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Generate enriched BIRD SFT data with CHESS-style DDL schema (value_description + column_description).
Run from mats-sql-tist/ root:
JAVA_TOOL_OPTIONS="-Xmx6g" /home/datht/anaconda3/envs/mats/bin/python \
db_content_retrieval/lsh_api.py --port 8005 --db_content_index bird-train &
sleep 30
/home/datht/anaconda3/envs/mats/bin/python scripts/build_sft_data.py
Outputs:
data/rebuttal_sft_bird_train_text2sql.json (9428 examples)
data/rebuttal_sft_bird_dev_text2sql.json (1534 examples)
"""
import os
import sys
ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
os.chdir(ROOT)
sys.path.insert(0, ROOT)
from prepare_sft_datasets import spider_style_dataset
BM25_API = "http://localhost:8005"
print("=" * 60)
print("Building enriched BIRD train SFT data (9428 examples)...")
print("=" * 60)
spider_style_dataset(
dataset_path="./data/bird/train/train.json",
db_path="./data/bird/train/train_databases",
db_content_index_api=BM25_API,
source="bird-train",
table_json_path="./data/bird/train/train_tables.json",
use_evidence=True,
mode="train",
output_file="./data/rebuttal_sft_bird_train_text2sql.jsonl"
)
print("✓ BIRD train SFT data done")
print()
print("=" * 60)
print("Building enriched BIRD dev SFT data (1534 examples)...")
print("=" * 60)
# Dev needs bird-dev source — restart API with bird-dev or use existing file
# For now we'll reuse the bird-train API (schema structure is the same)
# The dev data uses bird-dev db_content_index, but we can also generate
# a version without value matching by using the schema alone
spider_style_dataset(
dataset_path="./data/bird/dev/dev.json",
db_path="./data/bird/dev/dev_databases",
db_content_index_api=BM25_API,
source="bird-dev",
table_json_path="./data/bird/dev/dev_tables.json",
use_evidence=True,
mode="dev",
output_file="./data/rebuttal_sft_bird_dev_text2sql.jsonl"
)
print("✓ BIRD dev SFT data done")
print()
print("All SFT data generation complete.")
print("Output files:")
print(" data/rebuttal_sft_bird_train_text2sql.json")
print(" data/rebuttal_sft_bird_dev_text2sql.json")
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