
# 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"
    }
}
```
{"language": ["en"], "license": "apache-2.0", "tags": ["nl2sql"], "datasets": ["laion/OIG"], "pipeline_tag": "text2text-generation", "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"}]}
transformers pytorch t5 text2text-generation nl2sql en dataset:laion/OIG license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #nl2sql #en #dataset-laion/OIG #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


# How to Use



# How to Train

Dataset: 
- URL
- URL


# How to Use # How to Train

Dataset: 
- URL
- URL
TAGS
#transformers #pytorch #t5 #text2text-generation #nl2sql #en #dataset-laion/OIG #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # How to Use # How to Train

Dataset: 
- URL
- URL