dnrops
/
sqlcoder
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Update README.md

This commit is contained in:
Rishabh Srivastava 2023-08-18 06:10:53 +08:00 committed by GitHub
parent 844109b0a6
commit 53c7bcc4ab
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 42 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

44
README.md
View File

@ -1,2 +1,42 @@
# sql-coder
SoTA LLM for converting natural language questions to SQL queries
# Defog SQLCoder
Defog's SQLCoder is a state of the art LLM for converting natural language questions to SQL queries.
[🤗 HF Repo](https://huggingface.co/defog/sql-coder) | [Twitter](https://twitter.com/defogdata)
## TL;DR
SQLCoder slightly outperforms gpt-3.5-turbo for natural language to SQL generation tasks on our [sql-eval](https://github.com/defog-ai/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](https://defog.ai/blog/defog-sql-coder-dataset-creation).
## 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 | wizardcoder | starcoder |
|-|-|-|-|-|-|-|-|
| 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](https://defog.ai/sqlcoder).
## Todo
[x] Open-source the 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