Datasets:

ronke21
/

HeRE

---
license: cc-by-sa-4.0
language:
  - he
task_categories:
  - text-classification
task_ids:
  - natural-language-inference
pretty_name: HeRE
size_categories:
  - 1M<n<10M
configs:
  - config_name: gold
    data_files:
      - split: validation
        path: gold/validation.parquet
      - split: test
        path: gold/test.parquet
  - config_name: silver
    data_files:
      - split: train
        path: silver/here_silver.parquet
  - config_name: silver_all_signals
    data_files:
      - split: train
        path: silver/silver_all_signals.parquet
---
# HeRE: Hebrew Relation Extraction benchmark

Data release for **HeRE: A Novel Benchmark for Hebrew Relation Extraction via LLM-Guided Denoising of Knowledge Graph Alignments** (Ron Keinan, Amir David Nissan Cohen, Reut Tsarfaty; AACL 2026). Code: https://github.com/Ronke21/HeRE.

Every row is a Hebrew Wikipedia passage aligned by distant supervision (CROCODILE) to a Wikidata triple (subject, predicate, object) whose two entities are mentioned in the passage. The label answers one question: **does the passage express the stated relation between the subject and the object?**

## Configs

| Config | Split | Rows | Label |
|---|---|---|---|
| `gold` | `validation` | 500 | human, dual-annotated and adjudicated; the set on which the denoising pipeline was selected |
| `gold` | `test` | 500 | human, dual-annotated and adjudicated; never used for selection (203 unseen-predicate rows, 97 depth rows, 200 silver-drawn rows) |
| `silver` | `train` | 1,491,070 | `agree3`: unanimous vote of Gemma-4-31B-it (few-shot), Gemma-3-27B-it (few-shot) and DictaLM-3.0-24B (HebNLI-fine-tuned NLI); 1,080,605 positive / 410,465 negative |
| `silver_all_signals` | `train` | 2,564,034 | every silver row with the raw score and hard prediction of all ten deployed signals and four consensus rules; `agree3_label` is -1 where the three models disagree |

`kg/here_kg_edges.tsv.gz` (1,080,605 rows) is **HeRE-KG**: the positive `agree3` edges as `subject, predicate, object, docid, n_signals_pos, mean_score`, i.e. a Hebrew knowledge graph in which every edge points to the passage (`docid`, join on `silver`) that expresses it. Statistics in `kg/here_kg_stats.md`.

## Columns

Gold: `item_id, docid, title, text, subject, predicate, object, label, annotator1, annotator2, notes, word_count, selection_reason` (`validation`, or in the test set `new_predicate` 203, `depth_boost` 97, `silver_validation_200` 200). `annotator1` and `annotator2` are the two annotators' labels (annotator 2 annotated blind); `label` is the adjudicated label used throughout the paper (identical to `annotator1`, whose column absorbed the adjudication decisions); `notes` are annotator 2's free-text remarks. Inter-annotator agreement before adjudication: Cohen's kappa 0.809 (validation), 0.757 (test).

Silver: `docid, title, text, subject, predicate, object, label` (or `agree3_label`), then for each signal `<family>__<model>__score` (float) and `<family>__<model>__pred` (0/1; -5 or null where the signal abstains) for

| Column stem | Strategy | Model |
|---|---|---|
| `opensource_llm__gemma4_31b` | 4, few-shot | Gemma-4-31B-it |
| `opensource_llm__gemma3_27b` | 4, few-shot | Gemma-3-27B-it |
| `opensource_llm__dictalm3` | 4, few-shot | DictaLM-3.0-24B-Base |
| `finetuned_nli__dictalm24b` | 2, LoRA NLI | DictaLM-3.0-24B |
| `finetuned_nli__aya32b` | 2, LoRA NLI (threshold 0.9) | Aya-Expanse-32b |
| `finetuned_nli__gemma2_9b` | 2, LoRA NLI (threshold 0.9) | Gemma-2-9B |
| `encoder_nli__neodictabert` | 1, encoder NLI | NeoDictaBERT |
| `encoder_nli__xlmroberta` | 1, encoder NLI | XLM-RoBERTa-large |
| `cross_rc__neodictabert_k3` | 3, cross-trained | NeoDictaBERT, K=3 |
| `cross_rc__mmbert_k5` | 3, cross-trained | mmBERT, K=5 |

Consensus columns: `vote_all__pred`, `vote7__pred`, `agree4__pred`, `agree2__pred` (-5 = abstain).

## Quality

Measured on the held-out gold test set: the best single deployed signal (Gemma-4-31B-it) reaches F1 0.844 (0.944 on predicates seen during development, 0.776 on unseen ones). The published `agree3` label covers 66.8% of the test rows and on them reaches precision 0.899, recall 0.934, F1 0.916 (95% CI 0.886 to 0.944). Use the per-signal scores to choose a different precision/recall operating point.

On HeRE-KG, a TransE model trained on the denoised graph beats a size-matched random subset of the raw alignments by 24% relative MRR (three seeds). Every number behind the paper, with confidence intervals, is in the code repository under `results/` (https://github.com/Ronke21/HeRE/tree/main/results).

## Fine-tuned models

All HeRE resources are grouped in the Hub collection https://huggingface.co/collections/ronke21/here-hebrew-relation-extraction-6aa82bf21db8a9c955cf1cd1. The HebNLI-fine-tuned denoisers behind the released signals and the per-strategy winners:

| Model | Role in HeRE | Repo |
|---|---|---|
| DictaLM-3.0-24B (Thinking) HebNLI LoRA | Strategy 2; one of the three `agree3` models behind the published label | https://huggingface.co/ronke21/hebnli-dictalm-3.0-24b-lora |
| NeoDictaBERT HebNLI | Strategy 1; deployed signal `encoder_nli__neodictabert` | https://huggingface.co/ronke21/hebnli-neodictabert |
| XLM-RoBERTa-large HebNLI | Strategy 1; deployed signal `encoder_nli__xlmroberta` | https://huggingface.co/ronke21/hebnli-xlm-roberta-large |
| XLM-RoBERTa-XL HebNLI | Strategy 1 best (F1 0.779) | https://huggingface.co/ronke21/hebnli-xlm-roberta-xl |
| Qwen3.5-35B-A3B-Base HebNLI LoRA | Strategy 2 best (F1 0.873) | https://huggingface.co/ronke21/hebnli-qwen3.5-35b-a3b-lora |
| Gemma-4-31B HebNLI LoRA | Strategy 2 (F1 0.849) | https://huggingface.co/ronke21/hebnli-gemma-4-31b-lora |
| AlephBERT HebNLI | Strategy 1 (F1 0.618) | https://huggingface.co/ronke21/hebnli-alephbert |

The Strategy 4 signals (Gemma-4-31B-it, Gemma-3-27B-it, DictaLM-3.0-24B-Base) are prompted public models; prompts are in the code repository.

## Contamination note

The 500 test rows were drawn from the silver corpus; all 500 are **withheld from every silver and KG file** here. Do not add them back when training.

## Sources and licenses

Text: Hebrew Wikipedia (early-2025 dump), CC BY-SA 4.0, which this release inherits. Triples: Wikidata, CC0. Alignment: CROCODILE (Huguet Cabot and Navigli, 2021). Text was cleaned of wiki markup; entity fields were normalised.

## Citation

Keinan, R., Cohen, A. D. N., and Tsarfaty, R. (2026). HeRE: A Novel Benchmark for Hebrew Relation Extraction via LLM-Guided Denoising of Knowledge Graph Alignments. In *Proceedings of AACL 2026*.

	---
	license: cc-by-sa-4.0
	language:
	- he
	task_categories:
	- text-classification
	task_ids:
	- natural-language-inference
	pretty_name: HeRE
	size_categories:
	- 1M<n<10M
	configs:
	- config_name: gold
	data_files:
	- split: validation
	path: gold/validation.parquet
	- split: test
	path: gold/test.parquet
	- config_name: silver
	data_files:
	- split: train
	path: silver/here_silver.parquet
	- config_name: silver_all_signals
	data_files:
	- split: train
	path: silver/silver_all_signals.parquet
	---

	# HeRE: Hebrew Relation Extraction benchmark

	Data release for HeRE: A Novel Benchmark for Hebrew Relation Extraction via LLM-Guided Denoising of Knowledge Graph Alignments (Ron Keinan, Amir David Nissan Cohen, Reut Tsarfaty; AACL 2026). Code: https://github.com/Ronke21/HeRE.

	Every row is a Hebrew Wikipedia passage aligned by distant supervision (CROCODILE) to a Wikidata triple (subject, predicate, object) whose two entities are mentioned in the passage. The label answers one question: does the passage express the stated relation between the subject and the object?

	## Configs

	\| Config \| Split \| Rows \| Label \|
	\|---\|---\|---\|---\|
	\| `gold` \| `validation` \| 500 \| human, dual-annotated and adjudicated; the set on which the denoising pipeline was selected \|
	\| `gold` \| `test` \| 500 \| human, dual-annotated and adjudicated; never used for selection (203 unseen-predicate rows, 97 depth rows, 200 silver-drawn rows) \|
	\| `silver` \| `train` \| 1,491,070 \| `agree3`: unanimous vote of Gemma-4-31B-it (few-shot), Gemma-3-27B-it (few-shot) and DictaLM-3.0-24B (HebNLI-fine-tuned NLI); 1,080,605 positive / 410,465 negative \|
	\| `silver_all_signals` \| `train` \| 2,564,034 \| every silver row with the raw score and hard prediction of all ten deployed signals and four consensus rules; `agree3_label` is -1 where the three models disagree \|

	`kg/here_kg_edges.tsv.gz` (1,080,605 rows) is HeRE-KG: the positive `agree3` edges as `subject, predicate, object, docid, n_signals_pos, mean_score`, i.e. a Hebrew knowledge graph in which every edge points to the passage (`docid`, join on `silver`) that expresses it. Statistics in `kg/here_kg_stats.md`.

	## Columns

	Gold: `item_id, docid, title, text, subject, predicate, object, label, annotator1, annotator2, notes, word_count, selection_reason` (`validation`, or in the test set `new_predicate` 203, `depth_boost` 97, `silver_validation_200` 200). `annotator1` and `annotator2` are the two annotators' labels (annotator 2 annotated blind); `label` is the adjudicated label used throughout the paper (identical to `annotator1`, whose column absorbed the adjudication decisions); `notes` are annotator 2's free-text remarks. Inter-annotator agreement before adjudication: Cohen's kappa 0.809 (validation), 0.757 (test).

	Silver: `docid, title, text, subject, predicate, object, label` (or `agree3_label`), then for each signal `<family>__<model>__score` (float) and `<family>__<model>__pred` (0/1; -5 or null where the signal abstains) for

	\| Column stem \| Strategy \| Model \|
	\|---\|---\|---\|
	\| `opensource_llm__gemma4_31b` \| 4, few-shot \| Gemma-4-31B-it \|
	\| `opensource_llm__gemma3_27b` \| 4, few-shot \| Gemma-3-27B-it \|
	\| `opensource_llm__dictalm3` \| 4, few-shot \| DictaLM-3.0-24B-Base \|
	\| `finetuned_nli__dictalm24b` \| 2, LoRA NLI \| DictaLM-3.0-24B \|
	\| `finetuned_nli__aya32b` \| 2, LoRA NLI (threshold 0.9) \| Aya-Expanse-32b \|
	\| `finetuned_nli__gemma2_9b` \| 2, LoRA NLI (threshold 0.9) \| Gemma-2-9B \|
	\| `encoder_nli__neodictabert` \| 1, encoder NLI \| NeoDictaBERT \|
	\| `encoder_nli__xlmroberta` \| 1, encoder NLI \| XLM-RoBERTa-large \|
	\| `cross_rc__neodictabert_k3` \| 3, cross-trained \| NeoDictaBERT, K=3 \|
	\| `cross_rc__mmbert_k5` \| 3, cross-trained \| mmBERT, K=5 \|

	Consensus columns: `vote_all__pred`, `vote7__pred`, `agree4__pred`, `agree2__pred` (-5 = abstain).

	## Quality

	Measured on the held-out gold test set: the best single deployed signal (Gemma-4-31B-it) reaches F1 0.844 (0.944 on predicates seen during development, 0.776 on unseen ones). The published `agree3` label covers 66.8% of the test rows and on them reaches precision 0.899, recall 0.934, F1 0.916 (95% CI 0.886 to 0.944). Use the per-signal scores to choose a different precision/recall operating point.

	On HeRE-KG, a TransE model trained on the denoised graph beats a size-matched random subset of the raw alignments by 24% relative MRR (three seeds). Every number behind the paper, with confidence intervals, is in the code repository under `results/` (https://github.com/Ronke21/HeRE/tree/main/results).

	## Fine-tuned models

	All HeRE resources are grouped in the Hub collection https://huggingface.co/collections/ronke21/here-hebrew-relation-extraction-6aa82bf21db8a9c955cf1cd1. The HebNLI-fine-tuned denoisers behind the released signals and the per-strategy winners:

	\| Model \| Role in HeRE \| Repo \|
	\|---\|---\|---\|
	\| DictaLM-3.0-24B (Thinking) HebNLI LoRA \| Strategy 2; one of the three `agree3` models behind the published label \| https://huggingface.co/ronke21/hebnli-dictalm-3.0-24b-lora \|
	\| NeoDictaBERT HebNLI \| Strategy 1; deployed signal `encoder_nli__neodictabert` \| https://huggingface.co/ronke21/hebnli-neodictabert \|
	\| XLM-RoBERTa-large HebNLI \| Strategy 1; deployed signal `encoder_nli__xlmroberta` \| https://huggingface.co/ronke21/hebnli-xlm-roberta-large \|
	\| XLM-RoBERTa-XL HebNLI \| Strategy 1 best (F1 0.779) \| https://huggingface.co/ronke21/hebnli-xlm-roberta-xl \|
	\| Qwen3.5-35B-A3B-Base HebNLI LoRA \| Strategy 2 best (F1 0.873) \| https://huggingface.co/ronke21/hebnli-qwen3.5-35b-a3b-lora \|
	\| Gemma-4-31B HebNLI LoRA \| Strategy 2 (F1 0.849) \| https://huggingface.co/ronke21/hebnli-gemma-4-31b-lora \|
	\| AlephBERT HebNLI \| Strategy 1 (F1 0.618) \| https://huggingface.co/ronke21/hebnli-alephbert \|

	The Strategy 4 signals (Gemma-4-31B-it, Gemma-3-27B-it, DictaLM-3.0-24B-Base) are prompted public models; prompts are in the code repository.

	## Contamination note

	The 500 test rows were drawn from the silver corpus; all 500 are withheld from every silver and KG file here. Do not add them back when training.

	## Sources and licenses

	Text: Hebrew Wikipedia (early-2025 dump), CC BY-SA 4.0, which this release inherits. Triples: Wikidata, CC0. Alignment: CROCODILE (Huguet Cabot and Navigli, 2021). Text was cleaned of wiki markup; entity fields were normalised.

	## Citation

	Keinan, R., Cohen, A. D. N., and Tsarfaty, R. (2026). HeRE: A Novel Benchmark for Hebrew Relation Extraction via LLM-Guided Denoising of Knowledge Graph Alignments. In Proceedings of AACL 2026.