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 (arXiv:2609.03350). Sister releases: COPA-AI/armstem and 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
@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}
}