datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
seneca-cybench
Seneca-CyBench - Cybersecurity LLM Benchmark
Seneca-CyBench: A comprehensive benchmark system designed to evaluate Large Language Models (LLMs) on cybersecurity domain knowledge. Features GPT-4o-based automated scoring for objective assessment of model capabilities across security topics.
620 questions (310 MCQ + 310 SAQ) covering all major cybersecurity domains including GRC, Security Architecture, Cloud Security, IAM, and more.
🌟 Supported Providers
🔵 OpenAI… See the full description on the dataset page: https://huggingface.co/datasets/AlicanKiraz0/seneca-cybench.sob-ft-finetune-ready
SOB-FT Finetune Ready
~100k source rows → ~152k chat SFT examples for fine-tuning a small language model on JSON extraction (generation) and JSON error detection / repair (correction), with prompts aligned to our SOB extraction and zero-shot repair benchmarks.
Derived from mariem123kfg/sob-ft-extract (multi-source structured extraction corpus, excluding original SOB benchmark rows). Errors were injected in-house, then rows were materialized into ready-to-train prompt/target… See the full description on the dataset page: https://huggingface.co/datasets/seneca-center/sob-ft-finetune-ready.seneca-trbench
🇹🇷 Seneca-TRBench Leaderboard
Seneca-TRBench is a comprehensive benchmark for evaluating Large Language Models (LLMs) on Turkish language proficiency.
📊 Benchmark Overview
Test Formats
MCQ (Multiple Choice Questions)
131 questions across 13 categories
Tests structural knowledge of Turkish morphology, phonology, and syntaxBinary scoring: correct/incorrect
SAQ (Short Answer Questions)
422 questions across 39 categories
Assesses… See the full description on the dataset page: https://huggingface.co/datasets/AlicanKiraz0/seneca-trbench.SOB-with-errors-injection
SOB With Errors Injection — CAREBench Repair Eval Holdout
Frozen JSON repair / error-detection benchmark used in CAREBench. Each row pairs a schema-aligned gold answer with a synthetically corrupted JSON candidate and structured error metadata.
Dataset: seneca-center/SOB-with-errors-injection
Dataset
Rows
5,000 (test split)
Task
Detect errors in a candidate JSON and produce corrected JSON matching validated_output under json_schema
Eval mode in… See the full description on the dataset page: https://huggingface.co/datasets/seneca-center/SOB-with-errors-injection.f1-seneca