datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
LDS-retrain-bank-adamw-N16k-bs256-gpt2-mediumPARTIAL_LDS-retrain-bank-gpt2medium-16k-bs32GPT-wiki-intro
GPT Wiki Intro
Overview
Dataset for training models to classify human written vs GPT/ChatGPT generated text.
This dataset contains Wikipedia introductions and GPT (Curie) generated introductions for 150k topics.
Prompt used for generating text
200 word wikipedia style introduction on '{title}'
{starter_text}
where title is the title for the wikipedia page, and starter_text is the first seven words of the wikipedia introduction.
Here's an example of prompt used to… See the full description on the dataset page: https://huggingface.co/datasets/aadityaubhat/GPT-wiki-intro.bergson-wikitext-gpt2-leaderboard-bank
bergson leaderboard: retrain banks, scores and LDS/QLD results (WikiText GPT-2)
Everything behind the numbers on the bergson leaderboard,
for the model at EleutherAI/bergson-wikitext-gpt2-leaderboard.
path
what it is
bank/
the LDS ground truth: 100 random leave-1%-out subsets of the 4,608 training chunks (subsets.json) and each subset's measured loss change on the 50 test queries (validation.csv)
random/retrained/{base,subset_0..99}
the retrained models themselves… See the full description on the dataset page: https://huggingface.co/datasets/EleutherAI/bergson-wikitext-gpt2-leaderboard-bank.gpt4_bias
Assessing GPT-4’s Potential for Perpetuating Racial and Gender Biases in Healthcare
This repository accompanies the paper "Coding Inequity: Assessing GPT-4’s Potential for Perpetuating Racial and Gender Biases in Healthcare".
Overview
The data is available in the data_to_share folder. This can be broken into several pieces:
simulated_pt_distribution --- here is where we store all the information for generating patient demographic distributions. We store the outputs of… See the full description on the dataset page: https://huggingface.co/datasets/katielink/gpt4_bias.GPTMicro-Nanowire-Sintering
GPTMicro — Nanowire Sintering & Symbolic Regression Dataset
Curated data for data-driven discovery of governing equations in nanowire
sintering. It pairs raw molecular-dynamics (MD) trajectories with the ML-ready
train/validation/test splits used to learn closed-form models for the sintering
dynamics (change in flattening ddelta and rotation dtheta) and for two
effective material properties (effective diffusion coefficient D_eff and
effective relaxation/viscosity coefficient… See the full description on the dataset page: https://huggingface.co/datasets/Kiarash99/GPTMicro-Nanowire-Sintering.arc-agi-3-schema-traces-gpt56
ARC-AGI-3 Schema Gameplay Trajectories — GPT-5.6 Sol
This release contains every gpt-5.6-sol gameplay trajectory produced on our
cluster with the world_model_v5 agent harness — 100 runs across the 25 public
ARC-AGI-3 games — plus a dependency-free scoring utility.
It is the GPT-5.6 Sol member of a family built by the same harness and the same
sanitizer, so trajectories can be compared game by game:
arc-agi-3-schema-traces-fable5 — Claude Fable 5, best per game (25)… See the full description on the dataset page: https://huggingface.co/datasets/guanning/arc-agi-3-schema-traces-gpt56.patents-classified-2106-gpt5-minigptchemmt_bench_single_score_gpt4_judgementOpenChatData
OpenChatData
OpenChatData is an anonymized dataset derived from database dumps from a discontinued AI chatbot service that routed model requests through OpenRouter.
The dataset contains 20,949 chat-log records collected between February 4, 2026 and April 5, 2026, covering usage across 27 model identifiers.
Important: OpenChatData does not contain the text of user prompts or model responses. The released data consists of metadata and aggregate measurements such as token, word… See the full description on the dataset page: https://huggingface.co/datasets/gptforfree/OpenChatData.gpt-image-edit-benchmark-results
GPT-Image-Edit — Benchmark Results
This repository contains evaluation results of GPT-Image-Edit across four standard image-editing benchmarks. All scores were computed using the official evaluation scripts provided by each benchmark.
📊 Benchmarks
Benchmark
Metrics
Folder
GEdit-EN
12 editing categories + Avg
gedit/
Complex-Edit
IF, IP, PQ, Overall
complex_edit/
ImgEdit-Full
10 editing operations + Overall
imgedit/
OmniContext
Contextual edit scores… See the full description on the dataset page: https://huggingface.co/datasets/UCSC-VLAA/gpt-image-edit-benchmark-results.defuser-grounding-coordinates_resultsGPTtrainlogsgpt_routing
Model Description
This model is designed for prompt routing to determine whether a prompt should be handled by GPT-4o or GPT-3.5. The goal is to reduce costs, as GPT-4o is significantly more expensive (10x the cost of GPT-3.5).
The dataset is intended for few-shot learning, which is why it contains a small number of prompts in two languages: Vietnamese and English.
Labels were manually assigned based on specific use cases. You can expand the classification to include other models… See the full description on the dataset page: https://huggingface.co/datasets/mrzaizai2k/gpt_routing.gpt4.1_promptA_results_aggregatedarc-agi-3-schema-traces-gpt56-xhigh
ARC-AGI-3 Schema Gameplay Trajectories — GPT-5.6 Sol (xhigh)
The best gpt-5.6-sol trajectory at xhigh reasoning effort for each of the
25 public ARC-AGI-3 games, produced with the world_model_v5 agent harness.
This release exists to make the cross-model comparison single-effort on all
sides. Its siblings are each one model at one effort, but the
gpt-5.6-sol collection in
arc-agi-3-schema-gameplay
is a mix of xhigh and max (16 games + 9 games), so it is not directly
comparable to… See the full description on the dataset page: https://huggingface.co/datasets/guanning/arc-agi-3-schema-traces-gpt56-xhigh.small-GPT-wiki-intro-features
Small-GPT-wiki-intro-features dataset
This dataset is based on aadityaubhat/GPT-wiki-intro.
It contains 100k randomly selected texts (50k from Wikipedia and 50k generated by ChatGPT).
For each text, various complexity measures were calculated, including e.g. readibility, lexical richness etc.
It can be used for text classification or analysis of linguistic features of human-generated and ChatGPT-generated texts.
Dataset structure
Features were calculated using… See the full description on the dataset page: https://huggingface.co/datasets/julia-lukasiewicz-pater/small-GPT-wiki-intro-features.bergson-magic-scores-gpt-2
bergson MAGIC scores — GPT-2 / wikitext-2 / random validation
Per-document MAGIC attribution scores for a GPT-2 model fine-tuned on
Salesforce/wikitext wikitext-2-raw-v1 train, attributing the
training-loss gradient with respect to the model's output on
test[3:4] (a single test example, taken as the query).
Scores were produced by the bergson
attribution toolkit's MAGIC subcommand, which back-propagates through
the entire training trajectory to compute, for each training doc d… See the full description on the dataset page: https://huggingface.co/datasets/EleutherAI/bergson-magic-scores-gpt-2.finbert-gptmt_bench_pairwise_comparison_gpt4_judgmentsgpt3.5_tweetsdefuser-vqa-oe_resultsGPT-wiki-intro-features
Small-GPT-wiki-intro-features dataset
This dataset is based on aadityaubhat/GPT-wiki-intro.
It contains 150k short texts from Wikipedia (label 0) and corresponding texts generated by ChatGPT (label 1) (together 300k texts).
For each text, various complexity measures were calculated, including e.g. readability, lexical diversity etc.
It can be used for text classification or analysis of linguistic features of human-generated and ChatGPT-generated texts.
For a smaller version… See the full description on the dataset page: https://huggingface.co/datasets/julia-lukasiewicz-pater/GPT-wiki-intro-features.scores-bin-curated-gpt4gpt89-v2qwen_2.5_7b_soln_gpt_4o_verifydefuser-grounding-som_resultsexplanation_gpt
Dataset Card for Dataset Name
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Dataset Details
Dataset Description
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Dataset Sources [optional]
Repository: [More… See the full description on the dataset page: https://huggingface.co/datasets/neenax/explanation_gpt.expert-element-grounding_results