Hugging Face's logo

cssupport
/
t5-small-awesome-text-to-sql

Text2Text Generation
Transformers
PyTorch
English
t5
text-generation-inference
Inference Endpoints
Model card Files Community
5
t5-small-awesome-text-to-sql
File size: 5,638 Bytes 
1c52c6e
 
e690707
 
 
 
 
 
1c52c6e
e690707
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2f1fd09
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e690707
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
 
---
license: apache-2.0
datasets:
- Clinton/Text-to-sql-v1
- b-mc2/sql-create-context
language:
- en
pipeline_tag: text2text-generation
---
# Model Card for Model ID

<!-- Based on  https://huggingface.co/t5-small, model generates SQL from text given table list with "CREATE TABLE" statements. 
This is a very light weigh model and could be used in multiple analytical applications. -->

Based on  [t5-small](https://huggingface.co/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](https://huggingface.co/datasets/b-mc2/sql-create-context) and [Clinton/Text-to-sql-v1](https://huggingface.co/datasets/Clinton/Text-to-sql-v1) dataset.
Contact us for more info: support@cloudsummary.com


## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** cssupport (support@cloudsummary.com)
- **Model type:** Language model
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model :** [t5-small](https://huggingface.co/t5-small)

### Model Sources 

<!-- Provide the basic links for the model. -->

Please refer [t5-small](https://huggingface.co/t5-small) for Model Sources.

## How to Get Started with the Model

Use the code below to get started with the model.

```python
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

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

[More Information Needed]

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
Could used in application where natural language is to be converted into SQL queries. 
[More Information Needed]



### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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

[t5-small](https://huggingface.co/t5-small)

### Compute Infrastructure



#### Hardware

one A100-80

#### Software

Pytorch and HuggingFace 


## Model Card Contact

cssupport (support@cloudsummary.com)