| | --- |
| | license: apache-2.0 |
| | datasets: |
| | - laion/OIG |
| | language: |
| | - en |
| | pipeline_tag: text2text-generation |
| | tags: |
| | - nl2sql |
| | widget: |
| | - text: 'Given the following schema:\ntrack (Track_ID, Name, Location, Seating, Year_Opened)\nrace (Race_ID, Name, Class, Date, Track_ID)\nWrite a SQL query to count the number of tracks.' |
| | example_title: 'count' |
| | - text: 'Given the following schema:\nmountain (Mountain_ID, Name, Height, Prominence, Range, Country)\nclimber (Climber_ID, Name, Country, Time, Points, Mountain_ID)\nWrite a SQL query to list the countries that have more than one mountain.' |
| | example_title: 'having' |
| | - text: 'Given the following schema:\nairports (apid, name, city, country, x, y, elevation, iata, icao)\nroutes (rid, dst_apid, dst_ap, src_apid, src_ap, alid, airline, codeshare)\nairlines (alid, name, iata, icao, callsign, country, active)\nWrite a SQL query to find the number of routes for each source airport and the airport name.' |
| | example_title: 'join' |
| | --- |
| | |
| | # How to Use |
| |
|
| | ```python |
| | import torch |
| | from transformers import T5ForConditionalGeneration, AutoTokenizer |
| | |
| | device = torch.device("cuda:0") |
| | |
| | tokenizer = AutoTokenizer.from_pretrained("LarkAI/codet5p-770m_nl2sql_oig") |
| | model = T5ForConditionalGeneration.from_pretrained("LarkAI/codet5p-770m_nl2sql_oig").to(device) |
| | |
| | text = "Given the following schema:\ntrack (Track_ID, Name, Location, Seating, Year_Opened)\nrace (Race_ID, Name, Class, Date, Track_ID)\nWrite a SQL query to count the number of tracks." |
| | inputs = tokenizer.encode(text, return_tensors="pt").to(device) |
| | output_ids = model.generate(inputs, max_length=512) |
| | response_text = tokenizer.decode(outputs[0], skip_special_tokens=True) |
| | # SELECT COUNT( * ) FROM track |
| | ``` |
| |
|
| | # How to Train |
| |
|
| | Dataset: |
| | - https://huggingface.co/datasets/laion/OIG#unified_sqlv1jsonl-17000 |
| | - https://huggingface.co/datasets/laion/OIG#unified_sqlv2jsonl24000 |
| |
|
| | ```json |
| | { |
| | "text":"<human>: Given the following schema:\nlocation (restaurant_id, house_number, street_name, city_name)\nrestaurant (id, name, food_type, city_name, rating)\ngeographic (city_name, county, region)\nWrite a SQL query to give me some good arabic -s on buchanan in san francisco ?\n<bot>: SELECT location.house_number , restaurant.name FROM location , restaurant WHERE location.city_name = \"san francisco\" AND location.street_name = \"buchanan\" AND restaurant.food_type = \"arabic\" AND restaurant.id = location.restaurant_id AND restaurant.rating > 2.5 ;", |
| | "metadata":{ |
| | "source":"unified_sqlv1" |
| | } |
| | } |
| | ``` |
