Model: SwastikM/bart-large-nl2sql
Author: SwastikM
Library: transformers
Tags: transformers, safetensors, bart, text2text-generation, natural language, SQL, text2sql, nl2sql, en, dataset:gretelai/synthetic_text_to_sql, arxiv:1910.13461, base_model:facebook/bart-large-cnn, base_model:finetune:facebook/bart-large-cnn, model-index, co2_eq_emissions, endpoints_compatible, region:us
Base models (finetune): id: facebook/bart-large-cnn
Card metadata: widget: text: sql_prompt: What is the monthly voice usage for each customer in the Mumbai region? sql_context: CREATE TABLE customers (customer_id INT, name VARCHAR(50), voice_usage_minutes FLOAT, region VARCHAR(50)); INSERT INTO customers (customer_id, name, voice_usage_minutes, region) VALUES (1, 'Aarav Patel', 500, 'Mumbai'), (2, 'Priya Shah', 700, 'Mumbai');
example_title: Example1, text: sql_prompt: How many wheelchair accessible vehicles are there in the 'Train' mode of transport? sql_context: CREATE TABLE Vehicles(vehicle_id INT, vehicle_type VARCHAR(20), mode_of_transport VARCHAR(20), is_wheelchair_accessible BOOLEAN); INSERT INTO Vehicles(vehicle_id, vehicle_type, mode_of_transport, is_wheelchair_accessible) VALUES (1, 'Train_Car', 'Train', TRUE), (2, 'Train_Engine', 'Train', FALSE), (3, 'Bus', 'Bus', TRUE);
example_title: Example2, text: sql_prompt: Which economic diversification efforts in the 'diversification' table have a higher budget than the average budget for all economic diversification efforts in the 'budget' table? sql_context: CREATE TABLE diversification (id INT, effort VARCHAR(50), budget FLOAT); CREATE TABLE budget (diversification_id INT, diversification_effort VARCHAR(50), amount FLOAT);
example_title: Example3
language: en
datasets: gretelai/synthetic_text_to_sql
metrics: rouge
library_name: transformers
base_model: facebook/bart-large-cnn
model-index: name: SwastikM/bart-large-nl2sql
results: task: type: text2text-generation
dataset: name: gretelai/synthetic_text_to_sql
type: gretelai/synthetic_text_to_sql
split: train, test
metrics: name: ROUGE-1
type: rouge
value: 55.69
verified: no, name: ROUGE-2
type: rouge
value: 42.99
verified: no, name: ROUGE-L
type: rouge
value: 51.43
verified: no, name: ROUGE-LSUM
type: rouge
value: 51.4
verified: no
github: https://github.com/swastikmaiti/SwastikM-bart-large-nl2sql.git
co2_eq_emissions: emissions: 160
source: ML CO2 Impact https://mlco2.github.io/impact/#home)
training_type: fine-tuning
hardware_used: TESLA P100
tags: natural language, SQL, text2sql, nl2sql
Likes: 7 | All-time downloads: 3065
Created: 2024-04-25 12:43:37