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ilyyeees/leetspeak-to-english

1337speak-to-English V3 Dataset Dataset Description This is a large-scale synthetic dataset designed to train models (like ByT5) to decode "Leetspeak", internet slang, and corrupted text back into clean, standard English. It contains ~720k pairs of text, generated through a sophisticated hybrid pipeline that combines rule-based corruption with LLM-generated slang and semantic alterations. Goal To enable robust text normalization models that can… See the full description on the dataset page: https://huggingface.co/datasets/ilyyeees/leetspeak-to-english.

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1337speak-to-English V3 Dataset

<div align="center"> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformerslogoname.png" width="300"/> </div>

Dataset Description

This is a large-scale synthetic dataset designed to train models (like ByT5) to decode "Leetspeak", internet slang, and corrupted text back into clean, standard English.

It contains ~720k pairs of text, generated through a sophisticated hybrid pipeline that combines rule-based corruption with LLM-generated slang and semantic alterations.

Goal

To enable robust text normalization models that can handle:

  • —Visual substitutions: 3 for e, $ for s, |_ for L
  • —Phonetic slang: wut → what, kewl → cool
  • —Grammar/Shortening: idk → I don't know, ur → your/you're
  • —Edge cases: Preserving numbers, dates, and special characters correctly.

Data Generation Pipeline

The dataset was constructed using a "Reverse Pipeline" approach (Clean → Corrupt), utilizing three primary sources:

  1. 1.WikiText: For formal, grammatically complex sentence structures.
  2. 2.ELI5 (Explain Like I'm 5): For conversational, question-answer style natural language.
  3. 3.LLM Synthetic: Fully synthetic sentences generated to include modern internet culture, gaming references (gg, noob), and Gen Z slang (no cap, bussin).

Corruption Strategy

Unlike simple find-and-replace scripts, this dataset uses a Hybrid Corruption Engine:

  1. 1.LLM Layer: First revises clean text to include slang, grammar lazy-ness, and internet-speak (e.g., changing "I am going to" to "imma").
  2. 2.Script Layer: Applies randomized, weighted visual substitutions (Leetspeak) with varying intensities (Light, Medium, Heavy, 1337).
  3. 3.Adversarial Filtering: (Optional subset) Samples were filtered to prioritize "hard" negatives that previous model versions failed to decode.

Dataset Structure

The dataset consists of a single JSONL-style split with the following columns:

ColumnTypeDescription
input_textstringThe corrupted leetspeak/slang text input.
target_textstringThe ground-truth clean English text.

Example Data

input_texttarget_text
h3ll0 w0rldHello world
idk wh4t 2 d0 tbhI don't know what to do to be honest
1 h4v3 2 c4t5I have 2 cats
n0 c4p th15 5l4p5No lie this is great

Usage

python
from datasets import load_dataset

dataset = load_dataset("ilyyeees/leetspeak-to-english-v3")

# Print first example
print(dataset['train'][0])
# {'input_text': '...', 'target_text': '...'}

License

MIT License. Free to use for research and commercial applications.