Model Card for Model ID
Based on t5-small, model generates SQL from text given table list with "CREATE TABLE" statements. Supports multiple tables with joins. This is a very light weigh model and could be used in multiple analytical applications. Used combination of b-mc2/sql-create-context and Clinton/Text-to-sql-v1 dataset. Contact us for more info: support@cloudsummary.com
Model Details
Model Description
- Developed by: cssupport (support@cloudsummary.com)
- Model type: Language model
- Language(s) (NLP): English
- License: Apache 2.0
- Finetuned from model : t5-small
Model Sources
Please refer t5-small for Model Sources.
How to Get Started with the Model
Use the code below to get started with the model.
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration
# Initialize the tokenizer from Hugging Face Transformers library
tokenizer = T5Tokenizer.from_pretrained('t5-small')
# Load the model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = T5ForConditionalGeneration.from_pretrained('cssupport/t5-small-awesome-text-to-sql')
model = model.to(device)
model.eval()
def generate_sql(input_prompt):
# Tokenize the input prompt
inputs = tokenizer(input_prompt, padding=True, truncation=True, return_tensors="pt").to(device)
# Forward pass
with torch.no_grad():
outputs = model.generate(**inputs, max_length=512)
# Decode the output IDs to a string (SQL query in this case)
generated_sql = tokenizer.decode(outputs[0], skip_special_tokens=True)
return generated_sql
# Test the function
#input_prompt = "tables:\n" + "CREATE TABLE Catalogs (date_of_latest_revision VARCHAR)" + "\n" +"query for: Find the dates on which more than one revisions were made."
#input_prompt = "tables:\n" + "CREATE TABLE table_22767 ( \"Year\" real, \"World\" real, \"Asia\" text, \"Africa\" text, \"Europe\" text, \"Latin America/Caribbean\" text, \"Northern America\" text, \"Oceania\" text )" + "\n" +"query for:what will the population of Asia be when Latin America/Caribbean is 783 (7.5%)?."
#input_prompt = "tables:\n" + "CREATE TABLE procedures ( subject_id text, hadm_id text, icd9_code text, short_title text, long_title text ) CREATE TABLE diagnoses ( subject_id text, hadm_id text, icd9_code text, short_title text, long_title text ) CREATE TABLE lab ( subject_id text, hadm_id text, itemid text, charttime text, flag text, value_unit text, label text, fluid text ) CREATE TABLE demographic ( subject_id text, hadm_id text, name text, marital_status text, age text, dob text, gender text, language text, religion text, admission_type text, days_stay text, insurance text, ethnicity text, expire_flag text, admission_location text, discharge_location text, diagnosis text, dod text, dob_year text, dod_year text, admittime text, dischtime text, admityear text ) CREATE TABLE prescriptions ( subject_id text, hadm_id text, icustay_id text, drug_type text, drug text, formulary_drug_cd text, route text, drug_dose text )" + "\n" +"query for:" + "what is the total number of patients who were diagnosed with icd9 code 2254?"
input_prompt = "tables:\n" + "CREATE TABLE student_course_attendance (student_id VARCHAR); CREATE TABLE students (student_id VARCHAR)" + "\n" + "query for:" + "List the id of students who never attends courses?"
generated_sql = generate_sql(input_prompt)
print(f"The generated SQL query is: {generated_sql}")
#OUTPUT: The generated SQL query is: SELECT student_id FROM students WHERE NOT student_id IN (SELECT student_id FROM student_course_attendance)
Uses
[More Information Needed]
Direct Use
Could used in application where natural language is to be converted into SQL queries. [More Information Needed]
Out-of-Scope Use
[More Information Needed]
Bias, Risks, and Limitations
[More Information Needed]
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
Technical Specifications
Model Architecture and Objective
Compute Infrastructure
Hardware
one A100-80
Software
Pytorch and HuggingFace
Model Card Contact
cssupport (support@cloudsummary.com)