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sKT-Ai-Labs
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Q-Augmented

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Tags:
dataset
question-pairs
data-augmentation
synthetic-data
llm-training
evaluation
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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
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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SKT DATA AUGMENTATION SUITE

Q-AUGMENTED

SUPERCHARGE YOUR LLM TRAINING

High-Quality Question Pairs • Synthetic Augmentation • Evaluation Ready

A premium collection of augmented question pairs designed to enhance model robustness. Perfect for SFT, RLHF, and rigorous benchmarking without answer bias.

Q-Augmented Dataset

🔄 DATA AUGMENTATION ❓ QUESTION PAIRS 🧠 REASONING BOOST ⚡ UNBIASED EVAL

Dataset Overview

Q-Augmented provides a diverse set of high-quality question pairs generated via advanced augmentation techniques. Unlike standard datasets, this collection focuses on input diversity to help models generalize better across different phrasing styles and complexities.

✨ Why Use Q-Augmented?

  • Robustness Training: Expose your model to varied question structures for the same underlying intent.
  • Evaluation Benchmark: Test if your model truly understands meaning or just memorizes patterns.
  • No Answer Leakage: Pure input pairs allow you to generate fresh answers with your own system prompts.
  • SFT & RLHF Ready: Ideal base for creating preference pairs or expanding instruction datasets.

AUGMENT YOUR INTELLIGENCE

Better inputs lead to better models. Start augmenting today.

🛠️ How to Use

1. 🐍 Python (Hugging Face Datasets) 

pip install datasets

from datasets import load_dataset

# Load Q-Augmented
dataset = load_dataset("sKT-Ai-Labs/Q-Augmented")

# Inspect structure
print(dataset['train'][0])

# Example: Batch processing for evaluation
for batch in dataset['train']:
    q1 = batch['question_1']
    q2 = batch['question_2']
    
    # Compare embeddings or test generation consistency
    # ...

2. 🎯 Recommended Use Cases

Use Case Description
Semantic Similarity Train embedding models to recognize equivalent questions.
Paraphrase Detection Fine-tune classifiers for duplicate question detection.
Generation Diversity Use as prompts to measure output variance in LLMs.
Curriculum Learning Start with simple pairs, progress to complex augmentations.

⚖️ License & Attribution

This dataset is released under the Apache-2.0 License.

  • Created by: SKT AI LABS
  • Source: Synthetically augmented from high-quality seed data.
  • Attribution: Please cite "sKT-Ai-Labs/Q-Augmented" when used in research.

Made with ❤️ by SKT AI LABS

Building the foundation for next-gen AI reasoning.
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