license: cc-by-nc-4.0
language:
- en
Jellyfish-0-13B
Model Details
Jellyfish is a model designed specifically for data preprocessing tasks, including entity matching, data imputation, error detection, and schema matching.
We fine-tuned Open-Orca/OpenOrca-Platypus2-13B using data derived from benchmark datasets for those data preprocessing tasks.
Its performance is competitive, standing up well against prior state-of-the-art algorithms and larger language models like GPT 3.5 and GPT 4.
We have released two versions of Jellyfish: the main version and a variant called "Jellyfish-reasoning."
As the names suggest, the main version focuses on providing accurate, direct answers. In contrast, Jellyfish-reasoning can generate not only the final answer to data processing queries but also the reasons behind its decisions.
- Developed by: Haochen Zhang, Yuyang Dong, Chuan Xiao, Masafumi Oyamada
- Funded by: NEC Corporation, Osaka University
- Language(s) (NLP): English
- License: Non-Commercial Creative Commons license (CC BY-NC-4.0)
- Finetuned from model: Open-Orca/OpenOrca-Platypus2-13B
Prompt Template
### Instruction:
<prompt> (without the <>)
### Response:
Training Details
Training Data
We utilized the training and validation sets of datasets for 4 data processing tasks from the paper Can Foundation Models Wrangle Your Data? to fine-tune both versions of Jellyfish.
The original datasets can be accessed from their GitHub project page HazyResearch/fm_data_tasks
Through meticulous prompt-engineering, we constructed our datasets suitable for fine-tuning LLM, mirroring the style of OpenOrca.
Training Method
We used LoRA to speed up the training process, targeting the q_proj and v_proj modules.
Uses
Here are the prompts we used for both fine-tuning the model and for inference. Feel free to explore different prompts on your own to achieve the best generation quality.
For JellyFish-main
You are tasked with determining whether two records listed below are the same based on the information provided. Carefully compare the {attribute 1}, {attribute 2}... for each record before making your decision.
Note: Missing values (N/A or \"nan\") should not be used as a basis for your decision.
Record A: [{attribute 1}: {attribute 1 value}, {attribute 2}: {attribute 2 value}...]\nProduct B: [{attribute 1}: {attribute 1 value}, {attribute 2}: {attribute 2 value}...]
Are record A and record B the same entity? Choose your answer from: [Yes, No]
For JellyFish-reasoning
You are tasked with determining whether two products listed below are the same based on the information provided. Carefully examine all the attributes before making your decision.
Note: Missing values (N/A or \"nan\") should not be used as a basis for your decision.
Record A: [{attribute 1}: {attribute 1 value}, {attribute 2}: {attribute 2 value}...]\nProduct B: [{attribute 1}: {attribute 1 value}, {attribute 2}: {attribute 2 value}...]
Are record A and record B the same entity?
After your reasoning, finish your response in a separate line with and ONLY with your final answer. Choose your final answer from [Yes, No].",
Bias, Risks, and Limitations
As of now, we've tested Jellyfish exclusively with the test sets from the benchmark datasets mentioned earlier.
We're in the process of assessing its performance on additional datasets.
Citation
@article{
title = {Can Foundation Models Wrangle Your Data?},
author = {Avanika Narayan, Ines Chami, Laurel Orr, Simran Arora, Christopher Ré},
booktitle = {arXiv:2205.09911},
year = {2022}
}
@software{hunterlee2023orcaplaty1
title = {OpenOrcaPlatypus: Llama2-13B Model Instruct-tuned on Filtered OpenOrcaV1 GPT-4 Dataset and Merged with divergent STEM and Logic Dataset Model},
author = {Ariel N. Lee and Cole J. Hunter and Nataniel Ruiz and Bleys Goodson and Wing Lian and Guan Wang and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B},
}
@article{platypus2023,
title={Platypus: Quick, Cheap, and Powerful Refinement of LLMs},
author={Ariel N. Lee and Cole J. Hunter and Nataniel Ruiz},
booktitle={arXiv preprint arxiv:2308.07317},
year={2023}
}
@software{OpenOrcaxOpenChatPreview2,
title = {OpenOrcaxOpenChatPreview2: Llama2-13B Model Instruct-tuned on Filtered OpenOrcaV1 GPT-4 Dataset},
author = {Guan Wang and Bleys Goodson and Wing Lian and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B},
}
@software{openchat,
title = {{OpenChat: Advancing Open-source Language Models with Imperfect Data}},
author = {Wang, Guan and Cheng, Sijie and Yu, Qiying and Liu, Changling},
doi = {10.5281/zenodo.8105775},
url = {https://github.com/imoneoi/openchat},
version = {pre-release},
year = {2023},
month = {7},
}
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{touvron2023llama,
title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom},
year={2023},
eprint= arXiv 2307.09288
}
@misc{longpre2023flan,
title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning},
author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts},
year={2023},
eprint={2301.13688},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
@article{hu2021lora,
title={LoRA: Low-Rank Adaptation of Large Language Models},
author={Hu, Edward J. and Shen, Yelong and Wallis, Phillip and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Shean and Chen, Weizhu},
journal={CoRR},
year={2021}
}