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--- |
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dataset_info: |
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features: |
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- name: text |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 4483331 |
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num_examples: 342 |
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- name: validation |
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num_bytes: 622617 |
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num_examples: 39 |
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download_size: 2534957 |
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dataset_size: 5105948 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: validation |
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path: data/validation-* |
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license: apache-2.0 |
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--- |
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# Q Code Pretraining Corpus |
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This dataset provides a corpus of Q programming language code and documentation, curated for pretraining large language models and code models. |
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## 📊 Dataset Overview |
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- **Total Data**: Over 1.6 million Q tokens, 5+ million characters |
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- **Documents**: 342 training chunks, 39 validation chunks |
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- **Source Types**: |
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- Open-source Q repositories (MIT/Apache 2.0 licenses) |
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- Official KDB+/Q documentation and tutorials |
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- Hand-curated code snippets and scripts |
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- **Format**: Cleaned, deduplicated, chunked for efficient pretraining |
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## 🎯 Key Features |
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- **Q-Only**: All data is pure Q language (no mixed Python or non-code noise) |
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- **Permissive Licensing**: All source code is MIT or Apache 2.0, suitable for both research and commercial use |
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- **Coverage**: Includes code from analytics, time-series, database queries, and utilities |
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- **Filtered & Scored**: LLM-assisted quality scoring plus manual review for top-tier data fidelity |
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- **Chunked & Ready**: Delivered as 4k-token chunks for immediate use with Hugging Face, TRL, or custom pipelines |
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## 🏗️ Dataset Structure |
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Each record is a text chunk, containing code or documentation in Q. |
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Splits: |
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- `train`: Main corpus for pretraining (342 chunks) |
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- `validation`: Holdout set for evaluation (39 chunks) |
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Sample record: |
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```python |
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{ |
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"text": str # Raw Q code or documentation chunk |
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} |
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``` |
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## 🧑💻 Usage |
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### Loading the Dataset |
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```python |
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from datasets import load_dataset |
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# Load the full Q pretraining dataset |
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dataset = load_dataset("morganstanley/q_pretrained_dataset") |
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# Access splits |
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train_data = dataset["train"] |
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val_data = dataset["validation"] |
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``` |
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### Example: Previewing Data |
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```python |
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sample = dataset["train"][0] |
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print(sample["text"]) |
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``` |
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### Training Usage |
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This dataset is designed for language model pretraining using next-token prediction or masked language modeling objectives. |
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Supports efficient training with Hugging Face Transformers, TRL, or custom frameworks. |
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## 🔤 About Q Programming Language |
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Q is a vector and array programming language developed by Kx Systems for high-performance analytics, finance, and time-series applications. |
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It features: |
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- Concise, functional, array-oriented syntax |
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- Powerful built-in operators for large-scale data manipulation |
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- Industry adoption in trading, banking, and real-time analytics |
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## 📁 Source Repositories |
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Major open-source Q repos included: |
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- DataIntellectTech/TorQ |
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- psaris/qtips |
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- psaris/funq |
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- KxSystems/ml |
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- finos/kdb |
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- LeslieGoldsmith/qprof |
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- jonathonmcmurray/reQ |
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- ...and more |
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All with permissive licenses (MIT or Apache 2.0). |
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## 📈 Data Preparation & Filtering |
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- **Automated Scoring**: Qwen-2.5-32B was used to score each file (0–10) for quality and relevance; only files scoring ≥4 were included. |
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- **Manual Review**: Additional cleaning to remove non-Q files or low-value content. |
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- **Deduplication**: Duplicate and boilerplate code removed. |
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## 📝 Citation |
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If you use this dataset in your research, please cite: |
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```bibtex |
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@dataset{q_pretraining_corpus_2024, |
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title={Q Code Pretraining Corpus}, |
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author={Brendan Rappazzo Hogan}, |
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year={2024}, |
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url={https://huggingface.co/datasets/bhogan/q-pretraining-corpus}, |
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note={Dataset for domain-adaptive pretraining of language models on the Q programming language} |
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} |
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``` |
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**Associated Paper:** [Link to paper will be added here] |
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