Update README.md

README.md

@@ -17,4 +17,94 @@ tags:
 pretty_name: bookcorpus
 size_categories:
 - 10M<n<100M
----
+---
+
+# BookCorpus — Cleaned for Pre-training LLMs
+
+A cleaned, deduplicated, document-segmented version of
+[`SamuelYang/bookcorpus`](https://huggingface.co/datasets/SamuelYang/bookcorpus)
+
+## TL;DR
+
+| Property | Value |
+|---|---|
+| Rows (sentences) | **33,649,142** |
+| Documents (books) | **4,086** |
+| Format | CSV, 3 columns: `doc_id`, `sent_id`, `text` |
+| Language | English (lowercased) |
+| Source | `SamuelYang/bookcorpus` (74,004,228 raw rows) |
+
+## Schema
+
+| Column | Type | Description |
+|---|---|---|
+| `doc_id` | int | Inferred document/book identifier. Sentences sharing the same `doc_id` come from the same book. |
+| `sent_id` | int | Sentence position within its document (0-indexed). Preserves original order. |
+| `text` | string | Cleaned sentence text (lowercased, normalized). |
+
+## How to use it
+
+### Quick load
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("kd13/bookcorpus-clean", split="train")
+print(ds[0])
+# {'doc_id': 0, 'sent_id': 0, 'text': 'i wish i had a better answer ...'}
+```
+
+## Cleaning pipeline
+
+Applied in this order to the source dataset:
+
+1. **Unicode + whitespace normalization** — NFKC normalization, collapse
+   consecutive whitespace, strip.
+2. **Document segmentation** — since the source is a flat stream of sentences
+   without book IDs, document boundaries are inferred from telltale markers
+   at the start of books:
+   - ISBN lines (e.g. `isbn : 1492913731`)
+   - Copyright declarations (`copyright 2013 ...`)
+   - `all rights reserved`
+   - `chapter 1`
+3. **Line-level filters** — sentences are dropped if they:
+   - have fewer than **20** or more than **1000** characters
+   - match boilerplate patterns (copyright, ISBN, "all rights reserved")
+   - have an alphabetic-character ratio below **0.6**
+   - have a digit ratio above **0.3**
+   - contain no alphabetic characters
+4. **Language filter** — cheap English stop-word ratio check (≥ 5% of tokens
+   must be in a small English stop-word set; short lines pass through).
+5. **Within-document exact dedup** — SHA-1 hashing drops repeated sentences
+   inside the same book (e.g. recurring chapter headers, section dividers).
+   Note: dedup is *not* applied globally — sentences like "he nodded." occur
+   legitimately across many books.
+6. **Document filter** — books with fewer than **8** surviving sentences are
+   dropped (not enough context for NSP).
+7. **Cross-document near-duplicate removal** — a SHA-1 fingerprint of each
+   document's first 5 sentences identifies same-book re-uploads; duplicates
+   are dropped.
+
+## Cleaning statistics
+
+| Metric | Value |
+|---|---|
+| Raw rows (sentences) in source | 74,004,228 |
+| Documents detected | 6,779 |
+| Documents kept | **4,086** |
+| Documents dropped (< 8 sentences) | 973 |
+| Documents dropped (near-duplicate) | 1,720 |
+| Sentences kept | **33,649,142** |
+
+Drop rate: ~40% of detected documents removed (mostly same-book re-uploads
+and too-short documents).
+
+## Source & licensing
+
+- **Source dataset:** [`SamuelYang/bookcorpus`](https://huggingface.co/datasets/SamuelYang/bookcorpus)
+- **Original corpus:** BookCorpus (Zhu et al., 2015), originally scraped from
+  Smashwords. The original BookCorpus has well-documented provenance and
+  consent concerns; downstream users should review them before commercial use.
+- This cleaned derivative is released under the **MIT License** for the
+  cleaning code and structuring effort. The underlying text retains whatever
+  rights apply to the upstream source.