Add dataset card

README.md

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+---
+license: cc-by-sa-4.0
+language:
+  - so
+size_categories:
+  - 100K<n<1M
+task_categories:
+  - text-generation
+  - fill-mask
+pretty_name: SomaliWeb v1
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: train.jsonl
+      - split: validation
+        path: validation.jsonl
+tags:
+  - somali
+  - low-resource
+  - pretraining
+  - web-corpus
+  - deduplicated
+  - quality-filtered
+---
+
+# SomaliWeb v1 — Quality-filtered Somali web corpus
+
+**SomaliWeb v1** is a cleaned, deduplicated, and quality-filtered Somali-language web corpus of **~303 million tokens** (819,322 documents), built by aggregating three public Somali-heavy web distributions (HPLT v2, CC100, Somali Wikipedia) and passing them through a reproducible six-stage pipeline.
+
+This is, to our knowledge, the first dedicated and versioned Somali-only pretraining corpus released on Hugging Face with a complete dataset card.
+
+## Dataset summary
+
+- **Language**: Somali (Standard Somali, Latin script). No Maay Maay detected in the final distribution.
+- **Token count**: ~303M approximate tokens (`words × 1.3`), **233M whitespace-separated words**.
+- **Document count**: 819,322 (train: 778,355 · validation: 40,967, 95/5 split).
+- **Script**: Latin.
+- **Period of source content**: ~2019–2024, inherited from HPLT v2 and CC100.
+- **License**: CC-BY-SA 4.0 (see Licensing below; sources have compatible terms).
+
+## Why this dataset
+
+Somali is a low-resource language with ~25 million speakers worldwide. Existing Somali text appears *inside* multilingual dumps (HPLT, CC100, mC4, OSCAR) but no standalone, named, versioned Somali pretraining corpus has been published with a documented construction pipeline. SomaliWeb v1 fills that gap and ships with:
+
+- A reproducible pipeline anyone can re-run and audit.
+- A comparison baseline tokenizer trained on HPLT-raw, released alongside.
+- An evaluation against FLORES-200 Somali showing measurable fertility improvements over general-purpose tokenizers.
+
+## Dataset structure
+
+### Data fields
+
+Each line in `train.jsonl` and `validation.jsonl` is one JSON object:
+
+- `id` (string): source-prefixed unique identifier (e.g. `hplt2-so_12345`).
+- `text` (string): the document body, cleaned and length-filtered.
+- `source` (string): one of `hplt2-so`, `cc100-so`, `wikipedia-so`.
+- `n_words` (int): whitespace-separated word count after cleaning.
+- `quality_score` (float): Phase-5 quality score (0–1, higher = cleaner Somali).
+- `lang_conf` (float): langdetect top-1 confidence for Somali.
+- `dialect_tag` (string, optional): GlotLID v3 dialect tag (almost always `som_Latn`).
+
+### Data splits
+
+| Split | Documents | Approx tokens |
+|---|---:|---:|
+| `train` (95%) | 778,355 | ~288M |
+| `validation` (5%) | 40,967 | ~15M |
+
+### Source composition (of the combined train + validation)
+
+| Source | Documents | Fraction |
+|---|---:|---:|
+| HPLT v2 `som_Latn` | 582,257 | 71.07% |
+| CC100-so (statmt.org) | 233,394 | 28.49% |
+| Somali Wikipedia (dump 2023-11-01) | 3,671 | 0.45% |
+
+## Construction pipeline
+
+Full details + intermediate metrics in the companion GitHub repository's `reports/` directory. The pipeline is six phases:
+
+1. **Merge + byte-exact dedup.** Combine the three sources under a unified schema; drop documents whose SHA-256 of `lowercase(whitespace-collapsed(text))` is already seen (first-seen wins). Removed **189,692 duplicates (13.83%)**, of which **166,628 (17.3%) came from within HPLT v2's own "cleaned" distribution**, demonstrating that HPLT's dedup is not byte-exact.
+
+2. **Clean + normalize.** Mojibake fix via `ftfy`, whitespace collapse, repeated-char-run collapse, minimum-length filter (≥ 50 words). **ftfy fixed mojibake in 615,314 documents (52.04% of input)** — notably **56.1% of HPLT v2's documents** contained fixable encoding artifacts.
+
+3. **LID verification.** `langdetect` (seeded) on every surviving document; keep only top-1 = `so` with confidence ≥ 0.50. Drop rate 0.21% — HPLT/CC100 already LID-filtered solidly. GlotLID v3 second pass tagged dialect: `som_Latn` 99.96%, `ymm_Latn` (Maay Maay) 0.00%.
+
+4. **MinHash near-duplicate detection.** Word-3-gram shingles hashed inline to 31-bit ints, 64 MinHash signatures, (16, 4) LSH banding (`s* ≈ 0.50`), τ = 0.80 Jaccard verification, union-find clustering, `keep-longest` rule. Removed **113,896 near-duplicates (10.57%)**. Largest cluster: 75 copies of one article — HPLT missed it.
+
+5. **Quality filter.** Character 5-gram coverage against a clean Somali Wikipedia seed (2,221 articles ≥ 200 words, 828K unique 5-grams). Dropped bottom 15% by coverage score (threshold 0.9029). Filter catches template-like listings and encoding-degraded content; a known false-positive rate applies to news with heavy proper-noun density.
+
+6. **Release structuring.** Shuffled (seed 0), 95/5 train/val split, metadata consolidated.
+
+## Evaluation
+
+### Tokenizer fertility on FLORES-200 Somali devtest (1,012 held-out sentences)
+
+| Tokenizer | Training corpus | Vocab | Tokens | Fertility ↓ |
+|---|---|---:|---:|---:|
+| **SomaliWeb-v1** (this dataset) | 350M tokens (cleaned) | 16K | 35,867 | **1.538** |
+| HPLT-raw | 505M tokens (raw HPLT v2) | 16K | 35,854 | 1.537 |
+| GPT-4 `cl100k_base` | proprietary | 100K | 60,010 | 2.573 |
+
+**Two results:**
+
+- SomaliWeb v1 matches HPLT-raw's tokenizer fertility at 30% smaller training corpus — our dedup + quality filter preserves tokenizer quality.
+- SomaliWeb v1's tokenizer is **40.2% more token-efficient than GPT-4's `cl100k_base`** on Somali text. The "tokenization tax" that penalizes low-resource languages is concretely measurable here.
+
+The companion tokenizer is published alongside as `tokenizer_somaliweb.json` (HF tokenizers JSON format).
+
+## Limitations and considerations
+
+- **Source inheritance.** The upstream sources (HPLT v2, CC100, Somali Wikipedia) each have their own coverage biases. HPLT and CC100 are CC-derived and therefore dominated by whatever Somali content happened to be on the public web during ~2019–2024.
+- **No Maay Maay.** GlotLID v3 tagged 0 documents as `ymm_Latn` in the final distribution. Either the upstream distributions filtered it out, or GlotLID undercounts Maay on web text — either way, this corpus should be described as **Standard Somali only**. Future versions should source Maay Maay content separately.
+- **Quality filter false positives.** The char-5-gram filter can drop real news articles heavy in proper nouns because the seed is predominantly Wikipedia encyclopedic prose. v2 should use a mixture seed or a trained classifier.
+- **Known encoding artifacts caught, not created.** 52% of docs had mojibake fixed by `ftfy`. We did not find evidence of residual mojibake, but this hasn't been exhaustively audited.
+- **Dialect scope.** Standard Somali only.
+- **PII.** We did not run a dedicated PII-removal pass. Upstream sources may retain names, phone numbers, or emails embedded in news content. Empirical scan of the released splits: **~7.9% of documents contain at least one email-shaped string** (mostly newsroom contact addresses such as `editor@…`, `support@…`, but also occasional personal `gmail.com` / `hotmail.com` addresses). Presidio does not cover Somali; a Somali-specific PII filter is planned for v2. **Users building consumer-facing applications must apply additional PII handling.**
+- **Copyright.** Sources are public web documents. Somali news sites are the largest contributors via HPLT/CC100. Users should consider downstream licensing obligations for their specific use case.
+- **Bias.** The corpus inherits any biases of the upstream crawls — the Somali internet skews toward diaspora, news, politics, and religion. Under-represented registers include conversational speech, technical writing, and long-form fiction.
+
+## Usage
+
+### Load with `datasets`
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("khaledyusuf44/somaliweb-v1")
+print(ds)
+# DatasetDict({'train': Dataset(num_rows=778355), 'validation': Dataset(num_rows=40967)})
+
+sample = ds["train"][0]
+# {'id': 'hplt2-so_12345', 'text': '...', 'source': 'hplt2-so', ...}
+```
+
+### Load the companion tokenizer
+
+```python
+from tokenizers import Tokenizer
+tok = Tokenizer.from_file("tokenizer_somaliweb.json")
+print(tok.encode("Soomaaliya waa dal ku yaal geeska Afrika.").ids)
+```
+
+### Intended uses
+
+- Somali LLM pretraining (the primary use case).
+- Somali tokenizer training and evaluation.
+- Low-resource NLP research.
+- Somali downstream task fine-tuning (classification, NER, translation) — as a base for domain adaptation.
+
+### Out-of-scope uses
+
+- **Consumer-facing applications without additional PII filtering.**
+- **Claims of representing all Somali dialects.** This corpus is Standard Somali only.
+- **Evaluation tasks that require contamination control.** Source documents predate construction and may overlap with FLORES-200 or other public Somali evaluation sets. We recommend a contamination audit before using this corpus to train models you intend to evaluate on those benchmarks.
+
+## Dataset Curators
+
+Khalid Yusuf Dahir (`khaledyusuf44` on GitHub and Hugging Face).
+
+## Licensing
+
+Released under **CC-BY-SA 4.0**, inheriting the most restrictive license among the upstream sources:
+
+- HPLT v2: CC0 (permissive).
+- CC100: public domain / MIT-like (permissive).
+- Somali Wikipedia: CC-BY-SA 3.0 (requires attribution + share-alike).
+
+Users of SomaliWeb v1 must honor CC-BY-SA 4.0 (attribution + share-alike). Citations appreciated.
+
+## Citation
+
+```bibtex
+@misc{somaliweb2026,
+  title  = {SomaliWeb v1: A quality-filtered Somali web corpus},
+  author = {Khalid Yusuf Dahir},
+  year   = {2026},
+  howpublished = {\url{https://huggingface.co/datasets/khaledyusuf44/somaliweb-v1}},
+}
+```
+
+## Acknowledgments
+
+This dataset builds on:
+
+- **HPLT v2** (University of Helsinki / Turku / Edinburgh consortium) — the primary CC-derived source.
+- **CC100** (Wenzek et al., 2020) — the secondary CC-derived source distributed via statmt.org.
+- **Somali Wikipedia** contributors — the clean anchor and quality-filter seed.
+- **Common Crawl** — the underlying web archive all three upstream corpora were built from.
+- **FLORES-200** (NLLB Team et al., 2022) — held-out evaluation set.
+- **GlotLID** (Kargaran et al., 2023) — dialect tagging.
+
+## Changelog
+
+- **v1.0** (2026-04-23): Initial release.