Datasets:
File size: 3,710 Bytes
63e7929 a9cd4f1 63e7929 03a8ab0 63e7929 03a8ab0 63e7929 a9cd4f1 03a8ab0 a9cd4f1 03a8ab0 a9cd4f1 03a8ab0 a9cd4f1 03a8ab0 a9cd4f1 03a8ab0 a9cd4f1 03a8ab0 a9cd4f1 22e2f3c a9cd4f1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 | ---
language:
- en
license: apache-2.0
tags:
- wikipedia
- finewiki
- sampled
dataset_info:
features:
- name: text
dtype: string
- name: id
dtype: string
- name: wikiname
dtype: string
- name: page_id
dtype: int64
- name: title
dtype: string
- name: url
dtype: string
- name: date_modified
dtype: string
- name: in_language
dtype: string
- name: wikidata_id
dtype: string
- name: bytes_html
dtype: int64
- name: wikitext
dtype: string
- name: version
dtype: int64
- name: infoboxes
dtype: string
- name: has_math
dtype: bool
splits:
- name: train
num_examples: 52721
---
# FineWiki Sampled Dataset (1,000,000,332 tokens)
This is a sampled subset of [HuggingFaceFW/finewiki](https://huggingface.co/datasets/HuggingFaceFW/finewiki) containing approximately **1,000,000,332 tokens**.
## Dataset Details
### Source
- **Original Dataset**: HuggingFaceFW/finewiki (English subset, train split)
- **Sampling Method**: Reservoir sampling (unbiased random sampling)
- **Target Token Count**: 1,000,000,332 tokens
- **Tokenizer**: GPT-2 (50,257 vocabulary)
### Sampling Statistics
- **Documents Sampled**: 52,721
- **Average Tokens/Doc**: 18971.0
- **Random Seed**: 42
### Sampling Method
This dataset was created using **reservoir sampling**, which ensures:
- ✅ Unbiased random sample from the full dataset
- ✅ Every document has equal probability of being selected
- ✅ No distribution bias (early/late documents equally represented)
- ✅ Streaming-based (no need to download full dataset)
The sampling algorithm:
1. Streams through HuggingFaceFW/finewiki without downloading
2. Uses GPT-2 tokenizer to count tokens per document
3. Maintains a reservoir of documents using standard reservoir sampling
4. Stops when target token count is reached
## Usage
```python
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("codelion/finewiki-1B")
# Access the training data
for example in dataset['train']:
print(example['text'])
print(example['title'])
print(example['url'])
```
## Dataset Structure
Each example contains all fields from the original FineWiki dataset:
- **text** (string): The Wikipedia article text (primary content)
- **id** (string): Unique identifier
- **wikiname** (string): Wikipedia source name
- **page_id** (int64): Wikipedia page ID
- **title** (string): Article title
- **url** (string): Source Wikipedia URL
- **date_modified** (string): Last modification date
- **in_language** (string): Language code (always 'en' for this subset)
- **wikidata_id** (string): Wikidata identifier
- **bytes_html** (int64): Size of HTML content
- **wikitext** (string): Original wikitext markup
- **version** (int64): Article version number
- **infoboxes** (string): Extracted infobox data
- **has_math** (bool): Whether article contains mathematical formulas
## Use Cases
This sampled dataset is ideal for:
- 🔬 Small-scale language model pretraining experiments
- 📊 Dataset composition studies
- ⚡ Quick prototyping and testing
- 💰 Low-cost training runs
## Citation
If you use this model/dataset, please cite:
```bibtex
@article{sharma2025billion,
title={The 1 Billion Token Challenge: Finding the Perfect Pre-training Mix},
author={Sharma, Asankhaya},
year={2025},
url={https://huggingface.co/blog/codelion/optimal-dataset-mixing/}
}
```
For more details, see the [blog post](https://huggingface.co/blog/codelion/optimal-dataset-mixing/).
## License
Apache 2.0 (same as original FineWiki dataset)
## Dataset Card Authors
CodeLion
## Dataset Card Contact
For questions or issues, please open an issue on the dataset repository.
|