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import os
import json
import random
from typing import List, Dict, Any
from datasets import load_dataset
from tqdm import tqdm
# Configuration
OUTPUT_DIR = "data/raw_datasets"
# Dataset configurations
# Format: (huggingface_id, subset, split, text_column_name, approximate_limit)
SOURCES = [
{
"id": "lmsys/lmsys-chat-1m",
"subset": None,
"split": "train",
"type": "lmsys",
"limit": 200000
},
{
"id": "allenai/WildChat",
"subset": None,
"split": "train",
"type": "wildchat",
"limit": 150000
},
{
"id": "anon8231489123/ShareGPT_Vicuna_unfiltered",
"subset": None,
"split": "train",
"data_files": "ShareGPT_V3_unfiltered_cleaned_split.json",
"type": "sharegpt",
"limit": 50000
},
{
"id": "yahma/alpaca-cleaned",
"subset": None,
"split": "train",
"type": "alpaca",
"limit": 52000
},
{
"id": "Open-Orca/SlimOrca",
"subset": None,
"split": "train",
"type": "slimorca",
"limit": 100000
}
]
def ensure_english(text: str) -> bool:
# A simple heuristic to filter non-English text.
# For production, use langdetect or similar libraries.
# Here we check if a significant portion of characters are ASCII.
try:
return text.isascii()
except:
return False
def process_lmsys(example: Dict[str, Any]) -> str | None:
# LMSYS format: conversation is in 'conversation' list of dicts
try:
conversation = example.get("conversation", [])
if not conversation:
return None
# Get first user message
if conversation[0]["role"] == "user":
return conversation[0]["content"]
except:
pass
return None
def process_wildchat(example: Dict[str, Any]) -> str | None:
# WildChat format: 'conversation' list of dicts or 'prompt' column?
# Checking dataset viewer, it usually has 'conversation' with 'content' and 'role'
try:
conversation = example.get("conversation", [])
if not conversation:
return None
if conversation[0]["role"] == "user":
return conversation[0]["content"]
except:
pass
return None
def process_sharegpt(example: Dict[str, Any]) -> str | None:
# ShareGPT format: 'conversations' list
try:
conversations = example.get("conversations", [])
if not conversations:
return None
# Usually human/gpt or user/assistant
if conversations[0]["from"] in ["human", "user"]:
return conversations[0]["value"]
except:
pass
return None
def process_alpaca(example: Dict[str, Any]) -> str | None:
# Alpaca format: 'instruction' and 'input'. We combine them if input exists.
try:
instruction = example.get("instruction", "").strip()
inp = example.get("input", "").strip()
if inp:
return f"{instruction}\n\nInput: {inp}"
return instruction
except:
pass
return None
def process_slimorca(example: Dict[str, Any]) -> str | None:
# SlimOrca format: 'conversations' list of dicts (from, value)
# Similar to ShareGPT but keys might differ slightly
try:
conversations = example.get("conversations", [])
if not conversations:
return None
# Usually from: human/user
if conversations[0]["from"] in ["human", "user"]:
return conversations[0]["value"]
except:
pass
return None
def download_and_process():
os.makedirs(OUTPUT_DIR, exist_ok=True)
all_queries = []
# Target new sources only (alpaca and slimorca)
# You can comment out this filter if you want to re-run everything
new_types = ["alpaca", "slimorca"]
for source in SOURCES:
if source["type"] not in new_types:
continue
print(f"Processing {source['id']}...")
try:
# Load streaming to save disk/memory
kwargs = {"streaming": True}
if "data_files" in source:
kwargs["data_files"] = source["data_files"]
ds = load_dataset(source["id"], source["subset"], split=source["split"], **kwargs)
count = 0
limit = source["limit"]
for example in tqdm(ds, desc=f"Reading {source['id']}", total=limit):
if count >= limit:
break
query = None
if source["type"] == "lmsys":
query = process_lmsys(example)
elif source["type"] == "wildchat":
query = process_wildchat(example)
elif source["type"] == "sharegpt":
query = process_sharegpt(example)
elif source["type"] == "alpaca":
query = process_alpaca(example)
elif source["type"] == "slimorca":
query = process_slimorca(example)
# Basic cleaning
if query and len(query.strip()) > 5 and ensure_english(query):
all_queries.append({
"source": source["id"],
"query": query.strip()
})
count += 1
except Exception as e:
print(f"Error processing {source['id']}: {e}")
# Deduplicate based on query content
print(f"Total collected new items: {len(all_queries)}")
# Load existing if available to dedup against
output_path = os.path.join(OUTPUT_DIR, "combined_raw_queries.jsonl")
existing_data = []
if os.path.exists(output_path):
print("Loading existing data for deduplication...")
with open(output_path, "r", encoding="utf-8") as f:
for line in f:
if line.strip():
existing_data.append(json.loads(line))
combined = existing_data + all_queries
print(f"Total before final deduplication: {len(combined)}")
unique_queries = {item["query"]: item for item in combined}.values()
final_data = list(unique_queries)
print(f"Total after final deduplication: {len(final_data)}")
# Shuffle
random.shuffle(final_data)
# Save
print(f"Saving to {output_path}...")
with open(output_path, "w", encoding="utf-8") as f:
for item in final_data:
f.write(json.dumps(item, ensure_ascii=False) + "\n")
print("Done!")
if __name__ == "__main__":
download_and_process()
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