File size: 5,490 Bytes
c1f5657 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 | #!/usr/bin/env python3
"""ZabaanAI-v2 SFT Data Curation Pipeline
Downloads instruction datasets from HF Hub and converts to ChatML messages format
for Qwen2.5-7B-Instruct SFT training.
Supported formats -> ChatML conversion:
- Alpaca (instruction/input/output) -> messages
- Custom (text fields) -> messages
Run: python scripts/01_curate_sft_data.py
"""
import json
import argparse
from pathlib import Path
from datasets import load_dataset
DATASETS = {
"urdu_instruct": {
"source": "large-traversaal/urdu-instruct",
"split": "train",
"format": "alpaca",
"lang": "ur",
"instruction_col": "instruction",
"input_col": "input",
"output_col": "output",
},
"sindhi_intelligence": {
"source": "aakashMeghwar01/Sindhi-Intelligence-Core-SFT",
"split": "train",
"format": "alpaca",
"lang": "sd",
"instruction_col": "instruction",
"input_col": "input",
"output_col": "output",
},
"pashto_alpaca": {
"source": "saillab/alpaca_pashto_taco",
"split": "train",
"format": "alpaca",
"lang": "ps",
"instruction_col": "instruction",
"input_col": "input",
"output_col": "output",
},
"urdu_news_gen": {
"source": "AhmadMustafa/Urdu-Instruct-News-Article-Generation",
"split": "train",
"format": "alpaca",
"lang": "ur",
"instruction_col": "instruction",
"input_col": "input",
"output_col": "output",
},
"urdu_news_class": {
"source": "AhmadMustafa/Urdu-Instruct-News-Category-Classification",
"split": "train",
"format": "alpaca",
"lang": "ur",
"instruction_col": "instruction",
"input_col": "input",
"output_col": "output",
},
"urdu_news_headline": {
"source": "AhmadMustafa/Urdu-Instruct-News-Headline-Generation",
"split": "train",
"format": "alpaca",
"lang": "ur",
"instruction_col": "instruction",
"input_col": "input",
"output_col": "output",
},
"urdu_alpaca": {
"source": "ravithejads/alpaca_urdu_cleaned_instruction",
"split": "train",
"format": "alpaca",
"lang": "ur",
"instruction_col": "instruction",
"input_col": "input",
"output_col": "output",
},
"punjabi_alpaca": {
"source": "japneets/Alpaca_instruction_fine_tune_Punjabi",
"split": "train",
"format": "alpaca",
"lang": "pa",
"instruction_col": "instruction",
"input_col": "input",
"output_col": "output",
},
}
def alpaca_to_messages(example, inst_col, inp_col, out_col, lang):
"""Convert Alpaca format to ChatML messages format for SFT."""
instruction = str(example.get(inst_col, "")).strip()
inp = str(example.get(inp_col, "")).strip()
output = str(example.get(out_col, "")).strip()
if not instruction or not output:
return None
# Build user prompt
if inp and inp.lower() not in ["none", "nil", ""]:
user_content = f"{instruction}\n\n{inp}"
else:
user_content = instruction
# ChatML messages format (Qwen2.5 compatible)
messages = [
{"role": "system", "content": f"You are ZabaanAI, a helpful AI assistant fluent in {lang} and other Pakistan languages. Respond accurately and respectfully."},
{"role": "user", "content": user_content},
{"role": "assistant", "content": output},
]
return {"messages": messages, "language": lang}
def process_dataset(name, config, output_dir, max_samples=None):
"""Download and format a dataset."""
print(f"\nLoading: {name} ({config[\"source\"]})")
try:
ds = load_dataset(
config["source"],
split=config["split"],
trust_remote_code=True,
)
if max_samples:
ds = ds.select(range(min(max_samples, len(ds))))
print(f" Loaded {len(ds):,} examples")
except Exception as e:
print(f" Error: {e}")
return 0
formatted = []
for example in ds:
if config["format"] == "alpaca":
result = alpaca_to_messages(
example,
config["instruction_col"],
config["input_col"],
config["output_col"],
config["lang"],
)
else:
continue
if result:
formatted.append(result)
# Save
output_file = output_dir / f"{name}.jsonl"
with open(output_file, "w", encoding="utf-8") as f:
for item in formatted:
f.write(json.dumps(item, ensure_ascii=False) + "\n")
print(f" Saved {len(formatted):,} examples to {output_file.name}")
return len(formatted)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--output_dir", default="data/formatted")
parser.add_argument("--max_samples", type=int, default=None)
args = parser.parse_args()
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
print("=" * 60)
print(" ZabaanAI-v2 SFT Data Curation")
print("=" * 60)
total = 0
for name, config in DATASETS.items():
count = process_dataset(name, config, output_dir, args.max_samples)
total += count
print(f"\nTotal formatted examples: {total:,}")
print("=" * 60)
if __name__ == "__main__":
main()
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