| --- |
| license: cc-by-sa-4.0 |
| task_categories: |
| - table-question-answering |
| - question-answering |
| language: |
| - en |
| size_categories: |
| - 1K<n<10K |
| --- |
| # BIRD-SQL Train (Filtered) |
|
|
| A high-quality subset of the original BIRD train split for text-to-SQL finetuning. |
|
|
| ## Overview |
|
|
| Over the past year the community has shared many observations about data quality in BIRD. We performed a rigorous data quality check process to retain examples that are **consistent with schema** and **faithfully answer the question**. The resulting set keeps **6,601** instances out of **9,428** (≈70%), and serves as a drop-in replacement for training. |
|
|
| - **Original Train:** 9,428 |
| - **Filtered Train (this release):** 6,601 |
|
|
| The example code for training and inference can be found [here](https://github.com/bird-bench/mini_dev/tree/main) |
|
|
| ### For New Users |
| If you are new to BIRD project, you can download the complete databases for the training set using the following link: |
| [Download BIRD Train](https://bird-bench.oss-cn-beijing.aliyuncs.com/train.zip) |
|
|
| ### For Existing Users |
| If you have already downloaded the BIRD training databases, you can pull the latest filtered data updates through Hugging Face using the following scripts: |
|
|
| ```python |
| from datasets import load_dataset |
| # Load the dataset |
| dataset = load_dataset("birdsql/bird23-train-filtered") |
| # Access the dataset |
| print(dataset["train"][0]) |
| ``` |
|
|
| You can find the column meaning json file [here](https://huggingface.co/datasets/birdsql/bird23-train-filtered/resolve/main/train_column_meaning.json) the key is composed of `database_id|table_name|column_name`, and the value is key information about each column and their value summarized from raw CSVs. |
|
|
| ## Training Quality |
| We validate by finetuning a single open model with a standard SFT recipe and evaluating on the official BIRD Mini Dev and Dev set. We use the [Qwen/Qwen2.5-3B](https://huggingface.co/Qwen/Qwen2.5-3B) as the base model and follow the [Arctic-Text2SQL-R1 project ](https://www.snowflake.com/en/product/ai/ai-research/) data processing. You can find the original [repo](https://github.com/snowflakedb/ArcticTraining/tree/main/projects/arctic_text2sql_r1) and [paper](https://arxiv.org/abs/2505.20315) here. |
|
|
| ### Performance Comparison (EX) |
|
|
| | Setting | Mini-Dev | Dev | |
| | ------------------------- | -------- | -------- | |
| | **Baseline** | 26.2 | 31.88 | |
| | **Original Train** | 45.4 | 50.46 | |
| | **Filtered Train** | **46.0** | **50.0** | |
|
|
| Takeaway: with **~30%** fewer training items, the filtered set matches the full set on the Mini-Dev and Dev set. |
|
|
| ### Data scaling on the filtered set |
|
|
|
|
| <p align="left"> |
| <img src="https://cdn-uploads.huggingface.co/production/uploads/653693cb8ee17cfd44eed8ce/6_0KJzy4o1GfMDnqP3nA1.png" width="520"> |
| </p> |
|
|
| ## Dataset Introduction |
|
|
| The dataset contains the main following resources: |
|
|
| - `database`: The database should be stored under the [`./train_databases/`](./train_databases/). In each database folder, it has two components: |
| - `database_description`: the csv files are manufactured to describe database schema and its values for models to explore or references. |
| - `sqlite`: The database contents in BIRD. |
| - `data`: Each text-to-SQL pairs with the oracle knowledge evidence is stored as a JSONL file, i.e., `train.jsonl`. It has four main parts: |
| - `db_id`: the names of databases |
| - `question`: the questions curated by human crowdsourcing according to database descriptions, database contents. |
| - `evidence`: the external knowledge evidence annotated by experts for assistance of models or SQL annotators. |
| - `SQL`: SQLs annotated by crowdsource referring to database descriptions, database contents, to answer the questions accurately. |
|
|
| ## Acknowledgements |
| This work builds on the BIRD benchmark and the efforts of its creators and contributors. We thank the community for continuous feedback that helped shape this release. |
|
|
|
|
|
|
| ## Citation |
| Please cite the repo if you think our work is helpful to you. |
| ``` |
| @article{li2024can, |
| title={Can llm already serve as a database interface? a big bench for large-scale database grounded text-to-sqls}, |
| author={Li, Jinyang and Hui, Binyuan and Qu, Ge and Yang, Jiaxi and Li, Binhua and Li, Bowen and Wang, Bailin and Qin, Bowen and Geng, Ruiying and Huo, Nan and others}, |
| journal={Advances in Neural Information Processing Systems}, |
| volume={36}, |
| year={2024} |
| } |
| ``` |
