ADD full dataset card

README.md

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 ---
 license: cc-by-4.0
----
+language:
+- en
+size_categories:
+- 100B<n<1T
+pretty_name: DCLM Baseline 500B Tokens (Decontaminated)
+tags:
+- language-modeling
+- pretraining
+- memorization-research
+- hubble
+---
+
+# DCLM Baseline 500B Tokens (Decontaminated)
+
+## Dataset Description
+
+This dataset is a **decontaminated subset** of the [DCLM-Baseline](https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0) corpus, specifically prepared for the **Hubble** memorization research project. The dataset has been carefully processed to remove overlap with memorization evaluation data and subsampled around **500 billion tokens** of English text.
+
+This corpus serves as the foundational training data for all Hubble models, providing a clean baseline for studying memorization phenomena in large language models while attempting to remove confounding effects from contamination.
+
+### Dataset Summary
+
+- **Total Size**: ~500 billion tokens
+- **Language**: English
+- **Source**: Decontaminated DCLM-Baseline corpus
+- **Purpose**: Training language models for memorization research
+- **License**: CC-BY-4.0 (inherited from DCLM Baseline)
+
+## Data Revisions
+
+We provide multiple revisions of the training corpus corresponding to different Hubble models:
+
+| Revision | Description | Effective Token Count | Models Trained |
+|-----------------|-------------|-------------|----------|
+| `standard` | Full 500B token corpus | 500B | `hubble-{1/8}b-{100/500}b_toks-*-standard-*` |
+| `perturbed-500b` | Same as `standard` with perturbation data inserted across the 500B tokens used in training | 500B | `hubble-{1/8}b-500b_toks-perturbed-*` |
+| `perturbed-100b` | Same as `standard` with perturbation data inserted across the first 100B tokens used in training | 100B | `hubble-{1/8}b-100b_toks-perturbed-*` and  `hubble-1b-100b_toks-*_depth-perturbed-*` |
+| `perturbed-100b-paraphrased` | Same as `perturbed-100b` but with the paraphrased variants of MMLU and YAGO biographies | 100B |  `hubble-{1/8}b-100b_toks-paraphrased-perturbed-*`  |
+
+We do not release the corpora for the Timing and Interference experiments but these can be reproduced from the provided `standard` revision and tokenized perturbation data.
+
+## Dataset Structure
+
+The dataset repository contains the following structure:
+
+```
+dclm-baseline-500b_toks/
+├── tokenized/                                # (only in main) Tokenized perturbation data
+├── tokenized_paraphrase/                     # (only in main) Tokenized perturbation data with paraphrased YAGO and MMLU
+├── *-bin.md5sum.txt                          # MD5 checksums for tokenized corpus (bin file)
+├── standard_text_document.bin.zstd.part_**   # Shards of the compressed tokenized corpus (~22 GB each)
+├── standard_text_document.idx                # Index file for tokenized corpus (8.25 GB)
+├── *_perturbation_info.json                  # (only in perturbed revisions) Perturbation metadata (260 MB)
+├── *_perturbation_viz_docs.jsonl             # (only in perturbed revisions) Visualization documents (9.29 MB)
+├── *_test_indexmap_*_doc_idx.npy             # Test index mapping - doc indices (1.65 MB)
+├── *_test_indexmap_*_sample_idx.npy          # Test index mapping - sample indices (2.14 MB)
+├── *_test_indexmap_*_shuffle_idx.npy         # Test index mapping - shuffle indices (1.07 MB)
+├── *_train_indexmap_*_doc_idx.npy            # Train index mapping - doc indices (1.65 MB)
+├── *_train_indexmap_*_sample_idx.npy         # Train index mapping - sample indices (2.14 MB)
+├── *_train_indexmap_*_shuffle_idx.npy        # Train index mapping - shuffle indices (1.07 MB)
+├── *_valid_indexmap_*_doc_idx.npy            # Validation index mapping - doc indices (1.65 MB)
+├── *_valid_indexmap_*_sample_idx.npy         # Validation index mapping - sample indices (2.14 MB)
+├── *_valid_indexmap_*_shuffle_idx.npy        # Validation index mapping - shuffle indices (1.07 MB)
+```
+
+### File Types
+
+- **`.bin.zstd.part_*`**: Compressed data archives split into multiple parts. These need to be concatenated and uncompressed to obtain the tokenized dataset (`*.bin`) (~1TB uncompressed)
+- **`.idx`**: Index files recording the document boundaries in the tokenized corpus
+- **`perturbation_info.json`**: Metadata to identify the insertion position of the perturbation data
+- **`perturbation_viz_docs.jsonl`**: Sample of training sequences with inserted perturbation data
+- **`*_{train|valid|test}_indexmap_{num_samples}ns_{seq_length}sl_{seed}s_packedpi_ac_{doc|sample|shuffle}_idx.npy`**: NumPy arrays containing doc/sample/shuffle index mappings for a training run using `num_samples` training sequences, `seq_length` tokens per sequence and `seed` as the random seed for shuffling. Useful for reproducing the exact training order of sequences. 
+- **`.md5sum.txt`**: Checksum files for data integrity verification
+- **`tokenized/`**: Directory containing tokenized versions of the perturbation datasets
+- **`tokenized_paraphrase/`**: Directory containing tokenized paraphrase variations of perturbation datasets
+
+### Access Methods
+
+Refer to our [README](https://github.com/allegro-lab/hubble?tab=readme-ov-file#training-corpora) for instructions on downloading and preparing the corpus.
+
+## Dataset Creation
+
+### Source Data
+
+The dataset is derived from [DCLM-Baseline](https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0), which consists of:
+- **CommonCrawl** web scrapes
+- **Language identification** to retain English content
+- **Refined filtering** for quality and safety
+- **Extensive deduplication** to remove near-duplicates
+
+### Data Processing
+
+1. **Subsampling**: We use a subset of DCLM to retain around 500B tokens. The source files used are listed [here](https://github.com/allegro-lab/hubble/blob/main/scripts/dclm_files.txt). Note that we divided `global-shard_01_of_10` into `global-shard_01.0_of_10` and `global-shard_01.0_of_10` for ease of processing. 
+
+2. **Decontamination**: Systematic removal of text overlapping with [Hubble evaluation benchmarks](https://huggingface.co/collections/allegrolab/hubble-datasets) using [infini-gram](https://infini-gram.io/) as described in [this doc](https://github.com/allegro-lab/hubble/blob/main/scripts/decontamination/README.md). Candidate documents for decontamination include:
+   - Test sets (PopQA, MMLU, HellaSwag, PIQA, WinoGrande, Ellie, MUNCH)
+   - Passages (Wikipedia, Gutenberg)
+   - Paraphrases (MRPC, PAWS)
+   - Biographies (Synthetic YAGO, ECtHR)
+   - Chat logs (Personachat)
+
+## Uses
+
+### Direct Use
+
+This dataset is intended for pretraining language models for memorization research. The clean training data provides the foundation for the Hubble model suite. The dataset is released to support further research on memorization, mechanistic interpretability, study of training dynamics, and reproducibility.
+
+### Out-of-Scope Use
+
+This dataset should **NOT** be used for:
+- **Production language models** (research-focused, may contain biases)
+- **Commercial applications** without understanding license implications
+- **Safety-critical systems** (inherits web data biases and risks)
+
+## Bias, Risks, and Limitations
+
+### Known Biases
+
+**Inherited from Web Data:**
+- **Geographic bias**: Overrepresentation of content from certain regions
+- **Temporal bias**: Reflects internet content from specific time periods
+- **Platform bias**: Overrepresentation of certain websites and platforms
+
+**Language and Cultural Bias:**
+- **English-centric**: Only English content retained
+- **Socioeconomic bias**: Overrepresentation of content creators with internet access
+
+### Risks
+
+Certain revisions of the dataset explicitly contain private information and copyrighted material. Thus, we recommend not using this dataset for commercial purposes or general use language models.
+
+## Additional Information
+
+### Dataset Curators
+
+- **Hubble Research Team**: Johnny Tian-Zheng Wei*, Ameya Godbole*, Mohammad Aflah Khan*, Ryan Wang, Xiaoyuan Zhu, James Flemings, Nitya Kashyap
+- **Institutions**: University of Southern California, Max Planck Institute for Software Systems
+- **Based on**: DCLM corpus by ML Foundations
+
+### Licensing Information
+
+This dataset is distributed under the **CC-BY-4.0** (Creative Commons Attribution 4.0 International) license, inherited from the original DCLM-Baseline corpus. See [license details](https://creativecommons.org/licenses/by/4.0/) for full terms.
+
+### Citation Information
+
+If you use this dataset in your research, please cite both the Hubble project and the original DCLM work:
+
+```bibtex
+@misc{wei2025hubblemodelsuiteadvance,
+      title={Hubble: a Model Suite to Advance the Study of LLM Memorization}, 
+      author={Johnny Tian-Zheng Wei and Ameya Godbole and Mohammad Aflah Khan and Ryan Wang and Xiaoyuan Zhu and James Flemings and Nitya Kashyap and Krishna P. Gummadi and Willie Neiswanger and Robin Jia},
+      year={2025},
+      eprint={2510.19811},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2510.19811}, 
+}
+
+@misc{li2025datacomplmsearchgenerationtraining,
+      title={DataComp-LM: In search of the next generation of training sets for language models}, 
+      author={Jeffrey Li and Alex Fang and Georgios Smyrnis and Maor Ivgi and Matt Jordan and Samir Gadre and Hritik Bansal and Etash Guha and Sedrick Keh and Kushal Arora and Saurabh Garg and Rui Xin and Niklas Muennighoff and Reinhard Heckel and Jean Mercat and Mayee Chen and Suchin Gururangan and Mitchell Wortsman and Alon Albalak and Yonatan Bitton and Marianna Nezhurina and Amro Abbas and Cheng-Yu Hsieh and Dhruba Ghosh and Josh Gardner and Maciej Kilian and Hanlin Zhang and Rulin Shao and Sarah Pratt and Sunny Sanyal and Gabriel Ilharco and Giannis Daras and Kalyani Marathe and Aaron Gokaslan and Jieyu Zhang and Khyathi Chandu and Thao Nguyen and Igor Vasiljevic and Sham Kakade and Shuran Song and Sujay Sanghavi and Fartash Faghri and Sewoong Oh and Luke Zettlemoyer and Kyle Lo and Alaaeldin El-Nouby and Hadi Pouransari and Alexander Toshev and Stephanie Wang and Dirk Groeneveld and Luca Soldaini and Pang Wei Koh and Jenia Jitsev and Thomas Kollar and Alexandros G. Dimakis and Yair Carmon and Achal Dave and Ludwig Schmidt and Vaishaal Shankar},
+      year={2025},
+      eprint={2406.11794},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2406.11794}, 
+}
+```
+
+### Contact
+
+For questions about this dataset:
+- **GitHub Issues**: [Hubble Repository](https://github.com/allegro-lab/hubble)
+- **Project Website**: [https://allegro-lab.github.io/hubble/](https://allegro-lab.github.io/hubble/)
+- **Research Team**: Contact through institutional affiliations
+
+### Related Resources
+
+- **Hubble Models**: [allegrolab Collections](https://huggingface.co/allegrolab/collections)
+- **Perturbation Datasets**: [Hubble Datasets Collection](https://huggingface.co/collections/allegrolab/hubble-datasets)
+- **Original DCLM**: [mlfoundations/dclm-baseline-1.0](https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0)
+- **Project Documentation**: [Hubble README](https://github.com/allegro-lab/hubble)