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poseidon1113/
gpt2-medium-sql-generator

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GPT-2 Medium — SQL Query Generator

Fine-tuned GPT-2 Medium on the Spider text-to-SQL dataset to generate SQL queries from natural language questions.

Training

Base model: GPT-2 Medium (354M parameters)
Dataset: Spider (7000 train / 1034 validation examples)
Method: Full fine-tuning
Best checkpoint: Epoch 1 (val loss 1.410)

Usage

python

from transformers import GPT2LMHeadModel, GPT2Tokenizer

model = GPT2LMHeadModel.from_pretrained("your-username/gpt2-medium-sql-generator")
tokenizer = GPT2Tokenizer.from_pretrained("your-username/gpt2-medium-sql-generator")

prompt = "Question: How many singers are there?\nSQL:"
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Limitations

GPT-2 is a small model — output SQL may hallucinate table/column names
No schema awareness — works best on Singer/Concert domain from Spider training data
Intended as a learning project demonstrating full fine-tuning pipeline

poseidon1113/gpt2-medium-sql-generator

GPT-2 Medium — SQL Query Generator

Training

Usage

Limitations

poseidon1113/
gpt2-medium-sql-generator