Update README.md

README.md

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 ---
 license: cc-by-nc-4.0
+language:
+- en
 ---
+# Jellyfish-7B
+<!-- Provide a quick summary of what the model is/does. -->
+<!--
+<img src="https://i.imgur.com/d8Bl04i.png" alt="PicToModel" width="330"/>
+-->
+<img src="https://i.imgur.com/E1vqCIw.png" alt="PicToModel" width="330"/>
+
+
+## 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]]
+```