README.md

---
license: odc-by
language:
  - hy
task_categories:
  - text-generation
tags:
  - arxiv:2609.03350
  - armenian
  - pretraining
  - news
  - deduplicated
  - decontaminated
size_categories:
  - 1M<n<10M
pretty_name: ArmWeb
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train/*
      - split: validation
        path: data/validation/*
      - split: test_iid
        path: data/test_iid/*
      - split: test_tail
        path: data/test_tail/*
---

# ArmWeb

**ArmWeb** is a curated Armenian news corpus for language-model pretraining:
**4.37M documents / 11 GB / 3.3B Gemma-4 tokens** (1.15B tokens in the 32K
SentencePiece tokenizer used for the small-scale ablations) of deduplicated, decontaminated Eastern and
Western Armenian text with document-level provenance (URL, outlet, topic,
publication date). To our knowledge it is the first and largest open
pretraining corpus *built specifically for* Armenian — existing Armenian text
at scale exists only as slices of multilingual web crawls with no
Armenian-specific curation.

Paper: [*From Zero to Hero: An Open LLM Ecosystem for Armenian*](https://arxiv.org/abs/2609.03350)
(arXiv:2609.03350). Sister releases: [COPA-AI/armstem](https://huggingface.co/datasets/COPA-AI/armstem)
and [COPA-AI/arm-gemma-e4b](https://huggingface.co/COPA-AI/arm-gemma-e4b).

## Why another Armenian corpus?

Because the existing ones are contaminated and duplicated. Scanning the
Armenian slices of public corpora against ten Armenian benchmarks (13-gram
overlap, normalized text):

| Corpus | Docs | Gemma-4 tokens | Benchmark-contaminated |
|---|---|---|---|
| **ArmWeb (this release)** | 4.46M | 3.3B | **3.3% → removed** |
| CulturaX-hy | 2.96M | 4.5B | 7.9% |
| HPLT-v2-hy | 3.60M | 5.8B | 10.9% |
| FineWeb-2-hy | 1.76M | 2.3B | 17.4% |

Token sizes re-tokenize each corpus as distributed with the Gemma-4
vocabulary (ArmWeb as released), so they are comparable: ArmWeb has the most
documents but, being news, the least text of the three large corpora.

FineWeb-2's Armenian *training* split overlaps its own *test* split. Any
evaluation of Armenian-trained models without decontamination is inflated.
ArmWeb ships with its contamination **removed** and the scan code released.

ArmWeb is also complementary to the crawls, not redundant with them: in
controlled 410M ablations it is ~10% better bits-per-byte on held-out news,
and the **union** of ArmWeb + CulturaX beats every single corpus on the
panel mean (0.532 vs 0.545 bpb for the best single corpus) and is within
0.007 bpb of the best on every panel — a lead that widens at 1.3B (see
paper, Appendix).

## Contents

- **Source**: a single-operator crawl of public Armenian news sites,
  2011–2026, stored as a structured database (not raw HTML — near-zero
  boilerplate by construction).
- **Splits**: train (4.31M docs) / validation (20K) / test_iid (20K) /
  test_tail (20K; the final two months, for temporal generalization).
  Splits are stratified by outlet×month; deduplication ran **globally before
  splitting**, and three leakage gates are verified: zero exact cross-split
  collisions and near-duplicate rates ≤0.045% (val), 0.010% (test_iid),
  0.005% (test_tail) run as hard assertions in the release build, and a
  third gate counts cross-split shared paragraphs of ≥13 tokens: 30 (val),
  63 (test_iid), 10 (test_tail) per 20K-document split.
- **Fields**: `id`, `text` (NFC-normalized, otherwise verbatim), `source`
  (outlet), `url`, `topic`, `post_date` (publication), `scrape_date`
  (crawl), `dedup_cluster_id`, `cluster_size` (size of the near-duplicate
  cluster this document represents), `split`. Author names are not
  released.

## Pipeline (fully documented in the paper)

| Stage | Documents | Δ |
|---|---|---|
| Extracted | 5,918,811 | — |
| Language ID (GlotLID, hye/hyw) | 5,817,343 | −1.7% |
| Exact dedup (xxh128, keep-longest) | 5,436,984 | −6.5% |
| MinHash dedup (see below) | 4,515,497 | −16.9% |
| Repeated-paragraph removal | 4,515,484 | −0.0% |
| Splits held out (val/test_iid/test_tail, 20K each) | 4,455,484 | −60K |
| Benchmark decontamination (train side) | 4,308,383 | −3.3% |

The dedup engine is cross-checked twice: the reference datasketch library,
run with the identical recipe on a 1% sample, finds 449 in-sample
duplicates of which the engine had already flagged 95.8%; NVIDIA
NeMo-Curator at matched LSH geometry removes 23.5% vs our 22.4% on the
full corpus with 99.2% per-document agreement on a matched-scope sample,
disagreements confined to below-threshold borderline pairs (paper,
Appendix). The identical recipe applied to a Russian sister collection
from the same crawler removes only 0.9% of documents, so the high Armenian
removal rate reflects real news syndication, not an aggressive pipeline.

MinHash: word 5-gram shingles on an aggressive NFKC "signature view"
(Armenian ligature folding, punctuation stripping, digit-zeroing), 112
permutations, 14 bands × 8 rows (Jaccard ≈ 0.72). The lower-than-standard
threshold is deliberate: news syndication produces true reprints at far lower
Jaccard than web duplicates, and 0.8-threshold recipes miss most of them.
Decontamination: 13-gram overlap against ten Armenian evaluation sets
(Belebele, INCLUDE, HellaSwag-hy, MultiBLiMP, SIB-200, SynDARin, LR-Sum,
the FineWeb-2-hy test split, hyWiki eval, FLORES-200), 147,101 documents
removed. m-MMLU-hy and ARC-hy were not decontamination targets; a post-hoc
scan found **zero** of the 4.31M training documents share any 13-gram
with either benchmark.

## License and attribution

The **compilation** (selection, cleaning, deduplication, metadata, splits) is
released under **ODC-BY 1.0**. The crawl honored the sites' crawling rules,
including robots.txt directives. The underlying article texts remain the
copyright of their respective publishers; per-document `url` and `source`
fields preserve attribution. Author bylines are removed; a PII scan (942
e-mail addresses in 782 documents, 8,971 documents with phone-number-like
strings, overwhelmingly newsroom and institutional contact lines) ships
with the corpus. The corpus is released for research use,
consistent with text-and-data-mining practice for web-derived pretraining
corpora (C4, OSCAR, FineWeb). If you are a rights holder and want content
removed, open a discussion on this repository.

## Citation

```bibtex
@article{arakelyan2026armweb,
  title  = {From Zero to Hero: An Open LLM Ecosystem for Armenian},
  author = {Arakelyan, Erik and Avetisyan, Khatun and Davtyan, Meri and Grigoryan, Heghine and Khachatryan, Nane and Shahsuvaryan, Hayk and Sergoyan, Henrik and Martirosyan, Vahan},
  year   = {2026},
  journal = {arXiv preprint arXiv:2609.03350}
}
```