Add dataset card

README.md

@@ -1,97 +1,33 @@
 ---
-license: cc-by-3.0
 language:
 - he
-size_categories:
-- 100K<n<1M
-configs:
-  - config_name: default
-    data_files:
-      - split: train
-        path: HebNLI_train.jsonl
-      - split: dev
-        path: HebNLI_val.jsonl
-      - split: test
-        path: HebNLI_test.jsonl
-    features:
-    - name: original_annotator_labels
-      dtype: string
-    - name: genre
-      dtype: string
-    - name: original_label
-      dtype: string
-    - name: pairID
-      dtype: string
-    - name: promptID
-      dtype: int64
-    - name: sentence1
-      dtype: string
-    - name: translation1
-      dtype: string
-    - name: sentence2
-      dtype: string
-    - name: translation2
-      dtype: string
-    - name: hebrew_label
-      dtype: string
+task_categories:
+- natural-language-inference
+license: other
+pretty_name: HebNLI
+private: true
 ---
-# HebNLI - A Natural Language Inference Dataset in Hebrew
 
-## Summary
-HebNLI is a Hebrew dataset for natural language inference (NLI) tasks.
+# HebNLI
 
-## Introduction
-This dataset is the first of its kind in the Hebrew language and aims to serve as training data for NLI tasks.
-HebNLI is based on MultiNLI, a large crowd-sourced corpus of sentences from varied genres and writing styles in the English language.
-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.
-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.
-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.
+Hebrew NLI — entailment / contradiction / neutral
 
-## Genres/Sources in HebNLI
-HebNLI comprises 7 of the original 10 genres/sources that appeared in MultiNLI:
-1. Nine eleven - Written protocols from a commitee investigating the events of 9/11.
-2. Government - Reports, speeches and press releases published on U.S.A government websites.
-3. Letters - A database of letters written in the late 90's and early 2000's.
-4. OUP (Oxford University Press) - Publications about the textile industry and about child development.
-5. Slate - Pop-culture articles published in Slate magazine.
-6. Travel - Travel guides by Berlitz press.
-7. Fiction - Texts extracted from modern works of literature.
+## Source
 
-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). 
+Originally sourced from the Hebrew NLP benchmark collection.
+Google Drive: https://drive.google.com/drive/folders/1bM4GK9lYo8rSQqZCySCRNyr-KKBJi5M7
 
-## Dataset Statistics
-The table below shows the distribution of each source corpus within HebNLI (how many setences exist in the dataset from each source).
+## Files
 
-| Genre/Source     |  HebNLI Corpus   |
-|------------------|------------------|
-| Nine eleven      |   1878           |
-| Government       |   76953          |
-| Letters          |   1974           |
-| OUP              |   1986           |
-| Slate            |   71082          |
-| Travel           |   75776          |
-| Fiction          |   73734          |
+- `HebNLI_sampled_2000_v2.jsonl`
 
-Total # of sentences = 303,383.
+## Usage
 
-The table below shows the number of examples from each category in each of the splits:
+```python
+from datasets import load_dataset
+ds = load_dataset("HebArabNlpProject/HebNLI", token=HF_TOKEN)
+```
 
-| split |  total   | contradiction | entailment | neutral |
-|-------|----------|---------------|------------|---------|
-| train | 293,298  |   97,344      | 98,760     | 97,194  |
-| dev   |   5,000  |   1,679       | 1,682      | 1,639   |
-| test  |   5,000  |   1,682       | 1,638      | 1,680   |
+## Task type
 
-### HebNLI Blog Post
-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
-
-### Original MultiNLI Paper
-https://cims.nyu.edu/~sbowman/multinli/paper.pdf
-
-## Contributors
-HebNLI was translated and checked for quality by Webiks for MAFAT, as part of the National Natural Language Processing Plan of Israel. 
-
-Contributors: Hilla Merhav Fine (Webiks), Yaniv Maylik (Webiks), Carinne Cherf (Webiks), Tal Geva (MAFAT).
-
-## Acknowledgments
-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).
+`nli`