|
--- |
|
license: bsd-3-clause |
|
inference: |
|
parameters: |
|
do_sample: false |
|
max_length: 200 |
|
widget: |
|
- text: "CREATE TABLE stadium (\n stadium_id number,\n location text,\n name text,\n capacity number,\n)\n\n-- Using valid SQLite, answer the following questions for the tables provided above.\n\n-- how many stadiums in total?\n\nSELECT" |
|
example_title: "Number stadiums" |
|
- text: "CREATE TABLE work_orders ( ID NUMBER, CREATED_AT TEXT, COST FLOAT, INVOICE_AMOUNT FLOAT, IS_DUE BOOLEAN, IS_OPEN BOOLEAN, IS_OVERDUE BOOLEAN, COUNTRY_NAME TEXT, )\n\n-- Using valid SQLite, answer the following questions for the tables provided above.\n\n-- how many work orders are open?\n\nSELECT" |
|
example_title: "Open work orders" |
|
- text: "CREATE TABLE stadium ( stadium_id number, location text, name text, capacity number, highest number, lowest number, average number )\n\nCREATE TABLE singer ( singer_id number, name text, country text, song_name text, song_release_year text, age number, is_male others )\n\nCREATE TABLE concert ( concert_id number, concert_name text, theme text, stadium_id text, year text )\n\nCREATE TABLE singer_in_concert ( concert_id number, singer_id text )\n\n-- Using valid SQLite, answer the following questions for the tables provided above.\n\n-- What is the maximum, the average, and the minimum capacity of stadiums ?\n\nSELECT" |
|
example_title: "Stadium capacity" |
|
--- |
|
|
|
# NSQL (NSQL-350M) |
|
|
|
## Model Description |
|
|
|
NSQL is a family of autoregressive open-source large foundation models (FMs) designed specifically for SQL generation tasks. |
|
|
|
The checkpoint included in this repository is based on [CodeGen-Multi 350M](https://huggingface.co/Salesforce/codegen-350M-multi) from Salesforce and further pre-trained on a dataset of general SQL queries and then fine-tuned on a dataset composed of text-to-SQL pairs. |
|
|
|
## Training Data |
|
|
|
The general SQL queries are the SQL subset from [The Stack](https://huggingface.co/datasets/bigcode/the-stack), containing 1M training samples. The labeled text-to-SQL pairs come from more than 20 public sources across the web from standard datasets. We hold out Spider and GeoQuery datasets for use in evaluation. |
|
|
|
## Evaluation Data |
|
|
|
We evaluate our models on two text-to-SQL benchmarks: Spider and GeoQuery. |
|
|
|
## Training Procedure |
|
|
|
NSQL was trained using cross-entropy loss to maximize the likelihood of sequential inputs. For finetuning on text-to-SQL pairs, we only compute the loss over the SQL portion of the pair. The family of models is trained using 80GB A100s, leveraging data and model parallelism. We pre-trained for 3 epochs and fine-tuned for 10 epochs. |
|
|
|
## Intended Use and Limitations |
|
|
|
The model was designed for text-to-SQL generation tasks from given table schema and natural language prompts. The model works best with the prompt format defined below and outputting `SELECT` queries. |
|
|
|
## How to Use |
|
|
|
Example 1: |
|
|
|
```python |
|
from transformers import AutoTokenizer, AutoModelForCausalLM |
|
tokenizer = AutoTokenizer.from_pretrained("NumbersStation/nsql-350M") |
|
model = AutoModelForCausalLM.from_pretrained("NumbersStation/nsql-350M") |
|
|
|
text = """CREATE TABLE stadium ( |
|
stadium_id number, |
|
location text, |
|
name text, |
|
capacity number, |
|
highest number, |
|
lowest number, |
|
average number |
|
) |
|
|
|
CREATE TABLE singer ( |
|
singer_id number, |
|
name text, |
|
country text, |
|
song_name text, |
|
song_release_year text, |
|
age number, |
|
is_male others |
|
) |
|
|
|
CREATE TABLE concert ( |
|
concert_id number, |
|
concert_name text, |
|
theme text, |
|
stadium_id text, |
|
year text |
|
) |
|
|
|
CREATE TABLE singer_in_concert ( |
|
concert_id number, |
|
singer_id text |
|
) |
|
|
|
-- Using valid SQLite, answer the following questions for the tables provided above. |
|
|
|
-- What is the maximum, the average, and the minimum capacity of stadiums ? |
|
|
|
SELECT""" |
|
|
|
input_ids = tokenizer(text, return_tensors="pt").input_ids |
|
|
|
generated_ids = model.generate(input_ids, max_length=500) |
|
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True)) |
|
``` |
|
|
|
Example 2: |
|
|
|
```python |
|
from transformers import AutoTokenizer, AutoModelForCausalLM |
|
tokenizer = AutoTokenizer.from_pretrained("NumbersStation/nsql-350M") |
|
model = AutoModelForCausalLM.from_pretrained("NumbersStation/nsql-350M") |
|
|
|
text = """CREATE TABLE stadium ( |
|
stadium_id number, |
|
location text, |
|
name text, |
|
capacity number, |
|
) |
|
|
|
-- Using valid SQLite, answer the following questions for the tables provided above. |
|
|
|
-- how many stadiums in total? |
|
|
|
SELECT""" |
|
|
|
input_ids = tokenizer(text, return_tensors="pt").input_ids |
|
|
|
generated_ids = model.generate(input_ids, max_length=500) |
|
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True)) |
|
``` |
|
|
|
Example 3: |
|
|
|
```python |
|
from transformers import AutoTokenizer, AutoModelForCausalLM |
|
tokenizer = AutoTokenizer.from_pretrained("NumbersStation/nsql-350M") |
|
model = AutoModelForCausalLM.from_pretrained("NumbersStation/nsql-350M") |
|
|
|
text = """CREATE TABLE work_orders ( |
|
ID NUMBER, |
|
CREATED_AT TEXT, |
|
COST FLOAT, |
|
INVOICE_AMOUNT FLOAT, |
|
IS_DUE BOOLEAN, |
|
IS_OPEN BOOLEAN, |
|
IS_OVERDUE BOOLEAN, |
|
COUNTRY_NAME TEXT, |
|
) |
|
|
|
-- Using valid SQLite, answer the following questions for the tables provided above. |
|
|
|
-- how many work orders are open? |
|
|
|
SELECT""" |
|
|
|
input_ids = tokenizer(text, return_tensors="pt").input_ids |
|
|
|
generated_ids = model.generate(input_ids, max_length=500) |
|
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True)) |
|
``` |
|
|
|
For more information (e.g., run with your local database), please find examples in [this repository](https://github.com/NumbersStationAI/NSQL). |
|
|
