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

BART (large-sized model), fine-tuned on synthetic_text_to_sql

Generate SQL from Natural Language question with a SQL context.

Model Details

Model Description

BART from facebook/bart-large-cnn is fintuned on gretelai/synthetic_text_to_sql dataset to generate SQL from NL and SQL context

Model type: [BART]
Language(s) (NLP): English
License: openrail
Finetuned from model facebook/bart-large-cnn
Dataset: gretelai/synthetic_text_to_sql

Intended uses & limitations

Addressing the power of LLM in fintuned downstream task. Implemented as a personal Project.

How to use

Load model directly

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("SwastikM/bart-large-nl2sql")

model = AutoModelForSeq2SeqLM.from_pretrained("SwastikM/bart-large-nl2sql")

query_question_with_context = "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);"

sql = model.generate(text)

print(sql)

Training Details

Training Data

gretelai/synthetic_text_to_sql

[More Information Needed]

Training Procedure

HuggingFace Accelerate with Training Loop.

Preprocessing

Encoder Input: "sql_prompt: " + data['sql_prompt']+" sql_context: "+data['sql_context']
Decoder Input: data['sql']

Training Hyperparameters

Optimizer: AdamW
lr: 2e-5
decay: linear
num_warmup_steps: 0
batch_size: 8
num_training_steps: 12500

Evaluation

Rouge Score

Rouge1: 55.69
Rouge2: 42.99
RougeL: 51.43
RougeLsum: 51.40

Hardware

-GPU: P100

Citation

BibTeX:

@software{gretel-synthetic-text-to-sql-2024, author = {Meyer, Yev and Emadi, Marjan and Nathawani, Dhruv and Ramaswamy, Lipika and Boyd, Kendrick and Van Segbroeck, Maarten and Grossman, Matthew and Mlocek, Piotr and Newberry, Drew}, title = {{Synthetic-Text-To-SQL}: A synthetic dataset for training language models to generate SQL queries from natural language prompts}, month = {April}, year = {2024}, url = {https://huggingface.co/datasets/gretelai/synthetic-text-to-sql} }

@article{DBLP:journals/corr/abs-1910-13461, author = {Mike Lewis and Yinhan Liu and Naman Goyal and Marjan Ghazvininejad and Abdelrahman Mohamed and Omer Levy and Veselin Stoyanov and Luke Zettlemoyer}, title = {{BART:} Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension}, journal = {CoRR}, volume = {abs/1910.13461}, year = {2019}, url = {http://arxiv.org/abs/1910.13461}, eprinttype = {arXiv}, eprint = {1910.13461}, timestamp = {Thu, 31 Oct 2019 14:02:26 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1910-13461.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }

Model Card Authors

Swastik Maiti