File size: 27,796 Bytes
bb0efe6 f37b83c bb0efe6 f37b83c bb0efe6 f37b83c bb0efe6 f37b83c bb0efe6 f37b83c bb0efe6 f37b83c bb0efe6 f37b83c bb0efe6 f37b83c bb0efe6 f37b83c bb0efe6 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 | """
AIFinder Dataset Evaluator
Supports various HuggingFace dataset formats for evaluation.
"""
import os
import re
import json
import random
from collections import defaultdict
from typing import Any
from datasets import load_dataset
from tqdm import tqdm
HF_TOKEN = os.environ.get("HF_TOKEN")
SUPPORTED_FORMATS = {
"teichai_healer": {
"name": "TeichAI Healer Format",
"description": "TeichAI Healer-Alpha format with 'prompt' and 'response' fields",
"examples": ["TeichAI/Healer-Alpha-16k"],
"check": lambda row: (
"prompt" in row
and "response" in row
and isinstance(row.get("prompt"), (str, dict))
and isinstance(row.get("response"), (str, dict))
),
},
"teichai": {
"name": "TeichAI Format",
"description": "TeichAI dataset format with 'conversations' or 'messages' containing role/content",
"examples": [
"TeichAI/claude-4.5-opus-high-reasoning-250x",
"TeichAI/Claude-3.5-Sonnet-128k",
],
"check": lambda row: _check_conversations_format(row),
},
"combined": {
"name": "Combined Outputs",
"description": "Dataset with 'output', 'outputs', 'generated' or 'completion' field",
"examples": ["jacobmorrison/gpt-oss-20b-combined-outputs"],
"check": lambda row: (
"prompt" not in row
and "response" not in row
and not _check_conversations_format(row)
and (
any(k in row for k in ["output", "outputs", "generated", "completion"])
or (
isinstance(row.get("data"), str)
or isinstance(row.get("example"), str)
)
)
),
},
"conversations": {
"name": "Conversations Format",
"description": "Dataset with 'conversations' or 'messages' field containing role/content pairs",
"examples": [
"TeichAI/claude-4.5-opus-high-reasoning-250x",
"ianncity/Hunter-Alpha-SFT-300000x",
],
"check": lambda row: _check_conversations_format(row),
},
"chat": {
"name": "Chat Format",
"description": "Dataset with 'chat' or 'dialogue' field",
"examples": ["some/chat-dataset"],
"check": lambda row: ("chat" in row.keys() or "dialogue" in row.keys()),
},
"text": {
"name": "Text Field",
"description": "Dataset with a 'text' field containing the response",
"examples": ["some/text-dataset"],
"check": lambda row: "text" in row and isinstance(row.get("text"), str),
},
"response": {
"name": "Response Field",
"description": "Dataset with 'response' or 'output' field",
"examples": ["some/response-dataset"],
"check": lambda row: "response" in row or "output" in row,
},
"content": {
"name": "Content Field",
"description": "Dataset with 'content' field (single message)",
"examples": ["some/content-dataset"],
"check": lambda row: "content" in row and isinstance(row.get("content"), str),
},
"messages": {
"name": "Messages Array",
"description": "Dataset where each row is an array of message objects",
"examples": ["some/messages-dataset"],
"check": lambda row: isinstance(row, list)
and len(row) > 0
and isinstance(row[0], dict),
},
"sft": {
"name": "SFT Format",
"description": "Supervised Fine-Tuning format with 'prompt' and 'response' or 'completion'",
"examples": ["some/sft-dataset"],
"check": lambda row: "prompt" in row
and ("response" in row or "completion" in row),
},
"qa": {
"name": "Q&A Format",
"description": "Question-Answer format with 'question' and 'answer' fields",
"examples": ["some/qa-dataset"],
"check": lambda row: "question" in row and "answer" in row,
},
"combined": {
"name": "Combined Outputs",
"description": "Dataset with 'input', 'output', 'outputs' or combined text field",
"examples": ["jacobmorrison/gpt-oss-20b-combined-outputs"],
"check": lambda row: any(
k in row
for k in ["output", "outputs", "combined", "generated", "completion"]
)
or (isinstance(row.get("data"), str) or isinstance(row.get("example"), str)),
},
"completion": {
"name": "Completion Format",
"description": "Dataset with 'completion' field (like OpenAI fine-tuning)",
"examples": ["some/completion-dataset"],
"check": lambda row: "completion" in row
and isinstance(row.get("completion"), str),
},
"generations": {
"name": "Generations Format",
"description": "Dataset with 'generations' or 'generation' field (LLM outputs)",
"examples": ["some/generations-dataset"],
"check": lambda row: "generations" in row or "generation" in row,
},
}
def _check_conversations_format(row):
"""Check if row has conversations/messages with proper role/content structure."""
conv_key = (
"conversations"
if "conversations" in row
else "messages"
if "messages" in row
else None
)
if not conv_key:
return False
convos = row.get(conv_key)
if not isinstance(convos, list) or not convos:
return False
first_msg = convos[0]
if isinstance(first_msg, dict):
return "role" in first_msg and "content" in first_msg
return False
def detect_format(rows, sample_size=10):
"""Detect the dataset format from sample rows."""
if not rows:
return None, []
sample = rows[:sample_size]
for fmt_name, fmt_info in SUPPORTED_FORMATS.items():
check_func = fmt_info["check"]
matches = 0
for row in sample:
try:
if check_func(row):
matches += 1
except:
pass
if matches >= len(sample) * 0.6:
return fmt_name, SUPPORTED_FORMATS[fmt_name]
return None, []
def _parse_msg(msg):
"""Parse a message that may be a dict or a JSON string."""
if isinstance(msg, dict):
return msg
if isinstance(msg, str):
try:
parsed = json.loads(msg)
if isinstance(parsed, dict):
return parsed
except (ValueError, Exception):
pass
return {}
def _extract_response_only(content):
"""Extract only the final response, stripping CoT blocks."""
if not content:
return ""
think_match = re.search(r"</?think(?:ing)?>(.*)$", content, re.DOTALL)
if think_match:
response = think_match.group(1).strip()
if response:
return response
return content
def extract_texts_conversations(rows):
"""Extract from conversations/messages format."""
texts = []
for row in rows:
convos = row.get("conversations") or row.get("messages") or []
if not convos:
continue
for msg in convos:
msg = _parse_msg(msg)
role = msg.get("role", "")
content = msg.get("content", "")
if role in ("assistant", "gpt", "model", "ai") and content:
response_only = _extract_response_only(content)
if response_only and len(response_only) > 50:
texts.append(response_only)
return texts
def extract_texts_chat(rows):
"""Extract from chat/dialogue format."""
texts = []
for row in rows:
chat = row.get("chat") or row.get("dialogue") or []
if isinstance(chat, list):
for msg in chat:
msg = _parse_msg(msg)
role = msg.get("role", "")
content = msg.get("content", "")
if role in ("assistant", "ai") and content:
response_only = _extract_response_only(content)
if response_only and len(response_only) > 50:
texts.append(response_only)
return texts
def extract_texts_text_field(rows, field="text"):
"""Extract from a text field."""
texts = []
for row in rows:
content = row.get(field, "")
if content and len(str(content)) > 50:
response_only = _extract_response_only(str(content))
if response_only and len(response_only) > 50:
texts.append(response_only)
return texts
def extract_texts_sft(rows):
"""Extract from SFT format (prompt + response/completion)."""
texts = []
for row in rows:
response = row.get("response") or row.get("completion") or ""
if response and len(str(response)) > 50:
response_only = _extract_response_only(str(response))
if response_only and len(response_only) > 50:
texts.append(response_only)
return texts
def extract_texts_qa(rows):
"""Extract from Q&A format (use answer as response)."""
texts = []
for row in rows:
answer = row.get("answer", "")
if answer and len(str(answer)) > 50:
response_only = _extract_response_only(str(answer))
if response_only and len(response_only) > 50:
texts.append(response_only)
return texts
def extract_texts_messages_array(rows):
"""Extract from messages array format."""
texts = []
for row in rows:
if isinstance(row, list):
for msg in row:
msg = _parse_msg(msg)
role = msg.get("role", "")
content = msg.get("content", "")
if role in ("assistant", "ai", "model") and content:
response_only = _extract_response_only(content)
if response_only and len(response_only) > 50:
texts.append(response_only)
return texts
def extract_texts_teichai_healer(rows):
"""Extract from TeichAI Healer-Alpha format (prompt + response fields)."""
texts = []
for row in rows:
response = row.get("response")
if response:
if isinstance(response, dict):
response = response.get("content") or response.get("text") or ""
if response and len(str(response)) > 50:
response_only = _extract_response_only(str(response))
if response_only and len(response_only) > 50:
texts.append(response_only)
return texts
def _get_dataset_size(dataset_id, load_kwargs):
"""Get dataset size without loading all data."""
try:
ds = load_dataset(dataset_id, split="train", streaming=True, **load_kwargs)
return ds.info.num_rows
except Exception:
pass
try:
import pandas as pd
url = f"https://huggingface.co/api/datasets/{dataset_id}/parquet/default/train/0.parquet"
df = pd.read_parquet(url)
return len(df)
except Exception:
return 0
def _streaming_download_with_progress(
dataset_id, load_kwargs, progress_callback=None, max_rows=None
):
"""Download dataset using streaming with progress tracking."""
import pandas as pd
total_rows = _get_dataset_size(dataset_id, load_kwargs)
print(f"[PROGRESS] Dataset size: {total_rows} rows", flush=True)
download_limit = max_rows if max_rows and max_rows < total_rows else total_rows
if progress_callback:
progress_callback(0, download_limit, "fetching_info")
print(f"[PROGRESS] Initial callback: 0/{download_limit}", flush=True)
try:
ds = load_dataset(dataset_id, split="train", streaming=True, **load_kwargs)
rows = []
for i, row in enumerate(tqdm(ds, desc="Downloading", unit="rows")):
rows.append(row)
if progress_callback:
progress_callback(i + 1, download_limit, "downloading")
if i % 100 == 0:
print(f"[PROGRESS] Downloaded {i + 1}/{download_limit}", flush=True)
if max_rows and i + 1 >= max_rows:
print(f"[PROGRESS] Stopping at {i + 1} rows", flush=True)
break
return rows, min(len(rows), total_rows or len(rows))
except Exception as e:
print(f"[PROGRESS] Streaming failed: {e}", flush=True)
pass
try:
url = f"https://huggingface.co/api/datasets/{dataset_id}/parquet/default/train/0.parquet"
df = pd.read_parquet(url)
if max_rows and max_rows < len(df):
df = df.head(max_rows)
print(f"[PROGRESS] Limited to first {max_rows} rows", flush=True)
total = len(df)
if progress_callback:
progress_callback(0, total, "downloading")
rows = []
for i, row in enumerate(df.to_dict(orient="records")):
rows.append(row)
if progress_callback:
progress_callback(i + 1, total, "downloading")
return rows, total
except Exception as e:
raise e
def _load_sample_rows(dataset_id, sample_size, load_kwargs):
"""Load just a few rows for format detection."""
try:
ds = load_dataset(dataset_id, split="train", streaming=True, **load_kwargs)
return [next(iter(ds)) for _ in range(sample_size)]
except Exception:
pass
try:
import pandas as pd
url = f"https://huggingface.co/api/datasets/{dataset_id}/parquet/default/train/0.parquet"
df = pd.read_parquet(url)
return df.head(sample_size).to_dict(orient="records")
except Exception:
return []
def load_dataset_texts(
dataset_id,
max_samples=None,
sample_size=None,
progress_callback=None,
custom_format=None,
):
"""
Load a HuggingFace dataset and extract assistant response texts.
Returns: {
"texts": list of extracted texts,
"format": detected format name,
"format_info": format info dict,
"total_rows": total rows in dataset,
"supported": bool,
"error": error message if failed,
}
progress_callback: optional function(current, total, stage) -> None
stage can be: "fetching_info", "downloading", "extracting"
custom_format: optional custom format specification string
Examples:
- "column: response"
- "column: prompt, column: response"
- "pattern: user:, pattern: assistant:"
- "user:[startuser]assistant:[startassistant]"
"""
load_kwargs = {"token": HF_TOKEN} if HF_TOKEN else {}
rows = []
total_rows = 0
if sample_size:
total_rows = _get_dataset_size(dataset_id, load_kwargs)
if total_rows == 0:
return {
"texts": [],
"format": None,
"format_info": None,
"total_rows": 0,
"supported": False,
"error": "Dataset is empty",
}
rows = _load_sample_rows(dataset_id, sample_size, load_kwargs)
else:
if progress_callback:
try:
rows, total_rows = _streaming_download_with_progress(
dataset_id, load_kwargs, progress_callback, max_samples
)
except Exception as e:
fallback_error = None
try:
import pandas as pd
url = f"https://huggingface.co/api/datasets/{dataset_id}/parquet/default/train/0.parquet"
df = pd.read_parquet(url)
if max_samples and max_samples < len(df):
df = df.head(max_samples)
total_rows = len(df)
if progress_callback:
progress_callback(0, total_rows, "downloading")
rows = []
for i, row in enumerate(df.to_dict(orient="records")):
rows.append(row)
if progress_callback:
progress_callback(i + 1, total_rows, "downloading")
except Exception as e2:
fallback_error = str(e2)
return {
"texts": [],
"format": None,
"format_info": None,
"total_rows": 0,
"supported": False,
"error": f"Failed to load: {e}. Parquet fallback also failed: {fallback_error}",
}
else:
try:
ds = load_dataset(dataset_id, split="train", **load_kwargs)
total_rows = len(ds)
if max_samples and max_samples < total_rows:
total_rows = max_samples
rows = list(ds)[:max_samples] if max_samples else list(ds)
except Exception as e:
fallback_error = None
try:
import pandas as pd
url = f"https://huggingface.co/api/datasets/{dataset_id}/parquet/default/train/0.parquet"
df = pd.read_parquet(url)
if max_samples and max_samples < len(df):
df = df.head(max_samples)
total_rows = len(df)
rows = df.to_dict(orient="records")
except Exception as e2:
fallback_error = str(e2)
return {
"texts": [],
"format": None,
"format_info": None,
"total_rows": 0,
"supported": False,
"error": f"Failed to load: {e}. Parquet fallback also failed: {fallback_error}",
}
if not rows:
return {
"texts": [],
"format": None,
"format_info": None,
"total_rows": 0,
"supported": False,
"error": "Dataset is empty",
}
detect_rows = rows[:sample_size] if sample_size else rows
custom_format_spec = custom_format
if custom_format_spec and check_custom_format(detect_rows, custom_format_spec):
fmt_name = "custom"
fmt_info = {
"name": "Custom Format",
"description": f"Custom format: {custom_format_spec}",
"examples": [],
}
else:
fmt_name, fmt_info = detect_format(detect_rows, sample_size=sample_size or 10)
if fmt_name is None:
return {
"texts": [],
"format": None,
"format_info": None,
"total_rows": total_rows,
"supported": False,
"error": "Unknown dataset format. Supported formats: "
+ ", ".join(f["name"] for f in SUPPORTED_FORMATS.values()),
}
extractors = {
"teichai_healer": extract_texts_teichai_healer,
"teichai": extract_texts_conversations,
"conversations": extract_texts_conversations,
"chat": extract_texts_chat,
"text": lambda r: extract_texts_text_field(r, "text"),
"response": lambda r: extract_texts_text_field(r, "response")
or extract_texts_text_field(r, "output"),
"content": lambda r: extract_texts_text_field(r, "content"),
"messages": extract_texts_messages_array,
"sft": extract_texts_sft,
"qa": extract_texts_qa,
"combined": lambda r: (
extract_texts_text_field(r, "output")
or extract_texts_text_field(r, "outputs")
or extract_texts_text_field(r, "generated")
or extract_texts_text_field(r, "completion")
or extract_texts_text_field(r, "combined")
or extract_texts_text_field(r, "data")
or extract_texts_text_field(r, "example")
),
"completion": lambda r: extract_texts_text_field(r, "completion"),
"generations": lambda r: (
extract_texts_text_field(r, "generations")
or extract_texts_text_field(r, "generation")
),
"custom": lambda r: extract_texts_custom(r, custom_format_spec),
}
extractor = extractors.get(fmt_name)
texts = extractor(rows) if extractor else []
if max_samples and len(texts) > max_samples:
random.seed(42)
texts = random.sample(texts, max_samples)
return {
"texts": texts,
"format": fmt_name,
"format_info": fmt_info,
"total_rows": total_rows,
"supported": True,
"error": None,
}
def parse_custom_format_spec(spec):
"""
Parse custom format specification.
Supported formats:
- "column: <field_name>" - extract single field as text
- "column: <user_col>, column: <assistant_col>" - extract from two columns (user/assistant)
- "pattern: <start_marker>user<end_marker>, pattern: <start_marker>assistant<end_marker>" - use regex patterns
- "delimiter: <delim>" - use delimiter to split columns
Examples:
- "column: response"
- "column: prompt, column: response"
- "pattern: user:, pattern: assistant:"
- "user:[startuser]assistant:[startassistant]"
"""
if not spec:
return None
spec = spec.strip()
result = {
"type": None,
"user_field": None,
"assistant_field": None,
"user_pattern": None,
"assistant_pattern": None,
}
if spec.startswith("column:") or spec.startswith("col:"):
cols_spec = spec.replace("column:", "").replace("col:", "").strip()
if "," in cols_spec:
parts = [p.strip() for p in cols_spec.split(",")]
if len(parts) >= 2:
result["type"] = "two_column"
result["user_field"] = parts[0]
result["assistant_field"] = parts[1]
else:
result["type"] = "single_column"
result["assistant_field"] = cols_spec
return result
if spec.startswith("pattern:") or spec.startswith("regex:"):
patterns_spec = spec.replace("pattern:", "").replace("regex:", "").strip()
if "," in patterns_spec:
parts = [p.strip() for p in patterns_spec.split(",")]
if len(parts) >= 2:
result["type"] = "two_pattern"
result["user_pattern"] = parts[0]
result["assistant_pattern"] = parts[1]
else:
result["type"] = "single_pattern"
result["assistant_pattern"] = patterns_spec
return result
if "user:" in spec.lower() and "assistant:" in spec.lower():
import re
user_match = re.search(
r"user:\s*(\[.*?\]|(?:(?!\s+assistant:).)+)",
spec,
re.IGNORECASE | re.DOTALL,
)
assistant_match = re.search(
r"assistant:\s*(\[.*?\]|(?:(?:\s+user:|$).)+)",
spec,
re.IGNORECASE | re.DOTALL,
)
if user_match and assistant_match:
result["type"] = "two_pattern"
result["user_pattern"] = user_match.group(1).strip()
result["assistant_pattern"] = assistant_match.group(1).strip()
return result
if "[startuser]" in spec and "[startassistant]" in spec:
result["type"] = "two_pattern"
result["user_pattern"] = re.escape("[startuser]")
result["assistant_pattern"] = re.escape("[startassistant]")
return result
if "," in spec:
parts = [p.strip() for p in spec.split(",")]
if len(parts) >= 2:
result["type"] = "two_column"
result["user_field"] = parts[0]
result["assistant_field"] = parts[1]
return result
result["type"] = "single_column"
result["assistant_field"] = spec
return result
def extract_texts_custom(rows, format_spec):
"""Extract texts using custom format specification."""
parsed = parse_custom_format_spec(format_spec)
if not parsed or not parsed.get("type"):
return []
texts = []
if parsed["type"] == "single_column":
field = parsed["assistant_field"]
for row in rows:
content = row.get(field, "")
if content and len(str(content)) > 50:
response_only = _extract_response_only(str(content))
if response_only and len(response_only) > 50:
texts.append(response_only)
elif parsed["type"] == "two_column":
user_field = parsed.get("user_field")
assistant_field = parsed["assistant_field"]
for row in rows:
user_content = row.get(user_field, "") if user_field else ""
assistant_content = row.get(assistant_field, "")
if assistant_content and len(str(assistant_content)) > 50:
response_only = _extract_response_only(str(assistant_content))
if response_only and len(response_only) > 50:
texts.append(response_only)
elif parsed["type"] == "single_pattern":
pattern = parsed.get("assistant_pattern")
if pattern:
try:
regex = re.compile(pattern, re.DOTALL | re.IGNORECASE)
for row in rows:
row_str = str(row)
match = regex.search(row_str)
if match:
content = match.group(1) if match.groups() else match.group(0)
if content and len(content) > 50:
response_only = _extract_response_only(content)
if response_only and len(response_only) > 50:
texts.append(response_only)
except re.error:
pass
elif parsed["type"] == "two_pattern":
user_pattern = parsed.get("user_pattern")
assistant_pattern = parsed.get("assistant_pattern")
if assistant_pattern:
try:
user_regex = (
re.compile(user_pattern, re.DOTALL | re.IGNORECASE)
if user_pattern
else None
)
assistant_regex = re.compile(
assistant_pattern, re.DOTALL | re.IGNORECASE
)
for row in rows:
row_str = str(row)
match = assistant_regex.search(row_str)
if match:
content = match.group(1) if match.groups() else match.group(0)
if content and len(content) > 50:
response_only = _extract_response_only(content)
if response_only and len(response_only) > 50:
texts.append(response_only)
except re.error:
pass
return texts
def check_custom_format(rows, format_spec):
"""Check if custom format applies to the dataset."""
parsed = parse_custom_format_spec(format_spec)
if not parsed or not parsed.get("type"):
return False
if not rows:
return False
sample = rows[0]
if parsed["type"] == "single_column":
return parsed.get("assistant_field") in sample
if parsed["type"] == "two_column":
return parsed.get("assistant_field") in sample
if parsed["type"] in ("single_pattern", "two_pattern"):
pattern = parsed.get("assistant_pattern")
if pattern:
try:
regex = re.compile(pattern, re.DOTALL | re.IGNORECASE)
return regex.search(str(sample)) is not None
except re.error:
pass
return False
def get_supported_formats():
"""Return list of supported format info."""
return SUPPORTED_FORMATS
|