README.md

---
license: apache-2.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: text
    dtype: string
  - name: id
    dtype: string
  - name: dump
    dtype: string
  - name: url
    dtype: string
  - name: file_path
    dtype: string
  - name: language
    dtype: string
  - name: language_score
    dtype: float64
  - name: token_count
    dtype: int64
  - name: score
    dtype: float64
  - name: int_score
    dtype: int64
  - name: raw_text
    dtype: string
  - name: document_id
    dtype: string
  - name: overlap_score
    dtype: float64
  splits:
  - name: train
    num_bytes: 1049455
    num_examples: 100
  download_size: 625799
  dataset_size: 1049455
---

# FineWeb-Edu GPT-2 Tokenized Dataset

**Repository:** `LaughTaleAI/fineweb-edu-gpt2-tokenized`

This dataset contains a **tokenized version of the FineWeb-Edu dataset** using the **GPT-2 tokenizer** (`tiktoken`).  
The dataset is optimized for **training GPT-style causal language models** and stored as **binary token shards** for maximum training throughput.

---

# Overview

This dataset converts the original **FineWeb-Edu text corpus** into a **continuous stream of GPT-2 tokens** and stores them in binary shards.  

The format is designed for:

- fast training
- minimal preprocessing overhead
- efficient dataloading
- compatibility with GPT-style architectures

Each file contains a **contiguous token stream** that can be randomly sampled during training.

---

# Dataset Format

Each file is a **binary `.bin` file** containing tokens encoded as:

```

dtype = uint16

```

Each token corresponds to a **GPT-2 vocabulary token id**.

Example layout of a shard:

```

train_00000.bin
train_00001.bin
train_00002.bin
...

```

Each shard contains approximately:

```

100M tokens per file

```

(Actual size may vary slightly depending on the final shard.)

Binary size per shard:

```

~200MB per file

```

---

# Tokenization Details

Tokenization was performed using:

```

Tokenizer: GPT-2 BPE
Library: tiktoken
Vocabulary size: 50,257

```

Special tokens:

```

<|endoftext|> (50256)

```

An **EOS token is appended after every document** to preserve document boundaries.

Example token sequence:

```

[doc1 tokens] <EOS> [doc2 tokens] <EOS> [doc3 tokens]

```

---

# Preprocessing Pipeline

The preprocessing pipeline performs:

1. Load FineWeb-Edu parquet shards
2. Tokenize text using GPT-2 tokenizer
3. Append EOS token after each document
4. Concatenate tokens into a continuous stream
5. Write tokens into binary shards

The resulting dataset is **fully deterministic and reproducible**.

---

# Training Usage

This dataset is designed for **GPT-style causal language modeling**.

Typical training workflow:

```

1. Load .bin shard using numpy.memmap
2. Randomly sample token offsets
3. Extract fixed length sequences
4. Train autoregressive model

````

Example:

```python
import numpy as np

data = np.memmap("train_00000.bin", dtype=np.uint16, mode="r")

seq_len = 512
start = np.random.randint(0, len(data) - seq_len - 1)

x = data[start:start+seq_len]
y = data[start+1:start+seq_len+1]
````

This avoids padding and enables extremely fast dataloading.

---

# Advantages of Binary Token Datasets

Compared to text datasets:

| Feature             | Text Dataset | Token Dataset  |
| ------------------- | ------------ | -------------- |
| Tokenization cost   | high         | none           |
| Training throughput | medium       | very high      |
| Disk size           | larger       | smaller        |
| Loading speed       | slower       | extremely fast |

Binary token datasets are widely used in large-scale LLM training pipelines.

---

# Dataset Source

Original dataset:

```
karpathy/fineweb-edu-100b-shuffle
```

Source repository:

[https://huggingface.co/datasets/karpathy/fineweb-edu-100b-shuffle](https://huggingface.co/datasets/karpathy/fineweb-edu-100b-shuffle)

The dataset contains **educational web text filtered for high quality content**.

---

# Intended Use

This dataset is suitable for:

* GPT-style language model pretraining
* research experiments
* tokenizer experiments
* training small to medium sized LLMs

---

# Example Training Setup

Typical configuration used with this dataset:

```
sequence length: 512
batch size: 256
optimizer: AdamW
learning rate: 3e-4
```

The dataset can support **millions of training sequences** through random sampling.

---

# License

This dataset inherits the license of the original **FineWeb-Edu dataset**.

Please refer to the original dataset repository for licensing details.

---

# Citation

If you use this dataset, please cite the original FineWeb dataset.

```
@dataset{fineweb,
  title = {FineWeb Dataset},
  year = {2024},
  publisher = {HuggingFace}
}
```

---

# Acknowledgements

Thanks to the creators of:

* FineWeb dataset
* Hugging Face Datasets
* tiktoken tokenizer

````

---

# ⭐ Optional (Recommended)

You may also add a small metadata file:

`meta.json`

```json
{
  "tokenizer": "gpt2",
  "vocab_size": 50257,
  "dtype": "uint16",
  "tokens_per_shard": 100000000,
  "format": "binary_token_stream"
}
````