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# HeRo: RoBERTa and Longformer Hebrew Language Models

**Authors:** [Vitaly Shalumov](https://md.mova.elamur.ai/free/en/docs/papers/authors/vitaly-shalumov/); [Harel Haskey](https://md.mova.elamur.ai/free/en/docs/papers/authors/harel-haskey/) · **Year:** 2023 · **Venue:** arXiv:2304.11077 **Link:** [arxiv.org/abs/2304.11077](https://arxiv.org/abs/2304.11077) · library `langstream-2023-shalumov-hero-roberta-longformer-hebrew-language` · **Licence:** [arXiv non-exclusive license to distribute](https://arxiv.org/licenses/nonexclusive-distrib/1.0/license.html) — summary only

[**1** citations](https://openalex.org/works?filter=cites:W4366850652) openalex · 2026-10-18[**14** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/langstream-2023-shalumov-hero-roberta-longformer-hebrew-language/) of the 140673 papers whose bibliographies we read[arXiv:2304.11077](https://openalex.org/S4306400194)[licence](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

- [Publisher's page](https://arxiv.org/abs/2304.11077)
- [PDF](https://arxiv.org/pdf/2304.11077)
- [arXiv 2304.11077](https://arxiv.org/abs/2304.11077)
- [OpenAlex](https://openalex.org/W4366850652)

**Authors (2)**

1. [Vitaly Shalumov](https://openalex.org/A5000535266) [ORCID](https://orcid.org/0000-0002-9455-2134) · h 6 · 221 citations · 15 works
2. [Harel Haskey](https://openalex.org/A5017795900) h 1 · 1 citations · 2 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

The authors build a Hebrew pre-training corpus, HeDC4, from mC4 and OSCAR 22.01. They deduplicate it with MinHash, clean it, and reach about 2.5 times the size of the corpus used for AlephBERT. On it they train HeRo, a RoBERTa-base model for inputs up to 512 tokens, and LongHeRo, a Longformer started from a mid-training HeRo checkpoint for long documents. HeRo beats AlephBERT on sentiment (SMCD accuracy 89.56 against 89.02), on NER (BMC F1 93.56) and on ParaShoot QA (F1 58.2 against 49.6). LongHeRo reaches 89.1 F1 on a machine-translated Hyperpartisan set, close to the English Longformer (90.1).

## Ideas

- I1. Near-duplicate removal as a step before any Hebrew corpus is used: MinHash over shingles, applied within each source and across sources. Measure how many documents go and check the removed pairs by eye, as the paper shows one near-duplicate example. Targets the corpus pipeline of `tools/ocrfix` output and any web text Mova collects.
- I2. Report every Hebrew test in two views, as the paper does: morph-based (gold segmentation given) and token-based (raw tokens). Run `tools/hbo` on the same MACULA-derived gold both ways, so the segmenter's loss is separated from the parser's loss.
- I3. Translated test sets need a manual check on a sample. The authors machine-translated Hyperpartisan and checked only a subset by hand. Any Hebrew test Mova builds by translation should be sampled and checked the same way, and kept as a test, never as training data.

## For Mova

I2 goes into `tools/hbo` (the evaluation harness) and the segmentation chapter [1. Script and pointing](https://md.mova.elamur.ai/free/en/docs/languages/hbo/01-script/). I1 is a data step for the Hebrew corpora. I3 is a rule for any translated test set in `languages/he/`.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [arXiv 2304.11077](https://arxiv.org/abs/2304.11077)

```
@misc{shalumov2023hero,
  title = {{HeRo: RoBERTa and Longformer Hebrew Language Models}},
  author = {Shalumov, Vitaly and Haskey, Harel},
  year = {2023},
  eprint = {2304.11077},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2304.11077}
}
```

- [← previous ivrit.ai: A Comprehensive Dataset of Hebrew Speech for AI Research and Development](https://md.mova.elamur.ai/free/en/docs/papers/langstream-2023-marmor-ivrit-ai-comprehensive-dataset-hebrew/)
- [next → Introducing DictaLM -- A Large Generative Language Model for Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/langstream-2023-shmidman-introducing-dictalm-large-generative-language/)
- [↑ back to Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/he/)

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