README.md

metadata

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:

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

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"
}