validation path: 종교/validation-* - split: test path: 종교/test-* ---

YaoYX/llama_instruct_sample_second

YaoYX

--- license: apache-2.0 configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: instruction dtype: string - name: output sequence: string - name: scores sequence: float64 splits: - name: train num_bytes: 21672514206 num_examples: 61135 download_size: 7573735271 dataset_size: 21672514206 ---

dark9113/mtc1

dark9113

--- license: apache-2.0 ---

Fern1221/test

Fern1221

--- dataset_info: - config_name: en features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: audio dtype: audio splits: - name: train num_bytes: 1870258301.79 num_examples: 4633 download_size: 1434604695 dataset_size: 1870258301.79 - config_name: zh_tw features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: audio dtype: audio splits: - name: train num_bytes: 1491573523.458 num_examples: 3626 download_size: 1163252213 dataset_size: 1491573523.458 configs: - config_name: en data_files: - split: train path: en/train-* - config_name: zh_tw data_files: - split: train path: zh_tw/train-* ---

stzhao/ImageRewardModel-data

stzhao

--- license: apache-2.0 task_categories: - text-to-image tags: - images size_categories: - 1M<n<10M configs: - config_name: kadid data_files: kadid/*.parquet - config_name: koniq data_files: koniq/*.parquet - config_name: spaq data_files: spaq/*.parquet - config_name: agi data_files: agi/*.parquet - config_name: csiq data_files: csiq/*.parquet - config_name: live data_files: live/*.parquet - config_name: livec data_files: livec/*.parquet - config_name: imagerewarddb-test data_files: imagerewarddb/test/*.parquet - config_name: imagerewarddb-val data_files: imagerewarddb/validation/*.parquet - config_name: imagerewarddb-train data_files: imagerewarddb/train/*.parquet - config_name: ava data_files: ava/*.parquet - config_name: cgi data_files: cgi/*.parquet - config_name: hpdv2-train data_files: hpdv2/train/*.parquet ---

dgambettavuw/D_gen10_run1_llama2-7b_xlsum_doc1000_real32_synt96_vuw

dgambettavuw

--- dataset_info: features: - name: id dtype: int64 - name: doc dtype: string splits: - name: train num_bytes: 525938 num_examples: 1000 download_size: 264623 dataset_size: 525938 configs: - config_name: default data_files: - split: train path: data/train-* ---

davoesgoats/my-distiset-be899639

davoesgoats

--- size_categories: n<1K dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': genius '1': straight-forward splits: - name: train num_bytes: 5459 num_examples: 20 download_size: 5409 dataset_size: 5459 configs: - config_name: default data_files: - split: train path: data/train-* tags: - synthetic - distilabel - rlaif - datacraft --- <p align="left"> <a href="https://github.com/argilla-io/distilabel"> <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/> </a> </p> # Dataset Card for my-distiset-be899639 This dataset has been created with [distilabel](https://distilabel.argilla.io/). ## Dataset Summary This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI: ```console distilabel pipeline run --config "https://huggingface.co/datasets/davoesgoats/my-distiset-be899639/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/davoesgoats/my-distiset-be899639/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "label": 0, "text": "His groundbreaking work on non-Euclidean geometry led to a fundamental shift in our understanding of spatial relationships and the nature of reality itself, fundamentally altering the paradigm of mathematical inquiry." } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("davoesgoats/my-distiset-be899639", "default") ``` Or simply as it follows, since there's only one configuration and is named `default`: ```python from datasets import load_dataset ds = load_dataset("davoesgoats/my-distiset-be899639") ``` </details>

DT4LM/gp_mrpc_kuleshov_var_differential_original

DT4LM

--- dataset_info: features: - name: question1 dtype: string - name: question2 dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 135834.37190082646 num_examples: 537 download_size: 98742 dataset_size: 135834.37190082646 configs: - config_name: default data_files: - split: train path: data/train-* ---

DT4LM/t5v1-1ba_mrpc_kuleshov_var_differential_original

DT4LM

--- dataset_info: features: - name: question1 dtype: string - name: question2 dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 110388.25625920472 num_examples: 438 download_size: 79700 dataset_size: 110388.25625920472 configs: - config_name: default data_files: - split: train path: data/train-* ---

ravinpadhy/WASP3DLlama_Data

ravinpadhy

--- license: llama3.2 task_categories: - question-answering language: - en tags: - 3d - wasp3d - designer - xpress - pro pretty_name: Real-Time Graphic Solutions for Livestreamers & TV Broadcasters size_categories: - 1K<n<10K ---

Greys-An/agentharm

Greys-An

--- license: apache-2.0 ---

arumaekawa/wikipedia-ja

arumaekawa

--- dataset_info: - config_name: default features: - name: text dtype: string splits: - name: train num_bytes: 6850373251 num_examples: 1389467 download_size: 3859739520 dataset_size: 6850373251 - config_name: original features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 7039610767 num_examples: 1389467 download_size: 3935695644 dataset_size: 7039610767 configs: - config_name: default data_files: - split: train path: data/train-* - config_name: original data_files: - split: train path: original/train-* ---

edwardlinelytone/test

edwardlinelytone

--- license: llama3.2 language: - zh --- Test!!

ShunTatsukawa/roughness_pix2pix_dataset

ShunTatsukawa

--- license: mit ---

falan42/medlineplus1

falan42

--- license: apache-2.0 ---

Asap7772/MedQA

Asap7772

--- dataset_info: features: - name: question dtype: string - name: exp dtype: string - name: cop dtype: int64 - name: opa dtype: string - name: opb dtype: string - name: opc dtype: string - name: opd dtype: string - name: subject_name dtype: string - name: topic_name dtype: string - name: id dtype: string - name: choice_type dtype: string splits: - name: train num_bytes: 131903297 num_examples: 182822 - name: validation num_bytes: 2221428 num_examples: 4183 - name: test num_bytes: 1399350 num_examples: 6150 download_size: 88312052 dataset_size: 135524075 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

robinwitch/zeroeggs_mhubert1000_rvq

robinwitch

--- dataset_info: features: - name: file dtype: string - name: text sequence: string - name: type dtype: string splits: - name: all_data num_bytes: 22407184 num_examples: 2772 - name: train num_bytes: 22407184 num_examples: 2772 download_size: 1578278 dataset_size: 44814368 configs: - config_name: default data_files: - split: all_data path: data/all_data-* - split: train path: data/train-* ---

Selmher/bilder

Selmher

--- license: mit ---

Jake0808/generative-ai

Jake0808

--- license: mit ---

chungouse/bilder

chungouse

--- license: mit ---

yeganehmohammadi98/persian-multi-source-corpus

yeganehmohammadi98

--- license: mit language: - fa --- --- license: mit --- 📚 Persian Multi-Source Dataset Collection 🌟 Dataset Overview A comprehensive Persian language dataset combining diverse sources to create a rich training corpus for language models. 📊 Data Sources 🌐 Public Datasets MaralGPT Collections Persian blogs Persian quotes Story Collections TinyStories-Farsi FarsiTinyStories News & Media Farsi news corpus Persian daily news Persian blog posts Conversation & QA Persian QA pairs Telegram channel content Specialized Content ZharfaTech Open Platypus ZharfaTech OpenAssistant Guanaco Paraphrase detection corpus ParsinLu entailment dataset 💬 Custom Collections Nini Site Data User comments Discussion threads User issues and queries 📈 Statistics Multiple genres and writing styles Diverse vocabulary range Rich conversational patterns Real-world language usage 🎯 Use Cases Language Model Training Conversational AI Text Analysis Persian NLP Research ✨ Features 🔄 Merged and cleaned data 📝 Natural language variations 💡 Rich contextual information 🗣️ Authentic Persian conversations Perfect for training Persian language models with diverse, real-world language patterns! #PersianNLP #Dataset #MachineLearning #NaturalLanguageProcessing #DataScience

open-vdb/fashion-mnist-784-euclidean

open-vdb

--- configs: - config_name: train data_files: - split: train path: train/* - config_name: test data_files: - split: test path: test/* - config_name: neighbors data_files: - split: neighbors path: neighbors/* --- # Dataset Overview dataset: fashion-mnist-784-euclidean ## Metadata - **Creation Time**: 2025-01-07 11:02:55+0000 - **Update Time**: 2025-01-07 11:03:01+0000 - **Source**: https://github.com/erikbern/ann-benchmarks - **Task**: N/A - **Train Samples**: N/A - **Test Samples**: N/A - **License**: DISCLAIMER AND LICENSE NOTICE: 1. This dataset is intended for benchmarking and research purposes only. 2. The source data used in this dataset retains its original license and copyright. Users must comply with the respective licenses of the original data sources. 3. The ground truth part of the dataset (including but not limited to annotations, labels, and evaluation metrics) is licensed under Apache 2.0. 4. This dataset is provided 'AS IS' without any warranty. The dataset maintainers are not responsible for any copyright violations arising from the use of the source data. 5. If you are the copyright holder of any source data and believe it has been included inappropriately, please contact us for prompt removal. 6. Commercial use of this dataset must ensure compliance with the original data sources' licenses and obtain necessary permissions where required. ## Dataset Statistics | Split | Name | Size | Num Rows | Num Columns | Schema | Num Files | | --- | --- | --- | --- | --- | --- | --- | | train | fashion-mnist-784-euclidean | 29.228 MB | 60000 | 2 | {<br> "idx": "int64",<br> "emb": "list<element: float>"<br>} | 1 | | test | fashion-mnist-784-euclidean | 4.888 MB | 10000 | 2 | {<br> "idx": "int64",<br> "emb": "list<element: float>"<br>} | 1 | | neighbors | fashion-mnist-784-euclidean | 72.713 MB | 10000 | 8 | {<br> "idx": "int64",<br> "neighbors_id": "list<element: int64>",<br> "neighbors_distance": "list<element: double>",<br> "metric": "string",<br> "query_expr": "null",<br> "pk_field_name": "string",<br> "vector_field_name": "string",<br> "top_k": "int64"<br>} | 1 |

sarahgillet/BrainstormingDataset

sarahgillet

--- license: cc-by-nc-sa-4.0 pretty_name: Group-Brainstorming size_categories: - n>1T task_categories: - other language: - en tags: - human-robot interaction - group human-robot interaction - brainstorming - social behavior generation - heuristic demonstrations extra_gated_prompt: >- ### Brainstorming Dataset COMMUNITY LICENSE AGREEMENT Brainstorming Dataset Release Date: January 05, 2025 All the data within this repo are under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). extra_gated_fields: First Name: text Last Name: text Email: text Country: country Affiliation: text Phone: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Journalist - Other Research interest: text geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Brainstorming Dataset Privacy Policy: checkbox extra_gated_description: >- The information you provide will be collected, stored, processed and shared in accordance with the AgiBot Privacy Policy. extra_gated_button_content: Submit --- GitHub Badge

DT4LM/naive_t5v1-1base_mrpc_pair_faster-alzantot_old7

DT4LM

--- dataset_info: features: - name: question1 dtype: string - name: question2 dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 45508 num_examples: 199 download_size: 35774 dataset_size: 45508 --- # Dataset Card for "naive_t5v1-1base_mrpc_pair_faster-alzantot_old7" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

DT4LM/naive_t5v1-1base_mrpc_pair_faster-alzantot_original_old7

DT4LM

--- dataset_info: features: - name: question1 dtype: string - name: question2 dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 45034 num_examples: 199 download_size: 35153 dataset_size: 45034 --- # Dataset Card for "naive_t5v1-1base_mrpc_pair_faster-alzantot_original_old7" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

JasonAlbert/MATH80

JasonAlbert

--- license: mit ---

DT4LM/t5v1-1base_rte_kuleshov_var_original_old

DT4LM

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 42955 num_examples: 132 download_size: 33128 dataset_size: 42955 --- # Dataset Card for "t5v1-1base_rte_kuleshov_var_original_old" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

DT4LM/t5v1-1ba_rte_kuleshov_var_differential_original_old

DT4LM

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 33334 num_examples: 101 download_size: 26780 dataset_size: 33334 --- # Dataset Card for "t5v1-1ba_rte_kuleshov_var_differential_original_old" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

DT4LM/t5v1-1base_rte_pair_kuleshov_var_old

DT4LM

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 54713 num_examples: 166 download_size: 41409 dataset_size: 54713 --- # Dataset Card for "t5v1-1base_rte_pair_kuleshov_var_old" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

DT4LM/t5v1-1base_rte_pair_kuleshov_var_original_old

DT4LM

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 54499 num_examples: 166 download_size: 42564 dataset_size: 54499 --- # Dataset Card for "t5v1-1base_rte_pair_kuleshov_var_original_old" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

kaareej/bildertom2

kaareej

--- license: mit ---

atlasia/FineWeb2-Moroccan-Arabic-Predictions-0.9

atlasia

--- dataset_info: features: - name: text dtype: string - name: prediction struct: - name: prediction_confidence dtype: float64 - name: prediction_label dtype: string splits: - name: train num_bytes: 5595607628.85211 num_examples: 21420806 download_size: 2351016812 dataset_size: 5595607628.85211 configs: - config_name: default data_files: - split: train path: data/train-* --- # Moroccan Darija Dataset (Filtered from FineWeb2) This dataset contains Moroccan Darija samples extracted from the [FineWeb2](https://huggingface.co/datasets/Omartificial-Intelligence-Space/FineWeb2-Moroccan-Arabic) dataset. The extraction was performed using a [custom model](https://huggingface.co/atlasia/SfaIA-Arabic-Dialect-Identifier/tree/main/) trained to classify Arabic dialects, including Moroccan Darija (here we used the version `model_binary_v3_1fpr.bin`). Here, we kept samples with a high confidence score (above 0.9) for Moroccan Darija. This dataset aims to advance research and development in Moroccan Darija NLP tasks. --- ## Dataset Description Moroccan Darija, a widely spoken Arabic dialect in Morocco, is underrepresented in NLP resources. This dataset fills that gap by filtering FineWeb2 using an advanced classifier designed to accurately identify Moroccan Darija text. The resulting dataset is a valuable resource for tasks such as: - Language modeling - Sentiment analysis - Machine translation - Dialectal classification --- ## Extraction Methodology 1. **Base Dataset**: FineWeb2, a large-scale multilingual web dataset. 1. **First extraction using GlotLID**: [A version](https://huggingface.co/datasets/Omartificial-Intelligence-Space/FineWeb2-Moroccan-Arabic) with extraction using GlotLID. 2. **SfaIA Model**: A fasttext model trained to identify Arabic dialects, including Moroccan Darija with better performances than GlotLID. 3. **Pipeline**: - Text samples from the [dataset](https://huggingface.co/datasets/Omartificial-Intelligence-Space/FineWeb2-Moroccan-Arabic) were passed through the SfaIA classifier. - Only samples with a high confidence score (above 0.9) for Moroccan Darija were retained. --- ## Dataset Structure - `text`: The raw text sample in Moroccan Darija. - `prediction`: - `prediction_confidence`: Model confidence score for each sample. - `prediction_label`: Model predicted label. Example entry: ```json { "text": "السلام عليكم، كيف دايرين؟", "prediction": { 'prediction_confidence': 0.952466607093811, 'prediction_label': 'Morocco' } }

atlasia/FineWeb2-Moroccan-Arabic-Predictions-0.8

atlasia

--- dataset_info: features: - name: text dtype: string - name: prediction struct: - name: prediction_confidence dtype: float64 - name: prediction_label dtype: string splits: - name: train num_bytes: 6386386917.484202 num_examples: 24448025 download_size: 2817669136 dataset_size: 6386386917.484202 configs: - config_name: default data_files: - split: train path: data/train-* --- # Moroccan Darija Dataset (Filtered from FineWeb2) This dataset contains Moroccan Darija samples extracted from the [FineWeb2](https://huggingface.co/datasets/Omartificial-Intelligence-Space/FineWeb2-Moroccan-Arabic) dataset. The extraction was performed using a [custom model](https://huggingface.co/atlasia/SfaIA-Arabic-Dialect-Identifier/tree/main/) trained to classify Arabic dialects, including Moroccan Darija (here we used the version `model_binary_v3_1fpr.bin`). Here, we kept samples with a high confidence score (above 0.9) for Moroccan Darija. This dataset aims to advance research and development in Moroccan Darija NLP tasks. --- ## Dataset Description Moroccan Darija, a widely spoken Arabic dialect in Morocco, is underrepresented in NLP resources. This dataset fills that gap by filtering FineWeb2 using an advanced classifier designed to accurately identify Moroccan Darija text. The resulting dataset is a valuable resource for tasks such as: - Language modeling - Sentiment analysis - Machine translation - Dialectal classification --- ## Extraction Methodology 1. **Base Dataset**: FineWeb2, a large-scale multilingual web dataset. 1. **First extraction using GlotLID**: [A version](https://huggingface.co/datasets/Omartificial-Intelligence-Space/FineWeb2-Moroccan-Arabic) with extraction using GlotLID. 2. **SfaIA Model**: A fasttext model trained to identify Arabic dialects, including Moroccan Darija with better performances than GlotLID. 3. **Pipeline**: - Text samples from the [dataset](https://huggingface.co/datasets/Omartificial-Intelligence-Space/FineWeb2-Moroccan-Arabic) were passed through the SfaIA classifier. - Only samples with a high confidence score (above 0.8) for Moroccan Darija were retained. --- ## Dataset Structure - `text`: The raw text sample in Moroccan Darija. - `prediction`: - `prediction_confidence`: Model confidence score for each sample. - `prediction_label`: Model predicted label. Example entry: ```json { "text": "السلام عليكم، كيف دايرين؟", "prediction": { 'prediction_confidence': 0.852466607093811, 'prediction_label': 'Morocco' } }

atlasia/FineWeb2-Moroccan-Arabic-Predictions-0.6

atlasia

--- dataset_info: features: - name: text dtype: string - name: prediction struct: - name: prediction_confidence dtype: float64 - name: prediction_label dtype: string splits: - name: train num_bytes: 7436938915.663112 num_examples: 28469692 download_size: 3479461701 dataset_size: 7436938915.663112 configs: - config_name: default data_files: - split: train path: data/train-* --- # Moroccan Darija Dataset (Filtered from FineWeb2) This dataset contains Moroccan Darija samples extracted from the [FineWeb2](https://huggingface.co/datasets/Omartificial-Intelligence-Space/FineWeb2-Moroccan-Arabic) dataset. The extraction was performed using a [custom model](https://huggingface.co/atlasia/SfaIA-Arabic-Dialect-Identifier/tree/main/) trained to classify Arabic dialects, including Moroccan Darija (here we used the version `model_binary_v3_1fpr.bin`). Here, we kept samples with a high confidence score (above 0.6) for Moroccan Darija. This dataset aims to advance research and development in Moroccan Darija NLP tasks. --- ## Dataset Description Moroccan Darija, a widely spoken Arabic dialect in Morocco, is underrepresented in NLP resources. This dataset fills that gap by filtering FineWeb2 using an advanced classifier designed to accurately identify Moroccan Darija text. The resulting dataset is a valuable resource for tasks such as: - Language modeling - Sentiment analysis - Machine translation - Dialectal classification --- ## Extraction Methodology 1. **Base Dataset**: FineWeb2, a large-scale multilingual web dataset. 1. **First extraction using GlotLID**: [A version](https://huggingface.co/datasets/Omartificial-Intelligence-Space/FineWeb2-Moroccan-Arabic) with extraction using GlotLID. 2. **SfaIA Model**: A fasttext model trained to identify Arabic dialects, including Moroccan Darija with better performances than GlotLID. 3. **Pipeline**: - Text samples from the [dataset](https://huggingface.co/datasets/Omartificial-Intelligence-Space/FineWeb2-Moroccan-Arabic) were passed through the SfaIA classifier. - Only samples with a high confidence score (above 0.6) for Moroccan Darija were retained. --- ## Dataset Structure - `text`: The raw text sample in Moroccan Darija. - `prediction`: - `prediction_confidence`: Model confidence score for each sample. - `prediction_label`: Model predicted label. Example entry: ```json { "text": "السلام عليكم، كيف دايرين؟", "prediction": { 'prediction_confidence': 0.652466607093811, 'prediction_label': 'Morocco' } }

Orbina-development/web-search-sartlar

Orbina-development

--- dataset_info: features: - name: question dtype: string - name: url dtype: string - name: title dtype: string - name: snippet dtype: string - name: content dtype: string - name: instruction dtype: string - name: similarity_score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1048580 num_examples: 26 download_size: 497110 dataset_size: 1048580 configs: - config_name: default data_files: - split: train path: data/train-* ---

riswanahamed/Distilabel_demo

riswanahamed

--- license: mit ---

dstc12/bot_adversarial_dialogue

dstc12

--- dataset_info: - config_name: ar features: - name: context sequence: string - name: response dtype: string - name: safety_label dtype: int64 - name: metadata dtype: string splits: - name: train num_bytes: 64925668 num_examples: 65056 - name: val num_bytes: 6540675 num_examples: 6576 - name: test num_bytes: 2363628 num_examples: 2414 download_size: 17596883 dataset_size: 73829971 - config_name: de features: - name: context sequence: string - name: response dtype: string - name: safety_label dtype: int64 - name: metadata dtype: string splits: - name: train num_bytes: 53271040 num_examples: 65001 - name: val num_bytes: 5360026 num_examples: 6582 - name: test num_bytes: 1939938 num_examples: 2409 download_size: 12951155 dataset_size: 60571004 - config_name: en features: - name: context sequence: string - name: response dtype: string - name: safety_label dtype: int64 - name: metadata dtype: string splits: - name: train num_bytes: 51001236 num_examples: 69274 - name: val num_bytes: 5111274 num_examples: 7002 - name: test num_bytes: 1888499 num_examples: 2598 download_size: 7710984 dataset_size: 58001009 - config_name: es features: - name: context sequence: string - name: response dtype: string - name: safety_label dtype: int64 - name: metadata dtype: string splits: - name: train num_bytes: 46095752 num_examples: 61553 - name: val num_bytes: 4690932 num_examples: 6284 - name: test num_bytes: 1706463 num_examples: 2309 download_size: 10695746 dataset_size: 52493147 - config_name: fr features: - name: context sequence: string - name: response dtype: string - name: safety_label dtype: int64 - name: metadata dtype: string splits: - name: train num_bytes: 52349891 num_examples: 64188 - name: val num_bytes: 5239104 num_examples: 6468 - name: test num_bytes: 1898513 num_examples: 2377 download_size: 12670821 dataset_size: 59487508 - config_name: ja features: - name: context sequence: string - name: response dtype: string - name: safety_label dtype: int64 - name: metadata dtype: string splits: - name: train num_bytes: 62435092 num_examples: 65343 - name: val num_bytes: 6297396 num_examples: 6602 - name: test num_bytes: 2260734 num_examples: 2422 download_size: 18434564 dataset_size: 70993222 - config_name: pt features: - name: context sequence: string - name: response dtype: string - name: safety_label dtype: int64 - name: metadata dtype: string splits: - name: train num_bytes: 73137272 num_examples: 65536 - name: val num_bytes: 5128328 num_examples: 6632 - name: test num_bytes: 1856522 num_examples: 2436 download_size: 21047681 dataset_size: 80122122 - config_name: zh features: - name: context sequence: string - name: response dtype: string - name: safety_label dtype: int64 - name: metadata dtype: string splits: - name: train num_bytes: 45799844 num_examples: 65920 - name: val num_bytes: 4649404 num_examples: 6692 - name: test num_bytes: 1664162 num_examples: 2442 download_size: 13987297 dataset_size: 52113410 configs: - config_name: ar data_files: - split: train path: ar/train-* - split: val path: ar/val-* - split: test path: ar/test-* - config_name: de data_files: - split: train path: de/train-* - split: val path: de/val-* - split: test path: de/test-* - config_name: en data_files: - split: train path: en/train-* - split: val path: en/val-* - split: test path: en/test-* - config_name: es data_files: - split: train path: es/train-* - split: val path: es/val-* - split: test path: es/test-* - config_name: fr data_files: - split: train path: fr/train-* - split: val path: fr/val-* - split: test path: fr/test-* - config_name: ja data_files: - split: train path: ja/train-* - split: val path: ja/val-* - split: test path: ja/test-* - config_name: pt data_files: - split: train path: pt/train-* - split: val path: pt/val-* - split: test path: pt/test-* - config_name: zh data_files: - split: train path: zh/train-* - split: val path: zh/val-* - split: test path: zh/test-* --- Access to this dataset is restricted to DSTC12 Track 1 Participants. For more details please visit the [website](https://chateval.org/dstc12). Baseline results using `meta-llama/Llama-Guard-3-1B`: <table> <thead> <tr> <th scope="col">Language</th> <th scope="col">ROC-AUC</th> </tr> </thead> <tbody> <tr> <td>Arabic (ar)</td> <td>0.6884</td> </tr> <tr> <td>German (de)</td> <td>0.7241</td> </tr> <tr> <td>English (en)</td> <td>0.7571</td> </tr> <tr> <td>Spanish (es)</td> <td>0.6961</td> </tr> <tr> <td>French (fr)</td> <td>0.7113</td> </tr> <tr> <td>Japanese (ja)</td> <td>0.7041</td> </tr> <tr> <td>Portuguese (pt)</td> <td>0.7146</td> </tr> <tr> <td>Chinese (zh)</td> <td>0.7143</td> </tr> <tr> <td><b>Average (all)</b></td> <td>0.7137</td> </tr>

yxdu/news-commentary

yxdu

--- dataset_info: features: - name: zh_token sequence: int64 - name: en_token sequence: int64 - name: zh_text dtype: string - name: en_text dtype: string splits: - name: train num_bytes: 2894233399 num_examples: 314446 - name: dev num_bytes: 360129734 num_examples: 39233 - name: test num_bytes: 362192405 num_examples: 39305 download_size: 626007357 dataset_size: 3616555538 configs: - config_name: default data_files: - split: train path: data/train-* - split: dev path: data/dev-* - split: test path: data/test-* ---

zekeZZ/gpqa_chem

zekeZZ

--- dataset_info: features: - name: Subdomain dtype: string - name: question dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: train num_bytes: 125506.546875 num_examples: 183 download_size: 61576 dataset_size: 125506.546875 configs: - config_name: default data_files: - split: train path: data/train-* ---

zekeZZ/gpqa_physics

zekeZZ

--- dataset_info: features: - name: Subdomain dtype: string - name: question dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: train num_bytes: 128249.859375 num_examples: 187 download_size: 65815 dataset_size: 128249.859375 configs: - config_name: default data_files: - split: train path: data/train-* ---

manu-gaur/NDLB_data

manu-gaur

--- license: apache-2.0 ---

searchivarius/test2

searchivarius

--- license: apache-2.0 ---

manu-gaur/NDLB-TrueMatch-Benchmark

manu-gaur

--- license: apache-2.0 ---

ChicagoHAI/language-of-bargaining

ChicagoHAI

--- license: cc-by-nc-sa-4.0 viewer: false extra_gated_prompt: >- ### Language of Bargaining Data License Agreement All the data within this repo are under this [Data License Agreement](https://drive.google.com/file/d/1VXLYPIgd8rm6SoLXaH97KeDqwYmEmFc5/view?usp=share_link). extra_gated_fields: First Name: text Last Name: text Email: text Affiliation: text Job Title: text By clicking Submit below I acknowledge that I have read the data license agreement, understand it, and agree to be bound by its terms and conditions: checkbox extra_gated_button_content: Submit --- # Language of Bargaining Dataset ## Dataset Description - **Paper:** [https://arxiv.org/abs/2306.07117]() ## Dataset Summary This repo contains the Natural Language (NL) and Alternating Offer (AO) negotation transcript data as described in https://aclanthology.org/2023.acl-long.735.pdf. The data includes the "Bargaining Act" annotations for the NL setting. ## Dataset Information Within the `negotiations-data.zip` compressed folder you will find two folders containing the AO and NL data. ### Alternating Offers (AO) The AO data is found in the folder `ao`. The folder contains 230 files, each one representing a single negotation conducted in the AO setting. They are in JSONL format where each line adheres to the following schema: ```yaml { "id": string, "role": string seller|buyer, "message": float, "created_at": datetime, "status": string } ``` There are multiple ways you could load this data, here is an example using the [pandas Python package](https://pandas.pydata.org/) for loading a single negotiation transcript, e.g. `ao/299.jsonl`. ``` import pandas as pd ao_file = 'ao/299.jsonl' negotiation_df = pd.read_json(ao_file, orient='records', lines=True) ``` ### Natural language (NL) The NL data is found in the folder `nl`. The folder contains 178 files, each one representing a single negotation conducted in the NL setting. They are in JSON format and adhere to the following schema: ```yaml { "duration_min": float, "turns": [ [ { "ID": string, "Role": string seller|buyer, "Word": string, "Span": string, "Spoken Numeric": string, "Numeric": string, "Category": string, "Firm or Soft": string, "External Incentive": string }, ... { ... }, ] ] } ``` There are multiple ways you could load this data, here is an example using the json and [pandas Python package](https://pandas.pydata.org/) for loading a single negotiation transcript, e.g. `nl/100.json`. ``` import pandas as pd import json nl_file = 'nl/100.json' with open(nl_file, 'r') as f: data = json.load(f) # data contains the data, in json, of the entire negotiation for utt in data['turns']: turn_df = pd.DataFrame.from_records(utt) # turn_df contains a single utterance, as a dataframe, where each row corresponds to one transcribed word. # Process and do something with turn_df here... ``` ### Annotation Instructions Below is a relevant portion of the instructions provided to annotators. It shoud help clarify the data: - Span: - `x` on all tokens included in the actual offer - Be inclusive - main idea is to exclude entirely different sentences - Spoken Numeric - `t`: if thousands place is spoken aloud (234,000), else blank - Numeric (i.e. offer amount) - Single number (plain numeric, thousands) - 220 - Range or Bounds - [220, 225] [,225] [220,] - Category - `n`: Numeric Offer (common case) - used for new numeric offers (numbers that haven’t been mentioned yet) - `p`: Push (request the other person move in my direction, mainly needs to reference offer made by other party) - `a`: Allowance (offer to move in the other persons’ direction without explicit number, needs to reference offer made by other party) - `c`: Comparisons to prices of external stuff (price of comparable house, price of imaginary existing offers) - `r`: Repetition of previous offer, non-committal - `e`: End of negotiation via offer acceptance entering mutual common ground (e.g., offer given followed by, “That works for me, let’s do it.”) - explicitly only happens once. - Firm or Soft - `s`: Soft number (e.g., 220ish) - Suffixes: “-ish”, prefixes: “around”, etc. - `f`: Firm number - External Incentive - `y`: speaker incorporates as a part of the offer non-monetary incentives (landscaping, sale/offer timing, cash payment of amount vs mortgage), needs to reference an offer. ### Anonymizing We replaced all occurences of participant names with the token `[PERSON]`. ## Citation If you use this dataset in your research or publication, please cite it as: ``` @inproceedings{heddaya-etal-2023-language, title = "Language of Bargaining", author = "Heddaya, Mourad and Dworkin, Solomon and Tan, Chenhao and Voigt, Rob and Zentefis, Alexander", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.acl-long.735", pages = "13161--13185", } ```

Lots-of-LoRAs/task375_classify_type_of_sentence_in_debate

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task375_classify_type_of_sentence_in_debate dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 320 - name: valid num_examples: 40 - name: test num_examples: 40 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task375_classify_type_of_sentence_in_debate ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task1705_ljspeech_classification

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task1705_ljspeech_classification dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 80 - name: valid num_examples: 10 - name: test num_examples: 10 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1705_ljspeech_classification ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task1267_ted_translation_fa_es

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task1267_ted_translation_fa_es dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 5122 - name: valid num_examples: 640 - name: test num_examples: 641 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1267_ted_translation_fa_es ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task1621_menyo20k-mt_en_yo_language_identification

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task1621_menyo20k-mt_en_yo_language_identification dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 3183 - name: valid num_examples: 398 - name: test num_examples: 398 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1621_menyo20k-mt_en_yo_language_identification ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task1370_newscomm_classification

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task1370_newscomm_classification dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 1934 - name: valid num_examples: 242 - name: test num_examples: 242 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1370_newscomm_classification ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task539_alt_translation_ma_en

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task539_alt_translation_ma_en dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 479 - name: valid num_examples: 60 - name: test num_examples: 60 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task539_alt_translation_ma_en ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task1224_ted_translation_ja_ar

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task1224_ted_translation_ja_ar dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 5143 - name: valid num_examples: 643 - name: test num_examples: 643 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1224_ted_translation_ja_ar ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task561_alt_translation_en_bg

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task561_alt_translation_en_bg dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 480 - name: valid num_examples: 60 - name: test num_examples: 60 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task561_alt_translation_en_bg ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task368_synthetic_even_or_odd_calculation

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task368_synthetic_even_or_odd_calculation dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 5196 - name: valid num_examples: 649 - name: test num_examples: 650 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task368_synthetic_even_or_odd_calculation ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])

Lots-of-LoRAs/task1096_ted_translation_ja_it

Lots-of-LoRAs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task1096_ted_translation_ja_it dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 5128 - name: valid num_examples: 641 - name: test num_examples: 641 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1096_ted_translation_ja_it ## 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](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @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: ```bibtex @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](mailto:[email protected])