juanfra218/text2sql

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

Training Parameters

training_args = Seq2SeqTrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    weight_decay=0.01,
    save_total_limit=3,
    num_train_epochs=3,
    predict_with_generate=True,
    fp16=True,
    push_to_hub=False,
)

Usage

import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load the tokenizer and model
model_path = 'juanfra218/text2sql'
tokenizer = T5Tokenizer.from_pretrained(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path)
model.to(device)  

# 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")

    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.
• test_results.csv: Results on the testing set, contains: prompt, context, true_answer, predicted_answer, exact_match