Spaces:
Sleeping
Sleeping
File size: 17,383 Bytes
d939bae |
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 |
import json
import re
import os
from datasets import load_dataset
from tqdm import tqdm
# Regex để bắt điểm (ví dụ: 7 hoặc 7.5 hoặc 6.0)
FLOAT_RE = r"(\d+(?:\.\d+)?)"
def to_float_safe(x):
"""Chuyển đổi an toàn sang float, nếu lỗi trả về None"""
try:
val = float(x)
# Kiểm tra điểm hợp lệ (0-9)
if 0 <= val <= 9:
return val
return None
except Exception:
return None
def parse_chillies_dataset(dataset):
"""
Parser cho 'chillies/IELTS-writing-task-2-evaluation'.
Format: **Task Achievement: [7]** hoặc **Overall Band Score: [7.5]**
"""
print("Đang xử lý dataset 'chillies'...")
cleaned = []
bad_examples = 0
patterns = {
"task_response": re.compile(
r"\*\*Task Achievement:\s*\[?(" + FLOAT_RE + r")\]?\*\*",
re.I
),
"coherence_cohesion": re.compile(
r"\*\*Coherence and Cohesion:\s*\[?(" + FLOAT_RE + r")\]?\*\*",
re.I
),
"lexical_resource": re.compile(
r"\*\*Lexical Resource:\s*\[?(" + FLOAT_RE + r")\]?\*\*",
re.I
),
"grammatical_range": re.compile(
r"\*\*Grammatical Range and Accuracy:\s*\[?(" + FLOAT_RE + r")\]?\*\*",
re.I
),
}
for item in tqdm(dataset, desc="Parsing chillies"):
try:
prompt = item.get('prompt', '').strip()
essay = item.get('essay', '').strip()
evaluation_text = item.get('evaluation', '')
if not (prompt and essay and evaluation_text and len(essay) > 50):
bad_examples += 1
continue
scores = {}
for key, pattern in patterns.items():
match = pattern.search(evaluation_text)
if match:
score_str = match.group(1)
scores[key] = to_float_safe(score_str)
else:
scores[key] = None
if all(scores.values()):
standard_scores = {
"task_response": scores["task_response"],
"coherence_cohesion": scores["coherence_cohesion"],
"lexical_resource": scores["lexical_resource"],
"grammatical_range": scores["grammatical_range"]
}
cleaned.append({
"prompt_text": prompt,
"essay_text": essay,
"scores": standard_scores
})
else:
bad_examples += 1
except Exception:
bad_examples += 1
print(f" ✓ kept {len(cleaned)} samples, skipped {bad_examples}")
return cleaned
def parse_123harr_dataset(dataset):
"""
Parser cho '123Harr/IELTS-WT2-LLaMa3-1k'.
Lấy scores từ 'formatted' field
"""
print("Đang xử lý dataset '123Harr'...")
cleaned = []
bad_examples = 0
prompt_essay_re = re.compile(
r"<\|start_header_id\|>user<\|end_header_id\|>\n\n(.*?)<\|eot_id\|>",
re.S
)
score_patterns = {
"task_response": re.compile(
r"(?:###|##|\*\*)?Task Achievement(?:\*\*)?:[\s\S]*?(?:Suggested Band Score|Band Score)?[\s\S]*?" + FLOAT_RE + r"(?:\s|$)",
re.I | re.M
),
"coherence_cohesion": re.compile(
r"(?:###|##|\*\*)?Coherence and Cohesion(?:\*\*)?:[\s\S]*?(?:Suggested Band Score|Band Score)?[\s\S]*?" + FLOAT_RE + r"(?:\s|$)",
re.I | re.M
),
"lexical_resource": re.compile(
r"(?:###|##|\*\*)?Lexical Resource(?:\s*\(Vocabulary\))?(?:\*\*)?:[\s\S]*?(?:Suggested Band Score|Band Score)?[\s\S]*?" + FLOAT_RE + r"(?:\s|$)",
re.I | re.M
),
"grammatical_range": re.compile(
r"(?:###|##|\*\*)?Grammatical Range and Accuracy(?:\*\*)?:[\s\S]*?(?:Suggested Band Score|Band Score)?[\s\S]*?" + FLOAT_RE + r"(?:\s|$)",
re.I | re.M
),
}
for item in tqdm(dataset, desc="Parsing 123Harr"):
try:
formatted_text = item.get('formatted', '')
if not formatted_text:
bad_examples += 1
continue
matches = prompt_essay_re.findall(formatted_text)
if len(matches) < 2:
bad_examples += 1
continue
prompt = matches[0].strip()
essay = matches[1].strip()
if not prompt or not essay or len(essay) < 50:
bad_examples += 1
continue
scores = {}
for key, pattern in score_patterns.items():
match = pattern.search(formatted_text)
if match:
score_str = match.group(match.lastindex) if match.lastindex else match.group(1)
scores[key] = to_float_safe(score_str)
else:
scores[key] = None
if all(scores.values()):
standard_scores = {
"task_response": scores["task_response"],
"coherence_cohesion": scores["coherence_cohesion"],
"lexical_resource": scores["lexical_resource"],
"grammatical_range": scores["grammatical_range"]
}
cleaned.append({
"prompt_text": prompt,
"essay_text": essay,
"scores": standard_scores
})
else:
bad_examples += 1
except Exception:
bad_examples += 1
print(f" ✓ kept {len(cleaned)} samples, skipped {bad_examples}")
return cleaned
def parse_dpo_dataset(dataset):
"""
Parser cho 'chillies/DPO_ielts_writing'.
"""
print("Đang xử lý dataset 'DPO'...")
cleaned = []
bad_examples = 0
patterns_primary = {
"task_response": re.compile(
r"##\s*Task Achievement:[\s\S]*?Suggested Band Score:\s*" + FLOAT_RE,
re.I
),
"coherence_cohesion": re.compile(
r"##\s*Coherence and Cohesion:[\s\S]*?Suggested Band Score:\s*" + FLOAT_RE,
re.I
),
"lexical_resource": re.compile(
r"##\s*Lexical Resource(?:\s*\(Vocabulary\))?:[\s\S]*?Suggested Band Score:\s*" + FLOAT_RE,
re.I
),
"grammatical_range": re.compile(
r"##\s*Grammatical Range and Accuracy:[\s\S]*?Suggested Band Score:\s*" + FLOAT_RE,
re.I
),
}
patterns_fallback = {
"task_response": re.compile(r"(?:\*\*)?Task Achievement(?:\*\*)?:\s*" + FLOAT_RE, re.I),
"coherence_cohesion": re.compile(r"(?:\*\*)?Coherence and Cohesion(?:\*\*)?:\s*" + FLOAT_RE, re.I),
"lexical_resource": re.compile(r"(?:\*\*)?Lexical Resource(?:\s*\(Vocabulary\))?(?:\*\*)?:\s*" + FLOAT_RE, re.I),
"grammatical_range": re.compile(r"(?:\*\*)?Grammatical Range and Accuracy(?:\*\*)?:\s*" + FLOAT_RE, re.I),
}
for item in tqdm(dataset, desc="Parsing DPO"):
try:
prompt = item.get('prompt', '').strip()
essay = item.get('essay', '').strip()
chosen_text = item.get('chosen', '')
if not (prompt and essay and chosen_text and len(essay) > 50):
bad_examples += 1
continue
scores = {}
for key, pattern in patterns_primary.items():
match = pattern.search(chosen_text)
if match:
scores[key] = to_float_safe(match.group(1))
else:
scores[key] = None
if not all(scores.values()):
scores = {}
for key, pattern in patterns_fallback.items():
match = pattern.search(chosen_text)
if match:
scores[key] = to_float_safe(match.group(1))
else:
scores[key] = None
if all(scores.values()):
standard_scores = {
"task_response": scores["task_response"],
"coherence_cohesion": scores["coherence_cohesion"],
"lexical_resource": scores["lexical_resource"],
"grammatical_range": scores["grammatical_range"]
}
cleaned.append({
"prompt_text": prompt,
"essay_text": essay,
"scores": standard_scores
})
else:
bad_examples += 1
except Exception:
bad_examples += 1
print(f" ✓ kept {len(cleaned)} samples, skipped {bad_examples}")
return cleaned
def parse_hadeel_dataset(dataset):
"""
Parser cho 'hadeelbkh/tokenized-IELTS-writing-task-2-evaluation'.
"""
print("Đang xử lý dataset 'hadeel'...")
cleaned = []
bad_examples = 0
patterns = {
"task_response": re.compile(
r"(?:\*\*)?task achievement(?:\*\*)?:\s*-?\s*(" + FLOAT_RE + r")",
re.I
),
"coherence_cohesion": re.compile(
r"(?:\*\*)?coherence and cohesion(?:\*\*)?:\s*-?\s*(" + FLOAT_RE + r")",
re.I
),
"lexical_resource": re.compile(
r"(?:\*\*)?lexical resource(?:\s*\(vocabulary\))?(?:\*\*)?:\s*-?\s*(" + FLOAT_RE + r")",
re.I
),
"grammatical_range": re.compile(
r"(?:\*\*)?grammatical range and accuracy(?:\*\*)?:\s*-?\s*(" + FLOAT_RE + r")",
re.I
),
}
for item in tqdm(dataset, desc="Parsing hadeel"):
try:
prompt = item.get('prompt', '').strip()
essay = item.get('essay', '').strip()
evaluation_text = item.get('evaluation', '')
if not (prompt and essay and evaluation_text and len(essay) > 50):
bad_examples += 1
continue
scores = {}
for key, pattern in patterns.items():
match = pattern.search(evaluation_text)
if match:
score_str = match.group(1)
scores[key] = to_float_safe(score_str)
else:
scores[key] = None
if all(scores.values()):
standard_scores = {
"task_response": scores["task_response"],
"coherence_cohesion": scores["coherence_cohesion"],
"lexical_resource": scores["lexical_resource"],
"grammatical_range": scores["grammatical_range"]
}
cleaned.append({
"prompt_text": prompt,
"essay_text": essay,
"scores": standard_scores
})
else:
bad_examples += 1
except Exception:
bad_examples += 1
print(f" ✓ kept {len(cleaned)} samples, skipped {bad_examples}")
return cleaned
def parse_vietanh_dataset(dataset):
"""
Parser cho 'vietanh0802/ielts_writing_training_data_prepared'.
Format: <s>[INST] ... ### Prompt: ... ### Essay: ... [/INST] ...
"""
print("Đang xử lý dataset 'vietanh'...")
cleaned = []
bad_examples = 0
prompt_re = re.compile(r"### Prompt:\s*(.*?)(?=### Essay:|$)", re.S | re.I)
essay_re = re.compile(r"### Essay:\s*(.*?)(?=\[/INST\]|$)", re.S | re.I)
score_patterns = {
"task_response": re.compile(
r"(?:\*\*)?Task Achievement(?:\*\*)?:\s*\[?(" + FLOAT_RE + r")\]?",
re.I
),
"coherence_cohesion": re.compile(
r"(?:\*\*)?Coherence and Cohesion(?:\*\*)?:\s*\[?(" + FLOAT_RE + r")\]?",
re.I
),
"lexical_resource": re.compile(
r"(?:\*\*)?Lexical Resource(?:\s*\(Vocabulary\))?(?:\*\*)?:\s*\[?(" + FLOAT_RE + r")\]?",
re.I
),
"grammatical_range": re.compile(
r"(?:\*\*)?Grammatical Range and Accuracy(?:\*\*)?:\s*\[?(" + FLOAT_RE + r")\]?",
re.I
),
}
for item in tqdm(dataset, desc="Parsing vietanh"):
try:
training_text = item.get('training_text', '')
if not training_text:
bad_examples += 1
continue
prompt_match = prompt_re.search(training_text)
if not prompt_match:
bad_examples += 1
continue
prompt = prompt_match.group(1).strip()
essay_match = essay_re.search(training_text)
if not essay_match:
bad_examples += 1
continue
essay = essay_match.group(1).strip()
if not prompt or not essay or len(essay) < 50:
bad_examples += 1
continue
scores = {}
for key, pattern in score_patterns.items():
match = pattern.search(training_text)
if match:
scores[key] = to_float_safe(match.group(1))
else:
scores[key] = None
if all(scores.values()):
standard_scores = {
"task_response": scores["task_response"],
"coherence_cohesion": scores["coherence_cohesion"],
"lexical_resource": scores["lexical_resource"],
"grammatical_range": scores["grammatical_range"]
}
cleaned.append({
"prompt_text": prompt,
"essay_text": essay,
"scores": standard_scores
})
else:
bad_examples += 1
except Exception:
bad_examples += 1
print(f" ✓ kept {len(cleaned)} samples, skipped {bad_examples}")
return cleaned
def main():
print("Đang tải các dataset từ Hugging Face...\n")
cache_dir = "./.cache/huggingface_datasets"
all_data = []
# Dataset 1: chillies/IELTS-writing-task-2-evaluation
try:
ds_chillies = load_dataset(
"chillies/IELTS-writing-task-2-evaluation",
split="train",
cache_dir=cache_dir
)
all_data.append(("chillies", parse_chillies_dataset(ds_chillies)))
except Exception as e:
print(f"✗ Lỗi tải chillies: {e}\n")
# Dataset 2: 123Harr/IELTS-WT2-LLaMa3-1k
try:
ds_123harr = load_dataset(
"123Harr/IELTS-WT2-LLaMa3-1k",
split="train",
cache_dir=cache_dir
)
all_data.append(("123Harr", parse_123harr_dataset(ds_123harr)))
except Exception as e:
print(f"✗ Lỗi tải 123Harr: {e}\n")
# Dataset 3: chillies/DPO_ielts_writing
try:
ds_chillies_2 = load_dataset(
"chillies/DPO_ielts_writing",
split="train",
cache_dir=cache_dir
)
all_data.append(("DPO", parse_dpo_dataset(ds_chillies_2)))
except Exception as e:
print(f"✗ Lỗi tải DPO: {e}\n")
# Dataset 4: hadeelbkh/tokenized-IELTS-writing-task-2-evaluation
try:
ds_hadeel = load_dataset(
"hadeelbkh/tokenized-IELTS-writing-task-2-evaluation-DialoGPT-medium",
split="train",
cache_dir=cache_dir
)
all_data.append(("hadeel", parse_hadeel_dataset(ds_hadeel)))
except Exception as e:
print(f"✗ Lỗi tải hadeel: {e}\n")
# Dataset 5: vietanh0802/ielts_writing_training_data_prepared
try:
ds_vietanh = load_dataset(
"vietanh0802/ielts_writing_training_data_prepared",
split="train",
cache_dir=cache_dir
)
all_data.append(("vietanh", parse_vietanh_dataset(ds_vietanh)))
except Exception as e:
print(f"✗ Lỗi tải vietanh: {e}\n")
# Tính tổng
print("\n" + "="*60)
print("--- TỔNG HỢP ---")
print("="*60)
total = 0
for name, data in all_data:
count = len(data)
total += count
print(f"Dataset ({name:15}): {count:5d} mẫu")
print("="*60)
print(f"Tổng cộng mẫu hợp lệ: {total}")
print("="*60)
final_dataset = []
for name, data in all_data:
final_dataset.extend(data)
if not final_dataset:
print("✗ Lỗi: Không có dữ liệu nào được chuẩn hóa. Vui lòng kiểm tra lại script.")
return
output_dir = "data"
output_path = os.path.join(output_dir, "dataset_for_scorer.json")
if not os.path.exists(output_dir):
os.makedirs(output_dir)
print(f"✓ Đã tạo thư mục {output_dir}")
with open(output_path, "w", encoding="utf-8") as f:
json.dump(final_dataset, f, ensure_ascii=False, indent=2)
print(f"✓ Đã ghi {len(final_dataset)} mẫu vào file '{output_path}'.")
print("\n✓ Hoàn tất! Bây giờ bạn có thể chạy 'src/train.py' trên Colab!")
if __name__ == "__main__":
main() |