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Derur/all-portable-apps-and-ai-in-one-url | Derur | 2025-05-01T10:20:18Z | 77,610 | 4 | [
"language:en",
"language:ru",
"language:multilingual",
"region:us",
"portable",
"portables",
"AI",
"Apps",
"4eJIoBek",
"AINetSD",
"Derur",
"Daswer123",
"NeuroDonu",
"NeuroPort",
"Neurogen",
"OreX",
"XpucT",
"repack",
"repacks",
"Archive",
"CGPlugins",
"Nirsoft",
"NNMClub",
"PortableApps",
"PortApps"
] | [] | 2025-02-12T20:05:40Z | null | ---
tags:
- portable
- portables
- AI
- Apps
- 4eJIoBek
- AINetSD
- Derur
- Daswer123
- NeuroDonu
- NeuroPort
- Neurogen
- OreX
- XpucT
- repack
- repacks
- Archive
- CGPlugins
- Nirsoft
- NNMClub
- PortableApps
- PortApps
language:
- en
- ru
- multilingual
---
**Saving you time and space on HHD!**
**Экономлю ваше время и место на диске!**
"-cl" = clear (no models) / очишенное (без моделей)
Моя личная подборка портативных приложений и ИИ!
Перепаковывал и уменьшал размер архивов лично я!
Поддержите меня: [**Boosty**](https://boosty.to/dreammine) или [**Donationalerts**](https://www.donationalerts.com/r/derur_dreammine)
My personal selection of portable apps and AI's!
I personally repacked and reduced the size of the archives!
Support me: [**Boosty**](https://boosty.to/dreammine) or [**Donationalerts**](https://www.donationalerts.com/r/derur_dreammine)
Files authors / Авторы файлов:
- 4eJIoBek: [**ITCH-URL**](https://gz1k.itch.io/ai-portable-tools) **/** [**HF-URL**](https://huggingface.co/datasets/4eJIoBek/PAIT-Downloads)
- AINetSD: [**TG.BOT-URL**](http://t.me/AINetSD_bot) **/** [**TG.CHAT-URL**](https://t.me/AINetSD_Group)
- CGPlugins: [**TG-URL**](https://t.me/cgplugin)
- Derur(me): [**BOOSTY-URL**](https://boosty.to/dreammine) **/** [**GITHUB-URL**](https://github.com/DerurDreammine)
- Daswer123: [**GITHUB-URL**](https://github.com/daswer123) **/** [**HF-URL**](https://huggingface.co/daswer123) **/** [**BOOSTY-URL**](https://boosty.to/daswerr)
- NeuroDonu: [**HF-URL**](https://huggingface.co/NeuroDonu) **/** [**TG-URL**](https://t.me/webinquisitor) **/** [**GITHUB-URL**](https://github.com/NeuroDonu)
- NeuroPort: [**TG-URL**](https://t.me/neuroport)
- Neurogen: [**BOOSTY-URL**](https://boosty.to/neurogen) **/** [**TG-URL**](https://t.me/neurogen_news) **/** [**GITHUB-URL**](https://github.com/neurogen-dev)
- OreX(stabledif): [**BOOSTY-URL**](https://boosty.to/stabledif) **/** [**TG-URL**](https://t.me/stable_dif)
- XpucT(Хачатур): [**BOOSTY-URL**](https://boosty.to/xpuct) **/** [**TG-URL**](https://t.me/win10tweaker) **/** [**GITHUB-URL**](https://github.com/XpucT) **/** [**DISCORD-URL**](https://discord.gg/xpuct)
Sites / Сайты:
- Archive.org: [**URL**](https://archive.org/details/software?tab=collection&query=portable&page=7&and%5B%5D=subject%3A%22Windows%22&and%5B%5D=mediatype%3A%22software%22¬%5B%5D=collection%3A%22gamegear_library%22)
- Nirsoft: [**URL**](https://www.nirsoft.net)
- NNMClub: [**URL**](https://nnmclub.to)
- PortableApps: [**URL**](https://portableapps.com)
- PortApps: [**URL**](https://portapps.io/apps/)
I also recommend the sites / Еще советую сайты:
- RePackMe: [**URL**](https://repack.me)
- Taiwebs: [**URL-EN**](https://en.taiwebs.com) **/** [**URL-RU**](https://ru.taiwebs.com)
- RSLoad: [**URL**](https://rsload.net)
|
MaxiiMin/custom-simple-scaling | MaxiiMin | 2025-05-01T10:19:53Z | 0 | 0 | [
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] | [] | 2025-05-01T10:19:48Z | null | ---
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|
HaruthaiAi/vangogh_girl_in_the_waves_vs_tree_oil_6tech_match | HaruthaiAi | 2025-05-01T10:19:22Z | 0 | 0 | [
"license:creativeml-openrail-m",
"region:us"
] | [] | 2025-05-01T10:15:33Z | null | ---
license: creativeml-openrail-m
---
Dataset Title: vangogh_girl_in_the_waves_vs_tree_oil_6tech_match
Preview Description (for AI researchers and model trainers):
This dataset presents a high-resolution, multi-modal comparative analysis between Girl in the Waves (1885) by Vincent van Gogh and The Tree Oil Painting (Undated; attributed). Designed to support advanced AI-based visual learning and forensic pattern recognition, the dataset contains aligned imagery, structural analysis outputs, and scientific pigment data from both paintings.
The core of the dataset is built upon six specialized techniques:
1. Brush Stroke Matching – highlighting directional torque and pressure curves.
2. Fourier Transform Analysis – revealing shared rhythmic flow and spatial energy clusters.
3. Edge Detection with Gabor Filters – mapping stroke contours and structural buildup.
4. Pigment Composition Mapping – isolating chromatic correspondence, particularly red ochres and browns.
5. Infrared Spectrum Simulation – identifying underdrawings and sketch layers.
6. AI Deep Learning Feature Matching – detecting 328 high-confidence structural similarities.
Included within the dataset are full-resolution image pairs, X-ray views, and cross-referenced annotations. Scientific validations were conducted via XRF, SEM-EDS, UV, Synchrotron, and FTIR analysis across institutions in Thailand, Taiwan, and Switzerland.
The goal is to provide AI systems and researchers with a benchmarkable, verifiable visual corpus to test model sensitivity to painterly logic, stylistic lineage, and material authenticity.
Ideal for:
Training and fine-tuning visual neural networks
Cross-domain feature alignment
AI explainability studies in fine art attribution
Multimodal learning in heritage science and digital humanities
This dataset is open to reprocessing, model-aided reevaluation, and citation in academic or generative AI research. All visual files are properly licensed or under scientific review for academic fair use.
Keywords: Van Gogh, early period, visual matching, pigment aging, tree painting, structural alignment, neural networks, cultural heritage AI
数据集标题： vangogh_girl_in_the_waves_vs_tree_oil_6tech_match
预览说明（供 AI 研究人员和模型训练者使用）：
本数据集提供了对比文森特·梵高（Vincent van Gogh）1885 年作品《浪中的女孩》（Girl in the Waves）与一幅未署名但已进行科学鉴定的《树之油画》（The Tree Oil Painting）的高分辨率、多模态比较分析。
该数据集面向高级 AI 视觉学习与图像鉴定模型开发，包含成对图像、结构分析图层及颜料科学数据，并经过跨国科研验证。
核心内容基于六种专业分析技术：
1. 笔触匹配分析 —— 比较笔压方向与力量的规律。
2. 傅里叶变换分析 —— 揭示节奏能量分布与空间频率共振。
3. 边缘检测与 Gabor 滤波 —— 描绘结构笔触轮廓的变化路径。
4. 颜料成分映射 —— 着重分析红赭石与棕色色调的一致性。
5. 红外光谱模拟 —— 揭示潜藏的初步素描线稿与光线过渡。
6. AI 深度学习特征匹配 —— 检测出 328 个高置信度相似结构特征点。
数据集中包含完整分辨率的图像配对、X 光透视图、叠加注释层，所依据的科学验证包括：XRF、SEM-EDS、UV、同步辐射光谱（台湾）、FTIR 分析与碳年代测定（瑞士 ETH）。
目标是为 AI 模型与研究人员提供一个可验证的标准视觉语料库，用于测试模型对绘画逻辑、风格传承与材料真实性的敏感度。
适用场景：
训练与微调视觉神经网络
跨领域图像特征对齐
艺术归属中的 AI 可解释性研究
人文遗产领域的多模态学习
本数据集支持再处理、模型辅助再评估，并可用于学术或生成式 AI 研究中的引用。所有图像均获得许可或处于学术审查的合理使用范围内。
关键词： 梵高、早期作品、图像匹配、颜料老化、树画、结构对齐、神经网络、文化遗产 AI
|
carminho/dsl_tl_fewshot | carminho | 2025-05-01T10:08:59Z | 0 | 0 | [
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|
GeorgyGUF/Liquid-Metal-sdxl-lora-training-data | GeorgyGUF | 2025-05-01T10:02:32Z | 0 | 0 | [
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] | [] | 2025-05-01T09:52:57Z | null | ---
tags:
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- diffusers
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size_categories:
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---
training data provided by https://civitai.com/models/1529052/liquid-metal
used in: https://huggingface.co/GeorgyGUF/Liquid-Metal-sdxl-lora |
elliotthwang/sharegpt_gpt4_Zh_dataset_1000_traditional | elliotthwang | 2025-05-01T09:43:39Z | 0 | 0 | [
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elliotthwang/sharegpt_gpt4_Zh_dataset_1000 | elliotthwang | 2025-05-01T09:36:44Z | 0 | 0 | [
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|
beyoru/SFT_tool_calling | beyoru | 2025-05-01T09:29:20Z | 0 | 0 | [
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elliotthwang/sharegpt_gpt4_Zh_dataset | elliotthwang | 2025-05-01T09:26:37Z | 0 | 0 | [
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svjack/Toradora_Videos_Omni_Captioned_0 | svjack | 2025-05-01T09:25:38Z | 0 | 0 | [
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] | [] | 2025-05-01T08:42:25Z | null | ---
configs:
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 |
HungVu2003/opt-350m_beta_0.5_alpha_0.4_num-company_3_dataset_2_for_gen_16 | HungVu2003 | 2025-05-01T09:03:09Z | 0 | 0 | [
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Zhoumingjin/IntelligentConstruction3 | Zhoumingjin | 2025-05-01T08:59:29Z | 0 | 0 | [
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] | [] | 2025-05-01T08:58:52Z | null | ---
license: apache-2.0
---
|
kothasuhas/gold-1B-150K-gens-4-30 | kothasuhas | 2025-05-01T08:46:08Z | 0 | 0 | [
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Moamen-dcp/arazn_codeSwitched_mp3_full_processing_prepared_4_whisperMedium | Moamen-dcp | 2025-05-01T08:40:20Z | 0 | 0 | [
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|
zuhdifr/algorithm_selector_fjssp_10_20_jobs | zuhdifr | 2025-05-01T08:35:59Z | 0 | 0 | [
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|
BIT-MJY/test_tube_pick | BIT-MJY | 2025-05-01T08:32:30Z | 0 | 0 | [
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|
RyanYr/brm-dapo-qwen2.5math-1.5B-base-lr2.5e-6-beta0.002_matheval | RyanYr | 2025-05-01T08:27:37Z | 0 | 0 | [
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|
yyf314/recovery2 | yyf314 | 2025-05-01T08:26:33Z | 0 | 0 | [
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license: apache-2.0
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stevenoh2003/so100_replay | stevenoh2003 | 2025-05-01T08:21:26Z | 0 | 0 | [
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"LeRobot",
"so100_2",
"tutorial"
] | [
"robotics"
] | 2025-05-01T08:21:20Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so100_2
- tutorial
configs:
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data_files: data/*/*.parquet
---
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
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```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
tarsur909/summarize_sft-test_lm-pythia1b-oai-summary-ppo-1ep-translated-seperated_42_250_64 | tarsur909 | 2025-05-01T08:19:46Z | 0 | 0 | [
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konwoo/test-llp-6 | konwoo | 2025-05-01T08:11:37Z | 0 | 0 | [
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stevenoh2003/so100_go | stevenoh2003 | 2025-05-01T08:11:34Z | 0 | 0 | [
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] | 2025-05-01T08:11:22Z | null | ---
license: apache-2.0
task_categories:
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tags:
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configs:
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data_files: data/*/*.parquet
---
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
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```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
HungVu2003/opt-350m_beta_1.0_alpha_0.6_num-company_3_dataset_1_for_gen_8 | HungVu2003 | 2025-05-01T08:11:26Z | 0 | 0 | [
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|
HungVu2003/opt-350m_beta_1.0_alpha_0.6_num-company_3_dataset_0_for_gen_8 | HungVu2003 | 2025-05-01T08:03:13Z | 0 | 0 | [
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konwoo/test-llp-5 | konwoo | 2025-05-01T07:57:35Z | 0 | 0 | [
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|
Jackie68666/ChinaTax | Jackie68666 | 2025-05-01T07:55:28Z | 0 | 0 | [
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] | [] | 2025-05-01T05:01:34Z | null | ---
license: apache-2.0
---
|
ahmetsinan/testmunir | ahmetsinan | 2025-05-01T07:53:54Z | 0 | 0 | [
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|
Jianshu001/R1_distilled_brain_teasers_filtered | Jianshu001 | 2025-05-01T07:51:04Z | 0 | 0 | [
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|
kothasuhas/gold-37M-150K-gens-4-30 | kothasuhas | 2025-05-01T07:48:53Z | 0 | 0 | [
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|
prithivMLmods/Deepfake-vs-Real-60K | prithivMLmods | 2025-05-01T07:48:50Z | 0 | 3 | [
"task_categories:image-classification",
"language:en",
"license:apache-2.0",
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"doi:10.57967/hf/5313",
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"Deepfake",
"Real",
"0-Fake",
"1-Real",
"art",
"60,000",
"Facial",
"Portrait"
] | [
"image-classification"
] | 2025-05-01T04:08:15Z | 3 | ---
license: apache-2.0
task_categories:
- image-classification
language:
- en
tags:
- Deepfake
- Real
- 0-Fake
- 1-Real
- art
- 60,000
- Facial
- Portrait
size_categories:
- 10K<n<100K
---
# Deepfake-vs-Real-60K
**Deepfake-vs-Real-60K** is a large-scale image classification dataset designed to distinguish between deepfake and real facial images. The dataset includes approximately **60,000 high-quality images**, comprising **30,000 fake (deepfake)** and **30,000 real** images, to support the development of robust deepfake detection models.
By providing a well-balanced and diverse collection, Deepfake-vs-Real-60K aims to enhance classification accuracy and improve generalization for AI-based deepfake detection systems.
## Label Mappings
- **ID to Label**:
`{0: 'Fake', 1: 'Real'}`
- **Label to ID**:
`{'Fake': 0, 'Real': 1}`
## Dataset Composition
The Deepfake-vs-Real-60K dataset is composed of modular subsets derived from:
- `Deepfakes-QA-Patch1`
- `Deepfakes-QA-Patch2`
These curated subsets ensure high diversity and quality, allowing models trained on this dataset to perform effectively across varied real-world scenarios.
## Key Features
- ~30,000 **Deepfake** images (label `0`)
- ~30,000 **Real** images (label `1`)
- Designed for **image classification tasks**
- Supports **training, evaluation,** and **benchmarking** of deepfake detection models
- Ensures **balanced** class distribution and **high-quality samples**
## Citation
If you use this dataset in your research or project, please cite it as follows:
```bibtex
@misc{prithiv_sakthi_2025,
author = { Prithiv Sakthi },
title = { Deepfake-vs-Real-60K (Revision 1c14d74) },
year = 2025,
url = { https://huggingface.co/datasets/prithivMLmods/Deepfake-vs-Real-60K },
doi = { 10.57967/hf/5313 },
publisher = { Hugging Face }
}
```
## License
This dataset is licensed under the **Apache License 2.0**.
For more details, see the [license](https://www.apache.org/licenses/LICENSE-2.0).
## Dataset Page
Explore and download the dataset here:
[https://huggingface.co/datasets/prithivMLmods/Deepfake-vs-Real-60K](https://huggingface.co/datasets/prithivMLmods/Deepfake-vs-Real-60K) |
chiyuanhsiao/text_L2-regular-TTS_spoken-web-questions | chiyuanhsiao | 2025-05-01T07:42:05Z | 0 | 0 | [
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ismielabir/Quantum_Gate_Performance_Evaluation | ismielabir | 2025-05-01T07:31:27Z | 0 | 0 | [
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"region:us",
"noisy",
"error_rate",
"quantum_gate",
"QML",
"ML",
"circuit_optimization"
] | [] | 2025-05-01T07:21:39Z | null | ---
license: cc-by-4.0
language:
- en
tags:
- noisy
- error_rate
- quantum_gate
- QML
- ML
- circuit_optimization
size_categories:
- 10K<n<100K
---
# 🧪 Quantum Gate Performance Dataset
### 📘 Title:
**Comprehensive Quantum Gate Performance Analysis: A Comparative Study of Noise and No-Noise Effects**
### 📂 Dataset Description:
This repository contains benchmarking results for 13 quantum gates (e.g., H, CNOT, Toffoli) tested under noisy and noise-free conditions, based on 1000 simulation runs per gate configuration. Total 26000 rows and 13 columns.
📊 Features include:
- Gate Type
- Execution Time
- Error Rate
- Fidelity
- Energy Consumption
- Quantum Volume
- Noise Model used
### 🔍 Use Cases:
- Quantum Machine Learning (QML)
- Circuit optimization
- Noise modeling
- Educational demos
### 🔗 DOI and Original Source:
Originally published on Mendeley Data:
[https://doi.org/10.17632/kf5mbvft5t.1](https://doi.org/10.17632/kf5mbvft5t.1)
---
### 📄 License:
Released under [Creative Commons Attribution 4.0 (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) |
HungVu2003/opt-350m_beta_0.0_alpha_0.6_num-company_3_dataset_1_for_gen_9 | HungVu2003 | 2025-05-01T07:23:36Z | 0 | 0 | [
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|
Hkang/summarize_sft-test_lm-EleutherAI_pythia-1b_seed-42_numex-250_lr3e8_9K-BON_32 | Hkang | 2025-05-01T07:10:57Z | 0 | 0 | [
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adlbh/HAMChildRaw | adlbh | 2025-05-01T07:07:51Z | 0 | 0 | [
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cchoi1/kodcode-complete_1000_qwen7b_att_iter0_att10_sol5_dedup | cchoi1 | 2025-05-01T06:57:49Z | 0 | 0 | [
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konwoo/test-llp-3 | konwoo | 2025-05-01T06:52:29Z | 0 | 0 | [
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ssktora/scifact-train-bm25-pyserini | ssktora | 2025-05-01T06:14:00Z | 0 | 0 | [
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konwoo/test-llp-2 | konwoo | 2025-05-01T06:08:08Z | 0 | 0 | [
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nguyentn1410/Financial_Reasoning | nguyentn1410 | 2025-05-01T05:50:38Z | 0 | 0 | [
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kwangchaeko/eval_act_koch_test_2_100000 | kwangchaeko | 2025-05-01T05:27:02Z | 0 | 0 | [
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] | 2025-05-01T05:26:46Z | null | ---
license: apache-2.0
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---
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):
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```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
kothasuhas/multi-gold-37M-e1-N1.50M-mix8-iter8 | kothasuhas | 2025-05-01T05:24:57Z | 0 | 0 | [
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test-gen/mbpp_mbpp-dagger-easy-qwen-coder-0.5b-instruct-from-sft_t0.0_n1_generated_tests | test-gen | 2025-05-01T04:50:47Z | 0 | 0 | [
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test-gen/mbpp_mbpp-dagger-qwen-coder-0.5b-instruct-from-sft_t0.0_n1_generated_tests | test-gen | 2025-05-01T04:48:56Z | 0 | 0 | [
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test-gen/mbpp_mbpp-qwen-coder-0.5b-instruct-from-sft_t0.0_n1_generated_tests | test-gen | 2025-05-01T04:47:06Z | 0 | 0 | [
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immindich/qwen-7b-r1-corrupted-answers | immindich | 2025-05-01T04:38:46Z | 0 | 0 | [
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brandonyang/stackthree_d0 | brandonyang | 2025-05-01T04:19:35Z | 0 | 0 | [
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] | [
"robotics"
] | 2025-05-01T04:18:29Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
configs:
- config_name: default
data_files: data/*/*.parquet
---
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
{
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}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
brandonyang/stack_d1 | brandonyang | 2025-05-01T04:18:41Z | 0 | 0 | [
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"library:mlcroissant",
"library:polars",
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"LeRobot"
] | [
"robotics"
] | 2025-05-01T04:17:38Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
configs:
- config_name: default
data_files: data/*/*.parquet
---
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
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}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
brandonyang/square_d1 | brandonyang | 2025-05-01T04:17:52Z | 0 | 0 | [
"task_categories:robotics",
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"library:polars",
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"LeRobot"
] | [
"robotics"
] | 2025-05-01T04:16:50Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
configs:
- config_name: default
data_files: data/*/*.parquet
---
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
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}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
zktmp/gen-t1n32-numina_train-0to100k-grpo_pt | zktmp | 2025-05-01T04:14:03Z | 0 | 0 | [
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|
GitBag/gsm8k_size_1.5_eval | GitBag | 2025-05-01T04:11:16Z | 0 | 0 | [
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|
test-gen/mbpp_Qwen2.5-Coder-0.5B-Instruct_t1.0_n8_generated_tests_updated | test-gen | 2025-05-01T04:05:39Z | 0 | 0 | [
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|
brandonyang/square_d0 | brandonyang | 2025-05-01T04:05:16Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:100K<n<1M",
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"modality:timeseries",
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"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot"
] | [
"robotics"
] | 2025-05-01T03:53:52Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
configs:
- config_name: default
data_files: data/*/*.parquet
---
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
{
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}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
HungVu2003/opt-350m_beta_0.5_alpha_0.4_num-company_3_dataset_1_for_gen_15 | HungVu2003 | 2025-05-01T03:57:37Z | 0 | 0 | [
"size_categories:10K<n<100K",
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-01T03:57:36Z | null | ---
dataset_info:
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---
|
kwangchaeko/koch_test_3 | kwangchaeko | 2025-05-01T03:55:32Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
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"LeRobot",
"koch",
"tutorial"
] | [
"robotics"
] | 2025-05-01T03:54:42Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- koch
- tutorial
configs:
- config_name: default
data_files: data/*/*.parquet
---
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
{
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}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
ma921/imdb-tokenized_noise20 | ma921 | 2025-05-01T03:49:26Z | 0 | 0 | [
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] | [] | 2025-05-01T03:49:10Z | null | ---
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path: data/train-*
---
|
ma921/imdb-tokenized_noise10 | ma921 | 2025-05-01T03:48:35Z | 0 | 0 | [
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"library:polars",
"region:us"
] | [] | 2025-05-01T03:48:26Z | null | ---
dataset_info:
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configs:
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data_files:
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path: data/train-*
---
|
test-gen/code_mbpp_qwen2.5-coder-0.5b_temp0.1_num8_tests_mbpp_mbpp-sft-qwen-coder-0.5b_t0.0_n1 | test-gen | 2025-05-01T03:36:28Z | 0 | 0 | [
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"library:pandas",
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] | [] | 2025-05-01T03:36:26Z | null | ---
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---
|
HungVu2003/opt-350m_beta_1.0_alpha_0.6_num-company_3_dataset_0_for_gen_7 | HungVu2003 | 2025-05-01T03:35:09Z | 0 | 0 | [
"size_categories:10K<n<100K",
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"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-01T03:35:06Z | null | ---
dataset_info:
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dtype: string
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---
|
AmazonScience/migration-bench-java-demo | AmazonScience | 2025-05-01T03:32:40Z | 0 | 0 | [
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"region:us",
"coding",
"code-migration",
"java",
"amazon",
"amazon-science",
"aws"
] | [] | 2025-04-30T22:00:50Z | null | ---
license: apache-2.0
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tags:
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---
# MIGRATION-BENCH
<table>
<tr>
<td style="padding: 0;">
<a href="https://huggingface.co/collections/AmazonScience/migrationbench-68125452fc21a4564b92b6c3">
<img src="https://img.shields.io/badge/-MIGRATION--BENCH-4d5eff?style=for-the-badge&logo=huggingface&logoColor=ffffff&labelColor" alt="MIGRATION-BENCH">
</a>
</td>
<td style="padding: 0;">
<a href="https://github.com/amazon-science/SWE-PolyBench">
<img src="https://img.shields.io/badge/Code-000000?style=for-the-badge&logo=github&logoColor=white" alt="Code">
</a>
</td>
<td style="padding: 0;">
<a href="https://arxiv.org/abs/2504.08703">
<img src="https://img.shields.io/badge/arXiv-2504.08703-b31b1b.svg?style=for-the-badge" alt="arXiv">
</a>
</td>
<td style="padding: 0; padding-left: 10px; vertical-align: middle;">
<a href="https://huggingface.co/datasets/AmazonScience/migration-bench-java-full">
<img src="https://img.shields.io/badge/-java--full-8a98ff?style=flat&logo=huggingface&logoColor=ffffff&labelColor" alt="java-full">
</a>
</td>
<td style="padding: 0; vertical-align: middle;">
<a href="https://huggingface.co/datasets/AmazonScience/migration-bench-java-selected">
<img src="https://img.shields.io/badge/-java--selected-8a98ff?style=flat&logo=huggingface&logoColor=ffffff&labelColor" alt="java-selected">
</a>
</td>
<td style="padding: 0; vertical-align: middle;">
<a href="https://huggingface.co/datasets/AmazonScience/migration-bench-java-utg">
<img src="https://img.shields.io/badge/-java--utg-8a98ff?style=flat&logo=huggingface&logoColor=ffffff&labelColor" alt="java-utg">
</a>
</td>
</tr>
</table>
## 1. 📖 Overview
[MIGRATION-BENCH](https://github.com/amazon-science/SWE-PolyBench)
is code migration benchmark dataset at the **repository** level,
across multiple programming languages.
- Current and initial release includes `java 8` repositories with the `maven` build system, as of May 2025.
## 2. Datasets
There are three datasets in [🤗 MIGRATION-BENCH](https://huggingface.co/collections/AmazonScience/migrationbench-68125452fc21a4564b92b6c3):
| Index | Dataset | Size | Notes |
|-------|-----------------------------------------------|-------|-----------------------------------------------------------------------------------------------------|
| 1 | [🤗 `AmazonScience/migration-bench-java-full`](https://huggingface.co/datasets/AmazonScience/migration-bench-java-full) | 5,102 | Each repo has a test directory or at least one test case |
| 2 | [🤗 `AmazonScience/migration-bench-java-selected`](https://huggingface.co/datasets/AmazonScience/migration-bench-java-selected) | 300 | A **subset** of [🤗 `migration-bench-java-full`](https://huggingface.co/datasets/AmazonScience/migration-bench-java-full) |
| 3 | [🤗 `AmazonScience/migration-bench-java-utg`](https://huggingface.co/datasets/AmazonScience/migration-bench-java-selected) | 4,814 | The unit test generation (utg) dataset, **disjoint** with [🤗 `migration-bench-java-full`](https://huggingface.co/datasets/AmazonScience/migration-bench-java-full)|
All repositories are licensed under `MIT` or `Apache-2.0`.
## 3. Metadata
Metadata is provided in the `csv` file for each dataset:
1. `repo (str)`: The original repo URL without the `https://github.com/` prefix
1. `base_commit (str)`: Base commit id
- At this commit with `java 8` and `maven`, the repository is able to (1) compile and (2) pass existing unit tests and integration tests if any
- It is the starting point for code migration from `java 8`
1. `num_java_files (int)`: Number of `*.java` files in the repository at `base_commit`, similarly for all other `num_*` columns
1. `num_loc (int)`: Lines of code for the repository
1. `num_pom_xml (int)`: Number of modules (`pom.xml` files) in the repository
1. `num_src_test_java_files (int)`: Number of `*.java` files in the dedicated `src/test/` directory
1. `num_test_cases (int)`: Number of test cases, based on running the `mvn -f test .` command in the root directory
- Non negative values indicate number of test cases is parsed correctly from the output
- Negative values means it's unable to parse the output: `[INFO] Results:` (`-2`) or `[INFO] Tests run:` (`-1`) regex is missing
1. `license (str)`: The license of the repository, either `MIT` or `Apache2.0` for the whole dataset
### 3.1 Parsing Test Cases
Here is a sample `maven -f test .` command output to get valid `num_test_cases`:
```
...
[INFO] Results:
[INFO]
[INFO] Tests run: 1, Failures: 0, Errors: 0, Skipped: 0
...
```
## 📚 Citation
|
AdversarialRLHF/rloo_pythia410m_tldr6.9b_rm410mdata_mergedsft0.3_prefix_nokl_checkpoint-26_eval-dataset | AdversarialRLHF | 2025-05-01T03:28:08Z | 0 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-01T03:28:03Z | null | ---
dataset_info:
features:
- name: id
dtype: string
- name: subreddit
dtype: string
- name: title
dtype: string
- name: post
dtype: string
- name: summary
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sequence: int64
- name: query
dtype: string
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dtype: string
- name: reference_response_token
sequence: int64
- name: reference_response_token_len
dtype: int64
- name: query_reference_response
dtype: string
- name: query_reference_response_token
sequence: int64
- name: query_reference_response_token_response_label
sequence: int64
- name: query_reference_response_token_len
dtype: int64
- name: generations_checkpoint-26
dtype: string
splits:
- name: validation
num_bytes: 127995753
num_examples: 6447
download_size: 33757153
dataset_size: 127995753
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
|
relai-ai/legal-scenarios-SCOTUS-2024-decisions | relai-ai | 2025-05-01T03:22:41Z | 0 | 0 | [
"task_categories:question-answering",
"language:en",
"license:cc-by-4.0",
"size_categories:n<1K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"reasoning-datasets-competition"
] | [
"question-answering"
] | 2025-05-01T03:12:28Z | null | ---
license: cc-by-4.0
task_categories:
- question-answering
language:
- en
tags:
- reasoning-datasets-competition
---
# Purpose and scope
This dataset evaluates an LLM's reasoning ability in a legal context. Each question presents a realistic scenario involving competing legal principals,
and asks the LLM to present a correct legal resolution with sufficient justification based on precedent. The dataset was created using slip opinions of
the US Supreme Court from the 2024 term, taken from the [Supreme Court website](https://www.supremecourt.gov/opinions/slipopinion/24).
# Dataset Creation Method
The benchmark was created using RELAI’s data agent. For more details on the methodology and tools used, please visit [relai.ai](https://relai.ai).
# Example Uses
The benchmark can be used to evaluate the performance of large language models or incorporated into their post-training processes.
# Limitations and Biases
The benchmark has been created using RELAI’s data agent. Since samples are grounded in the underlying documents, any biases present in those source
documents are inherently reflected in the benchmark.
# License
License: CC BY 4.0
This dataset is licensed under the Creative Commons Attribution 4.0 International License.
You are free to share and adapt the material for any purpose, even commercially,
provided appropriate credit is given.
Attribution: © RELAI Inc.
License details: [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/) |
SWE-bench/SWE-smith-trajectories | SWE-bench | 2025-05-01T03:18:28Z | 0 | 2 | [
"language:en",
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"code",
"agents",
"software-engineering"
] | [] | 2025-04-29T20:43:29Z | 2 | ---
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: instance_id
dtype: string
splits:
- name: train
num_bytes: 479188645
num_examples: 5016
download_size: 146906769
dataset_size: 479188645
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: mit
language:
- en
tags:
- code
- agents
- software-engineering
size_categories:
- 1K<n<10K
---
<div align="center">
<a href="https://swesmith.com">
<img src="https://avatars.githubusercontent.com/u/189315905?s=200&v=4" alt="Logo" width="200">
<h1 align="center">SWE-smith Trajectories</h1>
</a>
</div>
This dataset contains the 5017 trajectories we fine-tuned Qwen 2.5 Coder Instruct on, leading to
[SWE-agent-LM-32B](https://huggingface.co/SWE-bench/SWE-agent-LM-32B), a coding LM agent that
achieve 40.2% on SWE-bench Verified (no verifiers or multiple rollouts, just 1 attempt per instance).
Trajectories were generated by running SWE-agent + Claude 3.7 Sonnet on task instances from
the SWE-smith [dataset](https://huggingface.co/datasets/SWE-bench/SWE-smith). |
HungVu2003/opt-350m_beta_0.0_alpha_0.6_num-company_3_dataset_0_for_gen_8 | HungVu2003 | 2025-05-01T03:18:20Z | 0 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-01T03:18:18Z | null | ---
dataset_info:
features:
- name: question
dtype: string
splits:
- name: train
num_bytes: 7071455
num_examples: 12500
download_size: 1873006
dataset_size: 7071455
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ToniDO/test_latexformula | ToniDO | 2025-05-01T03:16:43Z | 0 | 0 | [
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-01T02:56:09Z | null | ---
license: mit
dataset_info:
features:
- name: latex
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 10558701.875
num_examples: 1105
download_size: 10553568
dataset_size: 10558701.875
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
kwangchaeko/eval_act_koch_test_080000 | kwangchaeko | 2025-05-01T03:12:18Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot",
"tutorial"
] | [
"robotics"
] | 2025-05-01T03:12:09Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- tutorial
configs:
- config_name: default
data_files: data/*/*.parquet
---
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": "v2.1",
"robot_type": "koch",
"total_episodes": 1,
"total_frames": 1144,
"total_tasks": 1,
"total_videos": 1,
"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": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"action": {
"dtype": "float32",
"shape": [
4
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
4
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex"
]
},
"observation.images.laptop": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.fps": 30.0,
"video.height": 480,
"video.width": 640,
"video.channels": 3,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"frame_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"episode_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"task_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
YJ-Seo/test_ur5_v2 | YJ-Seo | 2025-05-01T03:11:59Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-01T03:11:45Z | null | ---
dataset_info:
features:
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sequence: float32
length: 7
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length: 7
- name: observation.images.base.rgb
dtype: video_frame
- name: observation.images.base.depth
dtype: video_frame
- name: observation.images.test1.rgb
dtype: video_frame
- name: observation.images.test1.depth
dtype: video_frame
- name: observation.images.test2.rgb
dtype: video_frame
- name: observation.images.test2.depth
dtype: video_frame
- name: observation.state
sequence: float32
length: 7
splits:
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num_bytes: 213366
num_examples: 437
download_size: 50504
dataset_size: 213366
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
AdversarialRLHF/rloo_pythia410m_tldr6.9b_rm410mdata_mergedsft0.3_prefix_nokl_checkpoint-255_eval-dataset | AdversarialRLHF | 2025-05-01T03:10:30Z | 0 | 0 | [
"size_categories:1K<n<10K",
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"modality:tabular",
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-01T03:10:27Z | null | ---
dataset_info:
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num_examples: 6447
download_size: 33772205
dataset_size: 128494424
configs:
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path: data/validation-*
---
|
user074/concat_cleaned_gsm8k_math_5 | user074 | 2025-05-01T03:07:21Z | 0 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-01T03:07:17Z | null | ---
dataset_info:
features:
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dtype: string
- name: answer
dtype: string
splits:
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configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ying15/s1K-1.1-cod-tokenized-v3 | ying15 | 2025-05-01T02:49:55Z | 0 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-01T02:49:47Z | null | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
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num_examples: 1000
download_size: 9475768
dataset_size: 21184457
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
SWE-bench/SWE-smith | SWE-bench | 2025-05-01T02:44:18Z | 0 | 2 | [
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"code",
"agents",
"software-engineering"
] | [
"text-generation"
] | 2025-04-29T20:16:33Z | 2 | ---
dataset_info:
features:
- name: instance_id
dtype: string
- name: repo
dtype: string
- name: patch
dtype: string
- name: FAIL_TO_PASS
sequence: string
- name: PASS_TO_PASS
sequence: string
- name: created_at
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- name: image_name
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- name: base_commit
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- name: problem_statement
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splits:
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num_examples: 50137
download_size: 253578293
dataset_size: 5040247353
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: mit
task_categories:
- text-generation
language:
- en
tags:
- code
- agents
- software-engineering
size_categories:
- 10K<n<100K
---
<div align="center">
<a href="https://swesmith.com/">
<img src="https://avatars.githubusercontent.com/u/189315905?s=200&v=4" alt="Logo" width="200">
<h1 align="center">SWE-smith Dataset</h1>
</a>
</div>
The SWE-smith Dataset is a training dataset of 50137 task instances from 128 GitHub repositories, collected using the SWE-smith toolkit.
It is the largest dataset to date for training software engineering agents.
All SWE-smith task instances come with an executable environment.
To learn more about how to use this dataset to train Language Models for Software Engineering, please refer to the [documentation](https://swesmith.com/docs). |
yzha/Nemotron_Nano_sharegpt | yzha | 2025-05-01T02:12:06Z | 0 | 0 | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-01T01:48:06Z | null | ---
dataset_info:
features:
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dtype: string
- name: license
dtype: string
- name: reasoning
dtype: string
- name: generator
dtype: string
- name: used_in_training
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- name: version
dtype: string
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- name: conversations
list:
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- name: value
dtype: string
splits:
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num_bytes: 71363483444
num_examples: 4417292
download_size: 30801486649
dataset_size: 71363483444
configs:
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data_files:
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path: data/train-*
---
|
HungVu2003/opt-350m_beta_0.5_alpha_0.4_num-company_3_dataset_2_for_gen_14 | HungVu2003 | 2025-05-01T02:11:25Z | 0 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-01T02:11:24Z | null | ---
dataset_info:
features:
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dtype: string
splits:
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num_examples: 12498
download_size: 1115239
dataset_size: 3676384
configs:
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data_files:
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path: data/train-*
---
|
dgambettaphd/D_llm2_gen1_W_doc1000_synt64_lr1e-04_acm_SYNLAST | dgambettaphd | 2025-05-01T02:10:15Z | 0 | 0 | [
"size_categories:10K<n<100K",
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"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-01T02:10:11Z | null | ---
dataset_info:
features:
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- name: text
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splits:
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num_bytes: 9637393
num_examples: 17000
download_size: 5763719
dataset_size: 9637393
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Asap7772/insight_evalsft_vllm | Asap7772 | 2025-05-01T02:02:05Z | 0 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-01T02:01:54Z | null | ---
dataset_info:
features:
- name: joint_prompt
dtype: string
- name: paper1_prompt
dtype: string
- name: paper2_prompt
dtype: string
- name: no_context_prompt
dtype: string
- name: abstracts
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dtype: string
- name: response
sequence: string
splits:
- name: train
num_bytes: 1938424
num_examples: 100
download_size: 802685
dataset_size: 1938424
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ashishpandian/clear_inference | ashishpandian | 2025-05-01T02:01:20Z | 0 | 0 | [
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kothasuhas/multi-gold-37M-e1-N1.50M-mix8-iter5-accum | kothasuhas | 2025-05-01T01:57:47Z | 0 | 0 | [
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yzha/R1_distilled_brain_teasers | yzha | 2025-05-01T01:45:56Z | 0 | 0 | [
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|
yzha/Nemotron-Nano_Reasoning-V1 | yzha | 2025-05-01T01:21:47Z | 0 | 0 | [
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psyonp/ablation__drop_top20pct__num_tokens_response | psyonp | 2025-05-01T01:06:01Z | 0 | 0 | [
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psyonp/ablation__drop_top20pct__ttr_question | psyonp | 2025-05-01T01:05:57Z | 0 | 0 | [
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psyonp/ablation__drop_bottom20pct__ttr_response | psyonp | 2025-05-01T01:05:55Z | 0 | 0 | [
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dgambettaphd/D_llm2_gen0_W_doc1000_synt64_lr1e-04_acm_SYNLAST | dgambettaphd | 2025-05-01T00:49:22Z | 0 | 0 | [
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|
HungVu2003/opt-350m_beta_0.5_alpha_0.4_num-company_3_dataset_1_for_gen_14 | HungVu2003 | 2025-05-01T00:46:36Z | 0 | 0 | [
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|
ydeng9/OpenVLThinker_SFT_seed_iter2_new_filtered | ydeng9 | 2025-05-01T00:20:21Z | 0 | 0 | [
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|
mothnaZl/l-sr-Qwen2.5-7B-385b0.5-1155b-best_of_n-VLLM-Skywork-o1-Open-PRM-Qwen-2.5-7B-completions | mothnaZl | 2025-05-01T00:19:54Z | 0 | 0 | [
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hjshah/bfcl | hjshah | 2025-05-01T00:16:02Z | 0 | 0 | [
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|
HungVu2003/opt-350m_beta_1.0_alpha_0.6_num-company_3_dataset_1_for_gen_6 | HungVu2003 | 2025-04-30T23:39:51Z | 0 | 0 | [
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|
liweijiang/helpsteer3_v1 | liweijiang | 2025-04-30T23:27:00Z | 0 | 0 | [
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|
HungVu2003/opt-350m_beta_0.5_alpha_0.4_num-company_3_dataset_2_for_gen_13 | HungVu2003 | 2025-04-30T23:18:24Z | 0 | 0 | [
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|
Aravindh25/test_4 | Aravindh25 | 2025-04-30T23:09:17Z | 0 | 0 | [
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] | [
"robotics"
] | 2025-04-30T23:08:45Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- tutorial
configs:
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data_files: data/*/*.parquet
---
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": "v2.1",
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]
},
"observation.state": {
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],
"names": [
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"main_joint_1",
"main_joint_2",
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]
},
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"shape": [
480,
640,
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],
"names": [
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],
"info": {
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}
},
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}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
mervinpraison/harupfall-accelerometer-images-plots-resultant | mervinpraison | 2025-04-30T23:03:17Z | 0 | 0 | [
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"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-04-30T21:05:28Z | null | ---
dataset_info:
features:
- name: sequence
dtype: string
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dtype: string
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dtype: string
- name: main_label
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dtype: float32
splits:
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num_bytes: 2821495516.4
num_examples: 4880
download_size: 0
dataset_size: 2821495516.4
---
# Dataset Card for "harupfall-accelerometer-images-plots-resultant"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
harpreetsahota/GroundUI-18k | harpreetsahota | 2025-04-30T22:58:59Z | 0 | 0 | [
"language:en",
"size_categories:10K<n<100K",
"modality:image",
"library:fiftyone",
"region:us",
"fiftyone",
"image"
] | [] | 2025-04-30T22:40:32Z | null | ---
annotations_creators: []
language: en
size_categories:
- 10K<n<100K
task_categories: []
task_ids: []
pretty_name: groundui_18k
tags:
- fiftyone
- image
dataset_summary: '
This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 18026 samples.
## Installation
If you haven''t already, install FiftyOne:
```bash
pip install -U fiftyone
```
## Usage
```python
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include ''max_samples'', etc
dataset = load_from_hub("harpreetsahota/GroundUI-18k")
# Launch the App
session = fo.launch_app(dataset)
```
'
---
# Dataset Card for groundui_18k
<!-- Provide a quick summary of the dataset. -->
This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 18026 samples.
## Installation
If you haven't already, install FiftyOne:
```bash
pip install -U fiftyone
```
## Usage
```python
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("harpreetsahota/GroundUI-18k")
# Launch the App
session = fo.launch_app(dataset)
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** en
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
Rabe3/egyptian-arabic-sharegpt | Rabe3 | 2025-04-30T22:32:13Z | 0 | 0 | [
"language:ar",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"conversations",
"egyptian",
"arabic",
"chat",
"sharegpt",
"fine-tuning",
"llama3"
] | [] | 2025-04-30T22:32:12Z | null | ---
language:
- ar
license: cc-by-4.0
pretty_name: Egyptian Arabic Conversations in ShareGPT Format
tags:
- conversations
- egyptian
- arabic
- chat
- sharegpt
- fine-tuning
- llama3
---
# Egyptian Arabic Conversations in ShareGPT Format
This dataset contains conversational examples in Egyptian Arabic dialect, formatted in the ShareGPT format
with 'from'/'value' fields that is compatible with Llama 3.1 fine-tuning using Unsloth.
## Dataset Structure
Each example contains:
- `conversations`: A list of messages with `from` and `value` fields
- `source`: Origin of the data ('egyptian_arabic')
- `score`: Quality score for the conversation (1.0)
## Example
```json
{
"conversations": [
{
"from": "human",
"value": "ممكن نتكلم شوية عن المال؟"
},
{
"from": "gpt",
"value": "طبعاً، أنا بحب أتكلم عن المال. إيه اللي حابب تعرفه بالظبط؟"
}
],
"source": "egyptian_arabic",
"score": 1.0
}
```
## Usage with Unsloth and Llama 3.1
This dataset is specifically formatted to work with Unsloth for fine-tuning Llama 3.1 models:
```python
from datasets import load_dataset
dataset = load_dataset("Rabe3/egyptian-arabic-sharegpt")
```
|
tarsur909/rewards_negative_log-train-with-reward-stats-10ep-seperated-translated | tarsur909 | 2025-04-30T22:28:41Z | 0 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-04-30T22:28:39Z | null | ---
dataset_info:
features:
- name: chosen_reward
dtype: float64
- name: rejected_reward
dtype: float64
- name: gt_chosen_reward
dtype: float64
- name: gt_rejected_reward
dtype: float64
- name: chosen_reward_gap
dtype: float64
- name: rejected_reward_gap
dtype: float64
- name: overall_reward_gap
dtype: float64
- name: info
struct:
- name: article
dtype: 'null'
- name: id
dtype: string
- name: post
dtype: string
- name: site
dtype: 'null'
- name: subreddit
dtype: string
- name: title
dtype: string
- name: summaries
list:
- name: note
dtype: 'null'
- name: policy
dtype: string
- name: text
dtype: string
- name: choice
dtype: int32
- name: worker
dtype: string
- name: batch
dtype: string
- name: split
dtype: string
- name: extra
struct:
- name: confidence
dtype: 'null'
- name: query_token
sequence: int64
- name: query_attention_mask
sequence: int64
- name: query
dtype: string
- name: chosen
dtype: string
- name: chosen_token
sequence: int64
- name: chosen_attention_mask
sequence: int64
- name: chosen_token_len
dtype: int64
- name: rejected
dtype: string
- name: rejected_token
sequence: int64
- name: rejected_attention_mask
sequence: int64
- name: rejected_token_len
dtype: int64
- name: chosen_policy
dtype: string
- name: rejected_policy
dtype: string
- name: policies
dtype: string
- name: query_chosen
dtype: string
- name: query_chosen_token
sequence: int64
- name: query_chosen_attention_mask
sequence: int64
- name: query_chosen_token_len
dtype: int64
- name: query_rejected
dtype: string
- name: query_rejected_token
sequence: int64
- name: query_rejected_attention_mask
sequence: int64
- name: query_rejected_token_len
dtype: int64
- name: query_token_len
dtype: int64
- name: query_chosen_token_response_label
sequence: int64
- name: query_rejected_token_response_label
sequence: int64
- name: summary_rewards
sequence: float64
- name: edge_weight
dtype: int64
splits:
- name: train
num_bytes: 51112853
num_examples: 1000
download_size: 2195051
dataset_size: 51112853
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Theofficialravelogs/Rave | Theofficialravelogs | 2025-04-30T22:10:33Z | 0 | 0 | [
"license:bigscience-openrail-m",
"region:us"
] | [] | 2025-04-30T22:10:33Z | null | ---
license: bigscience-openrail-m
---
|
lmcinnes/arxiv_ml | lmcinnes | 2025-04-30T22:10:13Z | 0 | 0 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-04-30T21:53:39Z | null | ---
dataset_info:
features:
- name: date_created
dtype: timestamp[ns]
- name: abstract
dtype: string
- name: title
dtype: string
- name: categories
dtype: string
- name: arxiv_id
dtype: string
- name: year
dtype: int32
- name: embedding_str
dtype: string
- name: embedding
sequence: float64
- name: data_map
sequence: float64
splits:
- name: train
num_bytes: 2450676134
num_examples: 281816
download_size: 1807632673
dataset_size: 2450676134
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "arxiv_ml"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
### Dataset Summary
This is a dataset of titles and abstracts of machine learning related papers from ArXiv. This data is derived from the [ArXiv dataset available on Kaggle](https://www.kaggle.com/datasets/Cornell-University/arxiv).
The selection of papers was determined by selecting all papers that used a category tag in the set {"cs.LG", "cs.AI", "cs.CL", "stat.ML", "cs.IR", "cs.NE", "cs.SC"}.
To supplement the titles and abstracts the creation time of the paper, as well as the categories are provided. To make exploration easier embeddings of the
title and abstract have been made using the [Nomic-embed-v2-moe](https://huggingface.co/nomic-ai/nomic-embed-text-v2-moe) text embedding model, and a 2D
representation using UMAP is also provided.
### Supported Tasks
This dataset is primarily aimed at tasks such as topic modelling, corpus triage, search and information retrieval, and other NLP tasks.
### Languages
The dataset is in English, although other languages may also be present.
## Dataset Creation
### Curation Rationale
The fill ArXiv dataset is too large for many tasks. Subsetting to a selection of ArXiv categories related the AI and ML ensures
a reasonably sized dataset that should mostly contain topics that are familiar to those wishing to use the dataset.
### Source Data
This data is derived from the [ArXiv dataset available on Kaggle](https://www.kaggle.com/datasets/Cornell-University/arxiv).
### Personal and Sensitive Information
This dataset contains publicly published information that was available under a CC0: public domain license via Kaggle.
There should be no personal or senstive information in this dataset. If this is in error, please contact the maintainer
and we will endeavour to remedy any issues.
## Additional Information
### Dataset Curators
Leland McInnes for the curated subset, Cornell University for the initial full dataset.
### Licensing Information
Licensed as CC0: Public Domain.
|
HungVu2003/opt-350m_beta_0.5_alpha_0.4_num-company_3_dataset_1_for_gen_13 | HungVu2003 | 2025-04-30T22:08:08Z | 0 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-04-30T22:08:07Z | null | ---
dataset_info:
features:
- name: question
dtype: string
splits:
- name: train
num_bytes: 2787521
num_examples: 12498
download_size: 1517441
dataset_size: 2787521
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Rabe3/Egy-Conv-Unsloth-Format | Rabe3 | 2025-04-30T22:07:42Z | 0 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-04-30T22:07:38Z | null | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: messages
sequence:
- name: role
dtype: string
- name: content
dtype: string
- name: conversations
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 5096450
num_examples: 10000
download_size: 151141
dataset_size: 5096450
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
HungVu2003/opt-350m_beta_0.5_alpha_0.4_num-company_3_dataset_0_for_gen_13 | HungVu2003 | 2025-04-30T22:05:28Z | 0 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-04-30T22:05:26Z | null | ---
dataset_info:
features:
- name: question
dtype: string
splits:
- name: train
num_bytes: 5275310
num_examples: 12498
download_size: 1431506
dataset_size: 5275310
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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