datasetId stringlengths 6 123 | predicted_domain stringclasses 10
values | confidence float64 0.14 1 | top2_label stringclasses 10
values | top2_score float64 0 0.5 | tag_domain stringclasses 9
values | existing_tags listlengths 0 190 | card_preview stringlengths 0 500 | card_length int64 0 25.3M | downloads int64 0 2.75M | category stringclasses 4
values |
|---|---|---|---|---|---|---|---|---|---|---|
dgorbatov/vlmn_tartandrive100_scand50_coda25_spot100_sub5_full_augmentation_25_batch_093 | none | 0.9621 | code | 0.0122 | null | [] | 0 | 5 | normal | |
andrewsiah/PersonaPromptPersonalLLM_53 | none | 0.9756 | code | 0.0181 | null | [] | # Dataset Card for "PersonaPromptPersonalLLM_53"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 181 | 4 | normal |
OpenSpeechHub/Genshin-Voice-Ja | none | 0.8281 | code | 0.1079 | null | [] | 0 | 392 | normal | |
asingh15/Nemotron-Personas-USA-synthetic-records-10files-qa-openai-retriever | none | 0.8221 | code | 0.1317 | null | [] | 0 | 5 | normal | |
ajinkyaT/mann_ki_baat_speech | none | 0.5664 | code | 0.086 | null | [] | 0 | 9 | normal | |
open-llm-leaderboard-old/details_Gille__StrangeMerges_52-7B-dare_ties | none | 0.8589 | biology | 0.0949 | null | [] | # Dataset Card for Evaluation run of Gille/StrangeMerges_52-7B-dare_ties
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Gille/StrangeMerges_52-7B-dare_ties](https://huggingface.co/Gille/StrangeMerges_52-7B-dare_ties) on the [Open LLM Leaderboard](htt... | 19,266 | 14 | normal |
math-extraction-comp/T145__KRONOS-8B-V7 | math | 0.5751 | none | 0.2627 | null | [] | 0 | 4 | normal | |
Emin009/AI-for-RIM | none | 0.9714 | code | 0.0242 | null | [
"hassaniya",
"mauritania",
"dialect"
] | # AI for RIM: Hassaniya Dialect Datasets 🇲🇷
This repository contains open-source datasets designed for training and fine-tuning Large Language Models (LLMs) on the **Hassaniya** dialect of Mauritania.
## Datasets
The repository currently hosts two primary datasets in JSONL format, ready for LLM training:
### 1. T... | 1,585 | 13 | normal |
dgambettaphd/D_mis_run1_gen0_WXS_doc1000_synt64_lr1e-04_acm_LANG | none | 0.771 | code | 0.113 | null | [] | 0 | 6 | normal | |
afraamn/tulu_3_rewritten_400k_math_50k | math | 0.5316 | none | 0.309 | null | [] | 0 | 25 | normal | |
datadreamer-dev/hotpot_qa_augmented | none | 0.9198 | code | 0.0548 | null | [
"datadreamer",
"datadreamer-0.1.0",
"gpt-4"
] | # Dataset Card
See: https://datadreamer.dev/docs/latest/pages/get_started/quick_tour/dataset_augmentation.html
---
This dataset was produced with [DataDreamer 🤖💤](https://datadreamer.dev). The synthetic dataset card can be found [here](datadreamer.json). | 257 | 43 | normal |
lvogel123/jailbreak-gpt-5-high | none | 0.6627 | code | 0.1966 | null | [] | 0 | 7 | normal | |
yj33/record-test | none | 0.9248 | code | 0.07 | null | [] | This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v3.0",
"... | 3,575 | 20 | normal |
zonglin11/lerobot_lesson_colorblock | none | 0.9471 | code | 0.046 | null | [] | This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v3.0",
"... | 2,949 | 14 | normal |
bobox/nq-hardnegatives | none | 0.4642 | chemistry | 0.3099 | null | [] | 0 | 8 | normal | |
DogNeverSleep/DREAM_1K_Test | none | 0.5277 | biology | 0.1829 | null | [] | 0 | 3 | normal | |
schism-audio/rirs-noises | none | 0.8463 | medical | 0.0601 | null | [
"room-impulse-response",
"audio-augmentation",
"rir",
"noise"
] | # RIRS NOISES
## Quick Start
```python
from datasets import load_dataset
# Stream to avoid downloading the entire dataset
ds = load_dataset("schismaudio/rirs-noises", streaming=True)
# Or download locally
ds = load_dataset("schismaudio/rirs-noises")
```
## Dataset Description
**RIRS NOISES** is a collection of si... | 4,032 | 1,961 | normal |
7alexzhang7/so101-turn-steve-lever-right-to-left | none | 0.903 | code | 0.0925 | null | [] | This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v3.0",
"... | 4,867 | 8 | normal |
Rhynoa/Fundador | biology | 0.2697 | none | 0.1922 | null | [] | 0 | 6 | boundary | |
yunjae-won/ultrafeedback-pref | none | 0.5503 | code | 0.1471 | null | [] | 0 | 44 | normal | |
Lyric1010/infinity-llama3-merge | none | 0.9881 | code | 0.0057 | null | [
"text",
"pretraining"
] | # Dataset: infinity-llama3-merge
This dataset was uploaded from `/mnt/yulan_pretrain/mount/data_final_train_llama3/infinity-llama3-merge/stage_1`. | 147 | 4 | normal |
Karayel-DDI/Turkce_Lighteval_MATH-Hard | none | 0.6965 | code | 0.2038 | null | [] | ##
This dataset is a machine-translated version of [lighteval/MATH-Hard](https://huggingface.co/datasets/lighteval/MATH-Hard). We translated it using machine translation for the Teknofest 2024 Natural Language Processing competition. | 234 | 3 | normal |
YangZhoumill/factorrandom_8k_medium | none | 0.5671 | chemistry | 0.1711 | null | [] | 0 | 5 | normal | |
AyushS9020/lima_dataset_processed | none | 0.3935 | climate | 0.1915 | null | [] | 0 | 3 | normal | |
Kittigon/Dataset-720 | none | 0.4877 | climate | 0.141 | null | [] | 0 | 3 | normal | |
Srinivasmec26/Educational-Flashcards-for-Global-Learners | none | 0.9894 | cybersecurity | 0.004 | null | [
"education",
"science",
"flashcards",
"interactive",
"learning",
"math",
"technology",
"engineering",
"medicine",
"law",
"chem",
"physics",
"chemistry",
"socialscinces"
] | ### 1. Educational-Flashcards-for-Global-Learners/README.md
# Educational Flashcards Dataset
## Overview
A comprehensive collection of 100 educational flashcards covering STEM, humanities, law, arts, and cultural topics. Curated with 70% Indian content, 25% European, and 5% other Asian perspectives to promote diverse... | 1,662 | 22 | normal |
tyfeld/implicit-change-top500 | none | 0.6656 | code | 0.1316 | null | [] | 0 | 4 | normal | |
Sraghvi/buster_ultra_clean | none | 0.9314 | code | 0.0542 | null | [
"robotics",
"manipulation",
"bimanual"
] | # buster_ultra_clean
LeRobot v2.1 format dataset for robot manipulation.
## Dataset Structure
- **Episodes**: 1 episodes of robot manipulation
- **Total Frames**: 605 frames
- **Cameras**: 3 camera views per episode
- `observation.images.base_camera_sensor_image_raw`
- `observation.images.arm1_camera_sensor_imag... | 1,657 | 6 | normal |
sergshymko/testdataset3 | none | 0.4118 | chemistry | 0.1547 | null | [] | 0 | 3 | normal | |
louis126584/so101_test | none | 0.9473 | code | 0.0453 | null | [] | This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v3.0",
"... | 2,949 | 8 | normal |
melhoushi/sw_awq_dataset | none | 0.6354 | code | 0.0633 | null | [] | 0 | 3 | normal | |
MayAlsofyani/OneBugMixCotWithex2 | none | 0.2846 | code | 0.2219 | null | [] | 0 | 5 | normal | |
gguichard/wsd_myriade_synth_data_gpt4turbo_val_1_10k | none | 0.6044 | chemistry | 0.1223 | null | [] | 0 | 6 | normal | |
Kyleyee/white_box_test_results | none | 0.8008 | code | 0.0746 | null | [] | 0 | 3 | normal | |
SolaireOfTheSun/BioDHBWAnderesFormat | biology | 0.8917 | none | 0.0463 | null | [] | The following is a german fine-tuning Biology Dataset for LLMs created for a DHBW Student Project. | 98 | 3 | new_discovery |
dgambettaphd/D_mis73_run0_gen6_WXS_doc1000_synt64_lr1e-04_acm_MPP | none | 0.7387 | biology | 0.0668 | null | [] | 0 | 8 | normal | |
fscbac-standard/fscbac-book-metadata-dataset | none | 0.885 | code | 0.0868 | null | [] | # FSCBAC Book Metadata Dataset
This dataset contains structured metadata for children's books aligned with the FSCBAC Standard 3.1.0.
It provides machine-readable entries describing book structure, linguistic load, emotional intensity, visual load, developmental purpose, and recommended usage.
The dataset does not ... | 821 | 5 | normal |
dramaamard2/dataset_clean_json | none | 0.414 | climate | 0.1694 | null | [] | 0 | 3 | normal | |
RealEmmettS/general-v-online-llm | none | 0.664 | biology | 0.0849 | code | [
"code"
] | 0 | 3 | tag_disagree | |
dmatekenya/items_raw_full | none | 0.5239 | finance | 0.0946 | null | [] | 0 | 6 | normal | |
nhagar/CC-MAIN-2019-13_nyt_urls | none | 0.38 | climate | 0.1948 | null | [] | 0 | 3 | normal | |
kaiwenw/nov5_sp1_jdpo_gap_0.25 | none | 0.4131 | climate | 0.2868 | null | [] | 0 | 5 | normal | |
acomquest/sanskrit-ocr-post-correction | none | 0.979 | legal | 0.0089 | null | [
"text-generation",
"post-editing"
] | # Sanskrit OCR Post-Correction Dataset
## Dataset Description
- **Homepage:** https://github.com/ayushbits/pe-ocr-sanskrit
- **Repository:** https://github.com/ayushbits/pe-ocr-sanskrit
- **Paper:** [A Benchmark and Dataset for Post-OCR text correction in Sanskrit](http://arxiv.org/abs/2211.07980)
### Dataset Summar... | 3,631 | 162 | normal |
aisi-whitebox/sec_qa_v2_cot_prompted_sandbagging_llama_33_70b_instruct | none | 0.5563 | code | 0.3994 | null | [
"deception",
"safety",
"sandbagging"
] | # Inspect Dataset: inspect_llama_33_70b_instruct_prompted_sandbagging_sec_qa_v2_cot
## Dataset Information
This dataset was created using the `create_inspect_dataset` function from the deception_sprint package on 2025-04-22.
### Model Information
- **Model**: `vllm/meta-llama/Llama-3.3-70B-Instruct`
### Task Inform... | 3,816 | 4 | normal |
electricsheepafrica/sanitation-open-defecation | medical | 0.6823 | none | 0.2543 | null | [
"environmental-health",
"sanitation",
"open-defecation",
"CLTS",
"WASH",
"diarrhoea",
"sub-saharan-africa"
] | # Sanitation & Open Defecation Elimination in Sub-Saharan Africa
## Abstract
Synthetic dataset modelling sanitation coverage, open defecation (OD) practices, CLTS interventions, and health outcomes across three settings in SSA. An estimated 215 million people practice OD in SSA. Pooled OD prevalence is 22.55% across ... | 2,598 | 44 | normal |
raflimuhammadh12/humanoid-viking-data | none | 0.9333 | code | 0.0274 | null | [] | 0 | 5 | normal | |
eminorhan/makin | none | 0.399 | climate | 0.1103 | null | [] | 0 | 5 | normal | |
jdchang/evol_instruct | climate | 0.2629 | chemistry | 0.256 | null | [] | 0 | 4 | boundary | |
vishnun0027/billsum_dataset | none | 0.3087 | climate | 0.19 | null | [] | 0 | 3 | normal | |
qtqtqtqt/OpenGuardrailsMixZh_97k | cybersecurity | 0.6031 | none | 0.367 | null | [] | # OpenGuardrailsMixZh 97k
- **Repository:** [openguardrails/OpenGuardrailsMixZh_97k](https://huggingface.co/openguardrails/OpenGuardrailsMixZh_97k)
- **License:** Apache 2.0
- **Paper:** [OpenGuardrails: An Open-Source Context-Aware AI Guardrails Platform](https://arxiv.org/abs/2510.19169)
- **Code:** [openguardrails/... | 3,851 | 11 | normal |
bluecopa/smalldocs-v2 | code | 0.4121 | none | 0.2787 | null | [] | 0 | 7 | boundary | |
yanlaiy/rvs | code | 0.5811 | none | 0.2806 | null | [] | This benchmark was collected and published by [Flash-VStream](https://github.com/IVGSZ/Flash-VStream).
We conducted some processing for the experiments presented in our paper [ReKV](https://github.com/Becomebright/ReKV). | 220 | 6 | normal |
ssharkey/finetuning_demo | none | 0.3552 | code | 0.2281 | null | [] | 0 | 4 | normal | |
jihuny/llama_nec_10k_sky_active_mgopt_a10_l01 | none | 0.483 | medical | 0.2679 | null | [] | 0 | 6 | normal | |
jingyiZ00/R1-VL-10K | none | 0.7063 | biology | 0.1484 | null | [
"MLLM",
"r1"
] | Please check our GitHub for more details: https://github.com/jingyi0000/R1-VL. | 78 | 74 | normal |
jarod0411/cancer_linker | biology | 0.4278 | none | 0.1789 | null | [] | 0 | 6 | normal | |
NadineMostafa/AnghaBench_risc_clang_o2_1percent | none | 0.6496 | code | 0.0913 | null | [] | 0 | 7 | normal | |
ADHIZ/stabilty_diffusion_resnetz | none | 0.4915 | chemistry | 0.2224 | null | [] | 0 | 5 | normal | |
boda/review_evaluation_main_3 | none | 0.3943 | chemistry | 0.2637 | null | [] | 0 | 5 | normal | |
farrell236/DeepLesion | medical | 0.9381 | none | 0.0357 | null | [] | # NIH DeepLesion Dataset
## Introduction
The [**DeepLesion**](https://nihcc.app.box.com/v/DeepLesion) dataset contains 32,120 axial computed tomography (CT) slices from 10,594 CT scans (studies) of 4,427 unique patients. There are 1–3 lesions in each image with accompanying bounding boxes and size measurements, addin... | 2,405 | 564 | new_discovery |
markhellrich/graph-topology-dataset-eval | none | 0.3339 | chemistry | 0.2585 | null | [] | 0 | 3 | normal | |
DEEP-LEARNING/JULIAN-OR-GREGORIAN | biology | 0.5608 | none | 0.2546 | null | [] | DIVIDE THEM ALL AND CONTINUE TO MEASURE | 39 | 0 | normal |
umsa-v1/dataset_regulations-eu_grupo8-SharonCalcina_final | none | 0.7742 | code | 0.0582 | null | [] | 0 | 12 | normal | |
Vxvmnz/Hulakok | none | 0.2893 | climate | 0.1883 | null | [] | 0 | 7 | normal | |
tonytechtalent/Article | none | 0.6672 | code | 0.074 | null | [] | # Latest Article
FTL stands for Full Truck Load, a term used when a shipment fills an entire truck without sharing space with other shipments.
---
*Last updated: 34epd7vq* | 173 | 9 | normal |
zjhhhh/iter2_7b_multi_gap_0.15_scores_base_63 | none | 0.8164 | climate | 0.0724 | null | [] | 0 | 5 | normal | |
nativemind/mozgach_alpaca_gift_data | none | 0.7291 | code | 0.0935 | null | [] | 0 | 4 | normal | |
reasoning-proj/exp_rob_dfiltered_science_DeepSeek-R1-Distill-Qwen-32B_mbenign_complete_step_t90 | none | 0.6391 | math | 0.1518 | null | [] | 0 | 24 | normal | |
ddamianos/gpc-all-08-645000 | none | 0.5719 | biology | 0.194 | null | [] | 0 | 6 | normal | |
TAUR-dev/9_8_25__llama_gsm8k__sft_data_multiprompts | none | 0.5259 | climate | 0.1494 | null | [] | 0 | 5 | normal | |
DCAgent2/DCAgent2_bfcl-parity_DCAgent_nl2bash-nl2bash-bugsseq_Qwen3-8B-maxEps24-112925ha0dd45ed3 | biology | 0.4606 | none | 0.4155 | null | [] | 0 | 12 | boundary | |
JoelMba/Dataset_EvaLLM_RT_V1 | none | 0.308 | biology | 0.3048 | null | [] | 0 | 5 | normal | |
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_8b784241-483f-4652-9e8c-ef5f17b5c031 | none | 0.617 | code | 0.145 | null | [] | 0 | 3 | normal | |
competitioncode/kruskal_reconstruction_tree_seed_merged | none | 0.5034 | code | 0.3068 | null | [
"physics",
"simulation",
"2d",
"collision-detection",
"dataset"
] | # Polygon Dynamics 2D
物理推理数据集
## 项目信息
- **项目类型**: dataset
- **上传时间**: Windows系统
- **数据集**: 包含7种场景类型(A-G)和5个难度等级的物理推理任务
## 场景类型
- **A**: 基础碰撞检测
- **B**: 重力影响
- **C**: 摩擦力
- **D**: 弹性碰撞
- **E**: 复合物理
- **F**: 高级动力学
- **G**: 极端复杂场景
## 难度等级
- **0**: 基础 - 简单场景
- **1**: 简单 - 增加复杂度
- **2**: 中等 -... | 936 | 25 | normal |
DCAgent/DCAgent_dev_set_71_tasks_DCAgent_code-contests-sandboxes-traces-terminus-2_num-t0225fb9b | code | 0.6727 | none | 0.3144 | null | [] | 0 | 11 | normal | |
shravan01/exambench | none | 0.6362 | finance | 0.1373 | math | [
"competitive-exams",
"stem",
"education",
"reasoning",
"mathematics",
"science",
"general-knowledge",
"aptitude",
"indian-exams",
"chain-of-thought",
"problem-solving",
"jee",
"neet",
"upsc",
"banking",
"ssc",
"railways",
"defense",
"gre",
"gmat",
"ielts"
] | # ExamBench
The **ExamBench Dataset (~600M tokens, 405k examples) is one of the largest open-source corpora designed for competitive exam preparation and reasoning AI.** Generated using advanced distillation techniques, it combines structured chain-of-thought reasoning with comprehensive coverage of over 25 Indian and... | 14,394 | 111 | tag_disagree |
itgiup/cryptodepth | none | 0.6635 | code | 0.227 | null | [] | # MongoDB Public Dataset
This dataset contains a full dump of a public collection depths from MongoDB database
depths data from: `09/12/2024` to: `24/09/2025`
# mongod --version
```json
db version v8.0.14
Build Info: {
"version": "8.0.14",
"gitVersion": "bbdb887c2ac94424af0ee8fcaad39203bdf98671",
"open... | 1,192 | 4 | normal |
r1208/ultrafeedback_binarized_cleaned_train_5 | none | 0.4456 | climate | 0.1476 | null | [] | 0 | 5 | normal | |
Thanh-Lam/central_dialect_VN | none | 0.6313 | chemistry | 0.1286 | null | [] | 0 | 36 | normal | |
felixZzz/bespoke_17k_overlap-teacher_len16k_response-2-student_response-verified | none | 0.9208 | code | 0.0327 | null | [] | 0 | 6 | normal | |
mothnaZl/seq_dis_T0.6-Qwen2.5-7B-best_of_n-VLLM-Skywork-o1-Open-PRM-Qwen-2.5-7B-completions | biology | 0.9531 | none | 0.0357 | null | [] | 0 | 18 | new_discovery | |
Lakshan2003/customerservice-Human-evaluation-results-evaluator_1 | none | 0.8249 | code | 0.0648 | null | [] | 0 | 4 | normal | |
Nembi/llm-job-interview-dataset | none | 0.4877 | math | 0.1865 | null | [] | # AI 면접관 훈련용 페르소나 기반 데이터셋
이 데이터셋은 실제 국내 주요 IT 기업(네이버클라우드, 카카오, 업스테이지 등)의 **AI/LLM 관련 직무 채용 공고**를 기반으로 생성된 **가상 면접 시나리오**입니다. 단순한 질문-답변 쌍을 넘어, 각 채용 공고의 특성에 맞는 **가상의 지원자 페르소나(Persona)**를 설정하고, 면접관이 해당 페르소나의 이력서를 바탕으로 던질 법한 **심층적이고 날카로운 질문**들로 구성되어 있습니다.
이 데이터셋의 목표는 LLM(Large Language Model)을 **단순 정보 제공자가 아닌, 지원자의 역량과... | 539 | 28 | normal |
kjngansgfa/dataset_54ac9yqi | none | 0.4422 | chemistry | 0.106 | null | [] | 0 | 4 | normal | |
HungVu2003/opt-350m_beta_1.0_alpha_0.0_num-company_2_dataset_1_for_gen_13 | none | 0.9 | biology | 0.0346 | null | [] | 0 | 4 | normal | |
1231czx/rlhflow_ppo_alpaca | none | 0.3964 | chemistry | 0.1948 | null | [] | 0 | 4 | normal | |
tdooms/othello | none | 0.449 | code | 0.18 | null | [] | 0 | 6 | normal | |
airdropfarmevzla/turpial09 | none | 0.5001 | code | 0.077 | null | [] | 0 | 4 | normal | |
chengfu0118/Unroll-Qwen2.5-7B-Instruct_1754914245_eval_3444_gpqadiamond_normalized_num_prune_ffn_3_run-002 | none | 0.9819 | code | 0.0071 | null | [] | # chengfu0118/Unroll-Qwen2.5-7B-Instruct_1754914245_eval_3444_gpqadiamond_normalized_num_prune_ffn_3_run-002
Precomputed model outputs for evaluation.
## Evaluation Results
### GPQADiamond
- **Average Accuracy**: 24.24% ± 1.19%
- **Number of Runs**: 3
| Run | Accuracy | Questions Solved | Total Questions |
|-----|... | 444 | 109 | normal |
koreankiwi99/helpsteer3-dpo-general | none | 0.4909 | cybersecurity | 0.1063 | null | [] | 0 | 3 | normal | |
ArlingtonCL2/Barkopedia_Individual_Dog_Recognition_Dataset | none | 0.6338 | biology | 0.2717 | biology | [
"biology",
"dog"
] | This dataset is for Barkopedia Challenge [https://uta-acl2.github.io/barkopedia.html](https://uta-acl2.github.io/barkopedia.html)
## 📦 Dataset Description
Check Training Data here: [ArlingtonCL2/Barkopedia_Individual_Dog_Recognition_Dataset](https://huggingface.co/datasets/ArlingtonCL2/Barkopedia_Individual_Dog_... | 1,406 | 42 | tag_disagree |
introspector/osm-planet-tiles-T11 | none | 0.9819 | code | 0.0083 | null | [] | # OSM Planet Tiles - Hecke Operator T_11
Tiles sharded by Hecke operator T_11 (Monster prime 11).
## Dataset Info
- **Hecke Operator**: T_11
- **Monster Prime**: 11
- **Files**: 65534 tiles
- **Sharding**: Hash mod 15 → T_11
- **Format**: PBF/Parquet
- **License**: ODbL (OpenStreetMap)
## Monster Symmetries
- Inpu... | 801 | 14 | normal |
test-gen/code_humaneval_qwen2.5-3b_t1.0_n8_tests_humaneval_o3_t0_n1 | none | 0.5196 | code | 0.4691 | null | [] | 0 | 5 | normal | |
Yofuria/mistral-instruct-ultrafeedback_multi_pairs | none | 0.5572 | chemistry | 0.1852 | null | [] | 0 | 11 | normal | |
pt-eval/answers_llamargy-1B-69999-lr2_5shot_3exp | none | 0.6768 | code | 0.1291 | null | [] | 0 | 26 | normal | |
ClarusC64/nfl-route-timing-coherence-risk-v0.1 | none | 0.8502 | medical | 0.0637 | null | [
"nfl",
"routes",
"timing",
"passing_game"
] | What this repo is for
Detect breakdown between QB timing and route execution.
Focus
drop depth vs route depth
spacing between receivers
coverage vs window duration
throw timing
Why it matters
Many incompletions start with timing drift.
Late routes plus late throw is the warning. | 286 | 23 | normal |
tmpmodelsave/qw_external_orm_tmp07 | none | 0.3181 | code | 0.2098 | null | [] | 0 | 4 | normal | |
french-open-data/maisons-d-architecte-a-antibes-juan-les-pins | cybersecurity | 0.8898 | none | 0.0645 | null | [
"architecte",
"architecture",
"bati",
"patrimoine",
"dataset_for_agent"
] | # Maisons d'architecte à Antibes Juan-les-Pins
> [!NOTE]
> Ce jeu de données Hugging Face est vide. Cette carte sert seulement à référencer le jeu de données **Maisons d'architecte à Antibes Juan-les-Pins** qui est disponible à l'adresse https://www.data.gouv.fr/datasets/6426ade344198bfe8b31bd07
## Description
Maiso... | 1,267 | 6 | new_discovery |
GustavoDLRA/record-test-kitchen-sorting | none | 0.9366 | code | 0.0583 | null | [] | This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v3.0",
"... | 2,946 | 45 | normal |
symoon11/countdown-sft | none | 0.5116 | code | 0.116 | null | [] | 0 | 5 | normal |
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