annotations_creators:
- other
language_creators:
- other
language:
- sk
license:
- other
- cc-by-sa-4.0
- cc-by-sa-3.0
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
- text-classification
- token-classification
task_ids:
- extractive-qa
- named-entity-recognition
- acceptability-classification
- natural-language-inference
- semantic-similarity-scoring
- sentiment-classification
- text-scoring
paperswithcode_id: skLEP
pretty_name: skLEP (General Language Understanding Evaluation benchmark for Slovak)
tags:
- qa-nli
- coreference-nli
- paraphrase-identification
config_names:
- hate-speech
- sentiment-analysis
- ner-wikigoldsk
- ner-uner
- pos
- question-answering
- rte
- nli
- sts
dataset_info:
- config_name: hate-speech
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': negative
'1': positive
- name: id
dtype: int32
splits:
- name: train
num_bytes: 1393604
num_examples: 10531
- name: test
num_bytes: 150919
num_examples: 1319
- name: validation
num_bytes: 160199
num_examples: 1339
download_size: 326394
dataset_size: 605704
- config_name: sentiment-analysis
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': negative
'1': positive
- name: id
dtype: int32
splits:
- name: train
num_bytes: 387491
num_examples: 3560
- name: test
num_bytes: 117983
num_examples: 1042
- name: validation
num_bytes: 117983
num_examples: 522
download_size: 326394
dataset_size: 605704
- config_name: ner-wikigoldsk
features:
- name: sentence
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-LOC
'2': I-LOC
'3': B-ORG
'4': I-ORG
'5': B-PER
'6': I-PER
'7': B-MISC
'8': I-MISC
- name: ner_tags_text
sequence: string
splits:
- name: train
num_bytes: 1885504
num_examples: 4687
- name: validation
num_bytes: 267514
num_examples: 669
- name: test
num_bytes: 532642
num_examples: 1340
- config_name: ner-uner
features:
- name: sentence
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-LOC
'2': I-LOC
'3': B-ORG
'4': I-ORG
'5': B-PER
'6': I-PER
'7': B-MISC
'8': I-MISC
- name: ner_tags_text
sequence: string
splits:
- name: train
num_bytes: 1786598
num_examples: 8483
- name: validation
num_bytes: 289084
num_examples: 1060
- name: test
num_bytes: 289026
num_examples: 1061
- config_name: pos
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: pos_tags
sequence: string
splits:
- name: train
num_bytes: 1786598
num_examples: 8483
- name: validation
num_bytes: 289084
num_examples: 1060
- name: test
num_bytes: 289026
num_examples: 1061
- config_name: question-answering
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
struct:
- name: text
sequence: string
- name: answer_start
sequence: int32
splits:
- name: train
num_bytes: 98742578
num_examples: 71999
- name: validation
num_bytes: 13100270
num_examples: 9583
- name: test
num_bytes: 12992195
num_examples: 9583
- config_name: rte
features:
- name: text1
dtype: string
- name: text2
dtype: string
- name: label
dtype:
class_label:
names:
'0': not entailment
'1': entailment
- name: idx
dtype: int32
- name: label_text
dtype: string
- name: text1_orig
dtype: string
- name: text2_orig
dtype: string
splits:
- name: train
num_bytes: 2134837
num_examples: 2490
- name: validation
num_bytes: 229013
num_examples: 277
- name: test
num_bytes: 1255739
num_examples: 1660
- config_name: nli
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': neutral
'1': entailment
'2': contradiction
- name: premise_orig
dtype: string
- name: hypothesis_orig
dtype: string
splits:
- name: train
num_bytes: 142579745
num_examples: 392702
- name: validation
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num_examples: 2490
- name: test
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num_examples: 5004
- config_name: sts
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: similarity_score
dtype: float64
- name: sentence1_orig
dtype: string
- name: sentence2_orig
dtype: string
splits:
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num_examples: 5604
num_bytes: 2184171
- name: validation
num_examples: 1481
num_bytes: 617309
- name: test
num_examples: 1352
num_bytes: 493116
configs:
- config_name: hate-speech
data_files:
- split: test
path: hate-speech/test.json
- split: train
path: hate-speech/train.json
- split: validation
path: hate-speech/validation.json
- config_name: sentiment-analysis
data_files:
- split: test
path: sentiment-analysis/test.json
- split: train
path: sentiment-analysis/train.json
- split: validation
path: sentiment-analysis/validation.json
- config_name: ner-wikigoldsk
data_files:
- split: test
path: ner-wikigoldsk/test.jsonl
- split: train
path: ner-wikigoldsk/train.jsonl
- split: validation
path: ner-wikigoldsk/dev.jsonl
- config_name: ner-uner
data_files:
- split: test
path: ner-uner/test.jsonl
- split: train
path: ner-uner/train.jsonl
- split: validation
path: ner-uner/dev.jsonl
- config_name: pos
data_files:
- split: test
path: pos/test.jsonl
- split: validation
path: pos/dev.jsonl
- split: train
path: pos/train.jsonl
- config_name: question-answering
data_files:
- split: test
path: question-answering/test.json
- split: validation
path: question-answering/validation.json
- split: train
path: question-answering/train.json
- config_name: rte
data_files:
- split: test
path: rte/test.json
- split: validation
path: rte/validation.json
- split: train
path: rte/train.json
- config_name: nli
data_files:
- split: test
path: nli/test.json
- split: validation
path: nli/validation.json
- split: train
path: nli/train.json
- config_name: sts
data_files:
- split: test
path: sts/test.json
- split: validation
path: sts/validation.json
- split: train
path: sts/train.json
Dataset Card for skLEP
Dataset Description
skLEP (General Language Understanding Evaluation benchmark for Slovak) is the first comprehensive benchmark specifically designed for evaluating Slovak natural language understanding (NLU) models. The benchmark encompasses nine diverse tasks that span token-level, sentence-pair, and document-level challenges, thereby offering a thorough assessment of model capabilities.
To create this benchmark, we curated new, original datasets tailored for Slovak and meticulously translated established English NLU resources with native speaker post-editing to ensure high quality evaluation.
Dataset Summary
skLEP, the General Language Understanding Evaluation benchmark for Slovak is a collection of resources for training, evaluating, and analyzing natural language understanding systems.
Supported Tasks and Leaderboards
skLEP includes nine tasks across three categories:
Token-Level Tasks:
- Part-of-Speech (POS) Tagging using Universal Dependencies
- Named Entity Recognition using Universal NER (UNER)
- Named Entity Recognition using WikiGoldSK (WGSK)
Sentence-Pair Tasks:
- Recognizing Textual Entailment (RTE)
- Natural Language Inference (NLI)
- Semantic Textual Similarity (STS)
Document-Level Tasks:
- Hate Speech Classification (HS)
- Sentiment Analysis (SA)
- Question Answering (QA) based on SK-QuAD
A public leaderboard is available at https://github.com/slovak-nlp/sklep
Languages
The language data in skLEP is in Slovak (BCP-47 sk)
Dataset Structure
Data Instances
The benchmark contains the following data splits:
- hate-speech: 10,531 train, 1,339 validation, 1,319 test examples
- sentiment-analysis: 3,560 train, 522 validation, 1,042 test examples
- ner-wikigoldsk: 4,687 train, 669 validation, 1,340 test examples
- ner-uner: 8,483 train, 1,060 validation, 1,061 test examples
- pos: 8,483 train, 1,060 validation, 1,061 test examples
- question-answering: 71,999 train, 9,583 validation, 9,583 test examples
- rte: 2,490 train, 277 validation, 1,660 test examples
- nli: 392,702 train, 2,490 validation, 5,004 test examples
- sts: 5,604 train, 1,481 validation, 1,352 test examples
Data Fields
Each task has specific data fields:
Token-level tasks (UD, UNER, WGSK): sentence, tokens, ner_tags/pos_tags, ner_tags_text
Sentence-pair tasks:
- RTE:
text1,text2,label,idx,label_text - NLI:
premise,hypothesis,label - STS:
sentence1,sentence2,similarity_score
Document-level tasks:
- Hate Speech/Sentiment:
text,label,id - Question Answering:
id,title,context,question,answers
Data Splits
All tasks follow a standard train/validation/test split structure. Some datasets (HS and QA) originally only had train/test splits, so validation sets were created by sampling from the training data to match the test set size.
Dataset Creation
Curation Rationale
skLEP was created to address the lack of a comprehensive benchmark for Slovak natural language understanding. While similar benchmarks exist for other Slavic languages (Bulgarian, Polish, Russian, Slovene), no equivalent existed for Slovak despite the emergence of several Slovak-specific large language models.
The benchmark was designed to provide a principled tool for evaluating language understanding capabilities across diverse tasks, enabling systematic comparison of Slovak-specific, multilingual, and English pre-trained models.
Source Data
Initial Data Collection and Normalization
Data was collected from multiple sources:
- Existing Slovak datasets: Universal Dependencies, Universal NER, WikiGoldSK, Slovak Hate Speech Database, Reviews3, SK-QuAD
- Translated datasets: RTE, NLI (XNLI), and STS were translated from English using machine translation services followed by native speaker post-editing
During preprocessing, duplicates were removed from XNLI and STS datasets. For STS, sentence pairs with identical text but non-perfect similarity scores were eliminated as translation artifacts.
Who are the source language producers?
The source language producers include:
- Native Slovak speakers for original Slovak datasets
- Professional translators and native Slovak post-editors for translated datasets
- Wikipedia contributors for WikiGoldSK and SK-QuAD
- Social media users for hate speech dataset
- Customer reviewers for sentiment analysis dataset
Annotations
Annotation process
Annotation processes varied by dataset:
- Token-level tasks: Following Universal Dependencies and Universal NER annotation guidelines
- WikiGoldSK: Manual annotation following BSNLP-2017 guidelines with CoNLL-2003 NER tagset
- Hate Speech: Expert annotation with quality filtering (removing annotators with >90% uniform responses or <70% agreement)
- Sentiment Analysis: Manual labeling by two annotators reaching consensus
- SK-QuAD: Created by 150+ volunteers and 9 part-time annotators, validated by 5 paid reviewers
- Translated datasets: Professional translation followed by native speaker post-editing
Who are the annotators?
Annotators include:
- Expert linguists and NLP researchers for token-level tasks
- Native Slovak speakers for post-editing translated content
- Domain experts for hate speech classification
- Trained volunteers and professional annotators for SK-QuAD
- Customer service experts for sentiment analysis
Personal and Sensitive Information
The hate speech dataset contains social media posts that may include offensive language by design. Personal information was removed during preprocessing. Other datasets (Wikipedia-based, customer reviews, translated content) have minimal personal information risk.
Considerations for Using the Data
Social Impact of Dataset
skLEP enables systematic evaluation and improvement of Slovak NLP models, supporting the development of better language technology for Slovak speakers. The hate speech detection task specifically contributes to online safety tools for Slovak social media platforms.
Discussion of Biases
Potential biases include:
- Domain bias: Wikipedia-heavy content in several tasks may not represent colloquial Slovak
- Translation bias: Translated tasks may carry over English linguistic patterns
- Social media bias: Hate speech dataset reflects specific online communities
- Geographic bias: May favor standard Slovak over regional variants
Other Known Limitations
- Some test sets differ from English counterparts due to translation and re-labeling requirements
- Dataset sizes vary significantly across tasks
- Limited coverage of specialized domains outside Wikipedia and social media
- Validation sets for some tasks were created by splitting training data rather than independent collection
Additional Information
Dataset Curators
skLEP was curated by researchers from:
- Comenius University in Bratislava, Slovakia
- Technical University of Košice, Slovakia
- Kempelen Institute of Intelligent Technologies, Bratislava, Slovakia
- Cisco Systems
Lead contact: Marek Šuppa ([email protected])
Licensing Information
The primary skLEP tasks are built on and derived from existing datasets. We refer users to the original licenses accompanying each dataset.
Citation Information
If you use skLEP, please cite the following paper:
@inproceedings{suppa-etal-2025-sklep,
title = "sk{LEP}: A {S}lovak General Language Understanding Benchmark",
author = "Suppa, Marek and
Ridzik, Andrej and
Hl{\'a}dek, Daniel and
Jav{\r{u}}rek, Tom{\'a}{\v{s}} and
Ondrejov{\'a}, Vikt{\'o}ria and
S{\'a}sikov{\'a}, Krist{\'i}na and
Tamajka, Martin and
Simko, Marian",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1371/",
pages = "26716--26743",
ISBN = "979-8-89176-256-5",
abstract = "In this work, we introduce skLEP, the first comprehensive benchmark specifically designed for evaluating Slovak natural language understanding (NLU) models. We have compiled skLEP to encompass nine diverse tasks that span token-level, sentence-pair, and document-level challenges, thereby offering a thorough assessment of model capabilities. To create this benchmark, we curated new, original datasets tailored for Slovak and meticulously translated established English NLU resources. Within this paper, we also present the first systematic and extensive evaluation of a wide array of Slovak-specific, multilingual, and English pre-trained language models using the skLEP tasks. Finally, we also release the complete benchmark data, an open-source toolkit facilitating both fine-tuning and evaluation of models, and a public leaderboard at \url{https://github.com/slovak-nlp/sklep} in the hopes of fostering reproducibility and drive future research in Slovak NLU."
}
Contributions
Contributions to skLEP include:
- First comprehensive Slovak NLU benchmark with 9 diverse tasks
- High-quality translations with native speaker post-editing
- Extensive baseline evaluations across multiple model types
- Open-source toolkit and standardized leaderboard
- Rigorous evaluation methodology with hyperparameter optimization
Future contributions and improvements are welcome through the project repository.