Datasets:

JibayAi
/

PRCC-Bench

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      JSON parse error: Invalid value. in row 0
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 324, in _generate_tables
                  df = pandas_read_json(f)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                         ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 815, in read_json
                  return json_reader.read()
                         ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1014, in read
                  obj = self._get_object_parser(self.data)
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1040, in _get_object_parser
                  obj = FrameParser(json, **kwargs).parse()
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1176, in parse
                  self._parse()
                  ~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1392, in _parse
                  ujson_loads(json, precise_float=self.precise_float), dtype=None
                  ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
              ValueError: Expected object or value
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4379, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2661, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2839, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 327, in _generate_tables
                  raise e
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
                  pa_table = paj.read_json(
                      io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
                  )
                File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0

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PRCC-BENCH

Persian Reasoning, Comprehension, Conversation Benchmark

Developed by Jibay Ai

Persian Benchmark for Evaluating Understanding, Reasoning, Question Answering, Information Extraction, Instruction Following and Analysis.

About

PRCC-BENCH is a large-scale Persian benchmark developed by Jibay Ai to evaluate real-world capabilities of language models.

Unlike traditional benchmarks that mainly focus on memorization or factual recall, PRCC-BENCH evaluates whether a model can truly:

Understand
Reason
Answer questions
Extract information
Follow instructions
Analyze complex situations

The benchmark aims to provide a practical and realistic measurement of Persian language intelligence.

Benchmark Information

Property	Value
Name	PRCC-BENCH
Organization	Jibay Ai
Language	Persian
Version	1.0.0
Total Questions	500
Scoring	+5 Correct
Incorrect	0
Blank	0
Evaluation	Exact + Semantic

Evaluation Categories

1. Comprehension

Measures whether a model truly understands language.

Capabilities:

Context understanding
Event tracking
Entity understanding
Reference resolution
Passage comprehension
Multi-step understanding

Examples:

Identify actors
Understand actions
Detect context relationships
Interpret meaning

2. Reasoning

Measures logical thinking ability.

Capabilities:

Deduction
Cause and effect
Multi-step reasoning
Temporal reasoning
Consistency

Examples:

Infer hidden conclusions
Connect multiple facts
Solve contextual tasks

3. Question Answering

Measures:

Accuracy
Completeness
Precision
Context awareness

Task types:

Direct QA
Contextual QA
Implicit answers
Multi-part questions

4. Information Extraction

Measures:

Entity extraction
Number extraction
Fact retrieval
Relationship understanding
Context filtering

Examples:

Extract important details
Ignore irrelevant information

5. Instruction Following

Measures:

Obeying requirements
Exact formatting
Rule execution
Constraint handling

Examples:

Structured output
Following conditions
Response discipline

6. Analysis

Measures:

Deep understanding
Comparison
Problem decomposition
Conclusion generation

Examples:

Analytical reasoning
Evidence comparison

Scoring System

Result	Score
Correct	+5
Incorrect	0
Blank	0
Near Match	0

Rule:

Only fully correct responses receive points.

Judging Modes

Exact Match

Normalization includes:

Spaces
Persian digits
Punctuation

Comparison occurs after normalization.

Semantic Match

Answers are accepted only if meaning remains fully equivalent.

List Exact

All required information must exist.

Order Exact

Required order must remain preserved.

Intended Use Cases

PRCC-BENCH may be used for:

AI Evaluation
LLM Benchmarking
Academic Research
Product Testing
Regression Testing
Persian Model Comparison
Quality Assurance

Philosophy

Understanding > Memorization

Reasoning > Pattern Matching

Execution > Generation

License

This project is licensed under:

CC BY-ND

Creative Commons Attribution — No Derivatives

Permission granted:

✅ Use
✅ Evaluate
✅ Publish Results
✅ Compare Models
✅ Share Benchmark

Conditions:

Attribution must remain visible.
Original benchmark identity must remain unchanged.

Additional Project Policy:

Modifying PRCC-BENCH is not authorized for official distribution.
Publishing altered versions as official releases is not permitted.
Publishing under another benchmark identity is not recognized as an official release.
Official upgrades and official benchmark publications are reserved exclusively for Jibay Ai.

Citation

If you use PRCC-BENCH:

PRCC-BENCH

Developed by Jibay Ai

فارسی

معرفی

PRCC-BENCH یک بنچمارک زبان فارسی توسعه‌یافته توسط Jibay Ai است.

هدف این پروژه ارزیابی توانایی واقعی مدل‌های هوش مصنوعی در انجام وظایف زبانی و تحلیلی است.

این بنچمارک بر موارد زیر تمرکز دارد:

درک
استدلال
سوال و جواب
استخراج اطلاعات
پیروی از دستورات
تحلیل

مشخصات

ویژگی	مقدار
نام	PRCC-BENCH
توسعه‌دهنده	Jibay Ai
زبان	فارسی
نسخه	1.0.0
تعداد سوال	500
امتیاز پاسخ صحیح	5
پاسخ غلط	0
ارزیابی	دقیق + معنایی

بخش‌های ارزیابی

درک

سنجش توانایی مدل در:

فهم متن
دنبال کردن رویداد
تشخیص ارجاع
درک رابطه‌ها

استدلال

بررسی:

منطق
علت و معلول
تحلیل چندمرحله‌ای
نتیجه‌گیری

سوال و جواب

ارزیابی:

دقت
کامل بودن
وابستگی به متن

استخراج اطلاعات

بررسی:

استخراج موجودیت
استخراج عدد
تشخیص اطلاعات مهم

پیروی از دستورات

بررسی:

رعایت محدودیت
اجرای دقیق
حفظ ساختار

تحلیل

بررسی:

تجزیه مسئله
مقایسه
استنتاج

سیستم امتیازدهی

پاسخ صحیح: ۵

سایر حالت‌ها: ۰

فقط پاسخ کاملاً صحیح امتیاز دریافت می‌کند.

مجوز

این پروژه تحت مجوز:

CC BY-ND

منتشر شده است.

همه افراد و سازمان‌ها مجاز هستند:

✅ استفاده کنند
✅ ارزیابی انجام دهند
✅ نتایج منتشر کنند
✅ مقایسه انجام دهند

اما:

❌ هیچ شخص یا سازمانی اجازه تغییر یا ارتقاء نسخه رسمی را ندارد.

❌ انتشار نسخه تغییر یافته به عنوان نسخه رسمی مجاز نیست.

❌ انتشار با نام دیگر به عنوان نسخه رسمی مجاز نیست.

✅ حق توسعه نسخه رسمی، ارتقاء و انتشارات رسمی فقط متعلق به Jibay Ai است.

PRCC-BENCH

by jibay.ir

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