Add dataset card

README.md

@@ -0,0 +1,70 @@
+---
+dataset_info:
+  features:
+    - name: text
+      dtype: string
+    - name: id
+      dtype: string
+    - name: url
+      dtype: string
+    - name: language
+      dtype: string
+    - name: language_score
+      dtype: float64
+    - name: fasttext_score
+      dtype: float64
+    - name: dataset
+      dtype: string
+  splits:
+    - name: train
+      num_examples: 89269902
+license: odc-by
+language:
+  - en
+size_categories:
+  - 10M<n<100M
+tags:
+  - pretraining
+  - smol-data
+pretty_name: DCLM 100BT
+---
+
+# DCLM 100BT
+
+A ~100 billion token English subset of [DCLM-Baseline 1.0](https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0-parquet), created for efficient pretraining experiments.
+
+Part of the [Smol-Data](https://huggingface.co/collections/HuggingFaceFW/smol-data) collection — tried and tested mixes for strong pretraining.
+
+## Dataset Description
+
+This dataset was created by randomly sampling from the full DCLM-Baseline 1.0 dataset (~3.5T tokens) to produce a ~100B token subset. Sampling was performed with a fixed seed (42) and a slight 1.05× oversampling factor to account for variance.
+
+A pre-shuffled version is available at [HuggingFaceFW/dclm_100BT-shuffled](https://huggingface.co/datasets/HuggingFaceFW/dclm_100BT-shuffled).
+
+## How It Was Created
+
+The dataset was generated using [datatrove](https://github.com/huggingface/datatrove) with the [smol_data.py](https://github.com/huggingface/datatrove/blob/main/examples/smol_data.py) script. The pipeline reads from the source dataset in streaming mode, applies a `SamplerFilter` to downsample, and writes the result back to the Hugging Face Hub.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("HuggingFaceFW/dclm_100BT", split="train", streaming=True)
+for sample in ds:
+    print(sample["text"][:200])
+    break
+```
+
+## Citation
+
+```bibtex
+@misc{niklaus2025dclm100bt,
+      title={DCLM 100BT},
+      author={Joel Niklaus and Hynek Kydl{\'\i}{\v{c}}ek},
+      year={2026},
+      publisher={Hugging Face},
+      journal={Hugging Face repository},
+      howpublished={\url{https://huggingface.co/datasets/HuggingFaceFW/dclm_100BT}}
+}
+```