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--- |
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license: cc-by-3.0 |
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language: |
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- he |
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size_categories: |
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- 100K<n<1M |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: train/*.jsonl |
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- split: dev |
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path: dev/*.jsonl |
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- split: test |
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path: test/*.jsonl |
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--- |
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# HebNLI - A Natural Language Inference Dataset in Hebrew |
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## Summary |
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HebNLI is a Hebrew dataset for natural language inference (NLI) tasks. |
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## Introduction |
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This dataset is the first of its kind in the Hebrew language and aims to serve as training data for NLI tasks. |
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HebNLI is based on MultiNLI, a large crowd-sourced corpus of sentences from varied genres and writing styles in the English language. |
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MultiNLI was originally built by collecting hundreds of thousands of base sentences from which different taggers derived follow-up sentences that stand in one of 3 logical relations to the base sentences: entailment, contradiction or neutral. |
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Different taggers were then given paired sentences - base sentence and a derived sentence. The logical relation between them was determined by the majority vote, and each pair of sentences was labled according to the determined logical relation. |
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In HebNLI we used machine translation (Google Gemini) to translate the English corpus to Hebrew, such that each base sentence and its compiled derivative sentences appear in Hebrew. |
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## Genres/Sources in HebNLI |
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HebNLI comprises 7 of the original 10 genres/sources that appeared in MultiNLI: |
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1. Nine eleven - Written protocols from a commitee investigating the events of 9/11. |
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2. Government - Reports, speeches and press releases published on U.S.A government websites. |
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3. Letters - A database of letters written in the late 90's and early 2000's. |
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4. OUP (Oxford University Press) - Publications about the textile industry and about child development. |
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5. Slate - Pop-culture articles published in Slate magazine. |
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6. Travel - Travel guides by Berlitz press. |
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7. Fiction - Texts extracted from modern works of literature. |
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The remaining three sources were found to either be too English-oriented to be properly translated to Hebrew by machine translation ("Verbatim" magazine source), or included too many broken sentences and filler-words to be properly translated to Hebrew by machine translation (face-to-face conversations and telephone conversations sources). |
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## Dataset Statistics |
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The table below shows the distribution of each source corpus within HebNLI (how many setences exist in the dataset from each source). |
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| Genre/Source | HebNLI Corpus | |
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|------------------|------------------| |
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| Nine eleven | 1878 | |
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| Government | 76953 | |
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| Letters | 1974 | |
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| OUP | 1986 | |
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| Slate | 71082 | |
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| Travel | 75776 | |
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| Fiction | 73734 | |
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Total # of sentences = 303,383. |
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The table below shows the number of examples from each category in each of the splits: |
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| split | total | contradiction | entailment | neutral | |
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|-------|----------|---------------|------------|---------| |
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| train | 293,298 | 97,344 | 98,760 | 97,194 | |
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| dev | 5,000 | 1,679 | 1,682 | 1,639 | |
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| test | 5,000 | 1,682 | 1,638 | 1,680 | |
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### HebNLI Blog Post |
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XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX |
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### Original MultiNLI Paper |
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https://cims.nyu.edu/~sbowman/multinli/paper.pdf |
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## Contributors |
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HebNLI was translated and checked for quality by Webiks for MAFAT, as part of the National Natural Language Processing Plan of Israel. |
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Contributors: Hilla Merhav Fine (Webiks), Yaniv Maylik (Webiks), Carinne Cherf (Webiks), Tal Geva (MAFAT). |
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## Acknowledgments |
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We would like to express our gratitude to Adina Williams, Nikita Nangia and Samuel R. Bowman, the creators of [the original NLI dataset MultiNLI](https://huggingface.co/datasets/nyu-mll/multi_nli). |
