File size: 11,708 Bytes
3ab9f4d | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 | #!/usr/bin/env python3
"""Inspect a Hugging Face dataset using the Dataset Viewer API.
This mirrors the useful parts of upstream ml-intern's hf_inspect_dataset tool:
status, configs/splits, schema, sample rows, and parquet file availability.
"""
from __future__ import annotations
import argparse
import json
import os
import sys
import urllib.error
import urllib.parse
import urllib.request
from concurrent.futures import ThreadPoolExecutor, as_completed
from typing import Any
BASE_URL = "https://datasets-server.huggingface.co"
MAX_SAMPLE_VALUE_LEN = 150
def fetch_json(path: str, params: dict[str, Any], token: str | None) -> dict[str, Any]:
query = urllib.parse.urlencode({k: v for k, v in params.items() if v is not None})
request = urllib.request.Request(f"{BASE_URL}{path}?{query}")
if token:
request.add_header("Authorization", f"Bearer {token}")
try:
with urllib.request.urlopen(request, timeout=30) as response:
return json.loads(response.read().decode("utf-8"))
except urllib.error.HTTPError as exc:
body = exc.read().decode("utf-8", errors="replace")
raise RuntimeError(f"{path} returned HTTP {exc.code}: {body[:500]}") from exc
def type_name(feature: Any) -> str:
if isinstance(feature, str):
return feature
if not isinstance(feature, dict):
return type(feature).__name__
feature_type = feature.get("_type")
if feature_type == "ClassLabel":
names = feature.get("names") or []
if 0 < len(names) <= 5:
values = ", ".join(f"{name}={idx}" for idx, name in enumerate(names))
return f"ClassLabel ({values})"
return f"ClassLabel ({len(names)} classes)"
if feature_type:
return feature_type
if "dtype" in feature:
return str(feature["dtype"])
return json.dumps(feature, ensure_ascii=False)[:120]
def extract_configs(splits_data: dict[str, Any]) -> list[dict[str, Any]]:
configs: dict[str, dict[str, Any]] = {}
for item in splits_data.get("splits", []):
config = item.get("config", "default")
split = item.get("split", "train")
row_count = item.get("num_rows") or item.get("num_examples")
configs.setdefault(config, {"name": config, "splits": []})
configs[config]["splits"].append({"name": split, "rows": row_count})
return list(configs.values())
def format_status(data: dict[str, Any]) -> str:
available = [
key
for key in ("viewer", "preview", "search", "filter", "statistics")
if data.get(key)
]
if available:
return f"## Status\nValid ({', '.join(available)})"
return "## Status\nDataset may have Dataset Viewer issues"
def format_structure(configs: list[dict[str, Any]], max_rows: int = 20) -> str:
lines = ["## Structure (configs & splits)", "| Config | Split | Rows |", "|---|---|---:|"]
total = sum(len(config["splits"]) for config in configs)
shown = 0
for config in configs:
for split in config["splits"]:
if shown >= max_rows:
break
rows = split["rows"] if split["rows"] is not None else "?"
lines.append(f"| {config['name']} | {split['name']} | {rows} |")
shown += 1
if shown >= max_rows:
break
if total > shown:
lines.append("| ... | ... | ... |")
lines.append(f"_Showing {shown} of {total} config/split rows._")
return "\n".join(lines)
def format_schema(info: dict[str, Any], config: str) -> str:
features = info.get("dataset_info", {}).get("features", {})
lines = [f"## Schema ({config})", "| Column | Type |", "|---|---|"]
if not features:
lines.append("| (none found) | unknown |")
for column, feature in features.items():
lines.append(f"| {column} | {type_name(feature)} |")
return "\n".join(lines)
def maybe_json(value: Any) -> Any:
if isinstance(value, str):
try:
return json.loads(value)
except json.JSONDecodeError:
return value
return value
def format_messages(messages: Any) -> str | None:
messages = maybe_json(messages)
if not isinstance(messages, list) or not messages:
return None
roles: set[str] = set()
keys: set[str] = set()
has_tool_calls = False
has_tool_results = False
example: dict[str, Any] | None = None
fallback: dict[str, Any] | None = None
for message in messages:
if not isinstance(message, dict):
continue
keys.update(message.keys())
if message.get("role"):
roles.add(str(message["role"]))
if message.get("tool_calls") or message.get("function_call"):
has_tool_calls = True
example = example or message
if message.get("role") in {"tool", "function"} or message.get("tool_call_id"):
has_tool_results = True
if message.get("role") == "assistant":
example = example or message
elif message.get("role") != "system":
fallback = fallback or message
example = example or fallback
lines = ["## Messages Column Format"]
lines.append(f"Roles: {', '.join(sorted(roles)) if roles else 'unknown'}")
common = ["role", "content", "tool_calls", "tool_call_id", "name", "function_call"]
lines.append("Message keys: " + ", ".join(f"{key} {'yes' if key in keys else 'no'}" for key in common))
if has_tool_calls:
lines.append("Tool calls: present")
if has_tool_results:
lines.append("Tool results: present")
if example:
cleaned = dict(example)
content = cleaned.get("content")
if isinstance(content, str) and len(content) > 100:
cleaned["content"] = content[:100] + "..."
lines.append("")
lines.append("Example message structure:")
lines.append("```json")
lines.append(json.dumps(cleaned, indent=2, ensure_ascii=False))
lines.append("```")
return "\n".join(lines)
def format_samples(rows_data: dict[str, Any], config: str, split: str, limit: int) -> str:
rows = rows_data.get("rows", [])[:limit]
lines = [f"## Sample Rows ({config}/{split})"]
first_messages: Any = None
for idx, row_wrapper in enumerate(rows, 1):
row = row_wrapper.get("row", {})
lines.append(f"**Row {idx}:**")
for key, value in row.items():
if key.lower() == "messages" and first_messages is None:
first_messages = value
text = str(value)
if len(text) > MAX_SAMPLE_VALUE_LEN:
text = text[:MAX_SAMPLE_VALUE_LEN] + "..."
lines.append(f"- {key}: {text}")
if not rows:
lines.append("(no rows returned)")
message_section = format_messages(first_messages) if first_messages is not None else None
if message_section:
lines.append("")
lines.append(message_section)
return "\n".join(lines)
def format_parquet(data: dict[str, Any], max_rows: int = 20) -> str | None:
files = data.get("parquet_files", [])
if not files:
return None
groups: dict[str, dict[str, int]] = {}
for item in files:
key = f"{item.get('config', 'default')}/{item.get('split', 'train')}"
groups.setdefault(key, {"count": 0, "size": 0})
groups[key]["count"] += 1
size = item.get("size") or 0
groups[key]["size"] += int(size) if isinstance(size, (int, float)) else 0
lines = ["## Files (Parquet)"]
for key, values in list(groups.items())[:max_rows]:
size_mb = values["size"] / (1024 * 1024)
lines.append(f"- {key}: {values['count']} file(s), {size_mb:.1f} MB")
if len(groups) > max_rows:
lines.append(f"- ... showing {max_rows} of {len(groups)} groups")
return "\n".join(lines)
def compatibility_notes(features: dict[str, Any]) -> str:
columns = set(features)
lines = ["## Training Compatibility"]
checks = {
"SFT": bool({"messages", "text"} & columns or {"prompt", "completion"} <= columns),
"DPO": {"prompt", "chosen", "rejected"} <= columns,
"GRPO": "prompt" in columns,
}
for method, ok in checks.items():
lines.append(f"- {method}: {'looks compatible' if ok else 'columns not sufficient'}")
if "messages" in columns:
lines.append("- Chat data: inspect the sample message roles before choosing a trainer template.")
return "\n".join(lines)
def inspect_dataset(dataset: str, config: str | None, split: str | None, sample_rows: int, token: str | None) -> str:
warnings: list[str] = []
with ThreadPoolExecutor(max_workers=3) as pool:
futures = {
pool.submit(fetch_json, "/is-valid", {"dataset": dataset}, token): "is-valid",
pool.submit(fetch_json, "/splits", {"dataset": dataset}, token): "splits",
pool.submit(fetch_json, "/parquet", {"dataset": dataset}, token): "parquet",
}
phase1: dict[str, Any] = {}
for future in as_completed(futures):
name = futures[future]
try:
phase1[name] = future.result()
except Exception as exc:
warnings.append(f"{name}: {exc}")
configs = extract_configs(phase1.get("splits", {}))
selected_config = config or (configs[0]["name"] if configs else "default")
selected_split = split or (configs[0]["splits"][0]["name"] if configs and configs[0]["splits"] else "train")
with ThreadPoolExecutor(max_workers=2) as pool:
futures = {
pool.submit(fetch_json, "/info", {"dataset": dataset, "config": selected_config}, token): "info",
pool.submit(
fetch_json,
"/first-rows",
{"dataset": dataset, "config": selected_config, "split": selected_split},
token,
): "first-rows",
}
phase2: dict[str, Any] = {}
for future in as_completed(futures):
name = futures[future]
try:
phase2[name] = future.result()
except Exception as exc:
warnings.append(f"{name}: {exc}")
features = phase2.get("info", {}).get("dataset_info", {}).get("features", {})
sections = [f"# {dataset}"]
if "is-valid" in phase1:
sections.append(format_status(phase1["is-valid"]))
if configs:
sections.append(format_structure(configs))
if "info" in phase2:
sections.append(format_schema(phase2["info"], selected_config))
sections.append(compatibility_notes(features))
if "first-rows" in phase2:
sections.append(format_samples(phase2["first-rows"], selected_config, selected_split, sample_rows))
parquet = format_parquet(phase1.get("parquet", {}))
if parquet:
sections.append(parquet)
if warnings:
sections.append("## Warnings\n" + "\n".join(f"- {warning}" for warning in warnings))
return "\n\n".join(sections)
def main() -> int:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("dataset", help="Dataset id, for example stanfordnlp/imdb")
parser.add_argument("--config", help="Config/subset name")
parser.add_argument("--split", help="Split for sample rows")
parser.add_argument("--sample-rows", type=int, default=3, help="Number of rows to show, max 10")
parser.add_argument("--token-env", default="HF_TOKEN", help="Environment variable containing an HF token")
args = parser.parse_args()
token = os.environ.get(args.token_env)
print(inspect_dataset(args.dataset, args.config, args.split, min(args.sample_rows, 10), token))
return 0
if __name__ == "__main__":
sys.exit(main())
|