Update README.md

README.md

@@ -20,4 +20,118 @@ configs:
   - split: validation
     path: data/validation-*
 license: apache-2.0
----
+---
+Absolutely, here’s the full dataset card as **plain Markdown** (just copy and paste as is).  
+Let me know if you want to tweak anything for your repo style.
+
+---
+
+```markdown
+# Q Code Pretraining Corpus
+
+This dataset provides a high-quality corpus of Q programming language code and documentation, curated for pretraining large language models and code models. It is designed to maximize coverage of Q syntax, idioms, and real-world usage for robust domain-adaptive pretraining.
+
+## 📊 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:
+```python
+{
+    "text": str   # Raw Q code or documentation chunk
+}
+```
+
+## 🧑‍💻 Usage
+
+### Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+# Load the full Q pretraining dataset
+dataset = load_dataset("bhogan/q-pretraining-corpus")
+
+# Access splits
+train_data = dataset["train"]
+val_data = dataset["validation"]
+```
+
+### Example: Previewing Data
+
+```python
+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:
+
+```bibtex
+@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]