Hub Card Data
Collection
2 items
•
Updated
•
4
Datasets:
datasetId
large_stringlengths 6
118
| author
large_stringlengths 2
42
| last_modified
large_stringdate 2021-04-29 15:34:29
2025-11-25 13:48:24
| downloads
int64 0
3.97M
| likes
int64 0
7.74k
| tags
large listlengths 1
7.92k
| task_categories
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48
| createdAt
large_stringdate 2022-03-02 23:29:22
2025-11-25 12:43:50
| trending_score
float64 0
170
| card
large_stringlengths 31
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|---|---|---|---|---|---|---|---|---|---|
rankiii/Vision-R1-DATA-RESIZED
|
rankiii
|
2025-05-09T04:45:59Z
| 0
| 0
|
[
"task_categories:image-text-to-text",
"license:cc-by-nc-3.0",
"region:us"
] |
[
"image-text-to-text"
] |
2025-05-09T04:34:44Z
| 0
|
---
license: cc-by-nc-3.0
task_categories:
- image-text-to-text
---
|
jplhughes2/alignment-faking-synthetic-chat-dataset-recall-20k-docs-0k-benign-0k-refusals
|
jplhughes2
|
2025-02-03T21:52:30Z
| 22
| 0
|
[
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-02-03T21:52:27Z
| 0
|
---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 70996039.0
num_examples: 20000
download_size: 36345565
dataset_size: 70996039.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
loki-r/pravinyam
|
loki-r
|
2025-06-19T14:42:48Z
| 0
| 0
|
[
"task_categories:text-generation",
"license:mit",
"size_categories:10K<n<100K",
"region:us"
] |
[
"text-generation"
] |
2025-06-19T14:37:50Z
| 0
|
---
license: mit
task_categories:
- text-generation
size_categories:
- 10K<n<100K
---
|
mteb/CLSClusteringP2P.v2
|
mteb
|
2025-05-06T09:25:26Z
| 0
| 0
|
[
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:derived",
"multilinguality:monolingual",
"source_datasets:C-MTEB/CLSClusteringP2P",
"language:cmn",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2209.05034",
"arxiv:2502.13595",
"arxiv:2210.07316",
"region:us",
"mteb",
"text"
] |
[
"text-classification"
] |
2025-05-06T09:25:19Z
| 0
|
---
annotations_creators:
- derived
language:
- cmn
license: apache-2.0
multilinguality: monolingual
source_datasets:
- C-MTEB/CLSClusteringP2P
task_categories:
- text-classification
task_ids:
- topic-classification
dataset_info:
features:
- name: sentences
dtype: string
- name: labels
dtype: int64
splits:
- name: test
num_bytes: 1186593
num_examples: 2048
download_size: 798216
dataset_size: 1186593
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
tags:
- mteb
- text
---
<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->
<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
<h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">CLSClusteringP2P.v2</h1>
<div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div>
<div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
</div>
Clustering of titles + abstract from CLS dataset. Clustering of 13 sets on the main category.
| | |
|---------------|---------------------------------------------|
| Task category | t2c |
| Domains | Academic, Written |
| Reference | https://arxiv.org/abs/2209.05034 |
## How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
```python
import mteb
task = mteb.get_tasks(["CLSClusteringP2P.v2"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
```
<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb).
## Citation
If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb).
```bibtex
@misc{li2022csl,
archiveprefix = {arXiv},
author = {Yudong Li and Yuqing Zhang and Zhe Zhao and Linlin Shen and Weijie Liu and Weiquan Mao and Hui Zhang},
eprint = {2209.05034},
primaryclass = {cs.CL},
title = {CSL: A Large-scale Chinese Scientific Literature Dataset},
year = {2022},
}
@article{enevoldsen2025mmtebmassivemultilingualtext,
title={MMTEB: Massive Multilingual Text Embedding Benchmark},
author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
publisher = {arXiv},
journal={arXiv preprint arXiv:2502.13595},
year={2025},
url={https://arxiv.org/abs/2502.13595},
doi = {10.48550/arXiv.2502.13595},
}
@article{muennighoff2022mteb,
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
url = {https://arxiv.org/abs/2210.07316},
doi = {10.48550/ARXIV.2210.07316},
}
```
# Dataset Statistics
<details>
<summary> Dataset Statistics</summary>
The following code contains the descriptive statistics from the task. These can also be obtained using:
```python
import mteb
task = mteb.get_task("CLSClusteringP2P.v2")
desc_stats = task.metadata.descriptive_stats
```
```json
{
"test": {
"num_samples": 2048,
"number_of_characters": 435264,
"min_text_length": 24,
"average_text_length": 212.53125,
"max_text_length": 1507,
"unique_texts": 448,
"min_labels_per_text": 18,
"average_labels_per_text": 1.0,
"max_labels_per_text": 920,
"unique_labels": 13,
"labels": {
"1": {
"count": 202
},
"5": {
"count": 920
},
"10": {
"count": 122
},
"9": {
"count": 184
},
"2": {
"count": 191
},
"12": {
"count": 28
},
"8": {
"count": 110
},
"11": {
"count": 59
},
"4": {
"count": 39
},
"6": {
"count": 87
},
"7": {
"count": 55
},
"3": {
"count": 33
},
"0": {
"count": 18
}
}
}
}
```
</details>
---
*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*
|
odysseywt/PdM_Library
|
odysseywt
|
2025-05-14T15:47:17Z
| 0
| 0
|
[
"task_categories:time-series-forecasting",
"language:en",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"region:us",
"PdM"
] |
[
"time-series-forecasting"
] |
2025-05-14T13:54:05Z
| 0
|
---
license: cc-by-4.0
language:
- en
task_categories:
- time-series-forecasting
tags:
- PdM
size_categories:
- 100K<n<1M
---
|
fridalex/llm-course-hw1
|
fridalex
|
2025-03-12T16:22:20Z
| 16
| 0
|
[
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-03-12T16:22:14Z
| 0
|
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 42016051
num_examples: 150553
download_size: 23821592
dataset_size: 42016051
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
easonjcc/so100_test-0
|
easonjcc
|
2025-04-02T13:16:21Z
| 36
| 0
|
[
"task_categories:robotics",
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot",
"so100",
"tutorial"
] |
[
"robotics"
] |
2025-04-02T13:16:12Z
| 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,
"total_frames": 298,
"total_tasks": 1,
"total_videos": 4,
"total_chunks": 1,
"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": {
"dtype": "float32",
"shape": [
6
],
"names": [
"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": {
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"shape": [
480,
640,
3
],
"names": [
"height",
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"channels"
],
"info": {
"video.fps": 30.0,
"video.height": 480,
"video.width": 640,
"video.channels": 3,
"video.codec": "h264",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"observation.images.phone": {
"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": "h264",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"frame_index": {
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"shape": [
1
],
"names": null
},
"episode_index": {
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"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]
```
|
LiSoViMa/AquaRat
|
LiSoViMa
|
2025-05-20T16:58:18Z
| 0
| 0
|
[
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-05-20T16:57:11Z
| 0
|
---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype: string
- name: support
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 41563861
num_examples: 97467
- name: validation
num_bytes: 116700
num_examples: 254
- name: test
num_bytes: 114853
num_examples: 254
download_size: 24330803
dataset_size: 41795414
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
Jeevesh2009/so101_gray_block_lowvar_test
|
Jeevesh2009
|
2025-06-11T06:53:42Z
| 0
| 0
|
[
"task_categories:robotics",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot",
"so101",
"tutorial"
] |
[
"robotics"
] |
2025-06-11T06:31:15Z
| 0
|
---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so101
- 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": "so101",
"total_episodes": 50,
"total_frames": 19795,
"total_tasks": 1,
"total_videos": 100,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:50"
},
"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": [
6
],
"names": [
"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.top": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
}
},
"observation.images.side": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"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]
```
|
Luffytaro-1/asr_en_ar_switch_split_72_final
|
Luffytaro-1
|
2025-02-16T06:20:29Z
| 17
| 0
|
[
"size_categories:n<1K",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-02-16T06:19:31Z
| 0
|
---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 5281616.0
num_examples: 54
download_size: 4660795
dataset_size: 5281616.0
---
# Dataset Card for "asr_en_ar_switch_split_72_final"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
alea-institute/kl3m-filter-data-dotgov-www.nlrb.gov
|
alea-institute
|
2025-02-04T18:34:50Z
| 14
| 0
|
[
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-02-04T18:34:46Z
| 0
|
---
dataset_info:
features:
- name: identifier
dtype: string
- name: dataset
dtype: string
- name: mime_type
dtype: string
- name: score
dtype: float64
- name: tokens
sequence: int64
splits:
- name: train
num_bytes: 58303093
num_examples: 342
download_size: 10973658
dataset_size: 58303093
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
NEXTLab-ZJU/popular-hook
|
NEXTLab-ZJU
|
2024-11-06T12:34:36Z
| 27,814
| 9
|
[
"size_categories:10K<n<100K",
"region:us",
"music",
"midi",
"emotion"
] |
[] |
2024-07-10T02:25:29Z
| 0
|
---
tags:
- music
- midi
- emotion
size_categories:
- 10K<n<100K
---
# Popular Hooks
This is the dataset repository for the paper: Popular Hooks: A Multimodal Dataset of Musical Hooks for Music Understanding and Generation, in 2024 IEEE International Conference on Multimedia and Expo Workshops (ICMEW).
## 1. Introduction
Popular Hooks, a shared multimodal music dataset consisting of **38,694** popular musical hooks for music understanding and generation; this dataset has the following key features:
- **Multimodal Music Data**
- **Accurate Time Alignment**
- **Rich Music Annotations**
## 2. Modalities
- Midi
- Lyrics
- Video (Youtube link provided, you need to download it by yourself)
- Audio
## 3. High Level Music Information
- Melody
- Harmony
- Structure
- Genre
- Emotion(Russell's 4Q)
- Region
## 4. Dataset File Structure
- info_tables.xlsx: it contains a list describing the baisc information of each midi file (index, path, song name, singer, song url, genres, youtube url, youtube video start time and end time/duration, language, tonalities)
- midi/{index}/{singer_name}/{song_name}:
- complete_text_emotion_result.csv: it contains the emotion class(4Q) which is predicted with the total lyrics of the song.
- song_info.json: it contains the song's section info, theorytab DB url and genres info.
- total_lyrics.txt: it contains the song's complete lyrics which is collected from music API(lyricsGenius, NetEase, QQMusic)
- youtube_info.json: it contains the url of the song in Youtube, the start time and end time/duration of the video section.
- ./{section}
- {section}.mid: the section in midi format
- {section}.txt: it contains the tonalites of the section.
- {section}_audio_emotion_result.csv: it contains the emotion class(4Q) which is predicted with the audio of the section.
- {section}_lyrics.csv: it contains the lyrics of the section.
- {section}_midi_emotion_result.csv: it contains the emotion class(4Q) which is predicted with the midi of the section.
- {section}_multimodal_emotion_result.csv: it contains the emotion class(4Q) which is selected from the multimodal emotions of the section.
- {section}_text_emotion_result.csv: it contains the emotion class(4Q) which is predicted with the lyrics of the section.
- {section}_video_emotion_result.csv: it contains the emotion class(4Q) which is predicted with the video of the section.
## 5. Demo
<img src='https://huggingface.co/datasets/NEXTLab-ZJU/popular-hook/resolve/main/imgs/popular_hooks_demo.png'>
|
adriansanz/Train_SQV_20241007101-232
|
adriansanz
|
2024-10-08T09:18:08Z
| 19
| 0
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-10-08T09:18:07Z
| 0
|
---
dataset_info:
features:
- name: document
dtype: string
- name: question
dtype: string
- name: context
dtype: string
splits:
- name: train
num_bytes: 1204577
num_examples: 4085
download_size: 153186
dataset_size: 1204577
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
uzair921/CONLL2003_LLM_BASELINE
|
uzair921
|
2024-10-01T12:44:41Z
| 18
| 0
|
[
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-10-01T12:44:36Z
| 0
|
---
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
'7': B-MISC
'8': I-MISC
splits:
- name: train
num_bytes: 2451027
num_examples: 9829
- name: validation
num_bytes: 866541
num_examples: 3250
- name: test
num_bytes: 784956
num_examples: 3453
download_size: 1004807
dataset_size: 4102524
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
autoevaluate/autoeval-staging-eval-project-a25a94fd-9305221
|
autoevaluate
|
2022-07-02T12:09:46Z
| 12
| 0
|
[
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"autotrain",
"evaluation"
] |
[] |
2022-07-01T08:08:40Z
| 0
|
---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- big_patent
eval_info:
task: summarization
model: google/bigbird-pegasus-large-bigpatent
metrics: ['rouge']
dataset_name: big_patent
dataset_config: all
dataset_split: validation
col_mapping:
text: description
target: abstract
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: google/bigbird-pegasus-large-bigpatent
* Dataset: big_patent
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@kayvane](https://huggingface.co/kayvane) for evaluating this model.
|
KBayoud/Darija-VLM-Dataset-BASE
|
KBayoud
|
2025-05-12T13:07:22Z
| 12
| 1
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-05-11T18:25:06Z
| 0
|
---
dataset_info:
features:
- name: image
dtype: image
- name: question
dtype: string
- name: answer
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 3421002825.492
num_examples: 1938
download_size: 2793518584
dataset_size: 3421002825.492
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
fabianrausch/financial-entities-values-augmented
|
fabianrausch
|
2022-06-20T09:50:29Z
| 23
| 1
|
[
"license:mit",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2022-06-20T09:12:30Z
| 0
|
---
license: mit
---
This dataset is contains 200 sentences taken from German financial statements. In each sentence financial entities and financial values are annotated. Additionally there is an augmented version of this dataset where the financial entities in each sentence have been replaced by several other financial entities which are hardly/not covered in the original dataset. The augmented version consists of 7287 sentences.
|
domhel/studytable_open_drawer_depth_1748246183
|
domhel
|
2025-05-26T08:29:57Z
| 0
| 0
|
[
"task_categories:robotics",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot"
] |
[
"robotics"
] |
2025-05-26T08:29:18Z
| 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": null,
"total_episodes": 50,
"total_frames": 22079,
"total_tasks": 1,
"total_videos": 300,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 10,
"splits": {
"train": "0:50"
},
"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": {
"observation.image.camera1_img": {
"dtype": "video",
"shape": [
3,
240,
320
],
"names": [
"channel",
"height",
"width"
],
"video_info": {
"video.fps": 10.0,
"video.is_depth_map": false
},
"info": {
"video.fps": 10.0,
"video.height": 240,
"video.width": 320,
"video.channels": 3,
"video.codec": "h264",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"observation.image.camera1_depth": {
"dtype": "video",
"shape": [
3,
240,
320
],
"names": [
"channel",
"height",
"width"
],
"video_info": {
"video.fps": 10.0,
"video.is_depth_map": false
},
"info": {
"video.fps": 10.0,
"video.height": 240,
"video.width": 320,
"video.channels": 3,
"video.codec": "h264",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"observation.image.camera2_img": {
"dtype": "video",
"shape": [
3,
240,
320
],
"names": [
"channel",
"height",
"width"
],
"video_info": {
"video.fps": 10.0,
"video.is_depth_map": false
},
"info": {
"video.fps": 10.0,
"video.height": 240,
"video.width": 320,
"video.channels": 3,
"video.codec": "h264",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"observation.image.camera2_depth": {
"dtype": "video",
"shape": [
3,
240,
320
],
"names": [
"channel",
"height",
"width"
],
"video_info": {
"video.fps": 10.0,
"video.is_depth_map": false
},
"info": {
"video.fps": 10.0,
"video.height": 240,
"video.width": 320,
"video.channels": 3,
"video.codec": "h264",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"observation.image.camera3_img": {
"dtype": "video",
"shape": [
3,
240,
320
],
"names": [
"channel",
"height",
"width"
],
"video_info": {
"video.fps": 10.0,
"video.is_depth_map": false
},
"info": {
"video.fps": 10.0,
"video.height": 240,
"video.width": 320,
"video.channels": 3,
"video.codec": "h264",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"observation.image.camera3_depth": {
"dtype": "video",
"shape": [
3,
240,
320
],
"names": [
"channel",
"height",
"width"
],
"video_info": {
"video.fps": 10.0,
"video.is_depth_map": false
},
"info": {
"video.fps": 10.0,
"video.height": 240,
"video.width": 320,
"video.channels": 3,
"video.codec": "h264",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"observation.state": {
"dtype": "float32",
"shape": [
7
],
"names": {
"motors": [
"motor_1",
"motor_2",
"motor_3",
"motor_4",
"motor_5",
"motor_6",
"gripper_7"
]
}
},
"observation.joint_velocities": {
"dtype": "float32",
"shape": [
7
],
"names": {
"motors": [
"motor_1",
"motor_2",
"motor_3",
"motor_4",
"motor_5",
"motor_6",
"gripper_7"
]
}
},
"action": {
"dtype": "float32",
"shape": [
7
],
"names": {
"motors": [
"motor_1",
"motor_2",
"motor_3",
"motor_4",
"motor_5",
"motor_6",
"gripper_7"
]
}
},
"observation.ee_pos_quat": {
"dtype": "float32",
"shape": [
7
],
"names": {
"motors": [
"motor_1",
"motor_2",
"motor_3",
"motor_4",
"motor_5",
"motor_6",
"gripper_7"
]
}
},
"observation.gripper_position": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"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]
```
|
alea-institute/kl3m-data-pacer-gasd
|
alea-institute
|
2025-04-11T01:45:54Z
| 9
| 0
|
[
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2504.07854",
"arxiv:2503.17247",
"region:us"
] |
[] |
2025-02-15T13:25:16Z
| 0
|
---
dataset_info:
features:
- name: identifier
dtype: string
- name: dataset
dtype: string
- name: mime_type
dtype: string
- name: tokens
sequence: int64
splits:
- name: train
num_bytes: 1293426117
num_examples: 97616
download_size: 280493322
dataset_size: 1293426117
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# KL3M Data Project
> **Note**: This page provides general information about the KL3M Data Project. Additional details specific to this dataset will be added in future updates. For complete information, please visit the [GitHub repository](https://github.com/alea-institute/kl3m-data) or refer to the [KL3M Data Project paper](https://arxiv.org/abs/2504.07854).
## Description
This dataset is part of the [ALEA Institute's](https://aleainstitute.ai/) KL3M Data Project, which provides copyright-clean training resources for large language models.
## Dataset Details
- **Format**: Parquet files containing document text and metadata
- **License**: [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
- **Tokenizer**: The `tokens` field uses the [kl3m-004-128k-cased](https://huggingface.co/alea-institute/kl3m-004-128k-cased) tokenizer, a case-sensitive 128K vocabulary tokenizer optimized for legal, financial, and enterprise documents
## Abstract
Practically all large language models have been pre-trained on data that is subject to global uncertainty related to copyright infringement and breach of contract. This creates potential risk for users and developers due to this uncertain legal status. The KL3M Data Project directly confronts this critical issue by introducing the largest comprehensive training data pipeline that minimizes risks related to copyright or breach of contract.
The foundation of this project is a corpus of over 132 million documents and trillions of tokens spanning 16 different sources that have been verified to meet the strict copyright and licensing protocol detailed in the project. We are releasing the entire pipeline, including:
1. The source code to acquire and process these documents
2. The original document formats with associated provenance and metadata
3. Extracted content in a standardized format
4. Pre-tokenized representations of the documents
5. Various mid- and post-train resources such as question-answer, summarization, conversion, drafting, classification, prediction, and conversational data
All of these resources are freely available to the public on S3, Hugging Face, and GitHub under CC-BY terms. We are committed to continuing this project in furtherance of a more ethical, legal, and sustainable approach to the development and use of AI models.
## Legal Basis
This dataset is fully compliant with copyright law and contractual terms. The content is included based on the following legal foundation:
- Public domain materials
- US government works
- Open access content under permissive licenses
- Content explicitly licensed for AI training
## Papers
For more information about the KL3M Data Project, please refer to:
- [The KL3M Data Project: Copyright-Clean Training Resources for Large Language Models](https://arxiv.org/abs/2504.07854)
- [KL3M Tokenizers: A Family of Domain-Specific and Character-Level Tokenizers for Legal, Financial, and Preprocessing Applications](https://arxiv.org/abs/2503.17247)
## Citation
If you use this dataset in your research, please cite:
```bibtex
@misc{bommarito2025kl3mdata,
title={The KL3M Data Project: Copyright-Clean Training Resources for Large Language Models},
author={Bommarito II, Michael J. and Bommarito, Jillian and Katz, Daniel Martin},
year={2025},
eprint={2504.07854},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@misc{bommarito2025kl3m,
title={KL3M Tokenizers: A Family of Domain-Specific and Character-Level Tokenizers for Legal, Financial, and Preprocessing Applications},
author={Bommarito II, Michael J. and Katz, Daniel Martin and Bommarito, Jillian},
year={2025},
eprint={2503.17247},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## About ALEA
The ALEA Institute is a non-profit research organization focused on advancing AI for business, law, and governance. Learn more at [https://aleainstitute.ai/](https://aleainstitute.ai/).
|
JesusAura999/MEMORYRECALL_DATASET_QWEN_FORMAT_CORRECTED
|
JesusAura999
|
2025-02-17T14:19:01Z
| 16
| 0
|
[
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-02-17T14:18:59Z
| 0
|
---
dataset_info:
features:
- name: Category
dtype: string
- name: input
dtype: string
- name: response
dtype: string
- name: conversations
dtype: string
- name: text
dtype: string
- name: source
dtype: string
- name: score
dtype: float64
- name: reasoning_process
dtype: string
splits:
- name: train
num_bytes: 118778565
num_examples: 50000
download_size: 1705043
dataset_size: 118778565
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Slim205/mathlib_benchmark_v09_new
|
Slim205
|
2025-06-17T22:20:49Z
| 0
| 0
|
[
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-06-17T22:20:42Z
| 0
|
---
dataset_info:
features:
- name: Context
dtype: string
- name: file_name
dtype: string
- name: start
dtype: int64
- name: end
dtype: int64
- name: theorem
dtype: string
- name: proof
dtype: string
splits:
- name: train
num_bytes: 455246519
num_examples: 42196
download_size: 167852854
dataset_size: 455246519
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
pepijn223/act_lekiwi_cam_test2
|
pepijn223
|
2025-03-12T08:56:39Z
| 33
| 0
|
[
"task_categories:robotics",
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot",
"tutorial"
] |
[
"robotics"
] |
2025-03-12T08:56:34Z
| 0
|
---
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": "lekiwi",
"total_episodes": 2,
"total_frames": 595,
"total_tasks": 1,
"total_videos": 4,
"total_chunks": 1,
"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": {
"dtype": "float32",
"shape": [
9
],
"names": [
"shoulder_pan",
"shoulder_lift",
"elbow_flex",
"wrist_flex",
"wrist_roll",
"gripper",
"x_mm",
"y_mm",
"theta"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
9
],
"names": [
"shoulder_pan",
"shoulder_lift",
"elbow_flex",
"wrist_flex",
"wrist_roll",
"gripper",
"x_mm",
"y_mm",
"theta"
]
},
"observation.images.front": {
"dtype": "video",
"shape": [
640,
480,
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
}
},
"observation.images.wrist": {
"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]
```
|
nguyentranai07/ToCode_HTrade1
|
nguyentranai07
|
2025-06-07T17:38:03Z
| 0
| 0
|
[
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-06-07T17:38:02Z
| 0
|
---
dataset_info:
features:
- name: Question
dtype: string
- name: Answer
dtype: string
splits:
- name: train
num_bytes: 674371
num_examples: 100
download_size: 314089
dataset_size: 674371
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
LEE181204/libero_object_poisoned_TI_2
|
LEE181204
|
2025-09-23T01:46:38Z
| 105
| 0
|
[
"task_categories:robotics",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:timeseries",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot",
"libero",
"panda",
"rlds"
] |
[
"robotics"
] |
2025-09-23T01:45:49Z
| 0
|
---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- libero
- panda
- rlds
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": "panda",
"total_episodes": 456,
"total_frames": 67233,
"total_tasks": 19,
"total_videos": 0,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 10,
"splits": {
"train": "0:456"
},
"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": {
"image": {
"dtype": "image",
"shape": [
256,
256,
3
],
"names": [
"height",
"width",
"channel"
]
},
"wrist_image": {
"dtype": "image",
"shape": [
256,
256,
3
],
"names": [
"height",
"width",
"channel"
]
},
"state": {
"dtype": "float32",
"shape": [
8
],
"names": [
"state"
]
},
"actions": {
"dtype": "float32",
"shape": [
7
],
"names": [
"actions"
]
},
"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]
```
|
neelabh17/new_news_exploded_prompt_n_5_d_perc_0_num_gen_10_Qwen2.5-0.5B-Instruct_dist_mcq
|
neelabh17
|
2025-05-17T17:20:04Z
| 0
| 0
|
[
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-05-17T17:20:03Z
| 0
|
---
dataset_info:
features:
- name: id
dtype: string
- name: name
dtype: string
- name: topic
dtype: string
- name: news
dtype: string
- name: category
dtype: string
- name: question
dtype: string
- name: option
sequence: string
- name: prompt
dtype: string
- name: response_0
dtype: string
- name: answer_0
dtype: string
- name: correct_0
dtype: int64
- name: response_1
dtype: string
- name: answer_1
dtype: string
- name: correct_1
dtype: int64
- name: response_2
dtype: string
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dtype: string
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dtype: int64
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dtype: string
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dtype: string
- name: correct_3
dtype: int64
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dtype: string
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dtype: string
- name: correct_4
dtype: int64
- name: response_5
dtype: string
- name: answer_5
dtype: string
- name: correct_5
dtype: int64
- name: response_6
dtype: string
- name: answer_6
dtype: string
- name: correct_6
dtype: int64
- name: response_7
dtype: string
- name: answer_7
dtype: string
- name: correct_7
dtype: int64
- name: response_8
dtype: string
- name: answer_8
dtype: string
- name: correct_8
dtype: int64
- name: response_9
dtype: string
- name: answer_9
dtype: string
- name: correct_9
dtype: int64
splits:
- name: train
num_bytes: 4121612
num_examples: 375
download_size: 1428016
dataset_size: 4121612
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
dataticon-dev/khmer_mpwt_speech
|
dataticon-dev
|
2024-11-06T08:40:13Z
| 19
| 0
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-11-06T08:40:00Z
| 0
|
---
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
- name: raw_transcription
dtype: string
- name: speaker_id
dtype: int64
splits:
- name: train
num_bytes: 28833809.002
num_examples: 2058
download_size: 27249237
dataset_size: 28833809.002
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Smudge-81/small_ultrafeedback
|
Smudge-81
|
2024-10-22T09:54:08Z
| 23
| 0
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-10-22T09:51:26Z
| 0
|
---
dataset_info:
features:
- name: source
dtype: string
- name: prompt
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: chosen-rating
dtype: float64
- name: chosen-model
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected-rating
dtype: float64
- name: rejected-model
dtype: string
splits:
- name: train
num_bytes: 28500982
num_examples: 6091
download_size: 15580887
dataset_size: 28500982
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
TheRealPilot638/Llama-3.1-8B-Instruct-BS16-PRM-Skywork-Math500
|
TheRealPilot638
|
2025-06-23T15:42:34Z
| 6
| 0
|
[
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-06-22T18:36:17Z
| 0
|
---
dataset_info:
- config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--m-4--iters-40--look-1--seed-0--agg_strategy--last
features:
- name: problem
dtype: string
- name: solution
dtype: string
- name: answer
dtype: string
- name: subject
dtype: string
- name: level
dtype: int64
- name: unique_id
dtype: string
- name: completions
sequence: string
- name: pred
dtype: string
- name: completion_tokens
sequence: int64
- name: scores
sequence:
sequence: float64
- name: agg_scores
sequence: float64
- name: pred_weighted@1
dtype: string
- name: pred_maj@1
dtype: string
- name: pred_naive@1
dtype: string
- name: pred_weighted@2
dtype: string
- name: pred_maj@2
dtype: string
- name: pred_naive@2
dtype: string
- name: pred_weighted@4
dtype: string
- name: pred_maj@4
dtype: string
- name: pred_naive@4
dtype: string
- name: pred_weighted@8
dtype: string
- name: pred_maj@8
dtype: string
- name: pred_naive@8
dtype: string
- name: pred_weighted@16
dtype: string
- name: pred_maj@16
dtype: string
- name: pred_naive@16
dtype: string
splits:
- name: train
num_bytes: 12937866
num_examples: 500
download_size: 2148602
dataset_size: 12937866
- config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--m-4--iters-40--look-1--seed-0--agg_strategy--last--evals
features:
- name: n
dtype: int64
- name: acc_naive
dtype: float64
- name: acc_weighted
dtype: float64
- name: acc_maj
dtype: float64
splits:
- name: train
num_bytes: 32
num_examples: 1
download_size: 1961
dataset_size: 32
configs:
- config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--m-4--iters-40--look-1--seed-0--agg_strategy--last
data_files:
- split: train
path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--m-4--iters-40--look-1--seed-0--agg_strategy--last/train-*
- config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--m-4--iters-40--look-1--seed-0--agg_strategy--last--evals
data_files:
- split: train
path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--m-4--iters-40--look-1--seed-0--agg_strategy--last--evals/train-*
---
|
helloTR/filtered-high-quality-dpo
|
helloTR
|
2025-04-20T06:56:52Z
| 23
| 0
|
[
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"instruction-tuning",
"dpo"
] |
[] |
2025-04-15T02:28:36Z
| 0
|
---
license: apache-2.0
language:
- en
tags:
- instruction-tuning
- dpo
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: score
dtype: int64
splits:
- name: train
num_bytes: 30757
num_examples: 10
download_size: 27909
dataset_size: 30757
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Filtered High-Quality Instruction-Output Dataset
This dataset contains high-quality (score = 5) instruction-output pairs generated via a reverse instruction generation pipeline using a fine-tuned backward model and evaluated by `LLaMA-2-7B-Chat`.
## Columns
- `instruction`: The generated prompt.
- `output`: The original response.
- `score`: Quality score assigned ( score = 5 retained).
|
israfelsr/img-wikipedia-simple
|
israfelsr
|
2022-08-26T16:13:05Z
| 57
| 0
|
[
"task_categories:image-to-text",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"language:en",
"size_categories:100K<n<1M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[
"image-to-text"
] |
2022-06-24T13:59:27Z
| 0
|
---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license: []
multilinguality:
- monolingual
pretty_name: image-wikipedia-simple
size_categories: []
source_datasets: []
task_categories:
- image-to-text
---
# Dataset Card for [Dataset Name]
## 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
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
|
mariaxclara/chatbot
|
mariaxclara
|
2025-01-14T21:15:09Z
| 24
| 0
|
[
"task_categories:question-answering",
"language:pt",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[
"question-answering"
] |
2025-01-14T21:13:19Z
| 0
|
---
task_categories:
- question-answering
language:
- pt
pretty_name: 'chatbot '
size_categories:
- n<1K
---
|
yunjae-won/mp_gemma9b_sft_40k_multisample_n2_mp_gemma9b_sft_dpo_beta1e-1_epoch4
|
yunjae-won
|
2025-05-21T12:43:53Z
| 0
| 0
|
[
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-05-21T12:43:41Z
| 0
|
---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: policy_logps
dtype: float64
- name: ref_logps
dtype: float64
splits:
- name: train
num_bytes: 236639815
num_examples: 80000
download_size: 107622892
dataset_size: 236639815
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Blancy/OpenThoughts-114k-Code_decontaminated-highquality-more
|
Blancy
|
2025-04-25T03:11:11Z
| 32
| 0
|
[
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-04-25T03:09:47Z
| 0
|
---
dataset_info:
features:
- name: problem
dtype: string
- name: solution
dtype: string
- name: answer
dtype: string
- name: problem_type
dtype: string
- name: question_type
dtype: string
- name: source
dtype: string
- name: uuid
dtype: string
- name: is_reasoning_complete
sequence: bool
- name: generations
sequence: string
- name: correctness_math_verify
sequence: bool
- name: correctness_llama
dtype: 'null'
- name: finish_reasons
sequence: string
- name: correctness_count
dtype: int64
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: generation
dtype: string
- name: level_num
dtype: int64
- name: token_length
dtype: int64
- name: avg_tokens_per_line
dtype: float64
- name: norm_level
dtype: float64
- name: norm_len
dtype: float64
- name: norm_line
dtype: float64
- name: final_score
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 847057124
num_examples: 10000
download_size: 365186401
dataset_size: 847057124
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
shashank2806/koch_test_3
|
shashank2806
|
2024-12-25T13:07:16Z
| 27
| 0
|
[
"task_categories:robotics",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot",
"tutorial"
] |
[
"robotics"
] |
2024-12-25T13:04:21Z
| 0
|
---
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": "v2.0",
"robot_type": "koch",
"total_episodes": 50,
"total_frames": 25575,
"total_tasks": 1,
"total_videos": 100,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:50"
},
"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": [
6
],
"names": [
"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": 480,
"video.width": 640,
"video.channels": 3,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"observation.images.phone": {
"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]
```
|
JingweiNi/test_meeting_Qwen3-8B_8_shard_0_shard_5_2
|
JingweiNi
|
2025-09-22T19:11:18Z
| 59
| 0
|
[
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-09-22T19:11:15Z
| 0
|
---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 29200
num_examples: 5
download_size: 11988
dataset_size: 29200
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
giux78/evalita-requests
|
giux78
|
2025-02-23T20:24:22Z
| 17
| 0
|
[
"license:apache-2.0",
"region:us"
] |
[] |
2025-02-19T21:03:17Z
| 0
|
---
license: apache-2.0
---
|
yiqingliang/sat-problems-dataset-mini
|
yiqingliang
|
2025-04-18T18:48:35Z
| 1
| 0
|
[
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-04-18T18:48:11Z
| 0
|
---
license: apache-2.0
dataset_info:
features:
- name: image
dtype: image
- name: problem
dtype: string
- name: solution
dtype: string
splits:
- name: test
num_bytes: 31192538
num_examples: 64
download_size: 31192538
dataset_size: 31192538
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
|
YYT-t/rs-math_gsm-Meta-Llama-3-8B-Instruct-iter_sample_7500_temp_1.0_gen_30_mlr5e-5
|
YYT-t
|
2025-05-02T08:40:33Z
| 0
| 0
|
[
"region:us"
] |
[] |
2025-05-02T08:40:24Z
| 0
|
---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: rational_answer
dtype: string
splits:
- name: train
num_bytes: 7215648
num_examples: 6149
download_size: 3705309
dataset_size: 7215648
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
dgambettavuw/D_gen8_run2_llama2-7b_sciabs_doc1000_real96_synt32_vuw
|
dgambettavuw
|
2024-12-26T02:26:41Z
| 53
| 0
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-12-26T02:26:37Z
| 0
|
---
dataset_info:
features:
- name: id
dtype: int64
- name: doc
dtype: string
splits:
- name: train
num_bytes: 754606
num_examples: 1000
download_size: 402235
dataset_size: 754606
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
littlestronomer/umamusume
|
littlestronomer
|
2025-09-25T15:15:51Z
| 59
| 0
|
[
"task_categories:text-to-image",
"task_categories:image-to-text",
"language:en",
"license:mit",
"size_categories:n<1K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"art",
"anime",
"safebooru",
"stable-diffusion",
"flux"
] |
[
"text-to-image",
"image-to-text"
] |
2025-09-25T14:58:19Z
| 0
|
---
license: mit
task_categories:
- text-to-image
- image-to-text
language:
- en
tags:
- art
- anime
- safebooru
- stable-diffusion
- flux
pretty_name: Safebooru Anime Dataset
size_categories:
- n<1K
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 328858750
num_examples: 389
download_size: 324178997
dataset_size: 328858750
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Safebooru Anime Dataset
This dataset contains 389 curated anime-style images from Safebooru with descriptive captions, prepared for fine-tuning text-to-image models like Stable Diffusion and FLUX.
## Dataset Structure
Each example in the dataset contains:
- `image`: The anime artwork image
- `text`: Descriptive caption/tags for the image
- `file_name`: The filename of the image
- `original_post_id`: Original Safebooru post ID
- `original_filename`: Original filename from Safebooru
## Usage
### Loading the Dataset
```python
from datasets import load_dataset
# Load from HuggingFace Hub
dataset = load_dataset("your-username/your-dataset-name")
# Access images and captions
for example in dataset['train']:
image = example['image'] # PIL Image
caption = example['text'] # String caption
print(f"Caption: {caption}")
image.show()
break
```
### For Fine-tuning
This dataset is formatted for fine-tuning text-to-image models:
```python
from datasets import load_dataset
from transformers import pipeline
dataset = load_dataset("your-username/your-dataset-name")
# Use with your training script
train_dataset = dataset['train']
```
### For FLUX LoRA Training
Compatible with the FLUX QLora training notebook:
https://huggingface.co/blog/flux-qlora
## Dataset Statistics
- **Total images**: 389
- **Image formats**: JPG, PNG
- **Caption style**: Booru-style tags in comma-separated format
- **Average caption length**: ~50-100 tokens
## Data Preprocessing
Images were:
1. Downloaded from Safebooru with safe content filters
2. Resized to maintain quality while being training-friendly
3. Paired with descriptive captions converted from booru tags
## License
MIT License - See individual image metadata for specific attribution requirements.
## Citation
If you use this dataset, please cite:
```bibtex
@dataset{safebooru_anime_dataset,
title={Safebooru Anime Dataset},
author={Your Name},
year={2024},
publisher={HuggingFace}
}
```
|
Burgermin/wiki_olympic_rag_eval_dataset
|
Burgermin
|
2025-06-18T08:40:55Z
| 0
| 0
|
[
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-06-18T08:40:52Z
| 0
|
---
dataset_info:
features:
- name: user_input
dtype: string
- name: reference
dtype: string
- name: qa_context
dtype: string
- name: response
dtype: string
- name: retrieved_contexts
sequence: string
splits:
- name: train
num_bytes: 109581
num_examples: 25
download_size: 32991
dataset_size: 109581
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
aisi-whitebox/sevenllm_mcq_en_non_sandbagging_llama_32_1b_instruct
|
aisi-whitebox
|
2025-05-01T11:45:44Z
| 0
| 0
|
[
"language:en",
"license:apache-2.0",
"region:us",
"evaluation",
"deception",
"safety",
"sandbagging"
] |
[] |
2025-05-01T11:45:40Z
| 0
|
---
language:
- en
license: apache-2.0
pretty_name: sevenllm mcq en non sandbagging llama 32 1b instruct
tags:
- evaluation
- deception
- safety
- sandbagging
dataset_info:
model: vllm/meta-llama/Llama-3.2-1B-Instruct
dataset_id: sevenllm_mcq_en_non_sandbagging_llama_32_1b_instruct
tasks: ['deception_sprint/sevenllm_mcq_en']
sandbagging_detection: False
sandbagging_filtering: False
creation_date: 2025-05-01
git_commit: dabcaf0ebb407cd902cc79f5bfb3d4bdd39e0a4d
git_branch: main
---
# Inspect Dataset: sevenllm_mcq_en_non_sandbagging_llama_32_1b_instruct
## Dataset Information
This dataset was created using the `create_inspect_dataset` function from the deception_sprint package on 2025-05-01.
### Model Information
- **Model**: `vllm/meta-llama/Llama-3.2-1B-Instruct`
### Task Information
- **Tasks**: `deception_sprint/sevenllm_mcq_en`
### System Prompts
- **Prompt 1**: Benign
```
You are a helpful, harmless and honest language model.
```
### Sandbagging Detection
- **Detection Enabled**: False
- **Filtering Applied**: False
### Dataset Splits
- **Split Applied**: False
- **Test Size**: 0.2
- **Validation Size**: 0.5
- **Random Seed**: 42
## Additional Parameters
- **limit**: 500
- **token_limit**: 4096
- **fail_on_error**: 0.2
- **epochs**: 1
- **max_connections**: 50
- **task_name**: sevenllm_mcq_en
## Git info
- **Git branch**: main
- **Git commit**: dabcaf0ebb407cd902cc79f5bfb3d4bdd39e0a4d
|
Asap7772/processed_image_seen_sc_ours_withjpg
|
Asap7772
|
2024-11-14T08:24:58Z
| 18
| 0
|
[
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-11-14T06:15:25Z
| 0
|
---
dataset_info:
features:
- name: user_id
dtype: int64
- name: caption
sequence: string
- name: split
dtype: string
- name: shot_id
dtype: int64
- name: preferred_image
sequence: binary
- name: dispreferred_image
sequence: binary
- name: preferred_image_uid
sequence: string
- name: dispreferred_image_uid
sequence: string
splits:
- name: test
num_bytes: 577417935
num_examples: 277
download_size: 574737834
dataset_size: 577417935
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
|
mteb/CzechProductReviewSentimentClassification
|
mteb
|
2025-05-06T11:20:04Z
| 0
| 0
|
[
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"task_ids:sentiment-scoring",
"task_ids:sentiment-classification",
"task_ids:hate-speech-detection",
"annotations_creators:derived",
"multilinguality:monolingual",
"language:ces",
"license:cc-by-nc-sa-4.0",
"modality:text",
"arxiv:2502.13595",
"arxiv:2210.07316",
"region:us",
"mteb",
"text"
] |
[
"text-classification"
] |
2025-05-06T11:19:58Z
| 0
|
---
annotations_creators:
- derived
language:
- ces
license: cc-by-nc-sa-4.0
multilinguality: monolingual
task_categories:
- text-classification
task_ids:
- sentiment-analysis
- sentiment-scoring
- sentiment-classification
- hate-speech-detection
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 4318722
num_examples: 24000
- name: validation
num_bytes: 534949
num_examples: 3000
- name: test
num_bytes: 370337
num_examples: 2048
download_size: 3576380
dataset_size: 5224008
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
tags:
- mteb
- text
---
<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->
<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
<h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">CzechProductReviewSentimentClassification</h1>
<div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div>
<div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
</div>
User reviews of products on Czech e-shop Mall.cz with 3 sentiment classes (positive, neutral, negative)
| | |
|---------------|---------------------------------------------|
| Task category | t2c |
| Domains | Reviews, Written |
| Reference | https://aclanthology.org/W13-1609/ |
## How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
```python
import mteb
task = mteb.get_tasks(["CzechProductReviewSentimentClassification"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
```
<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb).
## Citation
If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb).
```bibtex
@inproceedings{habernal-etal-2013-sentiment,
address = {Atlanta, Georgia},
author = {Habernal, Ivan and
Pt{\'a}{\v{c}}ek, Tom{\'a}{\v{s}} and
Steinberger, Josef},
booktitle = {Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis},
editor = {Balahur, Alexandra and
van der Goot, Erik and
Montoyo, Andres},
month = jun,
pages = {65--74},
publisher = {Association for Computational Linguistics},
title = {Sentiment Analysis in {C}zech Social Media Using Supervised Machine Learning},
url = {https://aclanthology.org/W13-1609},
year = {2013},
}
@article{enevoldsen2025mmtebmassivemultilingualtext,
title={MMTEB: Massive Multilingual Text Embedding Benchmark},
author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
publisher = {arXiv},
journal={arXiv preprint arXiv:2502.13595},
year={2025},
url={https://arxiv.org/abs/2502.13595},
doi = {10.48550/arXiv.2502.13595},
}
@article{muennighoff2022mteb,
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
url = {https://arxiv.org/abs/2210.07316},
doi = {10.48550/ARXIV.2210.07316},
}
```
# Dataset Statistics
<details>
<summary> Dataset Statistics</summary>
The following code contains the descriptive statistics from the task. These can also be obtained using:
```python
import mteb
task = mteb.get_task("CzechProductReviewSentimentClassification")
desc_stats = task.metadata.descriptive_stats
```
```json
{
"test": {
"num_samples": 2048,
"number_of_characters": 314089,
"number_texts_intersect_with_train": 506,
"min_text_length": 1,
"average_text_length": 153.36376953125,
"max_text_length": 2859,
"unique_text": 2003,
"unique_labels": 3,
"labels": {
"1": {
"count": 683
},
"0": {
"count": 682
},
"2": {
"count": 683
}
}
},
"train": {
"num_samples": 24000,
"number_of_characters": 3660165,
"number_texts_intersect_with_train": null,
"min_text_length": 1,
"average_text_length": 152.506875,
"max_text_length": 2603,
"unique_text": 20409,
"unique_labels": 3,
"labels": {
"1": {
"count": 8000
},
"0": {
"count": 8000
},
"2": {
"count": 8000
}
}
}
}
```
</details>
---
*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*
|
Swati-sd/clippededge_1
|
Swati-sd
|
2025-03-15T12:55:16Z
| 17
| 0
|
[
"size_categories:n<1K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-03-15T12:55:08Z
| 0
|
---
dataset_info:
features:
- name: prompt
dtype: string
- name: image
dtype: image
- name: mask_0
dtype: image
splits:
- name: train
num_bytes: 48825185.0
num_examples: 300
- name: test
num_bytes: 5205168.0
num_examples: 30
download_size: 53965027
dataset_size: 54030353.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_10c22d4d-1810-4647-a847-66278776a6cd
|
argilla-internal-testing
|
2024-10-09T07:39:33Z
| 19
| 0
|
[
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-10-09T07:39:32Z
| 0
|
---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': positive
'1': negative
splits:
- name: train
num_bytes: 111
num_examples: 3
download_size: 1454
dataset_size: 111
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
AI4Protein/VenusX_BindI_AlphaFold2_PDB
|
AI4Protein
|
2025-05-15T07:20:36Z
| 0
| 0
|
[
"license:apache-2.0",
"region:us"
] |
[] |
2025-05-15T07:05:57Z
| 0
|
---
license: apache-2.0
---
|
Lots-of-LoRAs/task175_spl_translation_en_pl
|
Lots-of-LoRAs
|
2025-01-05T14:29:23Z
| 64
| 0
|
[
"task_categories:text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2204.07705",
"arxiv:2407.00066",
"region:us"
] |
[
"text-generation"
] |
2025-01-05T14:29:21Z
| 0
|
---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
task_categories:
- text-generation
pretty_name: task175_spl_translation_en_pl
dataset_info:
config_name: plain_text
features:
- name: input
dtype: string
- name: output
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 287
- name: valid
num_examples: 36
- name: test
num_examples: 36
---
# Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task175_spl_translation_en_pl
## Dataset Description
- **Homepage:** https://github.com/allenai/natural-instructions
- **Paper:** https://arxiv.org/abs/2204.07705
- **Paper:** https://arxiv.org/abs/2407.00066
- **Point of Contact:** [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
## Additional Information
### Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
```bibtex
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks},
author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi},
year={2022},
eprint={2204.07705},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2204.07705},
}
```
More details can also be found in the following paper:
```bibtex
@misc{brüelgabrielsson2024compressserveservingthousands,
title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead},
author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon},
year={2024},
eprint={2407.00066},
archivePrefix={arXiv},
primaryClass={cs.DC},
url={https://arxiv.org/abs/2407.00066},
}
```
### Contact Information
For any comments or questions, please email [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
|
ScoutieAutoML/it_vacancies_vectorisation
|
ScoutieAutoML
|
2024-12-28T15:10:03Z
| 27
| 0
|
[
"task_categories:text-classification",
"task_categories:feature-extraction",
"task_categories:text-generation",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"language:ru",
"language:en",
"size_categories:10K<n<100K",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"russia",
"vectors",
"sentiment",
"ner",
"clusterisation",
"vacancies",
"it"
] |
[
"text-classification",
"feature-extraction",
"text-generation",
"zero-shot-classification",
"text2text-generation"
] |
2024-12-28T15:04:43Z
| 0
|
---
task_categories:
- text-classification
- feature-extraction
- text-generation
- zero-shot-classification
- text2text-generation
language:
- ru
- en
tags:
- russia
- vectors
- sentiment
- ner
- clusterisation
- vacancies
- it
pretty_name: IT vacancies
size_categories:
- 10K<n<100K
---
## Description in English:
The dataset is collected from Russian-language Telegram channels with information about various IT vacancies for ML, Data Science, Front-end, Back-end development,\
The dataset was collected and tagged automatically using the data collection and tagging service [Scoutie](https://scoutie.ru/).\
Try Scoutie and collect the same or another dataset using [link](https://scoutie.ru/) for FREE.
## Dataset fields:
**taskId** - task identifier in the Scouti service.\
**text** - main text.\
**url** - link to the publication.\
**sourceLink** - link to Telegram.\
**subSourceLink** - link to the channel.\
**views** - text views.\
**likes** - for this dataset, an empty field (meaning the number of emotions).\
**createTime** - publication date in unix time format.\
**createTime** - publication collection date in unix time format.\
**clusterId** - cluster id.\
**vector** - text embedding (its vector representation).\
**ners** - array of identified named entities, where lemma is a lemmatized representation of a word, and label is the name of a tag, start_pos is the starting position of an entity in the text, end_pos is the ending position of an entity in the text.\
**sentiment** - emotional coloring of the text: POSITIVE, NEGATIVE, NEUTRAL.\
**language** - text language RUS, ENG.\
**spam** - text classification as advertising or not NOT_SPAM - no advertising, otherwise SPAM - the text is marked as advertising.\
**length** - number of tokens in the text (words).\
**markedUp** - means that the text is marked or not within the framework of the Skauti service, takes the value true or false.
## Описание на русском языке:
Датасет собран из русскоязычных Telegram каналов с информацией о различных IT вакансиях для ML, Data Science, Front-end, Back-end разработке,\
Датасет был собран и размечен автоматически с помощью сервиса сбора и разметки данных [Скаути](https://scoutie.ru/).\
Попробуй Скаути и собери такой же или другой датасет по [ссылке](https://scoutie.ru/) БЕСПЛАТНО.
## Поля датасета:
**taskId** - идентификатор задачи в сервисе Скаути.\
**text** - основной текст.\
**url** - ссылка на публикацию.\
**sourceLink** - ссылка на Telegram.\
**subSourceLink** - ссылка на канал.\
**views** - просмотры текста.\
**likes** - для данного датасета пустое поле (означающее количество эмоций).\
**createTime** - дата публикации в формате unix time.\
**createTime** - дата сбора публикации в формате unix time.\
**clusterId** - id кластера.\
**vector** - embedding текста (его векторное представление).\
**ners** - массив выявленных именованных сущностей, где lemma - лемматизированное представление слова, а label это название тега, start_pos - начальная позиция сущности в тексте, end_pos - конечная позиция сущности в тексте.\
**sentiment** - эмоциональный окрас текста: POSITIVE, NEGATIVE, NEUTRAL.\
**language** - язык текста RUS, ENG.\
**spam** - классификация текста, как рекламный или нет NOT_SPAM - нет рекламы, иначе SPAM - текст помечен, как рекламный.\
**length** - количество токенов в тексте (слов).\
**markedUp** - означает, что текст размечен или нет в рамках сервиса Скаути принимает значение true или false.
|
daparasyte/prompt_scores
|
daparasyte
|
2024-11-04T10:22:13Z
| 23
| 0
|
[
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-11-04T10:21:27Z
| 0
|
---
dataset_info:
features:
- name: id
dtype: int64
- name: prompt
dtype: string
- name: score
dtype: int64
- name: embedding
sequence: float64
splits:
- name: train
num_bytes: 1387051325
num_examples: 109101
download_size: 1040902994
dataset_size: 1387051325
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
vlm-reasoning-cot/ARC-AGI-Chunk
|
vlm-reasoning-cot
|
2025-06-07T02:22:39Z
| 0
| 0
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-06-07T01:43:54Z
| 0
|
---
dataset_info:
- config_name: chunk_0001
features:
- name: question
dtype: string
- name: reasoning
dtype: string
- name: answer
dtype: string
- name: source_folder
dtype: string
- name: problem_image_1
dtype: image
- name: problem_image_2
dtype: image
- name: problem_image_3
dtype: image
- name: problem_image_4
dtype: image
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splits:
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num_bytes: 1047988015.0
num_examples: 1000
download_size: 869120847
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- config_name: chunk_0002
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dtype: image
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dtype: image
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dtype: image
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dtype: image
- name: reasoning_image_24
dtype: image
splits:
- name: train
num_bytes: 1113769233.0
num_examples: 1000
download_size: 926399415
dataset_size: 1113769233.0
configs:
- config_name: chunk_0001
data_files:
- split: train
path: chunk_0001/train-*
- config_name: chunk_0002
data_files:
- split: train
path: chunk_0002/train-*
---
|
cfy789/piper_real_test_0213_tt
|
cfy789
|
2025-02-13T05:45:53Z
| 31
| 0
|
[
"task_categories:robotics",
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"modality:image",
"modality:timeseries",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot",
"piper",
"tutorial"
] |
[
"robotics"
] |
2025-02-13T05:42:48Z
| 0
|
---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- piper
- 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": "piper",
"total_episodes": 1,
"total_frames": 177,
"total_tasks": 1,
"total_videos": 0,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 15,
"splits": {
"train": "0:1"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": null,
"features": {
"action": {
"dtype": "float32",
"shape": [
7
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_angle",
"main_wrist_roll",
"main_gripper"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
7
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_angle",
"main_wrist_roll",
"main_gripper"
]
},
"observation.images.laptop": {
"dtype": "image",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": null
},
"observation.images.phone": {
"dtype": "image",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": null
},
"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]
```
|
hendrydong/r1-93K
|
hendrydong
|
2025-02-12T06:05:01Z
| 15
| 0
|
[
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-02-12T06:04:29Z
| 0
|
---
dataset_info:
features:
- name: problem
dtype: string
- name: response
dtype: string
- name: len
dtype: int64
splits:
- name: train
num_bytes: 1457341962
num_examples: 93733
download_size: 650061855
dataset_size: 1457341962
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
yobro4619/skywork_Llama_3.1
|
yobro4619
|
2024-12-01T22:58:48Z
| 14
| 0
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-12-01T22:58:46Z
| 0
|
---
dataset_info:
features:
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: source
dtype: string
- name: reward_chosen
dtype: float64
- name: reward_rejected
dtype: float64
splits:
- name: train
num_bytes: 25019105
num_examples: 5000
download_size: 11754585
dataset_size: 25019105
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
michsethowusu/ganda-tumbuka_sentence-pairs
|
michsethowusu
|
2025-04-02T10:58:10Z
| 8
| 0
|
[
"size_categories:100K<n<1M",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-04-02T10:58:08Z
| 0
|
---
dataset_info:
features:
- name: score
dtype: float32
- name: Ganda
dtype: string
- name: Tumbuka
dtype: string
splits:
- name: train
num_bytes: 20117554
num_examples: 143005
download_size: 20117554
dataset_size: 20117554
configs:
- config_name: default
data_files:
- split: train
path: Ganda-Tumbuka_Sentence-Pairs.csv
---
# Ganda-Tumbuka_Sentence-Pairs Dataset
This dataset contains sentence pairs for African languages along with similarity scores. It can be used for machine translation, sentence alignment, or other natural language processing tasks.
This dataset is based on the NLLBv1 dataset, published on OPUS under an open-source initiative led by META. You can find more information here: [OPUS - NLLB-v1](https://opus.nlpl.eu/legacy/NLLB-v1.php)
## Metadata
- **File Name**: Ganda-Tumbuka_Sentence-Pairs
- **Number of Rows**: 143005
- **Number of Columns**: 3
- **Columns**: score, Ganda, Tumbuka
## Dataset Description
The dataset contains sentence pairs in African languages with an associated similarity score. Each row consists of three columns:
1. `score`: The similarity score between the two sentences (range from 0 to 1).
2. `Ganda`: The first sentence in the pair (language 1).
3. `Tumbuka`: The second sentence in the pair (language 2).
This dataset is intended for use in training and evaluating machine learning models for tasks like translation, sentence similarity, and cross-lingual transfer learning.
## References
Below are papers related to how the data was collected and used in various multilingual and cross-lingual applications:
[1] Holger Schwenk and Matthijs Douze, Learning Joint Multilingual Sentence Representations with Neural Machine Translation, ACL workshop on Representation Learning for NLP, 2017
[2] Holger Schwenk and Xian Li, A Corpus for Multilingual Document Classification in Eight Languages, LREC, pages 3548-3551, 2018.
[3] Holger Schwenk, Filtering and Mining Parallel Data in a Joint Multilingual Space ACL, July 2018
[4] Alexis Conneau, Guillaume Lample, Ruty Rinott, Adina Williams, Samuel R. Bowman, Holger Schwenk and Veselin Stoyanov, XNLI: Cross-lingual Sentence Understanding through Inference, EMNLP, 2018.
[5] Mikel Artetxe and Holger Schwenk, Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings arXiv, Nov 3 2018.
[6] Mikel Artetxe and Holger Schwenk, Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond arXiv, Dec 26 2018.
[7] Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman, WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia arXiv, July 11 2019.
[8] Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave and Armand Joulin CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB
[9] Paul-Ambroise Duquenne, Hongyu Gong, Holger Schwenk, Multimodal and Multilingual Embeddings for Large-Scale Speech Mining, NeurIPS 2021, pages 15748-15761.
[10] Kevin Heffernan, Onur Celebi, and Holger Schwenk, Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages
|
Voxel51/deeplesion-balanced-2k
|
Voxel51
|
2025-06-24T19:20:09Z
| 0
| 0
|
[
"task_categories:object-detection",
"language:en",
"size_categories:1K<n<10K",
"format:imagefolder",
"modality:image",
"library:datasets",
"library:mlcroissant",
"library:fiftyone",
"region:us",
"fiftyone",
"image",
"object-detection"
] |
[
"object-detection"
] |
2025-06-24T19:08:36Z
| 0
|
---
annotations_creators: []
language: en
size_categories:
- 1K<n<10K
task_categories:
- object-detection
task_ids: []
pretty_name: deeplesion_balanced
tags:
- fiftyone
- image
- object-detection
dataset_summary: '
This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 2161 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("pjramg/deeplesion_balanced_fiftyone")
# Launch the App
session = fo.launch_app(dataset)
```
'
---
# DeepLesion Benchmark Subset (Balanced 2K)
This dataset is a curated subset of the [DeepLesion dataset](https://nihcc.app.box.com/v/DeepLesion), prepared for demonstration and benchmarking purposes. It consists of 2,000 CT lesion samples, balanced across 8 coarse lesion types, and filtered to include lesions with a short diameter > 10mm.
## Dataset Details
- **Source**: [DeepLesion](https://nihcc.app.box.com/v/DeepLesion)
- **Institution**: National Institutes of Health (NIH) Clinical Center
- **Subset size**: 2,000 images
- **Lesion types**: lung, abdomen, mediastinum, liver, pelvis, soft tissue, kidney, bone
- **Selection criteria**:
- Short diameter > 10mm
- Balanced sampling across all types
- **Windowing**: All slices were windowed using DICOM parameters and converted to 8-bit PNG format
## License
This dataset is shared under the **[CC BY-NC-SA 4.0 License](https://creativecommons.org/licenses/by-nc-sa/4.0/)**, as specified by the NIH DeepLesion dataset creators.
> This dataset is intended **only for non-commercial research and educational use**.
> You must credit the original authors and the NIH Clinical Center when using this data.
## Citation
If you use this data, please cite:
```bibtex
@article{yan2018deeplesion,
title={DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning},
author={Yan, Ke and Zhang, Yao and Wang, Le Lu and Huang, Xuejun and Summers, Ronald M},
journal={Journal of medical imaging},
volume={5},
number={3},
pages={036501},
year={2018},
publisher={SPIE}
}
Curation done by FiftyOne.
@article{moore2020fiftyone,
title={FiftyOne},
author={Moore, B. E. and Corso, J. J.},
journal={GitHub. Note: https://github.com/voxel51/fiftyone},
year={2020}
}
```
## Intended Uses
- Embedding demos
- Lesion similarity and retrieval
- Benchmarking medical image models
- Few-shot learning on lesion types
## Limitations
- This is a small subset of the full DeepLesion dataset
- Not suitable for training full detection models
- Labels are coarse and may contain inconsistencies
## Contact
Created by Paula Ramos for demo purposes using FiftyOne and the DeepLesion public metadata.
|
espressovi/VALUED
|
espressovi
|
2025-01-28T19:38:00Z
| 31
| 1
|
[
"license:cc-by-4.0",
"region:us"
] |
[] |
2025-01-28T07:44:47Z
| 0
|
---
license: cc-by-4.0
---
# VALUED - Vision and Logical Understanding Evaluation Dataset.
---
This repository contains dataset associated with the paper at (https://data.mlr.press/assets/pdf/v01-13.pdf).
View samples from the dataset at the [dataset page](https://espressovi.github.io/VALUED).
## Authors
- [Soumadeep Saha](https://www.isical.ac.in/~soumadeep.saha_r)
- [Saptarshi Saha](https://openreview.net/profile?id=~Saptarshi_Saha1)
- [Utpal Garain](https://www.isical.ac.in/~utpal).
## Abstract
Starting with early successes in computer vision tasks, deep learning based techniques have since overtaken state of the art approaches in a multitude of domains. However, it has been demonstrated time and again that these techniques fail to capture semantic context and logical constraints, instead often relying on spurious correlations to arrive at the answer. Since application of deep learning techniques to critical scenarios are dependent on adherence to domain specific constraints, several attempts have been made to address this issue. One limitation holding back a thorough exploration of this area, is a lack of suitable datasets which feature a rich set of rules. In order to address this, we present the VALUE (Vision And Logical Understanding Evaluation) Dataset, consisting of 200,000+ annotated images and an associated rule set, based on the popular board game - chess. The curated rule set considerably constrains the set of allowable predictions, and are designed to probe key semantic abilities like localization and enumeration. Alongside standard metrics, additional metrics to measure performance with regards to logical consistency is presented. We analyze several popular and state of the art vision models on this task, and show that, although their performance on standard metrics are laudable, they produce a plethora of incoherent results, indicating that this dataset presents a significant challenge for future works.
---
## Usage
### Download data
- The generated train/test set along with all labels can be found [here](https://zenodo.org/records/10607059).
- The DOI for the dataset is 10.5281/zenodo.8278014.
## Cite
If you find our work useful, please cite:
```
@article{saha2024valued,
title={{VALUED} - Vision and Logical Understanding Evaluation Dataset},
author={Soumadeep Saha and Saptarshi Saha and Utpal Garain},
journal={Journal of Data-centric Machine Learning Research},
year={2024},
url={https://openreview.net/forum?id=nS9oxKyy9u}
}
```
|
koenvanwijk/insert5
|
koenvanwijk
|
2025-06-14T23:26:38Z
| 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:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot"
] |
[
"robotics"
] |
2025-06-14T23:26:23Z
| 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": "so101_follower",
"total_episodes": 2,
"total_frames": 4388,
"total_tasks": 1,
"total_videos": 4,
"total_chunks": 1,
"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": {
"dtype": "float32",
"shape": [
6
],
"names": [
"shoulder_pan.pos",
"shoulder_lift.pos",
"elbow_flex.pos",
"wrist_flex.pos",
"wrist_roll.pos",
"gripper.pos"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
6
],
"names": [
"shoulder_pan.pos",
"shoulder_lift.pos",
"elbow_flex.pos",
"wrist_flex.pos",
"wrist_roll.pos",
"gripper.pos"
]
},
"observation.images.front": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
}
},
"observation.images.laptop": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"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]
```
|
tmpmodelsave/llamasft_math_ift_balanced_moredata_100tmp10
|
tmpmodelsave
|
2025-01-21T05:53:08Z
| 16
| 0
|
[
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-01-21T05:53:06Z
| 0
|
---
dataset_info:
features:
- name: idx
dtype: int64
- name: gt
dtype: string
- name: prompt
dtype: string
- name: level
dtype: string
- name: type
dtype: string
- name: solution
dtype: string
- name: my_solu
sequence: string
- name: pred
sequence: string
- name: rewards
sequence: bool
splits:
- name: train
num_bytes: 85917567
num_examples: 30000
download_size: 31416891
dataset_size: 85917567
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
vlm-reasoning-cot/Visual_Search_New_1000
|
vlm-reasoning-cot
|
2025-06-12T17:21:12Z
| 0
| 0
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-06-12T17:20:50Z
| 0
|
---
dataset_info:
config_name: chunk_0001
features:
- name: question
dtype: string
- name: reasoning
dtype: string
- name: answer
dtype: string
- name: source_folder
dtype: string
- name: problem_image_1
dtype: image
- name: reasoning_image_1
dtype: image
splits:
- name: train
num_bytes: 1018252583.0
num_examples: 1000
download_size: 946548220
dataset_size: 1018252583.0
configs:
- config_name: chunk_0001
data_files:
- split: train
path: chunk_0001/train-*
---
|
mteb/VieStudentFeedbackClassification
|
mteb
|
2025-06-20T19:12:38Z
| 0
| 0
|
[
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"task_ids:sentiment-scoring",
"task_ids:sentiment-classification",
"task_ids:hate-speech-detection",
"annotations_creators:human-annotated",
"multilinguality:monolingual",
"source_datasets:uitnlp/vietnamese_students_feedback",
"language:vie",
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2502.13595",
"arxiv:2210.07316",
"region:us",
"mteb",
"text"
] |
[
"text-classification"
] |
2025-06-20T19:12:16Z
| 0
|
---
annotations_creators:
- human-annotated
language:
- vie
license: mit
multilinguality: monolingual
source_datasets:
- uitnlp/vietnamese_students_feedback
task_categories:
- text-classification
task_ids:
- sentiment-analysis
- sentiment-scoring
- sentiment-classification
- hate-speech-detection
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 1023776
num_examples: 11426
- name: validation
num_bytes: 136041
num_examples: 1583
- name: test
num_bytes: 180695
num_examples: 2048
download_size: 608176
dataset_size: 1340512
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
tags:
- mteb
- text
---
<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->
<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
<h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">VieStudentFeedbackClassification</h1>
<div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div>
<div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
</div>
A Vietnamese dataset for classification of student feedback
| | |
|---------------|---------------------------------------------|
| Task category | t2c |
| Domains | Reviews, Written |
| Reference | https://ieeexplore.ieee.org/document/8573337 |
Source datasets:
- [uitnlp/vietnamese_students_feedback](https://huggingface.co/datasets/uitnlp/vietnamese_students_feedback)
## How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
```python
import mteb
task = mteb.get_task("VieStudentFeedbackClassification")
evaluator = mteb.MTEB([task])
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
```
<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
To learn more about how to run models on `mteb` task check out the [GitHub repository](https://github.com/embeddings-benchmark/mteb).
## Citation
If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb).
```bibtex
@inproceedings{8573337,
author = {Nguyen, Kiet Van and Nguyen, Vu Duc and Nguyen, Phu X. V. and Truong, Tham T. H. and Nguyen, Ngan Luu-Thuy},
booktitle = {2018 10th International Conference on Knowledge and Systems Engineering (KSE)},
doi = {10.1109/KSE.2018.8573337},
number = {},
pages = {19-24},
title = {UIT-VSFC: Vietnamese Students’ Feedback Corpus for Sentiment Analysis},
volume = {},
year = {2018},
}
@article{enevoldsen2025mmtebmassivemultilingualtext,
title={MMTEB: Massive Multilingual Text Embedding Benchmark},
author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
publisher = {arXiv},
journal={arXiv preprint arXiv:2502.13595},
year={2025},
url={https://arxiv.org/abs/2502.13595},
doi = {10.48550/arXiv.2502.13595},
}
@article{muennighoff2022mteb,
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Loïc and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
url = {https://arxiv.org/abs/2210.07316},
doi = {10.48550/ARXIV.2210.07316},
}
```
# Dataset Statistics
<details>
<summary> Dataset Statistics</summary>
The following code contains the descriptive statistics from the task. These can also be obtained using:
```python
import mteb
task = mteb.get_task("VieStudentFeedbackClassification")
desc_stats = task.metadata.descriptive_stats
```
```json
{}
```
</details>
---
*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*
|
nvidia/PhysicalAI-Robotics-GR00T-GR1
|
nvidia
|
2025-06-14T04:34:00Z
| 8
| 0
|
[
"license:cc-by-4.0",
"size_categories:n<1K",
"modality:text",
"modality:video",
"library:datasets",
"library:mlcroissant",
"region:us"
] |
[] |
2025-06-14T00:33:27Z
| 0
|
---
license: cc-by-4.0
---
# GR1-100
92 Videos of a Fourier GR1-T2 robot performing various tasks in the lab from the third person perspective.<br>
This dataset is ready for commercial/non-commercial use.
## Dataset Owner(s):
NVIDIA Corporation (GEAR Lab)
## Dataset Creation Date:
May 1, 2025
## License/Terms of Use:
This dataset is governed by the [Creative Commons Attribution 4.0 International License (CC-BY-4.0)](https://creativecommons.org/licenses/by/4.0/deed.en).<br>
This dataset was created using a Fourier GR1-T2 robot.
## Intended Usage:
Training video dataset for robotics.
## Dataset Characterization
**Data Collection Method**<br>
- Human<br>
**Labeling Method**<br>
- Human <br>
## Dataset Format
MP4 files
## Dataset Quantification
Record count: 92 videos<br>
Total Size: 142.3MB
## Reference(s):
[DreamGen WhitePaper](https://arxiv.org/html/2505.12705v1)<br>
[IDM-GR1 Model](https://huggingface.co/seonghyeonye/IDM_gr1)
## Ethical Considerations:
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
|
nfiekas/fishnet-evals
|
nfiekas
|
2025-04-26T08:35:56Z
| 315
| 1
|
[
"license:cc0-1.0",
"size_categories:10B<n<100B",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-04-25T06:10:44Z
| 0
|
---
license: cc0-1.0
---
# Lichess database positions with Stockfish evaluations
Positions with evaluations extracted from https://database.lichess.org/.
Evaluations were computed using Lichess's distributed Stockfish analysis [fishnet](https://github.com/lichess-org/fishnet), using various Stockfish versions.
column | type | description
--- | --- | ---
`fen` | string | FEN of the evaluated position. En passant square included only if a fully legal en passant move exists.
`cp` / `mate` | int *or* null | Signed evaluation of the position in centipawns or moves to mate, as computed by Stockfish. Exactly one of these is not null. Always given from White's point of view, so positive if White has the advantage
`move` | string *or* null | UCI notation of the next move played by the **human** player, if any. Always in Chess960 mode (king move to rook for castling).
Known issues:
* Note that even `mate` evaluations are not completely guaranteed to be correct, especially those produced before 2016.
* 2016-12, 2020-07 and 2020-08 are intentionally omitted, because due to intermittent bugs, many broken evaluations were produced in those months.
|
HKrecords/MNLP_M3_dpo_dataset
|
HKrecords
|
2025-06-09T13:09:52Z
| 23
| 0
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-06-09T13:09:50Z
| 0
|
---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 10561822.421688953
num_examples: 3000
download_size: 6203661
dataset_size: 10561822.421688953
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
lihaoxin2020/ki_extraction-30b-lmeval-deepseek-reasoner-on-mmlu_pro-0shot_cot-scillm-b330c24d48-vw54
|
lihaoxin2020
|
2025-09-25T00:07:07Z
| 102
| 0
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-09-25T00:06:46Z
| 0
|
---
dataset_info:
- config_name: biology
features:
- name: data
list:
- name: role
dtype: string
- name: content
dtype: string
- name: raw_response
struct:
- name: text
dtype: string
- name: raw_text
dtype: string
- name: knowledge_pieces
list: string
- name: doc_id
dtype: int64
- name: native_id
dtype: int64
splits:
- name: train
num_bytes: 4477015
num_examples: 200
download_size: 1692020
dataset_size: 4477015
- config_name: chemistry
features:
- name: data
list:
- name: role
dtype: string
- name: content
dtype: string
- name: raw_response
struct:
- name: text
dtype: string
- name: raw_text
dtype: string
- name: knowledge_pieces
list: string
- name: doc_id
dtype: int64
- name: native_id
dtype: int64
splits:
- name: train
num_bytes: 6869958
num_examples: 200
download_size: 2525816
dataset_size: 6869958
- config_name: computer science
features:
- name: data
list:
- name: role
dtype: string
- name: content
dtype: string
- name: raw_response
struct:
- name: text
dtype: string
- name: raw_text
dtype: string
- name: knowledge_pieces
list: string
- name: doc_id
dtype: int64
- name: native_id
dtype: int64
splits:
- name: train
num_bytes: 6540940
num_examples: 200
download_size: 2355538
dataset_size: 6540940
- config_name: engineering
features:
- name: data
list:
- name: role
dtype: string
- name: content
dtype: string
- name: raw_response
struct:
- name: text
dtype: string
- name: raw_text
dtype: string
- name: knowledge_pieces
list: string
- name: doc_id
dtype: int64
- name: native_id
dtype: int64
splits:
- name: train
num_bytes: 8353562
num_examples: 200
download_size: 3128075
dataset_size: 8353562
- config_name: health
features:
- name: data
list:
- name: role
dtype: string
- name: content
dtype: string
- name: raw_response
struct:
- name: text
dtype: string
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dtype: string
- name: knowledge_pieces
list: string
- name: doc_id
dtype: int64
- name: native_id
dtype: int64
splits:
- name: train
num_bytes: 4668344
num_examples: 200
download_size: 1792046
dataset_size: 4668344
- config_name: math
features:
- name: data
list:
- name: role
dtype: string
- name: content
dtype: string
- name: raw_response
struct:
- name: text
dtype: string
- name: raw_text
dtype: string
- name: knowledge_pieces
list: string
- name: doc_id
dtype: int64
- name: native_id
dtype: int64
splits:
- name: train
num_bytes: 6458731
num_examples: 200
download_size: 2340058
dataset_size: 6458731
- config_name: physics
features:
- name: data
list:
- name: role
dtype: string
- name: content
dtype: string
- name: raw_response
struct:
- name: text
dtype: string
- name: raw_text
dtype: string
- name: knowledge_pieces
list: string
- name: doc_id
dtype: int64
- name: native_id
dtype: int64
splits:
- name: train
num_bytes: 7068469
num_examples: 200
download_size: 2558074
dataset_size: 7068469
configs:
- config_name: biology
data_files:
- split: train
path: biology/train-*
- config_name: chemistry
data_files:
- split: train
path: chemistry/train-*
- config_name: computer science
data_files:
- split: train
path: computer science/train-*
- config_name: engineering
data_files:
- split: train
path: engineering/train-*
- config_name: health
data_files:
- split: train
path: health/train-*
- config_name: math
data_files:
- split: train
path: math/train-*
- config_name: physics
data_files:
- split: train
path: physics/train-*
---
|
tinisoft/indicvoices-tamil-valid-subset
|
tinisoft
|
2025-06-03T06:08:36Z
| 0
| 0
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-06-03T06:07:59Z
| 0
|
---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: duration
dtype: float64
- name: lang
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---
|
lhkhiem28/MolT-Rex-datasets
|
lhkhiem28
|
2025-09-26T14:34:25Z
| 227
| 0
|
[
"region:us"
] |
[] |
2025-09-17T18:49:55Z
| 0
|
---
viewer: false
configs:
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data_files:
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data_files:
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---
|
cat-claws/hotpotqa_clustered_agglomerative_all-MiniLM-L6-v2_2
|
cat-claws
|
2025-09-20T07:34:55Z
| 74
| 0
|
[
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-09-20T07:34:47Z
| 0
|
---
dataset_info:
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dtype: string
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dtype: int64
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data_files:
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path: data/train-*
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path: data/validation-*
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path: data/test-*
---
|
test-gen/mbpp_mbpp-dagger-easy-qwen-coder-7b-instruct-from-sft-iter1_t0.0_n1_generated_tests
|
test-gen
|
2025-05-08T21:50:00Z
| 36
| 0
|
[
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] |
[] |
2025-05-06T15:04:48Z
| 0
|
---
dataset_info:
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---
|
lamm-mit/synthetic_graph_pathfinding_100K
|
lamm-mit
|
2024-12-15T13:33:32Z
| 28
| 1
|
[
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] |
[] |
2024-12-15T13:33:30Z
| 0
|
---
dataset_info:
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configs:
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data_files:
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---
|
Hanumansai/Question_answer_synthetic_dataset
|
Hanumansai
|
2025-03-22T09:39:19Z
| 16
| 0
|
[
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] |
[] |
2025-03-22T09:39:17Z
| 0
|
---
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configs:
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path: data/train-*
---
|
chiyuanhsiao/QA_ASR_TTS_phase-2_v2
|
chiyuanhsiao
|
2024-12-08T20:06:08Z
| 18
| 0
|
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] |
[] |
2024-12-08T18:21:47Z
| 0
|
---
dataset_info:
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configs:
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data_files:
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---
|
ashercn97/combined-reasoning-data
|
ashercn97
|
2024-11-28T04:41:37Z
| 15
| 0
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"region:us"
] |
[] |
2024-11-28T04:41:35Z
| 0
|
---
dataset_info:
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configs:
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data_files:
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path: data/train-*
---
|
LynxLegion/test2
|
LynxLegion
|
2025-01-01T11:23:31Z
| 16
| 0
|
[
"size_categories:n<1K",
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"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-12-31T16:37:16Z
| 0
|
---
dataset_info:
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configs:
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data_files:
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path: data/test-*
---
|
dbaeka/soen_691_msg_test_5000_hashed
|
dbaeka
|
2025-03-18T12:05:49Z
| 17
| 0
|
[
"size_categories:1K<n<10K",
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"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-03-17T07:01:39Z
| 0
|
---
dataset_info:
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download_size: 3228713
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configs:
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data_files:
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path: data/test-*
---
|
smui/llama_bts_ko
|
smui
|
2024-11-22T00:47:34Z
| 15
| 0
|
[
"license:apache-2.0",
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-11-22T00:45:10Z
| 0
|
---
license: apache-2.0
configs:
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data_files:
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path: data/train-*
dataset_info:
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dtype: string
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dtype: string
splits:
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num_bytes: 29225
num_examples: 43
download_size: 19249
dataset_size: 29225
---
|
gupta-tanish/Ultrafeedback-llama3-8b-Instruct-optimal-selection-grm-gemma2b
|
gupta-tanish
|
2025-03-26T21:29:52Z
| 7
| 0
|
[
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"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-03-26T21:28:41Z
| 0
|
---
dataset_info:
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num_examples: 1930
download_size: 1884694253
dataset_size: 4868725421
configs:
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data_files:
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path: data/train_prefs-*
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path: data/test_prefs-*
---
|
heig-vd-geo/STDL-soils
|
heig-vd-geo
|
2025-05-15T12:06:27Z
| 0
| 0
|
[
"task_categories:image-segmentation",
"language:en",
"license:mit",
"size_categories:1B<n<10B",
"region:us"
] |
[
"image-segmentation"
] |
2025-05-15T11:58:19Z
| 0
|
---
license: mit
task_categories:
- image-segmentation
language:
- en
size_categories:
- 1B<n<10B
---
|
danibor/wiki-paragraphs-es
|
danibor
|
2025-02-28T18:35:40Z
| 21
| 0
|
[
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
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"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-02-28T17:46:40Z
| 0
|
---
dataset_info:
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dtype: string
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dtype: string
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splits:
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num_bytes: 2677602228.2212667
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download_size: 1853359014
dataset_size: 2677602228.2212667
configs:
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data_files:
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path: data/train-*
---
|
villekuosmanen/agilex_tian_put_red_book_top
|
villekuosmanen
|
2025-02-13T22:16:15Z
| 33
| 0
|
[
"task_categories:robotics",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot"
] |
[
"robotics"
] |
2025-02-13T22:15:54Z
| 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": "v2.0",
"robot_type": "arx5_bimanual",
"total_episodes": 21,
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"total_tasks": 1,
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"fps": 25,
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"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
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14
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14
]
},
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"shape": [
14
]
},
"observation.images.cam_high": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.fps": 25.0,
"video.height": 480,
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"video.channels": 3,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
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}
},
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480,
640,
3
],
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}
},
"observation.images.cam_right_wrist": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
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"info": {
"video.fps": 25.0,
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"video.channels": 3,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"timestamp": {
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"shape": [
1
],
"names": null
},
"frame_index": {
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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": {
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1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
```
|
twei11/node1_round_60
|
twei11
|
2025-04-17T07:56:34Z
| 18
| 0
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-04-17T07:56:33Z
| 0
|
---
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 7204562
num_examples: 1800
download_size: 3534849
dataset_size: 7204562
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
sine/LeetCodeSample
|
sine
|
2024-10-21T18:54:40Z
| 41
| 1
|
[
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-10-21T17:42:38Z
| 0
|
---
configs:
- config_name: python
data_files:
- split: test
path: python/test-*
dataset_info:
config_name: python
features:
- name: id
dtype: string
- name: content
dtype: string
- name: test
struct:
- name: code
dtype: string
- name: labels
struct:
- name: programming_language
dtype: string
- name: execution_language
dtype: string
- name: questionId
dtype: string
- name: questionFrontendId
dtype: string
- name: questionTitle
dtype: string
- name: stats
struct:
- name: totalAccepted
dtype: string
- name: totalSubmission
dtype: string
- name: totalAcceptedRaw
dtype: int64
- name: totalSubmissionRaw
dtype: int64
- name: acRate
dtype: string
splits:
- name: test
num_bytes: 1236371
num_examples: 260
download_size: 502189
dataset_size: 1236371
---
|
gzx1988/gzx5w-3
|
gzx1988
|
2024-10-20T10:12:35Z
| 19
| 0
|
[
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-10-20T10:12:12Z
| 0
|
---
license: apache-2.0
---
|
nimapourjafar/mm_tqa
|
nimapourjafar
|
2024-06-15T01:29:14Z
| 32
| 1
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2024-06-15T01:29:01Z
| 1
|
---
dataset_info:
features:
- name: images
sequence:
image:
decode: false
- name: audios
sequence: 'null'
- name: videos
sequence: 'null'
- name: data
list:
- name: data
dtype: string
- name: modality
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 379242136
num_examples: 1493
download_size: 378257680
dataset_size: 379242136
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
collectivat/tv3_parla
|
collectivat
|
2024-11-25T15:21:20Z
| 287
| 3
|
[
"task_categories:automatic-speech-recognition",
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:ca",
"license:cc-by-nc-4.0",
"size_categories:100K<n<1M",
"region:us"
] |
[
"automatic-speech-recognition",
"text-generation"
] |
2022-03-02T23:29:22Z
| 0
|
---
annotations_creators:
- found
language_creators:
- found
language:
- ca
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- automatic-speech-recognition
- text-generation
task_ids:
- language-modeling
pretty_name: TV3Parla
---
# Dataset Card for TV3Parla
## 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
- **Homepage:** https://collectivat.cat/asr#tv3parla
- **Repository:**
- **Paper:** [Building an Open Source Automatic Speech Recognition System for Catalan](https://www.isca-speech.org/archive/iberspeech_2018/kulebi18_iberspeech.html)
- **Point of Contact:** [Col·lectivaT](mailto:info@collectivat.cat)
### Dataset Summary
This corpus includes 240 hours of Catalan speech from broadcast material.
The details of segmentation, data processing and also model training are explained in Külebi, Öktem; 2018.
The content is owned by Corporació Catalana de Mitjans Audiovisuals, SA (CCMA);
we processed their material and hereby making it available under their terms of use.
This project was supported by the Softcatalà Association.
### Supported Tasks and Leaderboards
The dataset can be used for:
- Language Modeling.
- Automatic Speech Recognition (ASR) transcribes utterances into words.
### Languages
The dataset is in Catalan (`ca`).
## Dataset Structure
### Data Instances
```
{
'path': 'tv3_0.3/wav/train/5662515_1492531876710/5662515_1492531876710_120.180_139.020.wav',
'audio': {'path': 'tv3_0.3/wav/train/5662515_1492531876710/5662515_1492531876710_120.180_139.020.wav',
'array': array([-0.01168823, 0.01229858, 0.02819824, ..., 0.015625 ,
0.01525879, 0.0145874 ]),
'sampling_rate': 16000},
'text': 'algunes montoneres que que et feien anar ben col·locat i el vent també hi jugava una mica de paper bufava vent de cantó alguns cops o de cul i el pelotón el vent el porta molt malament hi havia molts nervis'
}
```
### Data Fields
- `path` (str): Path to the audio file.
- `audio` (dict): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling
rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and
resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might
take a significant amount of time. Thus, it is important to first query the sample index before the `"audio"` column,
*i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- `text` (str): Transcription of the audio file.
### Data Splits
The dataset is split into "train" and "test".
| | train | test |
|:-------------------|-------:|-----:|
| Number of examples | 159242 | 2220 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[Creative Commons Attribution-NonCommercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/).
### Citation Information
```
@inproceedings{kulebi18_iberspeech,
author={Baybars Külebi and Alp Öktem},
title={{Building an Open Source Automatic Speech Recognition System for Catalan}},
year=2018,
booktitle={Proc. IberSPEECH 2018},
pages={25--29},
doi={10.21437/IberSPEECH.2018-6}
}
```
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
|
softjapan/jaquad-sft
|
softjapan
|
2025-09-24T14:32:53Z
| 72
| 0
|
[
"task_categories:question-answering",
"task_categories:text-generation",
"language:ja",
"license:mit",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"japanese",
"question-answering",
"sft",
"instruction-tuning"
] |
[
"question-answering",
"text-generation"
] |
2025-09-24T04:47:07Z
| 0
|
---
license: mit
task_categories:
- question-answering
- text-generation
language:
- ja
tags:
- japanese
- question-answering
- sft
- instruction-tuning
size_categories:
- 10K<n<100K
---
# softjapan/jaquad-sft
## データセットの概要
このデータセットは、JaQuAD（Japanese Question Answering Dataset）をSFT（Supervised Fine-Tuning）形式に変換したものです。日本語の質問応答タスクに特化したinstruction tuning用のデータセットです。
## データセットの詳細
- **言語**: 日本語
- **タスク**: 質問応答、instruction tuning
- **形式**: SFT（instruction/input/output）
- **訓練データ**: 31,748件
- **検証データ**: 3,939件
- **合計**: 35,687件
## データ形式
各サンプルは以下の形式で構成されています：
```json
{
"id": "tr-000-00-000",
"instruction": "次の文脈に基づいて質問に答えてください。可能なら短く正確に答えてください。",
"input": "【文脈】手塚治虫(てづかおさむ、本名:手塚治(読み同じ)、1928年(昭和3年)11月3日-1989年(平成元年)2月9日)は、日本の漫画家、アニメーター、アニメ監督である。\n戦後日本においてストーリー漫画の第一人者として、漫画表現の開拓者的な存在として活躍した。\n\n兵庫県宝塚市出身(出生は大阪府豊能郡豊中町、現在の豊中市)同市名誉市民である。\n大阪帝国大学附属医学専門部を卒業。\n医師免許取得のち医学博士(奈良県立医科大学・1961年)。\n\n【質問】戦後日本のストーリー漫画の第一人者で、医学博士の一面もある漫画家は誰?",
"output": "手塚治虫"
}
```
## 使用方法
### データセットの読み込み
```python
from datasets import load_dataset
# データセットを読み込み
dataset = load_dataset("softjapan/jaquad-sft")
# 訓練データの例を表示
print(dataset["train"][0])
```
### LoRAファインチューニングでの使用
```python
import os
import math
import random
import inspect
import transformers
from datasets import load_dataset
from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
DataCollatorForLanguageModeling,
Trainer,
)
# 衝突回避: HF の TrainingArguments を明示別名
from transformers import TrainingArguments as HFTrainingArguments
from peft import LoraConfig, get_peft_model, TaskType
# -------- 0) 再現性（任意） --------
SEED = int(os.environ.get("SEED", 42))
random.seed(SEED)
os.environ["PYTHONHASHSEED"] = str(SEED)
# -------- 1) データ読み込み --------
dataset = load_dataset("softjapan/jaquad-sft") # train / validation あり
# remove_columns 用に元カラム名を控える
if "train" in dataset:
original_columns = dataset["train"].column_names
else:
first_split = list(dataset.keys())[0]
original_columns = dataset[first_split].column_names
# -------- 2) トークナイザー & モデル --------
model_name = "Qwen/Qwen2-1.5B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"
# torch_dtype は非推奨 → dtype へ
model = AutoModelForCausalLM.from_pretrained(
model_name,
dtype="auto",
device_map="auto",
)
# -------- 3) LoRA 設定 --------
lora_config = LoraConfig(
r=8,
lora_alpha=16,
lora_dropout=0.05,
target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
task_type=TaskType.CAUSAL_LM,
bias="none",
)
model = get_peft_model(model, lora_config)
# -------- 4) 前処理（batched=False） --------
def format_row(ex):
inst = (ex.get("instruction") or "").strip()
inp = (ex.get("input") or "").strip()
out = (ex.get("output") or "").strip()
return f"### 指示\n{inst}\n\n### 入力\n{inp}\n\n### 応答\n{out}"
def tokenize(example):
text = format_row(example)
return tokenizer(
text,
truncation=True,
max_length=1024,
padding="max_length",
)
tokenized = {}
for split in dataset.keys():
tokenized[split] = dataset[split].map(
tokenize,
batched=False,
remove_columns=original_columns,
desc=f"Tokenizing {split}",
)
train_ds = tokenized.get("train")
eval_ds = tokenized.get("validation")
# validation が無い（将来の互換）場合のフォールバック
if train_ds is None:
only_name = list(tokenized.keys())[0]
only_ds = tokenized[only_name]
n = len(only_ds)
cut = max(1, int(n * 0.02))
eval_ds = only_ds.select(range(cut))
train_ds = only_ds.select(range(cut, n))
# -------- 5) Collator（Pad を -100 に）--------
collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
# -------- 6) TrainingArguments（衝突耐性） --------
print("Transformers version:", transformers.__version__)
common_args = dict(
output_dir="./qwen2-jaquad-lora",
num_train_epochs=3,
per_device_train_batch_size=2,
per_device_eval_batch_size=2,
gradient_accumulation_steps=8,
learning_rate=2e-4,
lr_scheduler_type="cosine",
warmup_ratio=0.03,
weight_decay=0.0,
logging_steps=50,
save_steps=500,
save_total_limit=2,
report_to="none",
bf16=True, # 対応GPUなら True
gradient_checkpointing=False, # VRAM節約
)
# 実際に使われるクラスのシグネチャを確認（名前衝突や古い版でも動くように）
sig = inspect.signature(HFTrainingArguments.__init__).parameters
if "evaluation_strategy" in sig:
training_args = HFTrainingArguments(
**common_args,
evaluation_strategy="steps",
eval_steps=500,
)
else:
# 古い版互換（基本的に今は通らない想定だが保険）
training_args = HFTrainingArguments(
**common_args,
do_eval=True,
)
# -------- 7) Trainer --------
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_ds,
eval_dataset=eval_ds,
data_collator=collator,
)
# -------- 8) 学習 & 評価 & 保存 --------
trainer.train()
metrics = trainer.evaluate()
eval_loss = metrics.get("eval_loss", None)
if eval_loss is not None:
try:
ppl = math.exp(eval_loss)
print(f"Eval loss: {eval_loss:.4f} | PPL: {ppl:.2f}")
except OverflowError:
print(f"Eval loss: {eval_loss:.4f} | PPL: overflow")
save_dir = "./qwen2-jaquad-lora-adapter"
trainer.model.save_pretrained(save_dir)
tokenizer.save_pretrained(save_dir)
print(f"Saved LoRA adapter to: {save_dir}")
```
## ライセンス
このデータセットはMITライセンスの下で公開されています。
## 引用
```bibtex
@dataset{softjapan/jaquad-sft,
title={softjapan/jaquad-sft: Japanese Question Answering Dataset for SFT},
author={Your Name},
year={2024},
url={https://huggingface.co/datasets/softjapan/jaquad-sft}
}
```
## 関連リンク
- [元データセット: SkelterLabsInc/jaquad](https://huggingface.co/datasets/SkelterLabsInc/jaquad)
- [変換スクリプト](https://github.com/softjapan/jaquad-to-sft)
|
shelbin/blocks2plate
|
shelbin
|
2025-04-16T08:45:24Z
| 18
| 1
|
[
"license:mit",
"size_categories:n<1K",
"modality:video",
"library:datasets",
"library:mlcroissant",
"region:us",
"LeRobot",
"SO-ARM100"
] |
[] |
2025-04-16T08:28:22Z
| 0
|
---
license: mit
tags:
- LeRobot
- SO-ARM100
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot) and [SO-ARM100](https://github.com/TheRobotStudio/SO-ARM100)
# Dataset Structure
```json
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "so100",
"total_episodes": 20,
"total_frames": 19038,
"total_tasks": 1,
"total_videos": 20,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:20"
},
"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": [
6
],
"names": [
"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.webcam": {
"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
}
}
}
```
|
yoonholee/completions_wstar-balanced_Qwen3-4B_HMMT2025
|
yoonholee
|
2025-05-13T11:52:34Z
| 0
| 0
|
[
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-05-13T11:52:33Z
| 0
|
---
dataset_info:
features:
- name: problem
dtype: string
- name: hint
dtype: string
- name: completions
sequence: string
- name: corrects
sequence: bool
- name: acc
dtype: float64
- name: answer
dtype: string
splits:
- name: train
num_bytes: 12354891
num_examples: 240
download_size: 4536451
dataset_size: 12354891
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
zhuang97/FourSquare-NYC-User-Profiles
|
zhuang97
|
2025-03-26T15:29:13Z
| 13
| 0
|
[
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] |
[] |
2025-03-26T15:29:03Z
| 0
|
---
dataset_info:
features:
- name: user_id
dtype: string
- name: user_profile
dtype: string
- name: traits
sequence: string
- name: attributes
sequence: string
- name: preferences
sequence: string
- name: routines
sequence: string
splits:
- name: train
num_bytes: 1468346
num_examples: 1047
download_size: 582745
dataset_size: 1468346
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "FourSquare-NYC-User-Profiles"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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
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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:
148 MB
Size of the auto-converted Parquet files:
148 MB
Number of rows:
172,428