datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
train-dpr-wikipedia
DPRWikipedia — Training, unified schema
A normalised copy of the dataset behind the mteb task DPRWikipedia, a retrieval training set built from Tevatron/wikipedia-nq-corpus. Same queries, documents
and relevance judgements as the benchmark evaluates — reshaped into one strict schema shared by every dataset
in this collection.
Source
Tevatron/wikipedia-nq-corpus @ 56c6e2438c13 (the revision pinned in mteb)
Domain · languages
Wikipedia QA (DPR) · eng
Queries /… See the full description on the dataset page: https://huggingface.co/datasets/Hyukkyu/train-dpr-wikipedia.wiki_dpr_token
Dataset Card for "wiki_dpr_token"
Distribution
[
{ // Token length
'~128': 2625007,
'128~256': 18370607,
'256~512': 19066,
'512~1024': 571,
'1024~2048': 47,
'2048~4096': 2,
'4096~8192': 0,
'8192~16384': 0,
'16384~32768': 0,
'32768~65536': 0,
'65536~128000': 0,
'128000~': 0,
},
{ // Text length
'~512': 86519,
'512~1024': 20927180,
'1024~2048': 1557,
'2048~4096': 43,
'4096~8192': 1,
'8192~16384':… See the full description on the dataset page: https://huggingface.co/datasets/seonglae/wiki_dpr_token.DPriv-Bench
DPrivBench: Benchmarking LLMs’ Reasoning for Differential Privacy
DPrivBench is a benchmark for evaluating whether language models can correctly reason about and verify claimed differential privacy (DP) guarantees from natural-language/LaTeX-format problem statements.
This release contains evaluation data from seven benchmark configs, along with one auxiliary function bank:
Category 1: 6 fundamental mechanism tracks, each with 98 questions.
Category 2: 125 more advanced… See the full description on the dataset page: https://huggingface.co/datasets/erchiw/DPriv-Bench.DPR
Diabetic Patient 30-Day Readmission Dataset
Dataset Summary
This dataset is derived from the Kaggle Diabetic Patients Readmission Prediction dataset. The original dataset contains electronic health record data from diabetic patient hospital encounters and is commonly used for hospital readmission prediction.
This released version organizes the repository into three levels of data:
Raw Data: the original downloaded source files.
Intermediate Data: derived files before… See the full description on the dataset page: https://huggingface.co/datasets/YPL67/DPR.telco-dpr-rag
Telco-DPR RAG
Dataset for Retrieval-Augmented Generation (RAG) based on Telco-DPR.
Structure
Subset
Splits
Description
corpus
train (default)
3GPP technical passages (text + tables) shared across all query splits
queries
train, dev, test
Synthetic telecom QA questions
qrels
train, dev, test
Relevance judgments (query ↔ passage)
answers
train, dev, test
Reference answers
Dataset statistics
Split
Queries
Corpus
train… See the full description on the dataset page: https://huggingface.co/datasets/DinoStackAI/telco-dpr-rag.massive_serve_dpr_wiki_contriever_ivfpqconvmix_dpr_wiki-text-6-3-tamber_top100dp_removed_aime_2026
dp_removed_aime_2026
Source: MathArena/aime_2026
Pinned source revision: d2de22f3c656b4f56cf8981212186377d1e23bc3. Config: default. Split: train. Retained rows: 30.
All retained rows and original columns are unchanged, including English problems, solutions, reasoning traces, prompt wrappers and source IDs. No translation, text repair, model generation, or added data columns were applied. Stable preparation IDs are recorded separately in provenance.json.
This evaluation split is… See the full description on the dataset page: https://huggingface.co/datasets/Seungjun/dp_removed_aime_2026.eval_DPRecordTestThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "so100",
"total_episodes": 1,
"total_frames": 224,
"total_tasks": 1,
"total_videos": 2,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:1"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/shylee/eval_DPRecordTest.massive_serve_dpr_wiki_e5_base_v2_ivfpqmassive_serve_dpr_wiki_qwen3_0.6b_ivfpqTopiOCQA_dpr_pyserini_top100_with_textDPR_Q0.5B_U10_beta0.10g0.30gamma0.30wikitext-2-raw-v1-preprocessed-200-PP_RIP_PTP_-wikipedia-dpr-k-2-OP-False-train-perplexitywikitext-2-raw-v1-preprocessed-200-PI_KFI_-wikipedia-dpr-k-1-OP-True-train-perplexitywikitext-2-raw-v1-preprocessed-200-PP_RIP_PTP_-wikipedia-dpr-k-2-OP-True-train-perplexitywikitext-2-raw-v1-preprocessed-200-PP_RIP_PTP_-wikipedia-dpr-k-1-OP-False-train-perplexitywikitext-2-raw-v1-preprocessed-200-PI_KFI_-wikipedia-dpr-k-2-OP-False-train-PI_KFI-perplexitywikitext-2-raw-v1-preprocessed-200-PI_KFI_-wikipedia-dpr-k-1-OP-False-train-perplexitywikitext-2-raw-v1-preprocessed-200-PI_KFI_-wikipedia-dpr-k-2-OP-True-train-perplexitymed_retrieved_dpr_ctxsDPR_Pm3B_U10_beta0.10g0.30gamma0.30DPR_Pm3B_U10_beta0.10g0.30gamma0.30wikitext-2-raw-v1-preprocessed-200-PI_KFI_-wikipedia-dpr-k-2-OP-True-train-PI_KFI-perplexitywikitext-2-raw-v1-preprocessed-200-PI_KFI_-wikipedia-dpr-k-1-OP-True-train-PI_KFI-perplexityPILE_Wikipedia_validation_set_insert_ret_tokens-wikipedia-dpr-k-1-OP-TruePILE_Wikipedia_validation_set_insert_ret_tokens-wikipedia-dpr-k-1-OP-FalseDPR_Q0.5B_U10_beta0.10g0.30gamma0.30wikitext-2-raw-v1-preprocessed-200-PI_KFI_-wikipedia-dpr-k-2-OP-True-train-PI_KFI_IK-perplexitywikitext-2-raw-v1-preprocessed-200-PI_KFI_-wikipedia-dpr-k-1-OP-False-train-PI_KFI_IK-perplexity