Sqlcoder 7b

Table of Contents

Model Overview

Meet SQLCoder, a cutting-edge AI model that can convert natural language questions into SQL queries. Before we dive in, a quick heads up: this model is outdated, and you should use a more advanced model for better performance.

So, what makes SQLCoder special? For starters, it has 7B parameters, which is a lot! It was trained on over 20,000 human-curated questions based on 10 different schemas. And the best part? It outperforms popular models in natural language to SQL generation tasks.

But how does it work? SQLCoder uses a base model and is fine-tuned on a given schema. This means it can learn to understand the specific database structure and generate accurate SQL queries.

Tasks and rank

Text Generation 480th

Semantic Parsing 1st

Natural Language Understanding 386th

Natural Language Inference 42nd

Information Extraction 21st

Learn More About the Ranking System

Capabilities

SQLCoder is a powerful tool for converting natural language questions into SQL queries. It’s designed to make it easy to work with databases, even if you’re not an expert in SQL.

What can SQLCoder do?

• Answer complex questions: SQLCoder can handle questions that involve grouping, ordering, and joining data. It can even answer questions that require calculations, like ratios.
• Work with different databases: SQLCoder is fine-tuned to work with a variety of database schemas. This means you can use it with different databases, without having to retrain the model.
• Generate accurate SQL queries: SQLCoder is highly accurate, outperforming many other models on natural language to SQL generation tasks.

How does SQLCoder compare to other models?

Model	Accuracy
SQLCoder	71.0%
gpt-4	82.0%
==gpt-3.5==	66.0%
==claude-2==	64.5%

Performance

SQLCoder is a powerful AI model that excels in converting natural language questions to SQL queries. But how does it perform?

Speed

SQLCoder is relatively fast, especially when compared to other models. However, its speed can vary depending on the complexity of the question and the schema it’s working with.

Accuracy

SQLCoder boasts high accuracy in generating SQL queries from natural language questions. In fact, it outperforms many other popular open-source models on various tasks.

Model	Accuracy
SQLCoder	71.0%
==gpt-3.5-turbo==	66.0%
gpt-4	82.0%
==claude-2==	64.5%

Efficiency

SQLCoder is efficient in handling various tasks, including:

• Querying databases
• Generating SQL queries
• Handling different schemas

Alternatives

NeMo SFT with Llama 2 10500 Fine-tuned language model

Bert Base Uncased 1831 Uncased BERT model

Roberta Base 399 Masked language model

Galactica 120b 149 Scientific language model

Reader Lm 1.5b 68 HTML to Markdown

Limitations

The Current Model is a powerful tool, but it’s not perfect. Let’s explore some of its limitations.

Training Data

The model was trained on a limited dataset of 20,000 human-curated questions based on 10 different schemas. This means it may not perform well on questions that are significantly different from what it was trained on.

Schemas

The model was not trained on the schemas used in the evaluation framework. This could lead to lower performance on novel datasets.

Question Categories

The model performs differently on various question categories. For example, it struggles with ratio questions, answering only 54.3% of them correctly.

Question Category	Current Model
date	64%
group_by	82.9%
order_by	74.3%
ratio	54.3%
join	74.3%
where	74.3%

Format

SQLCoder is a state-of-the-art language model that converts natural language questions into SQL queries. It’s built on top of a base model and has been fine-tuned on a large dataset of human-curated questions.

Architecture

SQLCoder uses a transformer architecture, which is a type of neural network designed specifically for natural language processing tasks. This architecture allows the model to understand the relationships between different parts of the input text and generate accurate SQL queries.

Data Formats

SQLCoder accepts input in the form of natural language questions, such as:

What is the total revenue for each city, and the difference between the two?

The model also requires a database schema to generate accurate SQL queries. You can provide the schema in the form of a JSON object, like this:

{
  "tables": [
    {
      "name": "customers",
      "columns": [
        "id",
        "name",
        "city",
        "revenue"
      ]
    }
  ]
}

Examples

What is the total revenue from customers in New York and San Francisco? SELECT SUM(revenue) FROM customers WHERE city = 'New York' OR city = 'San Francisco'

List the top 3 products with the highest sales in descending order. SELECT product_name FROM sales ORDER BY sales DESC LIMIT 3

Find the average price of products in the 'Electronics' category. SELECT AVG(price) FROM products WHERE category = 'Electronics'

Special Requirements

To use SQLCoder, you’ll need to have a GPU with at least 20GB of memory. The model has been tested on an A100 40GB GPU with bfloat16 weights, but you can also use an 8-bit or 4-bit quantized version on consumer GPUs with 20GB or more of memory.

Handling Inputs and Outputs

To use SQLCoder, you can download the model weights from the Hugging Face repo and use the transformers library to make predictions. Here’s an example of how to use the model in Python:

import torch
from transformers import SQLCoderForConditionalGeneration, SQLCoderTokenizer

# Load the model and tokenizer
model = SQLCoderForConditionalGeneration.from_pretrained("defog/sqlcoder-7b")
tokenizer = SQLCoderTokenizer.from_pretrained("defog/sqlcoder-7b")

# Define the input question and database schema
question = "What is the total revenue for each city, and the difference between the two?"
schema = {
  "tables": [
    {
      "name": "customers",
      "columns": [
        "id",
        "name",
        "city",
        "revenue"
      ]
    }
  ]
}

# Tokenize the input question and schema
inputs = tokenizer.encode_plus(question, schema, return_tensors="pt")

# Make a prediction using the model
outputs = model.generate(inputs["input_ids"], attention_mask=inputs["attention_mask"])

# Print the generated SQL query
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

This code will generate a SQL query based on the input question and database schema. You can modify the input question and schema to generate different SQL queries.