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---
license: cc-by-nc-4.0
language:
- en
---
# Jellyfish-7B
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## Model Details
Jellyfish-7B is a large language model equipped with 7 billion parameters.
We fine-tuned the [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) model using the datasets pertinent to data preprocessing tasks.
The training data include two parts:
* Jellyfish-13B training data
* GPT4 generated reasoning data for data preprocessing tasks.
More details about the model can be found in the [Jellyfish paper](https://arxiv.org/abs/2312.01678).
- **Developed by:** Haochen Zhang, Yuyang Dong, Chuan Xiao, Masafumi Oyamada
- **Contact: [email protected]**
- **Funded by:** NEC Corporation, Osaka University
- **Language(s) (NLP):** English
- **License:** Non-Commercial Creative Commons license (CC BY-NC-4.0)
- **Finetuned from model:** [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
## Citation
If you find our work useful, please give us credit by citing:
```
@article{zhang2023jellyfish,
title={Jellyfish: A Large Language Model for Data Preprocessing},
author={Zhang, Haochen and Dong, Yuyang and Xiao, Chuan and Oyamada, Masafumi},
journal={arXiv preprint arXiv:2312.01678},
year={2023}
}
```
## Performance on seen tasks
| Task | Type | Dataset | Non-LLM SoTA<sup>1</sup> | GPT-3.5<sup>2</sup> | GPT-4<sup>2</sup> | Jellyfish-13B| Jellyfish-7B |
| ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |
| Entity Matching | Seen | Fodors-Zagats | 100 | 100 | 100 | 100 | 100 |
| Entity Matching | Seen | Beer | 94.37| 96.30 | 100 | 96.77 | 96.55|
| Entity Matching | Seen | iTunes-Amazon | 97.06| 96.43 | 100 | 98.11 | 96.30|
| Entity Matching | Seen | DBLP-ACM | 98.99| 96.99 | 97.44 | 98.98 | 98.88|
| Entity Matching | Seen | DBLP-GoogleScholar | 95.60| 76.12 | 91.87 | 98.51 | 95.15|
| Entity Matching | Seen | Amazon-Google | 75.58| 66.53 | 74.21 | 81.34 | 80.83 |
| Entity Matching | Unseen | Walmart-Amazon | 86.76| 86.17 | 90.27 | 89.42 | 85.64 |
| Entity Matching | Unseen | Abt-Buy | 89.33 | -- | 92.77 | 89.58 | 82.38 |
| Data Imputation | Seen | Restaurant | 77.20| 94.19 | 97.67 | 94.19 | 88.37 |
| Data Imputation | Seen | Buy | 96.50| 98.46 | 100 | 100 | 96.62 |
| Data Imputation | Unseen | Filpkart | 68.00 | -- | 89.94 | 81.68 | 79.44|
| Data Imputation | Unseen | Phone | 86.70| -- | 90.79 | 87.21 | 85.00|
| Error Detection | Seen | Hosptial | 94.40| 90.74 | 90.74 | 95.59 | 96.27 |
| Error Detection | Seen | Adult | 99.10| 92.01 | 92.01 | 99.33 | 91.96 |
| Error Detection | Unseen | Flights | 81.00 | -- | 83.48 | 82.52 | 66.92 |
| Error Detection | Unseen | Rayyan | 79.00| -- | 81.95 | 90.65 | 69.82 |
| Schema Matching | Seen | Sythea | 38.50| 57.14 | 66.67 | 36.36 | 44.44 |
| Schema Matching | Seen | MIMIC | 20.00| -- | 40.00 | 40.00 | 40.00 |
| Schema Matching | Unseen | CMS | 50.00| -- | 19.35 | 59.29 | 13.79 |
_For GPT-3.5 and GPT-4, we used the few-shot approach on all datasets. However, for Jellyfish-13B and Jellyfish-Interpreter, the few-shot approach is disabled on seen datasets and enabled on unseen datasets._
_Accuracy as the metric for data imputation and the F1 score for other tasks._
## Performance on unseen tasks
### Column Type Annotation
| Dataset | RoBERTa (159 shots)<sup>1</sup> | GPT-3.5<sup>1</sup> | GPT-4 | Jellfish-13B| Jellyfish-7B |
| ---- | ---- | ---- | ---- | ---- | ----|
| SOTAB | 79.20 | 89.47 | 91.55 | 82.00 | 80.89 |
_Few-shot is disabled for Jellyfish-13B._
1. Results from [Column Type Annotation using ChatGPT](https://arxiv.org/abs/2306.00745)
### Attribute Value Extraction
| Dataset |Stable Beluga 2 70B<sup>1</sup> | SOLAR 70B<sup>1</sup> | GPT-3.5<sup>1</sup> | GPT-4 <sup>1</sup>| Jellfish-13B | Jellyfish-7B|
| ---- | ---- | ---- | ---- | ---- | ---- | ----|
| AE-110k | 52.10 | 49.20 | 61.30 | 55.50 | 58.12 | 76.85|
| OA-Mine | 50.80 | 55.20 | 62.70 | 68.90 | 55.96 | 76.04|
## Prompt Template
```
[INST]:
<prompt> (without the <>)
[\INST]]
```
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