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datasetId
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large_stringdate 2021-04-29 15:34:29
2025-06-25 02:40:10
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3.97M
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7.74k
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large_stringdate 2022-03-02 23:29:22
2025-06-25 00:32:52
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yxnd150150/uieb_llm | yxnd150150 | 2024-12-06T16:31:40Z | 20 | 0 | [
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] | [] | 2024-12-06T16:31:22Z | 0 | ---
dataset_info:
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configs:
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data_files:
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path: data/train-*
---
|
vente/corpuslens | vente | 2025-06-24T05:43:27Z | 0 | 0 | [
"license:fair-noncommercial-research-license",
"region:us"
] | [] | 2025-06-24T05:43:26Z | 0 | ---
license: fair-noncommercial-research-license
---
|
Sparx3d/t1 | Sparx3d | 2025-05-20T16:44:37Z | 0 | 0 | [
"task_categories:robotics",
"size_categories:n<1K",
"modality:video",
"library:datasets",
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"region:us",
"phosphobot",
"so100",
"phospho-dk"
] | [
"robotics"
] | 2025-05-20T16:35:41Z | 0 |
---
tags:
- phosphobot
- so100
- phospho-dk
task_categories:
- robotics
---
# t1
**This dataset was generated using a [phospho starter pack](https://robots.phospho.ai).**
This dataset contains a series of episodes recorded with a robot and multiple cameras. It can be directly used to train a policy using imitation learning. It's compatible with LeRobot and RLDS.
|
mertaylin/rearc_100k_DeepSeek-R1-Distill-Qwen-7B_responses | mertaylin | 2025-02-26T08:47:43Z | 63 | 0 | [
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] | [] | 2025-02-26T08:47:35Z | 0 | ---
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---
|
Bryce314/rewrite_0.5 | Bryce314 | 2025-03-29T15:08:56Z | 16 | 0 | [
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] | [] | 2025-03-29T15:08:54Z | 0 | ---
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|
ahmedheakl/arabic_examsv | ahmedheakl | 2024-10-29T09:18:44Z | 27 | 0 | [
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] | [] | 2024-10-12T10:35:16Z | 0 | ---
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|
HungVu2003/opt-350m_beta_0.0_alpha_0.0_num-company_3_dataset_2_for_gen_1 | HungVu2003 | 2025-04-14T04:39:44Z | 18 | 0 | [
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] | [] | 2025-04-14T04:39:43Z | 0 | ---
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|
test-gen/code_mbpp_qwen2.5-3b_t1.0_n8_tests_mbpp_qwen2.5-3b_t0.0_n1 | test-gen | 2025-05-22T15:12:54Z | 0 | 0 | [
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] | [] | 2025-05-22T15:12:53Z | 0 | ---
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---
|
gibbous-ml/623-dataset3 | gibbous-ml | 2024-12-07T17:17:05Z | 16 | 0 | [
"license:apache-2.0",
"size_categories:n<1K",
"format:text",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | [] | 2024-12-07T17:13:06Z | 0 | ---
license: apache-2.0
---
|
rricc22/so100_record50 | rricc22 | 2025-05-04T17:55:16Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"region:us",
"LeRobot",
"so100",
"tutorial"
] | [
"robotics"
] | 2025-05-04T17:54:43Z | 0 | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so100
- 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": "so100",
"total_episodes": 50,
"total_frames": 50542,
"total_tasks": 1,
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"chunks_size": 1000,
"fps": 30,
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},
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"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
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}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
yycgreentea/so100_test_v2 | yycgreentea | 2025-06-03T09:17:26Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
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"format:parquet",
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"library:dask",
"library:mlcroissant",
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"region:us",
"LeRobot",
"so100",
"tutorial"
] | [
"robotics"
] | 2025-06-03T06:57:34Z | 0 | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so100
- 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": "so100",
"total_episodes": 2,
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```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
mila-ai4h/AIMS.au | mila-ai4h | 2025-06-02T13:34:43Z | 543 | 2 | [
"task_categories:text-classification",
"language:en",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"arxiv:2502.07022",
"region:us",
"legal"
] | [
"text-classification"
] | 2025-02-26T23:03:34Z | 0 | ---
license: cc-by-4.0
task_categories:
- text-classification
language:
- en
tags:
- legal
pretty_name: AIMS.au
size_categories:
- 100K<n<1M
---
# AIMS.au
This repository contains the dataset associated with the publication
[AIMS.au: A Dataset for the Analysis of Modern Slavery Countermeasures in Corporate Statements](https://arxiv.org/abs/2502.07022) presented at ICLR 2025 and [AIMSCheck: Leveraging LLMs for AI-Assisted Review of Modern Slavery Statements Across Jurisdictions](LINK) presented at ACL 2025.
The dataset is composed of 5,731 modern slavery statements taken from the Australian Modern Slavery Register
and annotated at the sentence level by human annotators and domain expert analysts.
The dataset was created to help evaluate and fine-tune LLMs for the assessment of corporate statements on modern slavery.
The dataset also contains the annotated statements from the UK and Canada, used in the AIMSCheck paper.
You can access a more detailed dataset through this [Figshare](https://figshare.com/s/1b92ebfde3f2de2be0cf). Additional information can be found on the project's [GitHub page](https://github.com/mila-ai4h/ai4h_aims-au) or its [official website](https://mila.quebec/en/ai4humanity/applied-projects/ai-against-modern-slavery-aims). The AIMS.au dataset is also integrated into the [WikiRate platform] (https://wikirate.org/AIMS_au_A_Dataset_for_the_Analysis_of_Modern_Slavery_Countermeasures_in_Corporate_Statements). |
dgambettaphd/D_llm2_gen6_S_doc1000_synt64_lr1e-04_acm_SYNLAST | dgambettaphd | 2025-05-02T23:54:27Z | 0 | 0 | [
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] | [] | 2025-05-02T23:54:22Z | 0 | ---
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---
|
hirundo-io/MedHallu-mc-test | hirundo-io | 2025-04-21T08:24:17Z | 23 | 0 | [
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] | [] | 2025-04-20T14:25:06Z | 0 | ---
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|
jhoannarica/cebquad_split | jhoannarica | 2025-04-17T02:57:05Z | 56 | 0 | [
"task_categories:question-answering",
"size_categories:10K<n<100K",
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] | [
"question-answering"
] | 2025-04-04T04:42:57Z | 0 | ---
task_categories:
- question-answering
tags:
- code
- machine_learning
- natural_language_processing
size_categories:
- 1K<n<10K
---
Created for the Cebuano Question Answering System. \
Articles were scraped from the SunStar Superbalita website and were pseudonymized. \
The dataset for the articles are found [here](https://huggingface.co/datasets/jhoannarica/superbalita_split).
Researcher:\
**Jhoanna Rica Lagumbay**\
lagumbay.jhoanna@gmail.com\
University of the Philippines Cebu\
Department of the Computer Science |
Bertug1911/BrtAI_2500_Building | Bertug1911 | 2025-04-01T12:06:15Z | 22 | 0 | [
"task_categories:text-generation",
"language:tr",
"language:en",
"language:ar",
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"finance",
"biology",
"chemistry",
"code",
"art"
] | [
"text-generation"
] | 2025-04-01T12:05:25Z | 0 | ---
license: mit
task_categories:
- text-generation
language:
- tr
- en
- ar
tags:
- finance
- biology
- chemistry
- code
- art
size_categories:
- 1K<n<10K
--- |
villekuosmanen/agilex_put_orange_paperbox | villekuosmanen | 2025-02-13T05:11:01Z | 26 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
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"LeRobot"
] | [
"robotics"
] | 2025-02-13T05:10:35Z | 0 | ---
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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"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
sam749/slim-wiki-hindi | sam749 | 2024-11-21T15:42:11Z | 32 | 0 | [
"language:hi",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-11-19T02:32:47Z | 0 | ---
language:
- hi
dataset_info:
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dtype: string
- name: url
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dtype: string
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download_size: 227706910
dataset_size: 595628554.0
configs:
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data_files:
- split: train
path: data/train-*
---
|
JoelMba/Donnees_internes_retex_14 | JoelMba | 2025-05-29T14:30:15Z | 39 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-29T14:30:13Z | 0 | ---
dataset_info:
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dtype: string
- name: output
dtype: string
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num_bytes: 33469
num_examples: 41
download_size: 19178
dataset_size: 33469
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Donnees_internes_retex_14"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
wuulong/purchasing_exam_questions | wuulong | 2025-03-10T05:42:14Z | 73 | 1 | [
"license:cc-by-sa-4.0",
"size_categories:1K<n<10K",
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"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-03-07T07:19:23Z | 0 | ---
license: cc-by-sa-4.0
dataset_info:
features:
- name: 項目
dtype: string
- name: 題目類型
dtype: string
- name: 編號
dtype: string
- name: 答案
dtype: string
- name: 依據法源
dtype: string
- name: 試題
dtype: string
splits:
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num_bytes: 1083365
num_examples: 3695
download_size: 367748
dataset_size: 1083365
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
- 資料來源：[採購法規題庫](https://web.pcc.gov.tw/psms/plrtqdm/questionPublic/indexReadQuestion)
- 資料產生日期：114/03/07
- 大部分項目內本沒有 「依據法源」欄位，為求統一所以有欄位，為空值
- 順手用 colab 觀察資料內容：[採購網題庫1.ipynb](https://colab.research.google.com/drive/1LS1AZdVgAAut2v2UgK2Ku5F7hplqmdxm?usp=sharing)
|
SayantanJoker/Shrutilipi_Hindi_resampled_44100_merged_13_quality_metadata_description | SayantanJoker | 2025-05-04T20:45:51Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-04T20:13:26Z | 0 | ---
dataset_info:
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---
|
davidadamczyk/subset_imdb-5000 | davidadamczyk | 2024-10-11T19:23:02Z | 18 | 0 | [
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"library:polars",
"region:us"
] | [] | 2024-10-11T19:21:57Z | 0 | ---
dataset_info:
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configs:
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data_files:
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path: data/test-*
---
|
sharjeel103/filtered_fineweb_edu_350b | sharjeel103 | 2025-06-17T21:43:02Z | 0 | 0 | [
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"region:us"
] | [] | 2025-06-17T18:28:39Z | 0 | ---
dataset_info:
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download_size: 241125497
dataset_size: 416501795
configs:
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data_files:
- split: train
path: data/train-*
---
|
ayushchakravarthy/countdown-env | ayushchakravarthy | 2025-05-22T23:31:30Z | 63 | 0 | [
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"modality:text",
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"library:polars",
"region:us"
] | [] | 2025-05-17T04:15:59Z | 0 | ---
dataset_info:
features:
- name: prompt
list:
- name: content
dtype: string
- name: role
dtype: string
- name: metadata
struct:
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dtype: int64
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sequence: int64
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num_examples: 100000
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num_bytes: 101407
num_examples: 100
download_size: 34246054
dataset_size: 101492054
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: eval
path: data/eval-*
---
|
french-datasets/michsethowusu_french-kabyle_sentence-pairs | french-datasets | 2025-05-20T14:44:53Z | 0 | 0 | [
"language:fra",
"language:kab",
"region:us"
] | [] | 2025-05-20T14:43:08Z | 0 | ---
language:
- fra
- kab
viewer: false
---
Ce répertoire est vide, il a été créé pour améliorer le référencement du jeu de données [michsethowusu/french-kabyle_sentence-pairs](https://huggingface.co/datasets/michsethowusu/french-kabyle_sentence-pairs). |
PogusTheWhisper/fleurs-th_th-noise-augmented | PogusTheWhisper | 2025-05-29T10:16:35Z | 20 | 0 | [
"size_categories:1K<n<10K",
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"modality:audio",
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"library:dask",
"library:mlcroissant",
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] | [] | 2025-05-29T10:15:19Z | 0 | ---
dataset_info:
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'1': eastern_european_ee
'2': central_asia_middle_north_african_cmn
'3': sub_saharan_african_ssa
'4': south_asian_sa
'5': south_east_asian_sea
'6': chinese_japanase_korean_cjk
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---
|
aimagelab/ReflectiVA-Data | aimagelab | 2025-04-05T22:42:59Z | 248 | 0 | [
"task_categories:image-text-to-text",
"license:apache-2.0",
"arxiv:2411.16863",
"region:us"
] | [
"image-text-to-text"
] | 2025-03-25T14:37:42Z | 0 | ---
license: apache-2.0
task_categories:
- image-text-to-text
---
In this datasets space, you will find the data of [Augmenting Multimodal LLMs with Self-Reflective Tokens for Knowledge-based Visual Question Answering](https://huggingface.co/papers/2411.16863).
For more information, visit our [ReflectiVA repository](https://github.com/aimagelab/ReflectiVA), our [project page](https://aimagelab.github.io/ReflectiVA/) and [model space](https://huggingface.co/aimagelab/ReflectiVA).
## Citation
If you make use of our work, please cite our repo:
```bibtex
@inproceedings{cocchi2024augmenting,
title={{Augmenting Multimodal LLMs with Self-Reflective Tokens for Knowledge-based Visual Question Answering}},
author={Cocchi, Federico and Moratelli, Nicholas and Cornia, Marcella and Baraldi, Lorenzo and Cucchiara, Rita},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2025}
}
```
|
mrcuddle/Nifty-Authoritarian-Scrape | mrcuddle | 2024-12-21T17:11:01Z | 32 | 0 | [
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:text2text-generation",
"language:en",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"text-classification",
"text-generation",
"text2text-generation"
] | 2024-12-19T21:20:40Z | 0 | ---
language:
- en
size_categories:
- 1K<n<10K
task_categories:
- text-classification
- text-generation
- text2text-generation
---
Data Scrape from LGBT Literature Archive Nifty.Org
-Category: Authoritarian
|
d0rj/audiocaps | d0rj | 2023-06-30T12:17:56Z | 144 | 5 | [
"task_categories:text-to-speech",
"multilinguality:monolingual",
"source_datasets:original",
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"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"youtube",
"captions"
] | [
"text-to-speech"
] | 2023-06-29T19:10:43Z | 1 | ---
dataset_info:
features:
- name: audiocap_id
dtype: int64
- name: youtube_id
dtype: string
- name: start_time
dtype: int64
- name: caption
dtype: string
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num_examples: 49838
- name: validation
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num_examples: 2475
- name: test
num_bytes: 454652
num_examples: 4875
download_size: 2781679
dataset_size: 4816143
license: mit
task_categories:
- text-to-speech
language:
- en
multilinguality:
- monolingual
tags:
- youtube
- captions
pretty_name: AudioCaps
size_categories:
- 10K<n<100K
source_datasets:
- original
paperswithcode_id: audiocaps
---
# audiocaps
## Dataset Description
- **Homepage:** https://audiocaps.github.io/
- **Repository:** https://github.com/cdjkim/audiocaps
- **Paper:** [AudioCaps: Generating Captions for Audios in The Wild](https://aclanthology.org/N19-1011.pdf)
HuggingFace mirror of [official data repo](https://github.com/cdjkim/audiocaps). |
pvmoorthi/eval_telebuddy1750027646 | pvmoorthi | 2025-06-15T22:48:33Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot"
] | [
"robotics"
] | 2025-06-15T22:48:27Z | 0 | ---
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
{
"codebase_version": "v2.1",
"robot_type": "lekiwi_client",
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"fps": 10,
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"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": {
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],
"names": [
"arm_shoulder_pan.pos",
"arm_shoulder_lift.pos",
"arm_elbow_flex.pos",
"arm_wrist_flex.pos",
"arm_wrist_roll.pos",
"arm_gripper.pos",
"x.vel",
"y.vel",
"theta.vel"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
9
],
"names": [
"arm_shoulder_pan.pos",
"arm_shoulder_lift.pos",
"arm_elbow_flex.pos",
"arm_wrist_flex.pos",
"arm_wrist_roll.pos",
"arm_gripper.pos",
"x.vel",
"y.vel",
"theta.vel"
]
},
"observation.images.front": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
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"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 10,
"video.channels": 3,
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}
},
"observation.images.wrist": {
"dtype": "video",
"shape": [
640,
480,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 640,
"video.width": 480,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 10,
"video.channels": 3,
"has_audio": false
}
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
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"shape": [
1
],
"names": null
},
"episode_index": {
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1
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"names": null
},
"index": {
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1
],
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},
"task_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
martijn75/COHeN_heb_unvoc | martijn75 | 2025-01-11T15:41:48Z | 17 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-01-11T15:41:37Z | 0 | ---
dataset_info:
features:
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dtype: string
- name: Stage
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names:
'0': ABH
'1': CBH
'2': TBH
'3': LBH
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num_examples: 1197
download_size: 762130
dataset_size: 1586023
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: eval
path: data/eval-*
---
|
cobordism/mixed_pa-le-an-30k | cobordism | 2024-11-05T10:59:51Z | 21 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-11-05T10:59:14Z | 0 | ---
dataset_info:
features:
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dtype: image
- name: conversations
sequence:
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dtype: string
- name: value
dtype: string
splits:
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num_examples: 30000
download_size: 1177396671
dataset_size: 1218674370.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
RyanYr/reflect_llama8b-t0_mistlarge-t12_om2-4 | RyanYr | 2024-12-10T04:15:16Z | 16 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-12-10T04:15:14Z | 0 | ---
dataset_info:
features:
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dtype: string
- name: generated_solution
dtype: string
- name: answer
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sequence: string
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- name: response@2
sequence: string
splits:
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num_bytes: 57336765
num_examples: 10000
download_size: 24553575
dataset_size: 57336765
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
colabfit/discrepencies_and_error_metrics_NPJ_2023_enhanced_validation_set | colabfit | 2025-04-23T18:13:51Z | 20 | 0 | [
"license:cc-by-4.0",
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"molecular dynamics",
"mlip",
"interatomic potential"
] | [] | 2025-04-01T20:34:44Z | 0 | ---
configs:
- config_name: default
data_files: "main/*.parquet"
license: cc-by-4.0
tags:
- molecular dynamics
- mlip
- interatomic potential
pretty_name: discrepencies and error metrics NPJ 2023 enhanced validation set
---
# Dataset
discrepencies and error metrics NPJ 2023 enhanced validation set
### Description
Structures from discrepencies_and_error_metrics_NPJ_2023 validation set, enhanced by inclusion of rare events. The full discrepencies_and_error_metrics_NPJ_2023 dataset includes the original mlearn_Si_train dataset, modified with the purpose of developing models with better diffusivity scores by replacing ~54% of the data with structures containing migrating interstitials. The enhanced validation set contains 50 total structures, consisting of 20 structures randomly selected from the 120 replaced structures of the original training dataset, 11 snapshots with vacancy rare events (RE) from AIMD simulations, and 19 snapshots with interstitial RE from AIMD simulations. We also construct interstitial-RE and vacancy-RE testing sets, each consisting of 100 snapshots of atomic configurations with a single migrating vacancy or interstitial, respectively, from AIMD simulations at 1230 K.
<br>Additional details stored in dataset columns prepended with "dataset_".
### Dataset authors
Yunsheng Liu, Xingfeng He, Yifei Mo
### Publication
https://doi.org/10.1038/s41524-023-01123-3
### Original data link
https://github.com/mogroupumd/Silicon_MLIP_datasets
### License
CC-BY-4.0
### Number of unique molecular configurations
50
### Number of atoms
3198
### Elements included
Si
### Properties included
energy, atomic forces, cauchy stress
### Cite this dataset
Liu, Y., He, X., and Mo, Y. _discrepencies and error metrics NPJ 2023 enhanced validation set_. ColabFit, 2023. https://doi.org/10.60732/9c77bb8c |
claire-e-5/POS-tagged-MovieSummaries | claire-e-5 | 2024-10-25T18:42:00Z | 23 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-10-25T18:19:26Z | 0 | ---
dataset_info:
features:
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dtype: string
- name: Movie Description
dtype: string
- name: adjectives
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configs:
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path: data/train-*
---
|
SayantanJoker/Shrutilipi_Hindi_original_chunk_68 | SayantanJoker | 2025-04-15T03:24:28Z | 19 | 0 | [
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"library:polars",
"region:us"
] | [] | 2025-04-15T03:23:29Z | 0 | ---
dataset_info:
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path: data/train-*
---
|
pxyyy/NuminaMath-CoT-smp20k-removed-top3000-by-mp-1e-4 | pxyyy | 2025-05-02T16:21:58Z | 0 | 0 | [
"size_categories:10K<n<100K",
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"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-02T16:21:55Z | 0 | ---
dataset_info:
features:
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dtype: string
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download_size: 24494029
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configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "NuminaMath-CoT-smp20k-removed-top3000-by-mp-1e-4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pyterrier/nq.terrier | pyterrier | 2024-10-08T17:34:45Z | 32 | 0 | [
"task_categories:text-retrieval",
"region:us",
"pyterrier",
"pyterrier-artifact",
"pyterrier-artifact.sparse_index",
"pyterrier-artifact.sparse_index.terrier"
] | [
"text-retrieval"
] | 2024-10-08T17:30:12Z | 0 | ---
# pretty_name: "" # Example: "MS MARCO Terrier Index"
tags:
- pyterrier
- pyterrier-artifact
- pyterrier-artifact.sparse_index
- pyterrier-artifact.sparse_index.terrier
task_categories:
- text-retrieval
viewer: false
---
# nq.terrier
## Description
Terrier index for NQ (Natural Questions)
## Usage
```python
# Load the artifact
import pyterrier as pt
index = pt.Artifact.from_hf('pyterrier/nq.terrier')
index.bm25()
```
## Benchmarks
| name | nDCG@10 | R@1000 |
|:-------|----------:|---------:|
| bm25 | 0.2814 | 0.8906 |
| dph | 0.2846 | 0.8926 |
## Reproduction
```python
import pyterrier as pt
from tqdm import tqdm
import pandas as pd
import ir_datasets
from pyterrier_pisa import PisaIndex
dataset = ir_datasets.load('beir/nq')
meta_docno_len = dataset.metadata()['docs']['fields']['doc_id']['max_len']
indexer = pt.IterDictIndexer("./nq.terrier", meta={'docno': meta_docno_len, 'text': 4096})
docs = ({'docno': d.doc_id, 'text': d.default_text()} for d in tqdm(dataset.docs))
indexer.index(docs)
```
## Metadata
```
{
"type": "sparse_index",
"format": "terrier",
"package_hint": "python-terrier"
}
```
|
candywal/one_shot_logistic_code_sabotage_unsafe | candywal | 2025-05-20T03:35:46Z | 0 | 0 | [
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] | [] | 2025-05-20T03:35:44Z | 0 | ---
dataset_info:
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path: data/train-*
---
|
NicoHelemon/mmlu_STEM | NicoHelemon | 2025-05-26T20:58:05Z | 0 | 0 | [
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] | [] | 2025-05-26T20:58:01Z | 0 | ---
dataset_info:
features:
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dtype: string
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configs:
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data_files:
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path: data/test-*
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path: data/validation-*
- split: dev
path: data/dev-*
---
|
K1CH/so100_test08 | K1CH | 2025-03-28T11:24:08Z | 29 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot",
"so100",
"tutorial"
] | [
"robotics"
] | 2025-03-28T11:24:02Z | 0 | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so100
- 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": "v2.0",
"robot_type": "so100",
"total_episodes": 2,
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"total_tasks": 1,
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"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:2"
},
"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": {
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"shape": [
6
],
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"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.images.laptop": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.fps": 30.0,
"video.height": 640,
"video.width": 480,
"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": {
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"shape": [
1
],
"names": null
},
"task_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
jplhughes2/alignment-faking-synthetic-chat-dataset-recall-30k-benign-20k | jplhughes2 | 2025-02-03T16:38:23Z | 14 | 0 | [
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---
|
Ricky06662/refCOCOg_9k_840 | Ricky06662 | 2025-03-26T06:05:35Z | 250 | 0 | [
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dataset_info:
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---
|
katsuchi/paimon-voice-dataset | katsuchi | 2025-02-09T05:30:40Z | 27 | 0 | [
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] | [] | 2025-02-09T05:12:36Z | 0 | ---
dataset_info:
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---
|
gabrielegabellone/mediterraneo_triplets | gabrielegabellone | 2025-06-12T09:43:46Z | 1 | 0 | [
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---
|
dgambettaphd/D_llm2_gen1_WXS_doc1000_synt64_rnd42_lr5e-05_acm_SYNLAST | dgambettaphd | 2025-05-11T22:46:05Z | 0 | 0 | [
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] | [] | 2025-05-11T22:46:00Z | 0 | ---
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---
|
datacomp/ImageNetTraining20.0-frac-1over64 | datacomp | 2025-01-16T23:00:45Z | 14 | 0 | [
"license:cc",
"region:us"
] | [] | 2025-01-14T19:01:19Z | 0 | ---
title: ImageNetTraining20.0-frac-1over64
emoji: 😻
colorFrom: yellow
colorTo: blue
sdk: docker
pinned: false
license: cc
startup_duration_timeout: 5h
hf_oauth_expiration_minutes: 1440
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
Isotonic/reasoning-v0.1 | Isotonic | 2024-10-28T13:43:19Z | 30 | 1 | [
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] | [] | 2024-10-28T13:41:42Z | 0 | ---
dataset_info:
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path: data/train-*
---
|
Whimsyturtle/vpi-bench | Whimsyturtle | 2025-05-12T11:54:33Z | 0 | 0 | [
"language:en",
"license:cc-by-4.0",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"modality:image",
"library:datasets",
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"safety",
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"security",
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"data-exfiltration",
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"llm",
"agentic-ai",
"computer-use",
"browser-use",
"benchmark",
"dataset"
] | [] | 2025-05-10T13:40:38Z | 0 | ---
license: cc-by-4.0
language:
- en
tags:
- safety
- alignment
- security
- privacy
- multimodal
- visual
- image
- adversarial
- malicious
- robustness
- prompt-injection
- visual-prompt-injection
- data-exfiltration
- prompt-defense
- llm
- agentic-ai
- computer-use
- browser-use
- benchmark
- dataset
pretty_name: Computer-Use Agents Testcases & Web Platforms Dataset
size_categories:
- n<1K
configs:
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data_files:
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path: "main_benchmark.parquet"
--- |
SayantanJoker/Hindi_1000hr_Train_Subset_44100Hz_quality_metadata | SayantanJoker | 2025-04-13T10:31:09Z | 70 | 0 | [
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] | [] | 2025-04-13T10:31:08Z | 0 | ---
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---
|
timaeus/pythia-160m-pile-1m-ig-l7h4 | timaeus | 2025-01-31T19:08:48Z | 14 | 0 | [
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] | [] | 2025-01-31T19:08:45Z | 0 | ---
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---
|
HungVu2003/opt-350m_beta_0.5_alpha_0.2_num-company_3_dataset_2_for_gen_1 | HungVu2003 | 2025-04-29T16:52:31Z | 17 | 0 | [
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] | [] | 2025-04-29T16:52:28Z | 0 | ---
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path: data/train-*
---
|
HungVu2003/opt-350m_beta_0.0_alpha_1.0_num-company_2_dataset_0_for_gen_6 | HungVu2003 | 2025-04-10T12:31:11Z | 14 | 0 | [
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"library:polars",
"region:us"
] | [] | 2025-04-10T12:31:10Z | 0 | ---
dataset_info:
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---
|
colinrein/camel | colinrein | 2025-02-21T22:23:42Z | 14 | 0 | [
"license:apache-2.0",
"modality:image",
"region:us"
] | [] | 2025-02-21T22:06:32Z | 0 | ---
license: apache-2.0
---
|
nova07/naughtyamerica_json_6 | nova07 | 2025-02-09T14:09:29Z | 14 | 0 | [
"size_categories:n<1K",
"format:parquet",
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] | [] | 2025-02-09T14:09:26Z | 0 | ---
dataset_info:
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---
|
krutrim-ai-labs/IndicPope | krutrim-ai-labs | 2025-03-05T09:07:12Z | 158 | 0 | [
"language:as",
"language:hi",
"language:gu",
"language:ml",
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"arxiv:2502.15392",
"arxiv:2310.03744",
"arxiv:2305.10355",
"arxiv:2305.16307",
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] | [] | 2025-03-01T04:30:46Z | 0 | ---
dataset_info:
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configs:
- config_name: default
data_files:
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path: data/assamese-*
- split: bengali
path: data/bengali-*
- split: english
path: data/english-*
- split: gujarati
path: data/gujarati-*
- split: hindi
path: data/hindi-*
- split: kannada
path: data/kannada-*
- split: malayalam
path: data/malayalam-*
- split: marathi
path: data/marathi-*
- split: odia
path: data/odia-*
- split: sanskrit
path: data/sanskrit-*
- split: tamil
path: data/tamil-*
- split: telugu
path: data/telugu-*
license: other
license_name: krutrim-community-license-agreement-version-1.0
license_link: LICENSE.md
extra_gated_heading: Acknowledge license to accept the repository
extra_gated_button_content: Acknowledge license
language:
- as
- hi
- gu
- ml
- te
- ta
- kn
- or
- bn
- en
- mr
- sa
---
# IndicPope: Indian Multilingual Translation Dataset For Evaluating Large Vision Language Models
- You can find the performance of Chitrarth on IndicPope here : [**Paper**](https://arxiv.org/abs/2502.15392) | [**Github**](https://github.com/ola-krutrim/Chitrarth) | [**HuggingFace**](https://huggingface.co/krutrim-ai-labs/Chitrarth)
- Evaluation Scripts of BharatBench is available here : [**Github**](https://github.com/ola-krutrim/BharatBench)
## 1. Introduction
IndicPope is a new dataset designed for evaluating Large Vision-Language Models (LVLMs) on Visual Question Answering (VQA) tasks. It focuses on simple Yes-or-No questions probing objects in images (e.g., *Is there a car in the image?*).
This dataset is built upon **POPE: Polling-based Object Probing Evaluation for Object Hallucination** ([GitHub](https://github.com/AoiDragon/POPE)), which employs negative sampling techniques to test hallucination in vision-language models under **Random, Popular, and Adversarial** settings.
---
## 2. Dataset Details
IndicPope consists of **8.91k samples** spanning **12 Indic languages** along with English. Each sample includes:
- **Text**: The question about the image.
- **Category**: The type of sampling used (Random/Popular/Adversarial).
- **Label**: The answer (*Yes/No*).
### Supported Languages
- Assamese
- Bengali
- English
- Gujarati
- Hindi
- Kannada
- Malayalam
- Marathi
- Odia
- Sanskrit
- Tamil
- Telugu
---
## 3. How to Use and Run
You can load the dataset using the `datasets` library:
```python
from datasets import load_dataset
dataset = load_dataset("krutrim-ai-labs/IndicPope")
print(dataset)
```
---
## 4. License
This code repository and the model weights are licensed under the [Krutrim Community License.](LICENSE.md)
## 5. Citation
```
@article{khan2025chitrarth,
title={Chitrarth: Bridging Vision and Language for a Billion People},
author={Shaharukh Khan, Ayush Tarun, Abhinav Ravi, Ali Faraz, Akshat Patidar, Praveen Kumar Pokala, Anagha Bhangare, Raja Kolla, Chandra Khatri, Shubham Agarwal},
journal={arXiv preprint arXiv:2502.15392},
year={2025}
}
@misc{liu2023improvedllava,
title={Improved Baselines with Visual Instruction Tuning},
author={Liu, Haotian and Li, Chunyuan and Li, Yuheng and Lee, Yong Jae},
publisher={arXiv:2310.03744},
year={2023},
}
@misc{liu2023llava,
title={Visual Instruction Tuning},
author={Liu, Haotian and Li, Chunyuan and Wu, Qingyang and Lee, Yong Jae},
publisher={NeurIPS},
year={2023},
}
@article{li2023evaluating,
title={Evaluating object hallucination in large vision-language models},
author={Li, Yifan and Du, Yifan and Zhou, Kun and Wang, Jinpeng and Zhao, Wayne Xin and Wen, Ji-Rong},
journal={arXiv preprint arXiv:2305.10355},
year={2023}
}
@article{gala2023indictrans2,
title={Indictrans2: Towards high-quality and accessible machine translation models for all 22 scheduled indian languages},
author={Gala, Jay and Chitale, Pranjal A and AK, Raghavan and Gumma, Varun and Doddapaneni, Sumanth and Kumar, Aswanth and Nawale, Janki and Sujatha, Anupama and Puduppully, Ratish and Raghavan, Vivek and others},
journal={arXiv preprint arXiv:2305.16307},
year={2023}
}
```
## 6. Contact
Contributions are welcome! If you have any improvements or suggestions, feel free to submit a pull request on GitHub.
## 7. Acknowledgement
IndicPope is built with reference to the code of the following projects: [POPE](https://github.com/AoiDragon/POPE), and [LLaVA-1.5](https://github.com/haotian-liu/LLaVA). Thanks for their awesome work! |
AdoCleanCode/CI100_correct_distorted_v1 | AdoCleanCode | 2025-05-01T13:07:32Z | 0 | 0 | [
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] | [] | 2025-04-30T23:43:16Z | 0 | ---
dataset_info:
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---
|
Allen-UQ/cora_1_target_all_one_hop | Allen-UQ | 2025-06-18T04:07:02Z | 72 | 0 | [
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dataset_info:
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---
|
french-datasets/Geraldine-bso-publications-indexation-50k | french-datasets | 2025-03-29T20:37:43Z | 15 | 0 | [
"language:fra",
"region:us"
] | [] | 2025-03-29T20:37:34Z | 0 | ---
language: "fra"
viewer: false
---
Ce répertoire est vide, il a été créé pour améliorer le référencement du jeu de données huggingface.co/datasets/Geraldine/bso-publications-indexation-50k.
|
deepinfinityai/30_NLEM_Aug_audios_dataset | deepinfinityai | 2025-03-29T09:27:19Z | 17 | 0 | [
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---
|
aochongoliverli/R1-Distill-Qwen-1.5B-deepmath-level5-6-max-length-16384-rollout-8-temperature-0.5-rollouts | aochongoliverli | 2025-06-22T13:58:11Z | 11 | 0 | [
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] | [] | 2025-06-22T00:43:49Z | 0 | ---
dataset_info:
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configs:
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---
|
ebony59/gsm8k-gen-stepwise | ebony59 | 2025-04-24T16:54:00Z | 23 | 0 | [
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] | [] | 2025-04-23T21:27:36Z | 0 | ---
dataset_info:
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path: data/train-*
---
|
khr0516/SynthScars | khr0516 | 2025-03-21T08:59:19Z | 48 | 0 | [
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"library:mlcroissant",
"arxiv:2503.15264",
"region:us"
] | [] | 2025-03-21T06:26:47Z | 0 | ---
license: apache-2.0
---
## Citation
```
@misc{kang2025legionlearninggroundexplain,
title={LEGION: Learning to Ground and Explain for Synthetic Image Detection},
author={Hengrui Kang and Siwei Wen and Zichen Wen and Junyan Ye and Weijia Li and Peilin Feng and Baichuan Zhou and Bin Wang and Dahua Lin and Linfeng Zhang and Conghui He},
year={2025},
eprint={2503.15264},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2503.15264},
}
```
|
khuang2/Countdown-Tasks-3to4-query-gen-prompts-w-hint | khuang2 | 2025-02-19T08:09:53Z | 27 | 0 | [
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dataset_info:
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|
dgambettaphd/D_llm3_gen9_run0_X_doc1000_synt64_tot128_MPP | dgambettaphd | 2025-04-26T01:30:06Z | 31 | 0 | [
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|
jbloom-aisi/benton_et_al_sandbagging_response_llama_3_1_70B_Instruct | jbloom-aisi | 2025-02-14T14:41:21Z | 9 | 0 | [
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---
|
Langue-des-signes-francaise/dinlang-gespin-2023 | Langue-des-signes-francaise | 2025-03-29T22:04:05Z | 28 | 0 | [
"multilinguality:multilingual",
"language:fra",
"language:fsl",
"license:cc-by-4.0",
"region:us"
] | [] | 2024-10-22T13:59:37Z | 0 | ---
language:
- fra
- fsl
multilinguality:
- multilingual
viewer: false
license: cc-by-4.0
---
> [!NOTE]
> Dataset origin: https://www.ortolang.fr/market/corpora/dinlang-gespin-2023
## Description
You will find here the video excerpts related to the article "Coordinating eating and languaging: the choreography of speech, sign, gesture and action in family dinners" written for GESPIN 2023 by the DinLang project team.
## Citation
```
@misc{11403/dinlang-gespin-2023/v1,
title = {DinLang - GESPIN 2023},
author = {PRISMES and MoDyCo and SFL and DYLIS},
url = {https://hdl.handle.net/11403/dinlang-gespin-2023/v1},
note = {{ORTOLANG} ({Open} {Resources} {and} {TOols} {for} {LANGuage}) \textendash www.ortolang.fr},
copyright = {Licence Creative Commons - Attribution 4.0 International},
year = {2023}
}
``` |
lukicdarkoo/close_2_cameras | lukicdarkoo | 2025-06-14T17:44:45Z | 0 | 0 | [
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] | [] | 2025-06-14T17:42:31Z | 0 | ---
license: apache-2.0
---
|
NovaSky-AI/Sky-T1_preference_data_10k | NovaSky-AI | 2025-01-23T07:58:17Z | 73 | 13 | [
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] | [] | 2025-01-23T07:55:28Z | 0 | ---
license: apache-2.0
---
|
autobio-bench/insert-blender | autobio-bench | 2025-05-14T14:59:04Z | 0 | 0 | [
"task_categories:robotics",
"license:mit",
"modality:video",
"region:us",
"LeRobot",
"medical"
] | [
"robotics"
] | 2025-05-14T14:56:40Z | 0 | ---
license: mit
task_categories:
- robotics
tags:
- LeRobot
- medical
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:** mit
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.0",
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"total_episodes": 100,
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"train": "0:100"
},
"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": {
"state": {
"dtype": "float32",
"shape": [
7
],
"names": [
"state"
]
},
"actions": {
"dtype": "float32",
"shape": [
7
],
"names": [
"actions"
]
},
"image": {
"dtype": "video",
"shape": [
224,
224,
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],
"names": [
"height",
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"channel"
],
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}
},
"wrist_image": {
"dtype": "video",
"shape": [
224,
224,
3
],
"names": [
"height",
"width",
"channel"
],
"info": {
"video.fps": 50.0,
"video.height": 224,
"video.width": 224,
"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
},
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1
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},
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1
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1
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"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_7ec550b9-e3b0-4cc6-b781-7cc816e6ffab | argilla-internal-testing | 2024-12-13T13:06:23Z | 16 | 0 | [
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|
deepakkumar07/debug-llama2-1k | deepakkumar07 | 2024-10-26T18:12:52Z | 23 | 1 | [
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dataset_info:
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|
mlfoundations-dev/difficulty_sorting_medium_seed_code_w_openthoughts | mlfoundations-dev | 2025-02-18T00:59:55Z | 96 | 0 | [
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dataset_info:
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Senju2/context-aware-project | Senju2 | 2025-04-23T18:41:30Z | 16 | 0 | [
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Demonion/socks_basket_white | Demonion | 2025-04-12T10:04:40Z | 18 | 0 | [
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"socks_basket, so100,tutorial"
] | [
"robotics"
] | 2025-04-12T10:04:25Z | 0 | ---
license: apache-2.0
task_categories:
- robotics
tags:
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- socks_basket, so100,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]
``` |
OlafCC/lab1-llama3-tt | OlafCC | 2024-10-17T06:38:37Z | 14 | 0 | [
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license: apache-2.0
---
|
dvilasuero/jailbreak-classification-processed | dvilasuero | 2025-03-19T08:38:56Z | 20 | 0 | [
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|
thinkPy/title | thinkPy | 2024-11-05T17:01:07Z | 25 | 0 | [
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|
SreehariR/guanaco-llama2-1k | SreehariR | 2024-12-14T11:12:31Z | 15 | 0 | [
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|
EdinsonAcosta/DataSetKalmaPross | EdinsonAcosta | 2025-01-16T17:11:16Z | 22 | 0 | [
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] | 2025-01-16T17:02:30Z | 0 | ---
license: cc-by-nc-sa-4.0
task_categories:
- text2text-generation
language:
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tags:
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--- |
juliadollis/stf_regex_ner_pierre | juliadollis | 2024-11-28T16:53:04Z | 7 | 0 | [
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|
ed-donner/trade_code_data | ed-donner | 2024-11-21T16:24:31Z | 23 | 0 | [
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|
Luffytaro-1/asr_en_ar_switch_split_128_final | Luffytaro-1 | 2025-02-19T07:19:58Z | 16 | 0 | [
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|
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_17c677de-0ba3-46d4-abf6-2690adfe64bc | argilla-internal-testing | 2024-10-04T09:37:02Z | 17 | 0 | [
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|
SAA-Lab/test_march23-cwv-genrm_llama3b-ckptNone | SAA-Lab | 2025-05-12T20:04:23Z | 0 | 0 | [
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|
zerostratos/music2200 | zerostratos | 2025-05-06T15:15:08Z | 0 | 0 | [
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|
AbdallahhSaleh/asd-5-tokenized | AbdallahhSaleh | 2025-03-04T15:15:05Z | 15 | 0 | [
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] | [] | 2025-03-04T14:36:55Z | 0 | ---
dataset_info:
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|
gtsaidata/OCTImagesDataset | gtsaidata | 2025-02-27T10:37:55Z | 8 | 0 | [
"task_categories:image-classification",
"language:en",
"region:us",
"OCT Images Dataset",
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] | [
"image-classification"
] | 2025-02-27T10:32:44Z | 0 | ---
task_categories:
- image-classification
language:
- en
tags:
- OCT Images Dataset
- training AI models
---
Description:
<a href="https://gts.ai/dataset-download/oct-images-dataset/" target="_blank">👉 Download the dataset here</a>
Retinal Optical Coherence Tomography (OCT):
OCT is a non-invasive imaging technique that generates high-resolution cross-sectional images of the retina. This method is critical for diagnosing and monitoring retinal conditions by allowing clinicians to examine the internal structure of the retina in great detail. According to estimates, around 30 million OCT scans are conducted annually worldwide, underscoring the importance of efficient analysis and interpretation of these images. Traditional OCT image analysis demands significant time and expertise, which is why machine learning models trained on such datasets are increasingly being adopted to assist in the diagnostic process.
Download Dataset
Dataset Composition:
The dataset consists of retinal OCT images divided into four main categories of retinal conditions, enabling researchers to develop balanced models that can classify different eye conditions:
Choroidal Neovascularization (CNV): This condition involves the formation of new blood vessels in the choroid layer, typically associated with wet age-related macular degeneration (AMD). OCT scans reveal features such as subretinal fluid and the presence of neovascular membranes.
Image Characteristics: These images highlight areas where fluid has accumulated beneath the retina (subretinal fluid), which appears as dark spaces in the scans. The neovascular membrane can be identify as irregular structures disrupting the retinal layers.
Diabetic Macular Edema (DME): A complication of diabetes, DME occurs when fluid accumulates in the retina due to leaking blood vessels. Leading to swelling and impaired vision. OCT images of DME typically show increased retinal thickness and the presence of intraretinal fluid.
Image Characteristics: Retinal-thickening and pockets of intraretinal fluid can be seen as circular dark spaces in the middle layers of the retina, indicating areas affected by edema.
Balanced Version – Importance of Equal Representation:
This balanced version of the dataset ensures that each condition, including normal retinas, is equally represented. By balancing the dataset, researchers can train models that are less biased and can more accurately predict different retinal conditions, thereby improving the generalization ability of AI models.
Applications of the Dataset:
AI-Assisted Diagnostics: The dataset can be use to develop machine learning algorithms that assist ophthalmologists in diagnosing retinal diseases. Improving both the speed and accuracy of diagnosis.
Transfer Learning: Given the small size of the dataset, it is an excellent candidate for transfer learning applications. Where pre-train models can be fine-tuned for medical imaging tasks.
Research in Retinal Disease Progression: The dataset can also be use for studying disease progression by analyzing changes in OCT scans over time. Potentially offering insights into early intervention strategies.
In conclusion, this balance version of the OCT dataset provides a foundational resource for AI-driven medical research, facilitating advancements in the diagnosis, monitoring, and treatment of various retinal diseases. By ensuring equal representation across conditions. The dataset enables more robust model training. Which is essential for developing AI tools that are clinically useful in real-world settings.
This dataset is sourced from Kaggle. |
obiwan96/obiwan96open_web_math_qav3_150000_200000 | obiwan96 | 2025-02-21T05:14:09Z | 8 | 0 | [
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] | [] | 2025-02-20T23:37:28Z | 0 | ---
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|
BarryFutureman/vpt_data_8xx_shard0143 | BarryFutureman | 2025-06-11T02:01:22Z | 0 | 0 | [
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] | [
"robotics"
] | 2025-06-11T01:59:31Z | 0 | ---
license: apache-2.0
task_categories:
- robotics
tags:
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configs:
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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):
```json
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}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
tarscaleai/products-descriptions | tarscaleai | 2025-01-06T15:27:37Z | 15 | 0 | [
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|
timaeus/dsir-pile-100k-filtered-for-gutenberg-pg-19 | timaeus | 2024-11-15T17:56:56Z | 15 | 0 | [
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|
whitecrowdev/guanaco-llama1K | whitecrowdev | 2025-01-18T14:28:46Z | 16 | 0 | [
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|
ddureub/llama | ddureub | 2024-10-26T11:41:25Z | 19 | 0 | [
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mapau/br_queries_exfever | mapau | 2025-05-22T09:21:22Z | 0 | 0 | [
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dgambettaphd/D_llm3_gen7_run0_W_doc1000_synt120_SYNLAST | dgambettaphd | 2025-04-10T16:30:49Z | 15 | 0 | [
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giannhskp/scipar_ru_en_llm-backtranslation_sampling | giannhskp | 2025-05-24T22:51:57Z | 3 | 0 | [
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|
hardlyworking/HardlyRPv2 | hardlyworking | 2025-04-14T19:34:12Z | 41 | 0 | [
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] | [] | 2025-04-14T19:33:27Z | 0 | ---
license: apache-2.0
---
|
Roshal/AI4EO_DatasetsDiversity_Evals | Roshal | 2025-05-04T19:19:38Z | 0 | 0 | [
"license:apache-2.0",
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license: apache-2.0
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|
aklywtx/wiki_vi_sop | aklywtx | 2024-11-24T20:53:02Z | 16 | 0 | [
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|
End of preview. Expand
in Data Studio
Dataset Card for Hugging Face Hub Dataset Cards
This datasets consists of dataset cards for models hosted on the Hugging Face Hub. The dataset cards are created by the community and provide information about datasets hosted on the Hugging Face Hub. This dataset is updated on a daily basis and includes publicly available datasets on the Hugging Face Hub.
This dataset is made available to help support users wanting to work with a large number of Dataset Cards from the Hub. We hope that this dataset will help support research in the area of Dataset Cards and their use but the format of this dataset may not be useful for all use cases. If there are other features that you would like to see included in this dataset, please open a new discussion.
Dataset Details
Uses
There are a number of potential uses for this dataset including:
- text mining to find common themes in dataset cards
- analysis of the dataset card format/content
- topic modelling of dataset cards
- training language models on the dataset cards
Out-of-Scope Use
[More Information Needed]
Dataset Structure
This dataset has a single split.
Dataset Creation
Curation Rationale
The dataset was created to assist people in working with dataset cards. In particular it was created to support research in the area of dataset cards and their use. It is possible to use the Hugging Face Hub API or client library to download dataset cards and this option may be preferable if you have a very specific use case or require a different format.
Source Data
The source data is README.md files for datasets hosted on the Hugging Face Hub. We do not include any other supplementary files that may be included in the dataset directory.
Data Collection and Processing
The data is downloaded using a CRON job on a daily basis.
Who are the source data producers?
The source data producers are the creators of the dataset cards on the Hugging Face Hub. This includes a broad variety of people from the community ranging from large companies to individual researchers. We do not gather any information about who created the dataset card in this repository although this information can be gathered from the Hugging Face Hub API.
Annotations [optional]
There are no additional annotations in this dataset beyond the dataset card content.
Annotation process
N/A
Who are the annotators?
N/A
Personal and Sensitive Information
We make no effort to anonymize the data. Whilst we don't expect the majority of dataset cards to contain personal or sensitive information, it is possible that some dataset cards may contain this information. Dataset cards may also link to websites or email addresses.
Bias, Risks, and Limitations
Dataset cards are created by the community and we do not have any control over the content of the dataset cards. We do not review the content of the dataset cards and we do not make any claims about the accuracy of the information in the dataset cards. Some dataset cards will themselves discuss bias and sometimes this is done by providing examples of bias in either the training data or the responses provided by the dataset. As a result this dataset may contain examples of bias.
Whilst we do not directly download any images linked to in the dataset cards, some dataset cards may include images. Some of these images may not be suitable for all audiences.
Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
Citation
No formal citation is required for this dataset but if you use this dataset in your work, please include a link to this dataset page.
Dataset Card Authors
Dataset Card Contact
- Downloads last month
- 1,394
Curated by:
Daniel van Strien
Language(s) (NLP):
Dataset cards on the Hugging Face Hub are predominantly in English but may include other languages.
Size of downloaded dataset files:
139 MB
Size of the auto-converted Parquet files:
139 MB
Number of rows:
160,686