Datasets:

LakoreAI
/

bert-mlm-experiments-en

language:
  - en
license: openrail
size_categories:
  - 10M<n<100M
task_categories:
  - fill-mask
tags:
  - bert
  - mlm
  - masked-language-modeling
  - wikipedia
  - bookcorpus
dataset_info:
  features:
    - name: text
      dtype: string
  splits:
    - name: train
      num_bytes: 22046503784.16388
      num_examples: 72440303
    - name: validation
      num_bytes: 1224805681.247909
      num_examples: 4024461
    - name: test
      num_bytes: 1224805985.5882123
      num_examples: 4024462
  download_size: 15416904536
  dataset_size: 24496115451
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*

Unified English MLM Pre-training Corpus (80M Rows)

This dataset is a massive, diverse, multi-domain English text corpus explicitly engineered for pre-training and domain-adaptation of BERT-style models via Masked Language Modeling (MLM). It aggregates over 80 million rows of text, completely stripped of auxiliary metadata, labels, and identifiers to expose purely raw text strings.

Dataset Details

Repository ID: 8Opt/bert-mlm-experiments-en
Total Rows: 80,489,226
Format: Single-column (text) format optimized for fast, block-based tokenization pipelines.

Data Splits

Split	Number of Rows	Percentage
Train	72,440,303	~90%
Validation	4,024,461	~5%
Test	4,024,462	~5%

Note: This is the code I used to create this dataset.

Dataset Composition & Provenance

This corpus was programmatically compiled, cleaned, and standardized from the following source datasets:

BookCorpus (rojagtap/bookcorpus): High-quality narrative text from thousands of unpublished books, vital for capturing long-range context and literary style.
Wikipedia (wikimedia/wikipedia - split 20231101.en): Comprehensive, factual, and well-structured encyclopedic knowledge covering an expansive array of topics.
IMDb Movie Reviews (ajaykarthick/imdb-movie-reviews): Rich conversational, highly descriptive, and emotionally expressive language patterns.
STS (Semantic Textual Similarity) 2012-2015 (mteb/sts12-sts through sts15-sts): Highly nuanced paired sentence variations, flattened into individual rows to expose the model to standalone structured sentence syntaxes.

Processing Methodology

Schema Unification: All incoming dataset features (titles, bodies, labels, scores, URLs) were stripped out or merged.
Structural Flattening: For datasets containing sentence pairs (like STS) or multiple text fields (like Title/Body), the elements were completely flattened into standalone, individual rows under the text column to preserve line-by-line semantic structure.
Randomized Splitting: The final dataset dictionary was generated using a reproducible two-step deterministic shuffle and split (seed=42).

Intended Use & Quickstart

This dataset is primarily intended for Masked Language Modeling (MLM). Because of its massive validation split (~4M rows), it is highly recommended to subset the validation split during training check-ins to prevent evaluation bottlenecking.

How to Load

from datasets import load_dataset

# Load the full dataset dictionary (Train, Validation, Test)
dataset = load_dataset("8Opt/bert-mlm-experiments-en")

# Quick print overview
print(dataset)