Create README.md

README.md

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+---
+language:
+- en
+---
+## Model Description
+
+Graphix-T5 is a graph-aware semi-pretrained text-to-text PLM specifically designed to improve multi-hop reasoning for the complex text-to-SQL task. 
+This novel architecture enhances the structural encoding capabilities of the T5 model while preserving its powerful contextual encoding ability. 
+The experimental results demonstrate the effectiveness of GRAPHIX-T5 and underscore the importance of incorporating structural information in text-to-text PLMs for tackling intricate text-to-SQL challenges. 
+The smaller gap in performance between the dev and test sets indicates the stronger generalization capability of Graphix-T5.
+
+## Training Data
+Graphix-3B is trained based on SPIDER, a cross-domain text-to-SQL benchmark. And it's evaluated in vanilla SPIDER dev, test, and other variants: SPIDER-SYN, SPIDER-DK, 
+SPIDER-REALISTIC **without additional training**. This model will continue to be fine-tuned on more complex text-to-SQL data, 
+i.e. BIRD to deal with harder but more real applications
+
+## To Begin With
+
+You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
+```py
+from transformers import AutoTokenizer, AutoModel
+
+tokenizer = AutoTokenizer.from_pretrained("patrickNLP/Graphix-3B")
+
+model = AutoModel.from_pretrained("patrickNLP/Graphix-3B")
+```
+
+## Performance
+Graphix-3B w/ Picard maintains state-of-the-art (SOTA) semantic parsing capabilities, as demonstrated by its performance on the  
+[`SPIDER`](https://yale-lily.github.io/spider) leaderboard. Its only submission achieves **74.0%** on EM and **77.6%** on EX in the testing dataset.
+Please see [`Graphix Official Implementation`]() for details.
+
+## Reference
+1. [`Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing`](https://arxiv.org/abs/2301.07507)
+2. [`Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs`](https://arxiv.org/abs/2305.03111)
+3. [`Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task`](https://arxiv.org/abs/1809.08887)
+4. [`PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models`](https://arxiv.org/abs/2109.05093)
+
+
+## Citation
+```
+@misc{li2023graphixt5,
+      title={Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing}, 
+      author={Jinyang Li and Binyuan Hui and Reynold Cheng and Bowen Qin and Chenhao Ma and Nan Huo and Fei Huang and Wenyu Du and Luo Si and Yongbin Li},
+      year={2023},
+      eprint={2301.07507},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+
+