| # Qwen3-Inspired Pre-training Dataset | |
| ## Overview | |
| This dataset is a curated mixture of high-quality text data designed for large language model pre-training, inspired by the Qwen3 methodology. The dataset includes both training and validation splits. | |
| ## Dataset Statistics | |
| **Total Size:** 10.42 billion tokens | |
| - **Training Split:** 9.89 billion tokens (94.9%) | |
| - **Validation Split:** 0.53 billion tokens (5.1%) | |
| ### Data Sources (Combined) | |
| - **dclm_baseline**: 5.06B tokens (48.56%) - 4,088,916 documents | |
| - **the_stack**: 1.65B tokens (15.79%) - 383,490 documents | |
| - **common_corpus**: 1.5B tokens (14.36%) - 381,841 documents | |
| - **mini_pile**: 1.43B tokens (13.73%) - 999,858 documents | |
| - **math_pile**: 0.79B tokens (7.55%) - 72,936 documents | |
| ### Training Split Statistics | |
| - **dclm_baseline**: 4.81B tokens (48.61%) - 3,884,088 documents | |
| - **the_stack**: 1.58B tokens (15.97%) - 363,502 documents | |
| - **common_corpus**: 1.42B tokens (14.37%) - 361,913 documents | |
| - **mini_pile**: 1.36B tokens (13.78%) - 949,859 documents | |
| - **math_pile**: 0.72B tokens (7.26%) - 68,947 documents | |
| ### Validation Split Statistics | |
| - **dclm_baseline**: 0.25B tokens (47.69%) - 204,828 documents | |
| - **common_corpus**: 0.08B tokens (14.22%) - 19,928 documents | |
| - **math_pile**: 0.07B tokens (12.89%) - 3,989 documents | |
| - **mini_pile**: 0.07B tokens (12.86%) - 49,999 documents | |
| - **the_stack**: 0.07B tokens (12.33%) - 19,988 documents | |
| ## Data Processing Pipeline | |
| 1. **Data Collection**: Sourced from multiple high-quality datasets | |
| 2. **Standardization**: All data transformed to consistent format with `text`, `info`, and `source_data` fields | |
| 3. **Train/Validation Split**: Created 95%/5% splits within each source dataset | |
| 4. **Exact Deduplication**: Removed identical documents within each split | |
| 5. **Near Deduplication**: Used MinHashLSH with Jaccard similarity threshold of 0.85 | |
| 6. **Quality Filtering**: Applied content-based filtering during processing | |
| 7. **Shuffling**: Applied shuffling within each large shard for better data distribution | |
| ## Data Format | |
| Each example contains: | |
| - `text`: The main text content | |
| - `info`: Metadata from the original dataset (as string) | |
| - `source_data`: Source dataset identifier | |
| ## Splits | |
| The dataset contains two splits: | |
| - `train`: Training data (95% of each source dataset) | |
| - `validation`: Validation data (5% of each source dataset) | |
| ## Tokenization | |
| Token counts were computed using the Llama3 tokenizer (`meta-llama/Meta-Llama-3-8B`). | |
| ## Usage | |
| ```python | |
| from datasets import load_dataset | |
| # Load the entire dataset | |
| dataset = load_dataset("bluelightai-dev/qwen_clt_pretrain_data") | |
| # Load specific splits | |
| train_dataset = load_dataset("bluelightai-dev/qwen_clt_pretrain_data", split="train") | |
| val_dataset = load_dataset("bluelightai-dev/qwen_clt_pretrain_data", split="validation") | |
| ``` | |
| ## Dataset Sources | |
| The dataset combines data from the following sources: | |
| - **DCLM Baseline**: High-quality web text from DataComp-LM | |
| - **Common Corpus**: Multilingual web text corpus | |
| - **The Stack**: Deduplicated source code | |
| - **Mini Pile**: Academic and reference texts | |
| - **Math Pile**: Mathematical content and reasoning datasets | |
| ## License | |
| Please refer to the individual source dataset licenses. This mixture is provided for research purposes. | |
| ## Citation | |
| If you use this dataset, please cite the original source datasets and this work. | |