Commit
•
4844cec
1
Parent(s):
ee8bda4
Create README.md
Browse files
README.md
ADDED
@@ -0,0 +1,158 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
base_model: deepseek-ai/deepseek-coder-6.7b-instruct
|
3 |
+
tags:
|
4 |
+
- instruct
|
5 |
+
- finetune
|
6 |
+
library_name: transformers
|
7 |
+
license: cc-by-sa-4.0
|
8 |
+
pipeline_tag: text-generation
|
9 |
+
---
|
10 |
+
|
11 |
+
# **Natural-SQL-7B by ChatDB**
|
12 |
+
## Natural-SQL-7B is a model with very strong performance in Text-to-SQL instructions, has an excellent understanding of complex questions, and outperforms models of the same size in its space.
|
13 |
+
|
14 |
+
<img src="https://cdn-uploads.huggingface.co/production/uploads/648a374f00f7a3374ee64b99/hafdsfrFCqrVbATIzV_EN.png" width="600">
|
15 |
+
|
16 |
+
[ChatDB.ai](https://chatdb.ai) | [Notebook](https://github.com/cfahlgren1/natural-sql/blob/main/natural-sql-7b.ipynb) | [Twitter](https://twitter.com/calebfahlgren)
|
17 |
+
|
18 |
+
# **Benchmarks**
|
19 |
+
### *Results on Novel Datasets not trained on via SQL-Eval*
|
20 |
+
<img src="https://cdn-uploads.huggingface.co/production/uploads/648a374f00f7a3374ee64b99/5ynfoKPzI3_-WasQQt7qR.png" width="800">
|
21 |
+
|
22 |
+
<em>Big thanks to the [defog](https://huggingface.co/defog) team for open sourcing [sql-eval](https://github.com/defog-ai/sql-eval)</em>👏
|
23 |
+
|
24 |
+
Natural-SQL also can handle complex, compound questions that other models typically struggle with. There is a more detailed writeup Here is a write up, small test done [here](https://chatdb.ai/post/naturalsql-vs-sqlcoder-for-text-to-sql).
|
25 |
+
# Usage
|
26 |
+
|
27 |
+
Make sure you have the correct version of the transformers library installed:
|
28 |
+
|
29 |
+
```sh
|
30 |
+
pip install transformers==4.35.2
|
31 |
+
```
|
32 |
+
|
33 |
+
### Loading the Model
|
34 |
+
|
35 |
+
Use the following Python code to load the model:
|
36 |
+
|
37 |
+
```python
|
38 |
+
import torch
|
39 |
+
from transformers import AutoModelForCausalLM, AutoTokenizer
|
40 |
+
tokenizer = AutoTokenizer.from_pretrained("chatdb/natural-sql-7b")
|
41 |
+
model = AutoModelForCausalLM.from_pretrained(
|
42 |
+
"chatdb/natural-sql-7b",
|
43 |
+
device_map="auto",
|
44 |
+
torch_dtype=torch.float16,
|
45 |
+
)
|
46 |
+
```
|
47 |
+
|
48 |
+
### **License**
|
49 |
+
|
50 |
+
The model weights are licensed under `CC BY-SA 4.0`, with extra guidelines for responsible use.
|
51 |
+
Essentially, you're free to use and adapt the model, even commercially.
|
52 |
+
If you alter the weights, such as through fine-tuning, you must publicly share your changes under the same `CC BY-SA 4.0` license.
|
53 |
+
|
54 |
+
|
55 |
+
### Generating SQL
|
56 |
+
|
57 |
+
```python
|
58 |
+
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
|
59 |
+
generated_ids = model.generate(
|
60 |
+
**inputs,
|
61 |
+
num_return_sequences=1,
|
62 |
+
eos_token_id=100001,
|
63 |
+
pad_token_id=100001,
|
64 |
+
max_new_tokens=400,
|
65 |
+
do_sample=False,
|
66 |
+
num_beams=1,
|
67 |
+
)
|
68 |
+
|
69 |
+
outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
|
70 |
+
print(outputs[0].split("```sql")[-1])
|
71 |
+
```
|
72 |
+
# Prompt Template
|
73 |
+
|
74 |
+
```
|
75 |
+
# Task
|
76 |
+
Generate a SQL query to answer the following question: `{natural language question}`
|
77 |
+
|
78 |
+
### PostgreSQL Database Schema
|
79 |
+
The query will run on a database with the following schema:
|
80 |
+
|
81 |
+
<SQL Table DDL Statements>
|
82 |
+
|
83 |
+
# SQL
|
84 |
+
Here is the SQL query that answers the question: `{natural language question}`
|
85 |
+
'''sql
|
86 |
+
```
|
87 |
+
|
88 |
+
|
89 |
+
# Example SQL Output
|
90 |
+
|
91 |
+
### Example Schemas
|
92 |
+
|
93 |
+
```sql
|
94 |
+
CREATE TABLE users (
|
95 |
+
user_id SERIAL PRIMARY KEY,
|
96 |
+
username VARCHAR(50) NOT NULL,
|
97 |
+
email VARCHAR(100) NOT NULL,
|
98 |
+
password_hash TEXT NOT NULL,
|
99 |
+
created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP
|
100 |
+
);
|
101 |
+
CREATE TABLE projects (
|
102 |
+
project_id SERIAL PRIMARY KEY,
|
103 |
+
project_name VARCHAR(100) NOT NULL,
|
104 |
+
description TEXT,
|
105 |
+
start_date DATE,
|
106 |
+
end_date DATE,
|
107 |
+
owner_id INTEGER REFERENCES users(user_id)
|
108 |
+
);
|
109 |
+
CREATE TABLE tasks (
|
110 |
+
task_id SERIAL PRIMARY KEY,
|
111 |
+
task_name VARCHAR(100) NOT NULL,
|
112 |
+
description TEXT,
|
113 |
+
due_date DATE,
|
114 |
+
status VARCHAR(50),
|
115 |
+
project_id INTEGER REFERENCES projects(project_id)
|
116 |
+
);
|
117 |
+
CREATE TABLE taskassignments (
|
118 |
+
assignment_id SERIAL PRIMARY KEY,
|
119 |
+
task_id INTEGER REFERENCES tasks(task_id),
|
120 |
+
user_id INTEGER REFERENCES users(user_id),
|
121 |
+
assigned_date DATE NOT NULL DEFAULT CURRENT_TIMESTAMP
|
122 |
+
);
|
123 |
+
CREATE TABLE comments (
|
124 |
+
comment_id SERIAL PRIMARY KEY,
|
125 |
+
content TEXT NOT NULL,
|
126 |
+
created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
|
127 |
+
task_id INTEGER REFERENCES tasks(task_id),
|
128 |
+
user_id INTEGER REFERENCES users(user_id)
|
129 |
+
);
|
130 |
+
```
|
131 |
+
### Example SQL Outputs
|
132 |
+
|
133 |
+
**Question**: **Show me the day with the most users joining**
|
134 |
+
```sql
|
135 |
+
SELECT created_at::DATE AS day, COUNT(*) AS user_count
|
136 |
+
FROM users
|
137 |
+
GROUP BY day
|
138 |
+
ORDER BY user_count DESC
|
139 |
+
LIMIT 1;
|
140 |
+
```
|
141 |
+
**Question**: **Show me the project that has a task with the most comments**
|
142 |
+
```sql
|
143 |
+
SELECT p.project_name, t.task_name, COUNT(c.comment_id) AS comment_count
|
144 |
+
FROM projects p
|
145 |
+
JOIN tasks t ON p.project_id = t.project_id
|
146 |
+
JOIN comments c ON t.task_id = c.task_id
|
147 |
+
GROUP BY p.project_name, t.task_name
|
148 |
+
ORDER BY comment_count DESC
|
149 |
+
LIMIT 1;
|
150 |
+
```
|
151 |
+
|
152 |
+
**Question**: **What is the ratio of users with gmail addresses vs without?**
|
153 |
+
```sql
|
154 |
+
SELECT
|
155 |
+
SUM(CASE WHEN email ILIKE '%@gmail.com%' THEN 1 ELSE 0 END)::FLOAT / NULLIF(SUM(CASE WHEN email NOT ILIKE '%@gmail.com%' THEN 1 ELSE 0 END), 0) AS gmail_ratio
|
156 |
+
FROM
|
157 |
+
users;
|
158 |
+
```
|