explicit
siglip2-x256-explicit-contentbert-large-uncased-pdtb2-explicit-four-wayyuntian-deng_-_gpt2-explicit-cot-multiplication-20-digits-ggufsiglip2-x256p32-explicit-contentintent-aware-lfqa-qwen3-4b-intent-explicit-GGUFintent-aware-lfqa-qwen3-8b-intent-explicit-GGUFintent-aware-lfqa-llama3-8b-intent-explicit-GGUFpony-nsfw-explicit-raw-photography
Datasets
All datasets matching “explicit”explicit-edit-benchmark
Explicit Edit Benchmark
226 deterministic exact-edit tasks, run by different agents, harnesses, models and configurations. Every observation records what the harness did and whether the resulting files matched byte for byte.
Source code and benchmark runner: GitHub — Explicit Edit Benchmark
Open the interactive Explorer to compare agents, harnesses, models, versions, reasoning modes, correctness, recovery, time, cost and tokens.
Leaderboard by model route
Score v2… See the full description on the dataset page: https://huggingface.co/datasets/alexshpunt/explicit-edit-benchmark.task323_jigsaw_classification_sexually_explicit
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task323_jigsaw_classification_sexually_explicit
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task323_jigsaw_classification_sexually_explicit.prefeval_explicit
PrefEval Benchmark: Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMs
Welcome to the PrefEval dataset repository!
| Website | Paper | GitHub Repository |
Dataset Overview
We introduce PrefEval, a benchmark for evaluating LLMs' ability to infer, memorize and adhere to user preferences in a long-context conversational setting. The benchmark consists of three distinct preference forms, each requiring different levels of preference… See the full description on the dataset page: https://huggingface.co/datasets/siyanzhao/prefeval_explicit.hedgehog-schema-explicit
hedgehog-schema-explicit
Hedgehog — explicit-schema extraction training.
Contents
train.jsonl (1280 rows)
validation.jsonl (160 rows)
test.jsonl (192 rows)
Format
JSON Lines (.jsonl), one example per line.
Provenance
Original content for the Hedgehog extraction model (Michael Anthony Falabella).
annotator-9-11-explicit-rating-data-vocalsDanbooru2024_rating_explicit_prompts_without_character精选了danbooru2024里分级为explict且score>20的所有图像的prompt，去除了原角色的tag和相关特征，可以直接用于角色nsfw图像生成