---
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: 7088
---

# FineWiki Sampled Dataset (10,000,000 tokens)

This is a sampled subset of [HuggingFaceFW/finewiki](https://huggingface.co/datasets/HuggingFaceFW/finewiki) containing approximately **10,000,000 tokens**.

## Dataset Details

### Source
- **Original Dataset**: HuggingFaceFW/finewiki (English subset, train split)
- **Sampling Method**: Reservoir sampling (unbiased random sampling)
- **Target Token Count**: 10,000,000 tokens
- **Tokenizer**: GPT-2 (50,257 vocabulary)

### Sampling Statistics
- **Documents Sampled**: 7,088
- **Average Tokens/Doc**: 1411.0
- **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-10M")

# 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.