| --- |
| 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: 24496115451.0 |
| 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](https://colab.research.google.com/drive/1141Gi5gwB8KwnE-CQHAkTRpWSKuNZV1y?usp=sharing) I used to create this dataset. |
|
|
| --- |
|
|
| ## Dataset Composition & Provenance |
|
|
| This corpus was programmatically compiled, cleaned, and standardized from the following source datasets: |
|
|
| 1. **BookCorpus** (`rojagtap/bookcorpus`): High-quality narrative text from thousands of unpublished books, vital for capturing long-range context and literary style. |
| 2. **Wikipedia** (`wikimedia/wikipedia` - split `20231101.en`): Comprehensive, factual, and well-structured encyclopedic knowledge covering an expansive array of topics. |
| 3. **IMDb Movie Reviews** (`ajaykarthick/imdb-movie-reviews`): Rich conversational, highly descriptive, and emotionally expressive language patterns. |
| 4. **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 |
|
|
| ```python |
| 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) |
