adding readme

README.md

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+---
+language: 
+  - en
+tags:
+- text2sql
+datasets:
+- splash
+widget:
+ - text: "Give the name, population, and head of state for the country that has the largest area. || select name, population, continent from country order by surfacearea desc limit 1 || world_1 | country : name, population, headofstate, surfacearea || swap continent with head of state because it is not required."
+---
+## parkervg/destt5-text2sql
+
+Fine-tuned weights for the text2sql model described in [Correcting Semantic Parses with Natural Language through Dynamic
+Schema Encoding](https://arxiv.org/pdf/2305.19974.pdf), based on [t5-base](https://huggingface.co/t5-base).
+
+
+### Training Data
+
+The model has been fine-tuned on the 7,481 training examples in the [SPLASH interactive semantic parsing dataset](https://github.com/MSR-LIT/Splash).
+
+Rather than seeing the full database schema, it only received the filtered schema as predicted by the [destt5-schema-prediction model](https://huggingface.co/parkervg/destt5-schema-prediction)
+
+
+### Training Objective
+
+This model was initialized with [t5-base](https://huggingface.co/t5-base) and fine-tuned with the text-to-text generation objective.
+
+As this model works in the interactive setting, we utilize the standard text2sql features such as `question` and `db_schema`, in addition to `feedback` and `incorrect_parse`.
+
+Importantly, the `[table]`, `[column]`, `[content]` features are expected to be the 'gold' schema items, as predicted by an initial auxiliary schema prediction model.
+
+```
+[question] || [incorrect_parse] || [db_id] | [table] : [column] ( [content] , [content] ) , [column] ( ... ) , [...] | [table] : ... | ... || [feedback]
+```
+
+The model then attempts to parse the corrected SQL query, using the filtered database schema items. This is prefaced by the `db_id`.
+
+```
+[db_id] | [sql]
+```
+
+
+### Performance
+
+When this model receives the serialized database schema as predicted by [destt5-schema-prediction](https://huggingface.co/parkervg/destt5-schema-prediction), it achieves 53.43% correction accuracy (exact-match) on the SPLASH test set. 
+
+
+### References
+
+1. [Correcting Semantic Parses with Natural Language through Dynamic
+Schema Encoding](https://arxiv.org/pdf/2305.19974.pdf)
+
+2. [DestT5 codebase](https://github.com/parkervg/destt5)
+
+3. [Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback](https://arxiv.org/pdf/2005.02539v2.pdf)
+
+
+### Citation
+
+```bibtex
+@inproceedings{glenn2023correcting,
+  author = {Parker Glenn, Parag Pravin Dakle, Preethi Raghavan},
+  title = "Correcting Semantic Parses with Natural Language through Dynamic Schema Encoding",
+  booktitle = "Proceedings of the 5th Workshop on NLP for Conversational AI",
+  publisher = "Association for Computational Linguistics",
+  year = "2023"
+}
+```