---
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: true
    - name: ROUGE-2
      type: rouge
      value: 42.99
      verified: true
    - name: ROUGE-L
      type: rouge
      value: 51.43
      verified: true
    - name: ROUGE-LSUM
      type: rouge
      value: 51.4
      verified: true
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
---


# BART-LARGE-CNN fine-tuned on SYNTHETIC_TEXT_TO_SQL

Generate SQL query 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](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct.)**
- **Dataset:** [gretelai/synthetic_text_to_sql](https://huggingface.co/datasets/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

```python
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);"""
```

# Use a pipeline as a high-level helper
```python
from transformers import pipeline

sql_generator = pipeline("text2text-generation", model="SwastikM/bart-large-nl2sql")

sql = sql_generator(query_question_with_context)[0]['generated_text']

print(sql)
```

# Load model directly

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

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

inputs = tokenizer(query_question_with_context, return_tensors="pt").input_ids
outputs = model.generate(inputs, max_new_tokens=100, do_sample=False)

sql = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(sql)
```


## Training Details

### Training Data

[gretelai/synthetic_text_to_sql](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql)

### 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


#### Hardware

- **GPU:** P100


### Citing Dataset and BaseModel

```
  @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}
}

```

## Additional Information

- ***Github:*** [Repository](https://github.com/swastikmaiti/SwastikM-bart-large-nl2sql.git)

## Acknowledgment

Thanks to [@AI at Meta](https://huggingface.co/facebook) for adding the Pre Trained Model.
Thanks to [@Gretel.ai](https://huggingface.co/gretelai) for adding the datset.


## Model Card Authors

Swastik Maiti