Create README.md

README.md

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+---
+tags:
+- sql
+- spider
+- text-to-sql
+- sql finetune
+datasets:
+- spider
+- richardr1126/spider-natsql-skeleton-context-finetune
+library_name: transformers
+license: bigcode-openrail-m
+---
+## Citation
+
+Please cite the repo if you use the data or code in this repo.
+
+```
+@misc{luo2023wizardcoder,
+      title={WizardCoder: Empowering Code Large Language Models with Evol-Instruct}, 
+      author={Ziyang Luo and Can Xu and Pu Zhao and Qingfeng Sun and Xiubo Geng and Wenxiang Hu and Chongyang Tao and Jing Ma and Qingwei Lin and Daxin Jiang},
+      year={2023},
+}
+```
+```
+@article{yu2018spider,
+  title={Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task},
+  author={Yu, Tao and Zhang, Rui and Yang, Kai and Yasunaga, Michihiro and Wang, Dongxu and Li, Zifan and Ma, James and Li, Irene and Yao, Qingning and Roman, Shanelle and others},
+  journal={arXiv preprint arXiv:1809.08887},
+  year={2018}
+}
+```
+```
+@inproceedings{gan-etal-2021-natural-sql,
+    title = "Natural {SQL}: Making {SQL} Easier to Infer from Natural Language Specifications",
+    author = "Gan, Yujian  and
+      Chen, Xinyun  and
+      Xie, Jinxia  and
+      Purver, Matthew  and
+      Woodward, John R.  and
+      Drake, John  and
+      Zhang, Qiaofu",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
+    month = nov,
+    year = "2021",
+    address = "Punta Cana, Dominican Republic",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.findings-emnlp.174",
+    doi = "10.18653/v1/2021.findings-emnlp.174",
+    pages = "2030--2042",
+}
+```
+
+## Disclaimer
+
+The resources, including code, data, and model weights, associated with this project are restricted for academic research purposes only and cannot be used for commercial purposes. The content produced by any version of WizardCoder is influenced by uncontrollable variables such as randomness, and therefore, the accuracy of the output cannot be guaranteed by this project. This project does not accept any legal liability for the content of the model output, nor does it assume responsibility for any losses incurred due to the use of associated resources and output results.