metadata

license: mit
datasets:
  - b-mc2/sql-create-context
  - gretelai/synthetic_text_to_sql
language:
  - en
base_model: google-t5/t5-base
metrics:
  - exact_match
model-index:
  - name: juanfra218/text2sql
    results:
      - task:
          type: text-to-sql
        metrics:
          - name: exact_match
            type: exact_match
            value: 0.4322

Fine-Tuned Google T5 Model for Text to SQL Translation

A fine-tuned version of the Google T5 model, trained for the task of translating natural language queries into SQL statements.

Model Details

Architecture: Google T5 Base (Text-to-Text Transfer Transformer)
Task: Text to SQL Translation
Fine-Tuning Datasets:
- sql-create-context Dataset
- Synthetic-Text-To-SQL Dataset

Ongoing Work

Currently working to implement PICARD (Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models) to improve the results of this model. More details can be found in the original PICARD paper.

Results

Results are currently being evaluated and will be posted here soon.

Usage

import torch
from transformers import AutoTokenizer, T5ForConditionalGeneration

# Load the tokenizer and model
model_path = 'text2sql_model_path'
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path)

# Function to generate SQL queries
def generate_sql(prompt, schema):
    input_text = "translate English to SQL: " + prompt + " " + schema
    inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True, padding="max_length")

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    inputs = {key: value.to(device) for key, value in inputs.items()}

    max_output_length = 1024
    outputs = model.generate(**inputs, max_length=max_output_length)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Interactive loop
print("Enter 'quit' to exit.")
while True:
    prompt = input("Insert prompt: ")
    schema = input("Insert schema: ")
    if prompt.lower() == 'quit':
        break

    sql_query = generate_sql(prompt, schema)
    print(f"Generated SQL query: {sql_query}")
    print()

Files

optimizer.pt: State of the optimizer.
training_args.bin: Training arguments and hyperparameters.
tokenizer.json: Tokenizer vocabulary and settings.
spiece.model: SentencePiece model file.
special_tokens_map.json: Special tokens mapping.
tokenizer_config.json: Tokenizer configuration settings.
model.safetensors: Trained model weights.
generation_config.json: Configuration for text generation.
config.json: Model architecture configuration.