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metadata
dataset_info:
  features:
    - name: text
      dtype: string
  splits:
    - name: train
      num_bytes: 4483331
      num_examples: 342
    - name: validation
      num_bytes: 622617
      num_examples: 39
  download_size: 2534957
  dataset_size: 5105948
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
license: apache-2.0

Q Code Pretraining Corpus

This dataset provides a corpus of Q programming language code and documentation, curated for pretraining large language models and code models.

📊 Dataset Overview

  • Total Data: Over 1.6 million Q tokens, 5+ million characters
  • Documents: 342 training chunks, 39 validation chunks
  • Source Types:
    • Open-source Q repositories (MIT/Apache 2.0 licenses)
    • Official KDB+/Q documentation and tutorials
    • Hand-curated code snippets and scripts
  • Format: Cleaned, deduplicated, chunked for efficient pretraining

🎯 Key Features

  • Q-Only: All data is pure Q language (no mixed Python or non-code noise)
  • Permissive Licensing: All source code is MIT or Apache 2.0, suitable for both research and commercial use
  • Coverage: Includes code from analytics, time-series, database queries, and utilities
  • Filtered & Scored: LLM-assisted quality scoring plus manual review for top-tier data fidelity
  • Chunked & Ready: Delivered as 4k-token chunks for immediate use with Hugging Face, TRL, or custom pipelines

🏗️ Dataset Structure

Each record is a text chunk, containing code or documentation in Q.

Splits:

  • train: Main corpus for pretraining (342 chunks)
  • validation: Holdout set for evaluation (39 chunks)

Sample record:

{
    "text": str   # Raw Q code or documentation chunk
}

🧑‍💻 Usage

Loading the Dataset 

from datasets import load_dataset

# Load the full Q pretraining dataset
dataset = load_dataset("morganstanley/q_pretrained_dataset")

# Access splits
train_data = dataset["train"]
val_data = dataset["validation"]

Example: Previewing Data 

sample = dataset["train"][0]
print(sample["text"])

Training Usage

This dataset is designed for language model pretraining using next-token prediction or masked language modeling objectives.
Supports efficient training with Hugging Face Transformers, TRL, or custom frameworks.

🔤 About Q Programming Language

Q is a vector and array programming language developed by Kx Systems for high-performance analytics, finance, and time-series applications.

It features:

  • Concise, functional, array-oriented syntax
  • Powerful built-in operators for large-scale data manipulation
  • Industry adoption in trading, banking, and real-time analytics

📁 Source Repositories

Major open-source Q repos included:

  • DataIntellectTech/TorQ
  • psaris/qtips
  • psaris/funq
  • KxSystems/ml
  • finos/kdb
  • LeslieGoldsmith/qprof
  • jonathonmcmurray/reQ
  • ...and more

All with permissive licenses (MIT or Apache 2.0).

📈 Data Preparation & Filtering

  • Automated Scoring: Qwen-2.5-32B was used to score each file (0–10) for quality and relevance; only files scoring ≥4 were included.
  • Manual Review: Additional cleaning to remove non-Q files or low-value content.
  • Deduplication: Duplicate and boilerplate code removed.

📝 Citation

If you use this dataset in your research, please cite:

@dataset{q_pretraining_corpus_2024,
    title={Q Code Pretraining Corpus},
    author={Brendan Rappazzo Hogan},
    year={2024},
    url={https://huggingface.co/datasets/bhogan/q-pretraining-corpus},
    note={Dataset for domain-adaptive pretraining of language models on the Q programming language}
}

Associated Paper: [Link to paper will be added here]