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SwastikM/bart-large-nl2sql

sourceHugging Faceupdated 2y agoView on Hugging Face
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Model Card

BART-LARGE-CNN fine-tuned on SYNTHETICTEXTTO_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/synthetictextto_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

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

Training Procedure

HuggingFace Accelerate with Training Loop.

Preprocessing
  • —*Encoder Input:* "sqlprompt: " + data['sqlprompt']+" sqlcontext: "+data['sqlcontext']
  • —*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

Acknowledgment

Thanks to @AI at Meta for adding the Pre Trained Model. Thanks to @Gretel.ai for adding the datset.

Model Card Authors

Swastik Maiti