Create README.md

README.md

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+---
+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_rows: 72440303
+    - name: validation
+      num_rows: 4024461
+    - name: test
+      num_rows: 4024462
+---
+
+# 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 **BookCorpus** (`rojagtap/bookcorpus`): High-quality narrative text from thousands of unpublished books, vital for capturing long-range context and literary style.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)