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dc59b01 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 | import json
import os
import sqlite3
from datasets import Dataset
from transformers import T5Tokenizer
# =========================================================
# PROJECT ROOT (VERY IMPORTANT — fixes path issues)
# =========================================================
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
TRAIN_JSON = os.path.join(BASE_DIR, "data", "train_spider.json")
DEV_JSON = os.path.join(BASE_DIR, "data", "dev.json")
DB_FOLDER = os.path.join(BASE_DIR, "data", "database")
SAVE_TRAIN = os.path.join(BASE_DIR, "data", "tokenized", "train")
SAVE_DEV = os.path.join(BASE_DIR, "data", "tokenized", "validation")
os.makedirs(os.path.dirname(SAVE_TRAIN), exist_ok=True)
print("Project root:", BASE_DIR)
print("Train file:", TRAIN_JSON)
print("Database folder:", DB_FOLDER)
# =========================================================
# TOKENIZER
# =========================================================
tokenizer = T5Tokenizer.from_pretrained("t5-small")
# =========================================================
# READ DATABASE SCHEMA
# =========================================================
def get_schema(db_path):
conn = sqlite3.connect(db_path)
cursor = conn.cursor()
tables = cursor.execute(
"SELECT name FROM sqlite_master WHERE type='table';"
).fetchall()
schema_text = []
for table in tables:
table = table[0]
columns = cursor.execute(f"PRAGMA table_info({table});").fetchall()
col_names = [c[1] for c in columns]
schema_text.append(f"{table}({', '.join(col_names)})")
conn.close()
return "\n".join(schema_text)
# =========================================================
# BUILD TRAINING EXAMPLES
# =========================================================
def build_examples(spider_json):
print(f"\nBuilding dataset from: {spider_json}")
data = json.load(open(spider_json))
inputs = []
outputs = []
for ex in data:
question = ex["question"]
sql = ex["query"]
db_id = ex["db_id"]
db_path = os.path.join(DB_FOLDER, db_id, f"{db_id}.sqlite")
# skip if db missing (safety)
if not os.path.exists(db_path):
continue
schema = get_schema(db_path)
# ⭐ SCHEMA-AWARE PROMPT (VERY IMPORTANT)
input_text = f"""Database Schema:
{schema}
Translate English to SQL:
{question}
SQL:
"""
inputs.append(input_text)
outputs.append(sql)
return Dataset.from_dict({"input": inputs, "output": outputs})
# =========================================================
# TOKENIZE
# =========================================================
def tokenize(example):
model_input = tokenizer(
example["input"],
max_length=512,
padding="max_length",
truncation=True
)
label = tokenizer(
example["output"],
max_length=256,
padding="max_length",
truncation=True
)
model_input["labels"] = label["input_ids"]
return model_input
# =========================================================
# RUN PIPELINE
# =========================================================
print("\nBuilding TRAIN dataset...")
train_dataset = build_examples(TRAIN_JSON)
print("Tokenizing TRAIN dataset...")
tokenized_train = train_dataset.map(tokenize, batched=False)
print("Saving TRAIN dataset...")
tokenized_train.save_to_disk(SAVE_TRAIN)
print("\nBuilding VALIDATION dataset...")
val_dataset = build_examples(DEV_JSON)
print("Tokenizing VALIDATION dataset...")
tokenized_val = val_dataset.map(tokenize, batched=False)
print("Saving VALIDATION dataset...")
tokenized_val.save_to_disk(SAVE_DEV)
print("\nDONE ✔ Dataset prepared successfully!")
print("Train saved at:", SAVE_TRAIN)
print("Validation saved at:", SAVE_DEV) |