sqlcoder/README.md

Defog SQLCoder

Defog's SQLCoder is a state of the art LLM for converting natural language questions to SQL queries.

🤗 HF Repo | Twitter

TL;DR

SQLCoder slightly outperforms gpt-3.5-turbo for natural language to SQL generation tasks on our sql-eval framework, and significantly outperforms all popular open-source models. SQLCoder is fine-tuned on a base StarCoder model.

Results

model	perc_correct
gpt-4	74.3
defog-sql-coder	64.6
gpt-3.5-turbo	60.6
defog-easy	57.1
text-davinci-003	54.3
wizardcoder	52.0
starcoder	45.1

Training

Defog was trained on 10,537 human-curated questions across 2 epochs. These questions were based on 10 different schemas. None of the schemas in the training data were included in our evaluation framework.

Training happened in 2 phases. The first phase was on questions that were classified as "easy" or "medium" difficulty, and the second phase was on questions that were classified as "hard" or "extra hard" difficulty. You can read more about the dataset creation and classification process here.

Results by question category

We classified each generated question into one of 5 categories. These are the percentage of questions that each model got correct for each category

query_category	gpt-4	defog-sql-coder	gpt-3.5-turbo	defog-easy	text-davinci-003	wizard-coder	star-coder
group_by	82.9	77.1	71.4	62.9	62.9	68.6	54.3
order_by	71.4	65.7	60.0	68.6	60.0	54.3	57.1
ratio	62.9	57.1	48.6	40.0	37.1	22.9	17.1
table_join	74.3	57.1	60.0	54.3	51.4	54.3	51.4
where	80.0	65.7	62.9	60.0	60.0	60.0	45.7

Using SQLCoder

You can use SQLCoder via the transformers library by downloading our model weights from the HuggingFace repo. We have added sample code for inference here. You can also use a demo on our website here.

Todo

• Open-source the v1 model weights
• Train the model on more data, with higher data variance
• Tune the model further with Reward Modelling and RLHF
• Pretrain a model from scratch that specializes in SQL analysis