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math-extraction-comp/deepseek-ai__DeepSeek-R1-Distill-Qwen-1.5B | math-extraction-comp | 2025-01-26T13:29:41Z | 19 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-01-26T13:29:38Z | 0 | ---
dataset_info:
features:
- name: question
dtype: string
- name: gold
dtype: string
- name: target
dtype: string
- name: prediction
dtype: string
- name: subset
dtype: string
- name: lighteval-b200fe81_extracted_answer
dtype: string
- name: lighteval-b200fe81_score
dtype: float64
- name: qwen_extracted_answer
dtype: string
- name: qwen_score
dtype: float64
- name: harness_extracted_answer
dtype: string
- name: harness_score
dtype: float64
splits:
- name: train
num_bytes: 5546292
num_examples: 1324
download_size: 2496876
dataset_size: 5546292
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
tacofoundation/cloudsen12 | tacofoundation | 2025-01-05T14:47:21Z | 1,070 | 0 | [
"language:en",
"license:cc0-1.0",
"modality:geospatial",
"region:us",
"clouds",
"sentinel-2",
"image-segmentation",
"deep-learning",
"remote-sensing"
] | [] | 2024-12-04T16:39:44Z | 0 | ---
license:
- cc0-1.0
language:
- en
tags:
- clouds
- sentinel-2
- image-segmentation
- deep-learning
- remote-sensing
pretty_name: cloudsen12plus
viewer: false
---
<div style="text-align: center; border: 1px solid #ddd; border-radius: 10px; padding: 15px; max-width: 250px; margin: auto; background-color: #f9f9f9;">
[](https://cloudsen12.github.io/)
<b><p>This dataset follows the TACO specification.</p></b>
</div>
# [cloudsen12plus](https://cloudsen12.github.io/)
**Website:** https://cloudsen12.github.io/
**version:** 1.1.2
****The largest dataset of expert-labeled pixels for cloud and cloud shadow detection in Sentinel-2****
CloudSEN12+ version 1.1.0 is a significant extension of the CloudSEN12 dataset, which doubles the number of
expert-reviewed labels, making it, by a large margin, the largest cloud detection dataset to
date for Sentinel-2. All labels from the previous version have been curated and refined, enhancing
the dataset's truestworthiness. This new release is licensed under CC0, which puts it in the public
domain and allows anyone to use, modify, and distribute it without permission or attribution.
The images are padded from 509x509 to 512x512 and 2000x2000 to 2048x2048 to ensure that the
patches are divisible by 32. The padding is filled with zeros in the left and bottom sides of the
image. For those who prefer traditional storage formats, GeoTIFF files are available in our
[ScienceDataBank](https://www.scidb.cn/en/detail?dataSetId=2036f4657b094edfbb099053d6024b08&version=V1)
repository.
`CloudSEN12+` v.1.1.0 offers three distinct modes, tailored for diverse research and application needs:
- **`cloudsen12-l1c`**: Patches derived from Sentinel-2 Level-1C imagery, including high-quality labels, scribble annotations, and unlabeled data.
- **`cloudsen12-l2a`**: Similar to cloudsen12-l1c but based on Sentinel-2 Level-2A data as processed by Google Earth Engine.
- **`cloudsen12-extra`**: A supplementary collection of metadata to enhance contextual understanding of landscapes. Cloud masks from multiple sources have **NOT** been normalized to align with the CloudSEN12 class schema. This mode includes:
- **`elevation:`** Elevation data (meters) sourced from the Copernicus DEM GLO-30.
- **`lc10:`** ESA WorldCover 10m v100 land cover product.
- **`vv:`** Normalized Sentinel-1 Global Backscatter Model Land Surface (VV polarization).
- **`vh:`** Normalized Sentinel-1 Global Backscatter Model Land Surface (VH polarization).
- **`cloudmask_qa60:`** Cloud mask from Sentinel-2 Level-1C.
- **`cloudmask_sen2cor:`** Cloud mask from Sentinel-2 Level-2A.
- **`cloudmask_s2cloudless:`** Cloud mask generated by Sentinel Hub Cloud Detector.
- **`cloudmask_cloudscore_cs_v1:`** Cloud mask generated by [Pasquarella et al. 2023](https://openaccess.thecvf.com/content/CVPR2023W/EarthVision/html/Pasquarella_Comprehensive_Quality_Assessment_of_Optical_Satellite_Imagery_Using_Weakly_Supervised_CVPRW_2023_paper.html).
- **`cloudmask_cloudscore_cs_cdf_v1:`** Cloud mask generated by [Pasquarella et al. 2023](https://openaccess.thecvf.com/content/CVPR2023W/EarthVision/html/Pasquarella_Comprehensive_Quality_Assessment_of_Optical_Satellite_Imagery_Using_Weakly_Supervised_CVPRW_2023_paper.html).
- **`cloudmask_unetmobv2_v1:`** Cloud mask generated by [Aybar et al. 2022](https://www.nature.com/articles/s41597-022-01878-2).
- **`cloudmask_unetmobv2_v2:`** Cloud mask generated by [Aybar et al. 2024](https://www.sciencedirect.com/science/article/pii/S2352340924008163).
- **`cloudmask_sensei_v2:`** Cloud mask generated by [Alistair Francis 2024](https://ieeexplore.ieee.org/document/10505181).
- Changelog:
- Version 1.1.2:
- BUG FIX: cloudmask_qa60 now are in uint16 rather than uint8 to avoid overflow.
- Version 1.1.1:
- BUG FIX: VV and VH now are in int16 rather than uint16 to avoid overflow.
- Version 1.1.0:
- We save all GeoTIFF files with discard_lsb=2 to improve the compression ratio.
- Fixed 2000x2000 rotated patches. The datapoints are now correctly oriented. Check the patches:
- ROI_2526__20200709T105031_20200709T105719_T31UDQ
- ROI_0070__20190708T130251_20190708T130252_T24MUA
- ROI_4565__20200530T100029_20200530T100502_T32TQP
- Improved the quality of the following patches:
- ROI_1098__20200515T190909_20200515T191310_T11WPN
- ROI_1735__20190814T163849_20190814T164716_T15SXS
- ROI_0760__20190516T022551_20190516T022553_T56WMD
- ROI_3696__20200419T075611_20200419T080344_T35MRN
- ROI_2864__20170529T105621_20170529T110523_T31TCN
- We removed the following patches due to poor quality:
- ROI_3980__20190228T005641_20190228T005640_T58WDB
- ROI_1489__20210228T070831_20210228T070834_T40TDP
- Consideration:
- The field `roi_id` field serves as a unique identifier for the geographical location of each patch. In other words, it is used to link S2 images with
a specific geographic location. However, the roi_id between the 509x509 and 2000x2000 patches are
not the same. For example, the roid_id: `ROI_0008` in the 509x509 patches is not the same as the
`ROI_0008` in the 2000x2000 patches. In this version, we fixed this issue by summing the max value
of the 509x509 patches to the 2000x2000 patches. In this way, the `roi_id` between the 509x509 and
2000x2000 patches are unique. If users of 2000x2000 patches need to match the original roi_id published
in the previous version, they can use the following formula:
- `old_roi_id_2000 = old_roi_id_2000 - 12101`
where `12101` is the max value of the 509 patches. We also reported the previous roi as old_roi_id.
<center>
<img src='https://cdn-uploads.huggingface.co/production/uploads/6402474cfa1acad600659e92/9UA4U3WObVeq7BAcf37-C.png' alt='drawing' width='80%'/>
</center>
*CloudSEN12+ spatial coverage. The terms p509 and p2000 denote the patch size 509 × 509 and 2000 × 2000,
respectively. `high`, `scribble`, and `nolabel` refer to the types of expert-labeled annotations*
## 🔄 Reproducible Example
<a target="_blank" href="https://colab.research.google.com/drive/1U9n40rwdnn73bdWruONA3hIs1-H3f74Q">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
Load this dataset using the `tacoreader` library.
```python
import tacoreader
import rasterio as rio
print(tacoreader.__version__) # 0.5.3
# Remotely load the Cloud-Optimized Dataset
dataset = tacoreader.load("tacofoundation:cloudsen12-l1c")
#dataset = tacoreader.load("tacofoundation:cloudsen12-l2a")
#dataset = tacoreader.load("tacofoundation:cloudsen12-extra")
# Read a sample
sample_idx = 2422
s2_l1c = dataset.read(sample_idx).read(0)
s2_label = dataset.read(sample_idx).read(1)
# Retrieve the data
with rio.open(s2_l1c) as src, rio.open(s2_label) as dst:
s2_l1c_data = src.read([4, 3, 2], window=rio.windows.Window(0, 0, 512, 512))
s2_label_data = dst.read(window=rio.windows.Window(0, 0, 512, 512))
# Display
fig, ax = plt.subplots(1, 2, figsize=(10, 5))
ax[0].imshow(s2_l1c_data.transpose(1, 2, 0) / 3000)
ax[0].set_title("Sentinel-2 L1C")
ax[1].imshow(s2_label_data[0])
ax[1].set_title("Human Label")
plt.tight_layout()
plt.savefig("taco_check.png")
plt.close(fig)
```
<center>
<img src='https://cdn-uploads.huggingface.co/production/uploads/6402474cfa1acad600659e92/0nRv7sqMRMNY-TVkY2kh7.png' alt='drawing' width='70%'/>
</center>
## 🛰️ Sensor Information
The sensor related to the dataset: **sentinel2msi**
## 🎯 Task
The task associated with this dataset: **semantic-segmentation**
## 📂 Original Data Repository
Source location of the raw data:**[https://huggingface.co/datasets/isp-uv-es/CloudSEN12Plus](https://huggingface.co/datasets/isp-uv-es/CloudSEN12Plus)**
## 💬 Discussion
Insights or clarifications about the dataset: **[https://huggingface.co/datasets/tacofoundation/cloudsen12/discussions](https://huggingface.co/datasets/tacofoundation/cloudsen12/discussions)**
## 🔀 Split Strategy
How the dataset is divided for training, validation, and testing: **stratified**
## 📚 Scientific Publications
Publications that reference or describe the dataset.
### Publication 01
- **DOI**: [10.1038/s41597-022-01878-2](10.1038/s41597-022-01878-2)
- **Summary**: CloudSEN12 first release. Only 509 x 509 patches.
- **BibTeX Citation**:
```bibtex
@article{aybar2022cloudsen12,
title={CloudSEN12, a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2},
author={Aybar, Cesar and Ysuhuaylas, Luis and Loja, Jhomira and Gonzales, Karen and Herrera, Fernando and Bautista, Lesly and Yali, Roy and Flores, Angie and Diaz, Lissette and Cuenca, Nicole and others},
journal={Scientific Data},
volume={9},
number={1},
pages={782},
year={2022},
publisher={Nature Publishing Group UK London}
}
```
### Publication 02
- **DOI**: [10.1109/IGARSS52108.2023.10282381](10.1109/IGARSS52108.2023.10282381)
- **Summary**: Exploration of incorrect annotations in cloud semantic segmentation datasets.
- **BibTeX Citation**:
```bibtex
@inproceedings{aybar2023lessons,
title={Lessons Learned From Cloudsen12 Dataset: Identifying Incorrect Annotations in Cloud Semantic Segmentation Datasets},
author={Aybar, Cesar and Montero, David and Mateo-Garc{'\i}a, Gonzalo and G{'o}mez-Chova, Luis},
booktitle={IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium},
pages={892--895},
year={2023},
organization={IEEE}
}
```
### Publication 03
- **DOI**: [10.1016/j.dib.2024.110852](10.1016/j.dib.2024.110852)
- **Summary**: Extended version of CloudSEN12. We include 2000 x 2000 patches to the dataset.
- **BibTeX Citation**:
```bibtex
@article{aybar2024cloudsen12+,
title={CloudSEN12+: The largest dataset of expert-labeled pixels for cloud and cloud shadow detection in Sentinel-2},
author={Aybar, Cesar and Bautista, Lesly and Montero, David and Contreras, Julio and Ayala, Daryl and Prudencio, Fernando and Loja, Jhomira and Ysuhuaylas, Luis and Herrera, Fernando and Gonzales, Karen and others},
journal={Data in Brief},
volume={56},
pages={110852},
year={2024},
publisher={Elsevier}
}
```
## 🤝 Data Providers
Organizations or individuals responsible for the dataset.
|**Name**|**Role**|**URL**|
| :--- | :--- | :--- |
|Image & Signal Processing|host|[https://isp.uv.es/](https://isp.uv.es/)|
|European Space Agency (ESA)|producer|[https://www.esa.int/](https://www.esa.int/)|
## 🧑🔬 Curators
Responsible for structuring the dataset in the TACO format.
|**Name**|**Organization**|**URL**|
| :--- | :--- | :--- |
|Cesar Aybar|Image & Signal Processing|[https://csaybar.github.io/](https://csaybar.github.io/)|
## 🏷️ Labels
The dataset contains four classes: clear, thick cloud, thin cloud, and cloud shadow.
|**Name**|**Category**|**Description**|
| :--- | :--- | :--- |
|clear|0|Pixels without cloud and cloud shadow contamination. They are primarily identified using bands B4- B3-B2, B1-B12-B13, and the cirrus band.|
|thick cloud|1|Opaque clouds that block all the reflected light from the Earth's surface. We identify them by assuming clouds exhibit distinctive shapes and maintain higher reflectance values in bands B4-B3-B2, B1-B12-B13, and the cirrus band.|
|thin cloud|2|Semitransparent clouds that alter the surface spectral signal but still allow to recognize the background. This is the hardest class to identify. We utilize CloudApp [1] to better understand the background, both with and without cloud cover.|
|cloud shadow|3|Dark pixels where light is occluded by thick or thin clouds. Cloud shadows depend on clouds presence and, by considering the solar position, we can identify and map these shadows through a reasoned projection of the cloud shape.|
## 🌈 Optical Bands
No data value is always: **99**
### `tacofoundation:cloudsen12-l1c`
- `s2l1c`
|**Name**|**Common Name**|**Description**|**Center Wavelength** (nm)|**Full Width Half Max** (nm)|**Index**|**Scale Factor**|
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
|B01|Coastal aerosol|Band 1 - Coastal aerosol - 60m|443.5|17.0|0|0.0001|
|B02|Blue|Band 2 - Blue - 10m|496.5|53.0|1|0.0001|
|B03|Green|Band 3 - Green - 10m|560.0|34.0|2|0.0001|
|B04|Red|Band 4 - Red - 10m|664.5|29.0|3|0.0001|
|B05|Red edge 1|Band 5 - Vegetation red edge 1 - 20m|704.5|13.0|4|0.0001|
|B06|Red edge 2|Band 6 - Vegetation red edge 2 - 20m|740.5|13.0|5|0.0001|
|B07|Red edge 3|Band 7 - Vegetation red edge 3 - 20m|783.0|18.0|6|0.0001|
|B08|NIR|Band 8 - Near infrared - 10m|840.0|114.0|7|0.0001|
|B8A|Red edge 4|Band 8A - Vegetation red edge 4 - 20m|864.5|19.0|8|0.0001|
|B09|Water vapor|Band 9 - Water vapor - 60m|945.0|18.0|9|0.0001|
|B10|Cirrus|Band 10 - Cirrus - 60m|1375.5|31.0|10|N/A|
|B11|SWIR 1|Band 11 - Shortwave infrared 1 - 20m|1613.5|89.0|11|0.0001|
|B12|SWIR 2|Band 12 - Shortwave infrared 2 - 20m|2199.5|173.0|12|0.0001|
- `target`
The 'scribble' and 'nolabel' patches contains 99 values, which represent 'no data' in CloudSEN12.
|**Name**|**Common Name**|**Description**|**Center Wavelength**|**Full Width Half Max**|**Index**|**Scale Factor**|
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
|B01|Cloud Label|Cloud labels annotated by humans|-|-|0|1|
### `tacofoundation:cloudsen12-l2a`
- `s2l2a`
|**Band**|**Name**|**Description**|**Center Wavelength** (nm)|**Bandwidth** (nm)|**Index**|**Scale Factor**|
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
|B01|Coastal aerosol|Band 1 - Coastal aerosol - 60m|443.5|17.0|0|0.0001|
|B02|Blue|Band 2 - Blue - 10m|496.5|53.0|1|0.0001|
|B03|Green|Band 3 - Green - 10m|560.0|34.0|2|0.0001|
|B04|Red|Band 4 - Red - 10m|664.5|29.0|3|0.0001|
|B05|Red edge 1|Band 5 - Vegetation red edge 1 - 20m|704.5|13.0|4|0.0001|
|B06|Red edge 2|Band 6 - Vegetation red edge 2 - 20m|740.5|13.0|5|0.0001|
|B07|Red edge 3|Band 7 - Vegetation red edge 3 - 20m|783.0|18.0|6|0.0001|
|B08|NIR|Band 8 - Near infrared - 10m|840.0|114.0|7|0.0001|
|B8A|Red edge 4|Band 8A - Vegetation red edge 4 - 20m|864.5|19.0|8|0.0001|
|B09|Water vapor|Band 9 - Water vapor - 60m|945.0|18.0|9|0.0001|
|B11|SWIR 1|Band 11 - Shortwave infrared 1 - 20m|1613.5|89.0|10|0.0001|
|B12|SWIR 2|Band 12 - Shortwave infrared 2 - 20m|2199.5|173.0|11|0.0001|
|AOT|-|Aerosol Optical Thickness|-|-|12|0.001|
|WVP|-|Water Vapor Pressure. The height the water would occupy if the vapor were condensed into liquid and spread evenly across the column|-|-|13|0.001|
- `target`
The 'scribble' and 'nolabel' patches contains 99 values, which represent 'no data' in CloudSEN12.
|**Name**|**Common Name**|**Description**|**Center Wavelength**|**Full Width Half Max**|**Index**|**Scale Factor**|
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
|B01|Cloud Label|Cloud labels annotated by humans|-|-|0|1|
### `tacofoundation:cloudsen12-extra`
| **Band** | **Name** | **Description** | **Center Wavelength** (nm) | **Bandwidth** (nm) | **Index** | **Scale Factor** |
|-------------------------|----------------------------------|------------------------------------------------------------------------------------------------------------------|----------------------------|--------------------|-----------|------------------|
| elevation | Elevation | Elevation data (meters) sourced from the Copernicus DEM GLO-30. | - | - | 0 | 1 |
| lc10 | Landcover | ESA WorldCover 10m v100 land cover product. | - | - | 0 | 1 |
| vv | Vertical-Vertical Polarization | Normalized Sentinel-1 Global Backscatter Model (VV polarization). | - | - | 0 | 0.1 |
| vh | Vertical-Horizontal Polarization | Normalized Sentinel-1 Global Backscatter Model (VH polarization). | - | - | 0 | 0.1 |
| cloudmask_qa60 | QA60 | Cloud mask from Sentinel-2 Level-1C. | - | - | 0 | 1 |
| cloudmask_sen2cor | Sen2Cor | Cloud mask from Sentinel-2 Level-2A. | - | - | 0 | 1 |
| cloudmask_s2cloudless | S2Cloudless | Cloud mask generated by Sentinel Hub Cloud Detector. | - | - | 0 | 1 |
| cloudmask_cloudscore_cs_v1 | CloudScore v1 | Cloud mask generated by [Pasquarella et al. 2023](https://openaccess.thecvf.com/content/CVPR2023W/EarthVision/html/Pasquarella_Comprehensive_Quality_Assessment_of_Optical_Satellite_Imagery_Using_Weakly_Supervised_CVPRW_2023_paper.html) model. | - | - | 0 | 1 |
| cloudmask_cloudscore_cs_cdf_v1 | CloudScore CDF v1 | Cloud mask generated by [Pasquarella et al. 2023](https://openaccess.thecvf.com/content/CVPR2023W/EarthVision/html/Pasquarella_Comprehensive_Quality_Assessment_of_Optical_Satellite_Imagery_Using_Weakly_Supervised_CVPRW_2023_paper.html) model. | - | - | 0 | 1 |
| cloudmask_unetmobv2_v1 | UNetMobV2 v1 | Cloud mask generated by [Aybar et al. 2022](https://www.nature.com/articles/s41597-022-01878-2) model. | - | - | 0 | 1 |
| cloudmask_unetmobv2_v2 | UNetMobV2 v2 | Cloud mask generated by [Aybar et al. 2024](https://www.sciencedirect.com/science/article/pii/S2352340924008163) model.| - | - | 0 | 1 |
| cloudmask_sensei_v2 | Sensei v2 | Cloud mask generated by [Alistair Francis 2024](https://ieeexplore.ieee.org/document/10505181). | - | - | 0 | 1 |
## 👀 Additional metadata
In addition to the **`stac`** and **`rai`** fields, this dataset includes the following fields at the sample level.
| **ID** | **Description** |
|------------------------------|---------------------------------------------------------------------------------|
| `roi_id` | Unique identifier for the region of interest (ROI). |
| `old_roi_id` | Previous identifier for the region of interest, if applicable. See considerations section above. |
| `equi_id` | Identifier for the corresponding equi7grid system. |
| `equi_zone` | Zone or section within the equi7grid system. |
| `label_type` | Type of label assigned to the ROI. It can be: `high`. `scribble`, and `nolabe`. |
| `s2_id` | Identifier for the Sentinel-2 image ids. |
| `real_proj_shape` | Image in CloudSEN12 are padded with zeros (see description above). This field contains the shape of the original image. It can be: 2000 and 509.
| `s2_mean_solar_azimuth_angle` | Mean solar azimuth angle for the Sentinel-2 image (in degrees). |
| `s2_mean_solar_zenith_angle` | Mean solar zenith angle for the Sentinel-2 image (in degrees). |
| `thick_percentage` | Percentage of thick clouds in the ROI estimated by the annotator for the `high` label. For `scribble` and `nolabel` labels, this value is derived from UNetMobV2-V1 predictions. |
| `thin_percentage` | Percentage of thin clouds in the ROI estimated by the annotator for the `high` label. For `scribble` and `nolabel` labels, this value is derived from UNetMobV2-V1 predictions. |
| `cloud_shadow_percentage` | Percentage of cloud shadows in the ROI estimated by the annotator for the `high` label. For `scribble` and `nolabel` labels, this value is derived from UNetMobV2-V1 predictions. |
| `clear_percentage` | Percentage of clear sky in the ROI estimated by the annotator for the `high` label. For `scribble` and `nolabel` labels, this value is derived from UNetMobV2-V1 predictions. |
|
xzhe121/comaprative_analysis_results.csv | xzhe121 | 2024-11-29T04:18:07Z | 15 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-11-28T08:08:46Z | 0 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: base_output
dtype: string
- name: pairrm_output
dtype: string
- name: llm_judge_output
dtype: string
splits:
- name: train
num_bytes: 30275
num_examples: 10
download_size: 38195
dataset_size: 30275
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
shizi1011/xlam-function-calling-processed-1tool | shizi1011 | 2025-03-10T07:57:34Z | 19 | 1 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-03-06T06:59:56Z | 0 | ---
dataset_info:
features:
- name: query
dtype: string
- name: answers
sequence: string
- name: schemas
sequence: string
- name: system
list:
- name: content
dtype: string
- name: role
dtype: string
- name: conversation
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 5742846
num_examples: 3782
download_size: 1225273
dataset_size: 5742846
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
RLAIF/STAR-TRAIN-math_llama-star-iter3 | RLAIF | 2025-03-15T19:01:48Z | 17 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-03-15T19:01:46Z | 0 | ---
dataset_info:
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dtype: string
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dtype: int64
splits:
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num_examples: 3203
download_size: 2677615
dataset_size: 16206030.92375
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Locutusque/Math-Evol-Instruct-v0.1 | Locutusque | 2025-02-15T13:27:01Z | 26 | 2 | [
"language:en",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-02-08T05:55:08Z | 0 | ---
language:
- en
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 2632269.0
num_examples: 1288
download_size: 1220108
dataset_size: 2632269.0
configs:
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data_files:
- split: train
path: data/train-*
---
|
samsitol/so100_pretest | samsitol | 2025-06-10T15:40:30Z | 0 | 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"
] | 2025-06-10T15:40:22Z | 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": "so100",
"total_episodes": 2,
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"splits": {
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"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": {
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"main_shoulder_pan",
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"main_wrist_flex",
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]
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"main_shoulder_lift",
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"main_wrist_roll",
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]
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480,
640,
3
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"width",
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},
"observation.images.head": {
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],
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],
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"video.is_depth_map": false,
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"video.channels": 3,
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}
},
"observation.images.wrist": {
"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": {
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"shape": [
1
],
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},
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"shape": [
1
],
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},
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1
],
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},
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],
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},
"task_index": {
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1
],
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}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
anichris/eval_act_so100_test | anichris | 2025-05-01T16:54:12Z | 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",
"so100",
"tutorial"
] | [
"robotics"
] | 2025-05-01T16:47:41Z | 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",
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],
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"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
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},
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]
},
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480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.fps": 30.0,
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"video.width": 640,
"video.channels": 3,
"video.codec": "h264",
"video.pix_fmt": "yuv420p",
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}
},
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1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
supergoose/flan_combined_task308_jeopardy_answer_generation_all | supergoose | 2025-03-05T21:55:57Z | 17 | 0 | [
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"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-03-05T21:55:39Z | 0 | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: _template_idx
dtype: int64
- name: _task_source
dtype: string
- name: _task_name
dtype: string
- name: _template_type
dtype: string
splits:
- name: train
num_bytes: 13108461
num_examples: 19439
download_size: 3743834
dataset_size: 13108461
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
sywang/AttributeByUnlearning | sywang | 2024-11-24T20:09:16Z | 106 | 0 | [
"license:cc-by-nc-sa-4.0",
"size_categories:n<1K",
"format:text",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | [] | 2024-11-07T16:43:50Z | 0 | ---
license: cc-by-nc-sa-4.0
---
|
ganga4364/benchmark-stt-hs | ganga4364 | 2024-11-20T11:52:18Z | 8 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-11-20T11:52:15Z | 0 | ---
dataset_info:
features:
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num_bytes: 741026
num_examples: 1236
download_size: 1964568
dataset_size: 5703672
configs:
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data_files:
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path: data/Publishing_Year-*
- split: Name
path: data/Name-*
- split: Gender
path: data/Gender-*
- split: Age__Group
path: data/Age__Group-*
- split: place_of_origin
path: data/place_of_origin-*
- split: exiled_year
path: data/exiled_year-*
---
|
zijian2022/so100_test_1230vf | zijian2022 | 2024-12-30T20:56:01Z | 51 | 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"
] | 2024-12-30T20:55:57Z | 0 | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so100
- tutorial
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.0",
"robot_type": "so100",
"total_episodes": 5,
"total_frames": 823,
"total_tasks": 1,
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"fps": 30,
"splits": {
"train": "0:5"
},
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"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"action": {
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"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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480,
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],
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"height",
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"channels"
],
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"video.channels": 3,
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"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,
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}
},
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],
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1
],
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}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
DogNeverSleep/TEST_ocr_1 | DogNeverSleep | 2025-05-12T13:20:22Z | 0 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-12T13:20:13Z | 0 | ---
dataset_info:
features:
- name: index
dtype: int64
- name: video_index
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: eval_method
dtype: string
- name: option
sequence: string
- name: task_type
dtype: string
- name: task
dtype: string
splits:
- name: train
num_bytes: 191496
num_examples: 956
download_size: 70866
dataset_size: 191496
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
caiyan123/merged_other_task | caiyan123 | 2025-06-24T21:34:04Z | 0 | 0 | [
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-24T20:52:10Z | 0 | ---
license: cc-by-4.0
---
# Dataset: merged_other_task
This dataset contains merged robot manipulation episodes from six different tasks:
- open_cabinet
- close_cabinet
- open_fridge
- close_fridge
- open_dishwasher
- close_dishwasher
Each row represents one frame of an episode, with the following columns:
- `action`: a list of float values representing robot joint control signals.
- `observation.state`: a list of float values representing the robot's observed state at the frame.
- `timestamp`: frame timestamp (float)
- `label_index`: task label, where 0 = "open", 1 = "close"
- `task`: task name string (e.g., "open_fridge")
## Format
- File: `merged_other_task.csv`
- Rows: ~66,000
- Format: Tabular
- License: CC-BY-4.0
## License
This dataset is released under the [Creative Commons Attribution 4.0 License (CC-BY-4.0)](https://creativecommons.org/licenses/by/4.0/).
Please cite or credit the author if used in research or publication.
## Citation
Created by [caiyan123](https://huggingface.co/caiyan123).
|
supergoose/buzz_sources_277_xquery | supergoose | 2024-11-10T20:51:43Z | 16 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-11-10T20:51:42Z | 0 | ---
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: source
dtype: string
- name: stack
dtype: string
splits:
- name: train
num_bytes: 56935
num_examples: 38
download_size: 22691
dataset_size: 56935
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
friedrichor/MSR-VTT | friedrichor | 2025-05-20T08:01:59Z | 332 | 0 | [
"task_categories:text-to-video",
"task_categories:text-retrieval",
"task_categories:video-classification",
"language:en",
"size_categories:10K<n<100K",
"format:json",
"modality:tabular",
"modality:text",
"modality:video",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"text-to-video",
"text-retrieval",
"video-classification"
] | 2025-02-28T12:58:43Z | 0 | ---
configs:
- config_name: train_9k
data_files:
- split: train
path: "msrvtt_train_9k.json"
- config_name: train_7k
data_files:
- split: train
path: "msrvtt_train_7k.json"
- config_name: test_1k
data_files:
- split: test
path: "msrvtt_test_1k.json"
task_categories:
- text-to-video
- text-retrieval
- video-classification
language:
- en
size_categories:
- 1K<n<10K
---
[MSRVTT](https://openaccess.thecvf.com/content_cvpr_2016/html/Xu_MSR-VTT_A_Large_CVPR_2016_paper.html) contains 10K video clips and 200K captions.
We adopt the standard `1K-A split` protocol, which was introduced in [JSFusion](https://openaccess.thecvf.com/content_ECCV_2018/html/Youngjae_Yu_A_Joint_Sequence_ECCV_2018_paper.html) and has since become the de facto benchmark split in the `Text-Video Retrieval` field.
Train:
- train_7k: 7,010 videos, 140,200 captions
- train_9k: 9,000 videos, 180,000 captions
Test:
- test_1k: 1,000 videos, 1,000 captions
---
## 🌟 Citation
```bibtex
@inproceedings{xu2016msrvtt,
title={Msr-vtt: A large video description dataset for bridging video and language},
author={Xu, Jun and Mei, Tao and Yao, Ting and Rui, Yong},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2016}
}
```
|
datasets-CNRS/MPF | datasets-CNRS | 2025-03-29T21:40:30Z | 7 | 0 | [
"language:fra",
"license:cc-by-nc-sa-4.0",
"region:us"
] | [] | 2024-10-20T11:43:49Z | 0 | ---
language:
- fra
viewer: false
license: cc-by-nc-sa-4.0
---
> [!NOTE]
> Dataset origin: https://www.ortolang.fr/market/corpora/mpf
## Description
Conseil scientifique
Françoise Gadet (directrice), Emmanuelle Guérin, Amal Guha, Anaïs Moreno Kerdreux, RobertoPaternostro.
MPF est un corpus qui a été recueilli en région parisienne depuis 2010, auprès de populations « jeunes » connaissant des contacts multiculturels réguliers. La dénomination anglaise est due au contexte de son lancement, en 2010 : un projet franco-britannique (ANR-FRBR-09-037-01, 2010-2014). Sorti de ce cadre en 2014, il a été soutenu par la DGLFLF (budgets 2014, 2016, 2018, 2019), par Ortolang (budget 2016) et par le GTRC canadien Le français à la mesure d'un continent (F. Martineau dir., 2010-2017)
Ce corpus vise à documenter des évolutions en cours dans le français, sous la forme d’un "vernaculaire urbain contemporain", ainsi que les effets sur le français du contact avec les langues de l’immigration. Le principe privilégié pour le recueil a été la "proximité communicative" : aucun enregistrement n’a été fait avec des inconnus (liens de réseau, histoire conversationnelle antérieure). C’est pourquoi le recueil est le fruit du travail de 26 enquêteurs (dont le prénom engage le titre de l’enquête), qui ont joint 187 locuteurs différents, provenant de 28 communes d’Ile-de-France et de 4 arrondissements parisiens. En fonction de leurs caractéristiques linguistiques et langagières audibles (et non sur des critères socio-démographiques), les enregistrements ont été classés en 3 catégories : entretiens traditionnels, entretiens de proximité et événements écologiques (sans enquêteur). Le corpus comporte 106 enregistrements, de durées entre 6 et 153 mn, transcrits sous Praat (sauf 2 sous Transcriber). Les transcriptions ont été révisées et anonymisées (texte et son). Eniron 1 200 000 mots figurent sur le site, soit 83 heures (cf. conventions de transcription). En mars 2023, le corpus a fait l’objet de 97 publications (voir fichier pdf des références bibliographiques).
Chacune des enquêtes correspond à cinq catégories de fichiers dans le corpus :
- le fichier son (.wav),
- lat transcription (.TextGrid) - On peut la consulter de façon alignée avec le son (.wav) à l'aide du logiciel Praat),
- lat même transcription (.docx) - que'on peur consulter sans alignement avec le son,
- un fichier de métadonnées (.txt),
- une ou des fiche(s) signalétique(s) pour chaque locuteur, dans la mesure du possible (.txt).
Pour avoir un aperçu du contenu des transcriptions, des versions docx (Word) sont disponibles dans le dossier "MPF_Docx".
Créé à partir de ce corpus, mais utilisable par tout corpus de Textgrids, le programme Python extractionMots.py (adresse ci-dessous). Pour chaque "mot" spécifié en paramètre, le programme en extrait du corpus sous format csv toutes les occurrences (enquêtes et tours de parole - marinutage, locut·eur·rice).
https://github.com/aghnanterre/extract_from_TextGrid/edit/master/README.txt
Il est à noter que l'url https://ct3.ortolang.fr/teiconvert/index-en.html propose plusieurs outils de conversion en ligne (par exemple un TextGrid peut être converti en .doc ou en .txt.
## Citation
```
@misc{11403/mpf/v4.1,
title = {MPF},
author = {},
url = {https://hdl.handle.net/11403/mpf/v4.1},
note = {{ORTOLANG} ({Open} {Resources} {and} {TOols} {for} {LANGuage}) \textendash www.ortolang.fr},
copyright = {Licence Creative Commons Attribution - pans d'Utilisation Commerciale - panrtage dans les Mêmes Conditions 4.0 International},
year = {2023}
}
``` |
nicher92/verified_one | nicher92 | 2025-05-09T09:08:33Z | 0 | 0 | [
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] | [] | 2025-05-09T09:08:31Z | 0 | ---
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---
|
aishu15/aryaumeshl | aishu15 | 2025-02-19T02:24:53Z | 17 | 0 | [
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"library:polars",
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] | [] | 2025-02-18T07:06:57Z | 0 | ---
license: apache-2.0
---
|
AiArtLab/e-shuushuu | AiArtLab | 2024-12-17T09:40:41Z | 32 | 0 | [
"license:apache-2.0",
"region:us"
] | [] | 2024-12-16T16:13:09Z | 0 | ---
license: apache-2.0
---
|
uzair921/LLAMA7B_GUM_EMBEDDINGS_LLM_RAG_75_openai | uzair921 | 2025-02-11T11:25:59Z | 8 | 0 | [
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] | [] | 2025-02-11T11:20:51Z | 0 | ---
dataset_info:
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'13': B-place
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|
PrimeIntellect/SYNTHETIC-2-Base-v2-Math | PrimeIntellect | 2025-06-23T22:18:28Z | 0 | 0 | [
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] | [] | 2025-06-23T22:18:26Z | 0 | ---
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---
|
dgambettaphd/D_llm3_gen2_run0_WXS_doc1000_synt64_tot128_SYNLAST | dgambettaphd | 2025-04-25T10:49:36Z | 30 | 0 | [
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] | [] | 2025-04-25T10:49:34Z | 0 | ---
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---
|
ChavyvAkvar/synthetic-trades-ADA-batch-38 | ChavyvAkvar | 2025-06-04T10:34:58Z | 0 | 0 | [
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] | [] | 2025-06-04T10:33:54Z | 0 | ---
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---
|
HYAIYN/so100_get_orange_10epi | HYAIYN | 2025-03-20T13:08:18Z | 44 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
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"region:us",
"LeRobot",
"so100",
"tutorial",
"test"
] | [
"robotics"
] | 2025-03-20T13:06:13Z | 0 | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so100
- tutorial
- test
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
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"robot_type": "so100",
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}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
jb-balaji/pick_place_calib | jb-balaji | 2025-06-15T09:16:53Z | 0 | 0 | [
"task_categories:robotics",
"region:us",
"phosphobot",
"so100",
"phospho-dk"
] | [
"robotics"
] | 2025-06-15T09:16:50Z | 0 |
---
tags:
- phosphobot
- so100
- phospho-dk
task_categories:
- robotics
---
# pick_place_calib
**This dataset was generated using a [phospho starter pack](https://robots.phospho.ai).**
This dataset contains a series of episodes recorded with a robot and multiple cameras. It can be directly used to train a policy using imitation learning. It's compatible with LeRobot and RLDS.
|
weqweasdas/qwen7b_grpo_ver2_step80_with_score_passn | weqweasdas | 2025-05-09T05:02:20Z | 0 | 0 | [
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] | [] | 2025-05-09T05:02:17Z | 0 | ---
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|
test-gen/code_humaneval_qwen2.5-7b_t1.0_n8_tests_humaneval_qwen3-0.6b_t0.6_n1_think | test-gen | 2025-05-21T09:18:28Z | 0 | 0 | [
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] | [] | 2025-05-21T09:18:27Z | 0 | ---
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---
|
MossProphet/so100_folding_testrun | MossProphet | 2025-06-04T14:22:47Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:1K<n<10K",
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"modality:tabular",
"modality:timeseries",
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"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot",
"so100",
"tutorial"
] | [
"robotics"
] | 2025-06-04T14:22:42Z | 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
{
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"video.channels": 3,
"video.codec": "h264",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
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}
},
"observation.images.Arm_left": {
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],
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],
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}
},
"observation.images.Arm_right": {
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"shape": [
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],
"names": [
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}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
Isaac2489/rosa | Isaac2489 | 2025-04-23T15:39:17Z | 20 | 0 | [
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:text",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | [] | 2025-04-21T20:22:09Z | 0 | ---
license: apache-2.0
---
|
1231czx/w2r125k_r2r0k_r185k_ep3_tmp10 | 1231czx | 2025-01-10T04:01:29Z | 18 | 0 | [
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] | [] | 2025-01-09T16:50:51Z | 0 | ---
dataset_info:
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splits:
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num_examples: 5000
download_size: 6840631
dataset_size: 20226320
configs:
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data_files:
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path: data/train-*
---
|
andrewsiah/PersonaPromptPersonalLLM_813 | andrewsiah | 2024-11-15T06:01:21Z | 8 | 0 | [
"size_categories:1K<n<10K",
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"modality:tabular",
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-11-15T06:01:19Z | 0 | ---
dataset_info:
features:
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dtype: float64
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configs:
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data_files:
- split: train
path: data/train-*
---
# Dataset Card for "PersonaPromptPersonalLLM_813"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
cpondoc/noisy-nf-10771 | cpondoc | 2025-03-13T07:24:55Z | 29 | 0 | [
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] | [] | 2025-03-13T07:24:53Z | 0 | ---
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---
|
cm-upadhyay/TwinLlama-3.1-8B-results | cm-upadhyay | 2025-05-14T02:33:32Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-14T02:33:28Z | 0 | ---
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---
|
Asap7772/omnimath-hint-generator-0701 | Asap7772 | 2025-05-02T01:49:47Z | 0 | 0 | [
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] | [] | 2025-05-02T01:49:42Z | 0 | ---
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---
|
SadeqH/instruction-response-dataset | SadeqH | 2025-03-23T11:58:11Z | 17 | 0 | [
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|
swapnil7777/extracted_features_of_dermnet_images | swapnil7777 | 2024-12-27T00:45:14Z | 18 | 0 | [
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] | [] | 2024-12-26T18:33:22Z | 0 | ---
license: apache-2.0
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---
|
robotchip/eval_act_gf5_l2_9 | robotchip | 2025-06-11T14:53:52Z | 0 | 0 | [
"task_categories:robotics",
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"region:us",
"LeRobot",
"tutorial"
] | [
"robotics"
] | 2025-06-11T14:53:47Z | 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
{
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]
},
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}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
kornwtp/karonese-ind-classification | kornwtp | 2025-03-04T15:48:04Z | 28 | 0 | [
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] | [] | 2025-03-04T15:47:59Z | 0 | ---
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---
|
5525FP/minipile-spigot-100000-1-percent | 5525FP | 2025-04-23T22:14:42Z | 23 | 0 | [
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|
Sanket366/friend-chat | Sanket366 | 2024-12-28T09:23:08Z | 15 | 0 | [
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] | [] | 2024-12-28T09:23:07Z | 0 | ---
dataset_info:
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|
neelabh17/new_news_exploded_prompt_n_75_d_perc_0_num_gen_10_Qwen2.5-3B-Instruct_dist_mcq | neelabh17 | 2025-05-17T18:35:52Z | 0 | 0 | [
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] | [] | 2025-05-17T18:35:49Z | 0 | ---
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---
|
SayantanJoker/tts-rj-hi-karya-44100hz-part-22-quality | SayantanJoker | 2025-03-31T05:25:10Z | 16 | 0 | [
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] | [] | 2025-03-31T05:25:08Z | 0 | ---
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---
|
fluently-sets/reasoning-1-1k | fluently-sets | 2024-12-21T16:38:14Z | 64 | 25 | [
"task_categories:text-generation",
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"region:us",
"fluently-sets",
"sft",
"alpaca",
"synthetic",
"distilabel",
"rlaif",
"datacraft"
] | [
"text-generation",
"text2text-generation",
"question-answering"
] | 2024-12-21T16:16:43Z | 0 | ---
size_categories: 1K<n<10K
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dodalities: text
tags:
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- alpaca
- synthetic
- distilabel
- rlaif
- datacraft
license: mit
task_categories:
- text-generation
- text2text-generation
- question-answering
language:
- en
---
# Reasoning-1 1K
## Short about
This dataset will help in SFT training of LLM on the Alpaca format.
The goal of the dataset: to teach LLM to reason and analyze its mistakes using SFT training.
The size of 1.15K is quite small, so for effective training on SFTTrainer set *4-6* epochs instead of *1-3*.
*Made by Fluently Team ([@ehristoforu](https://huggingface.co/ehristoforu)) using [distilabel](https://github.com/argilla-io/distilabel) with love🥰*
## Dataset structure
This subset can be loaded as:
```python
from datasets import load_dataset
ds = load_dataset("fluently-sets/reasoning-1-1k", "default")
```
Or simply as it follows, since there's only one configuration and is named `default`:
```python
from datasets import load_dataset
ds = load_dataset("fluently-sets/reasoning-1-1k")
```
<p align="left">
<a href="https://github.com/argilla-io/distilabel">
<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
</a>
</p> |
Supa-AI/STEM-en-ms | Supa-AI | 2025-01-03T04:13:44Z | 84 | 7 | [
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"library:mlcroissant",
"library:polars",
"region:us",
"mathematics",
"physics",
"llms",
"Malaysia",
"Asia"
] | [] | 2024-12-27T06:50:06Z | 0 | ---
license: cc-by-4.0
dataset_info:
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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sequence: image
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sequence: string
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dtype: string
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num_examples: 614
download_size: 69119656
dataset_size: 69327096.0
tags:
- mathematics
- physics
- llms
- Malaysia
- Asia
size_categories:
- n<1K
configs:
- config_name: data_en
data_files:
- split: eval
path: data_en/train-*
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data_files:
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path: data_ms/train-*
language:
- en
- ms
---
# **A Bilingual Dataset for Evaluating Reasoning Skills in STEM Subjects**
This dataset provides a comprehensive evaluation set for tasks assessing reasoning skills in Science, Technology, Engineering, and Mathematics (STEM) subjects. It features questions in both English and Malay, catering to a diverse audience.
**Key Features**
* **Bilingual:** Questions are available in English and Malay, promoting accessibility for multilingual learners.
* **Visually Rich:** Questions are accompanied by figures to enhance understanding and support visual and contextual reasoning.
* **Focus on Reasoning:** The dataset emphasizes questions requiring logical reasoning and problem-solving skills, as opposed to simple recall of knowledge.
* **Real-World Context:** Questions are derived from real-world scenarios, such as past SPM (Sijil Pelajaran Malaysia) examinations, making them relatable to students.
**Dataset Structure**
The dataset is comprised of two configurations: `data_en` (English) and `data_ms` (Malay). Both configurations share the same features and structure.
**Data Fields**
* **FileName:** Unique identifier for the source file (alphanumeric).
* **IBSN:** International Standard Book Number of the source book (if available).
* **Subject:** Academic subject (e.g., Physics, Mathematics).
* **Topic:** Specific topic of the question within the subject (may be missing).
* **Questions:** Main body of the question or problem statement.
* **Figures:** List of associated image files related to the question (empty if no figures are present).
* **Label:** Original caption or description of each image in the `imgs` list.
* **Options:** Possible answer choices for the question, with keys (e.g., "A", "B", "C", "D") and corresponding text.
* **Answers:** Correct answer to the question, represented by the key of the correct option (e.g., "C").
---
## Data Instance Example
```json
{
"FileName": "FC064244",
"ISBN": "9786294703681",
"Subject": "Physics",
"Topic": "Measurement",
"Questions": "State the physical quantity that can be measured using the measuring device shown in Diagram 1.",
"Figures": [
{
"label": "Diagram 1",
"path": "FC064244_C1_Q12_ImageFile_0.png"
}
],
"Options": {
"A": "Weight",
"B": "Mass",
"C": "Amount of substance",
"D": "Volume"
},
"Answers": "B"
}
```
**Data Split**
The dataset is split between Physics and Mathematics subjects, with some questions lacking topic categorization.
| Subject | Instances with Topic | Instances without Topic | Total |
|-------------|----------------------|-------------------------|-------|
| Physics | 316 | 77 | 393 |
| Mathematics | 32 | 189 | 221 |
**Known Limitations**
* **Subject Coverage:** The current version focuses on Physics and Mathematics. Future releases will include more STEM subjects.
* **Answer Accuracy:** Answers are extracted from various sources and may contain inaccuracies.
**Source**
The dataset is derived from a combination of resources, including:
* SPM past-year exams
* SPM mock exams
* Educational exercise books
**Data Acquisition Method**
* Optical Character Recognition (OCR) for text extraction
* Manual quality control (QC) to ensure data accuracy
**Versioning and Maintenance**
* **Current Version:** 1.0.0
* **Release Date:** December 27, 2024
* **Contact:** We welcome any feedback or corrections to improve the dataset quality.
---
# License
This dataset is licensed under the [Creative Commons Attribution 4.0 International License (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/).
---
# Getting Started
You can access the dataset on Hugging Face using the following commands:
```bash
# For English data
pip install datasets
from datasets import load_dataset
dataset = load_dataset("Supa-AI/STEM-en-ms", name="data_en")
# For Malay data
dataset = load_dataset("Supa-AI/STEM-en-ms", name="data_ms")
```
---
# Bilingual STEM Dataset LLM Leaderboard
This document summarizes the evaluation results for various language models based on **5-shot** and **First Token Accuracy**. The evaluation was conducted across four configurations:
| **Model** | **en\_withfigures** | **en\_withoutfigures** | **ms\_withfigures** | **ms\_withoutfigures** |
| --------------------------------- | ------------------- | ---------------------- | ------------------- | ---------------------- |
| **gemini-2.0-flash-exp** | **63.70%** | <ins>75.16%</ins> | **63.36%** | <ins>75.47%</ins> |
| **gemini-1.5-flash** | 49.66% | 67.39% | 50.00% | 64.28% |
| **Qwen/Qwen2-VL-72B-Instruct** | <ins>58.22%</ins> | 69.25% | <ins>57.53%</ins> | 63.66% |
| **gpt-4o** | 47.95% | 66.15% | 50.00% | 68.01% |
| **gpt-4o-mini** | 41.10% | 55.90% | 38.36% | 52.80% |
| **pixtral-large-2411** | 42.81% | 64.29% | 35.27% | 60.87% |
| **pixtral-12b-2409** | 24.66% | 48.45% | 24.66% | 39.13% |
| **DeepSeek-V3** | None | **79.19%** | None | **76.40%** |
| **Qwen2.5-72B-Instruct** | None | 74.53% | None | 72.98% |
| **Meta-Llama-3.3-70B-Instruct** | None | 67.08% | None | 58.07% |
| **Llama-3.2-90B-Vision-Instruct** | None | 65.22% | None | 58.07% |
| **sail/Sailor2-20B-Chat** | None | 66.46% | None | 61.68% |
| **mallam-small** | None | 61.49% | None | 55.28% |
| **mistral-large-latest** | None | 60.56% | None | 53.42% |
| **google/gemma-2-27b-it** | None | 58.07% | None | 57.76% |
| **SeaLLMs-v3-7B-Chat** | None | 50.93% | None | 45.96% |
---
## Notes on eval.py
`eval.py` is a template for evaluating large language models (LLMs), update the script to integrate your _API calls_ or local model logic.
- The "First Token Accuracy" metric highlights initial token prediction accuracy.
- The evaluation results are based on the specific dataset and methodology employed.
- Further analysis might be needed to determine the models' suitability for specific tasks.
### Attribution for Evaluation Code
The `eval.py` script is based on work from the MMLU-Pro repository:
- Repository: [TIGER-AI-Lab/MMLU-Pro](https://github.com/TIGER-AI-Lab/MMLU-Pro)
- License: Apache License 2.0 (included in the `NOTICE` file)
---
# **Contributors**
- [**Gele**](https://huggingface.co/Geleliong)
- [**Ken Boon**](https://huggingface.co/caibcai)
- [**Wei Wen**](https://huggingface.co/WeiWen21)
|
Evan768/testEvan | Evan768 | 2024-12-27T12:18:25Z | 14 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-12-26T12:44:41Z | 0 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 3099451
num_examples: 6788
download_size: 1179658
dataset_size: 3099451
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Ttimofeyka/qa-assistant-llama3.2 | Ttimofeyka | 2025-04-15T13:04:20Z | 16 | 0 | [
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-04-15T13:03:48Z | 0 | ---
license: apache-2.0
---
|
villekuosmanen/pick_2_snackbars | villekuosmanen | 2025-05-18T17:20:04Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:text",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot"
] | [
"robotics"
] | 2025-05-18T11:58:45Z | 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": "arx5",
"total_episodes": 10,
"total_frames": 4759,
"total_tasks": 1,
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"fps": 20,
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"train": "0:10"
},
"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": [
14
]
},
"observation.state": {
"dtype": "float32",
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14
]
},
"observation.velocity": {
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14
]
},
"observation.effort": {
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14
]
},
"observation.eef_6d_pose": {
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"shape": [
12
]
},
"observation.images.front": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.fps": 20.0,
"video.height": 480,
"video.width": 640,
"video.channels": 3,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
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}
},
"observation.images.left_wrist": {
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480,
640,
3
],
"names": [
"height",
"width",
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],
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}
},
"observation.images.right_wrist": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.fps": 20.0,
"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": {
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},
"index": {
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},
"task_index": {
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1
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"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
FerronHooi/my-distiset-737c164c | FerronHooi | 2024-12-16T21:00:17Z | 9 | 0 | [
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"format:parquet",
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"library:datasets",
"library:pandas",
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"library:polars",
"library:distilabel",
"region:us",
"synthetic",
"distilabel",
"rlaif",
"datacraft"
] | [] | 2024-12-16T21:00:16Z | 0 | ---
size_categories: n<1K
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': home-appliance
'1': pc-accessory
'2': mobile-device
'3': gaming-console
'4': accessory
'5': wearable-technology
splits:
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num_bytes: 2581
num_examples: 10
download_size: 4101
dataset_size: 2581
configs:
- config_name: default
data_files:
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path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
- datacraft
---
<p align="left">
<a href="https://github.com/argilla-io/distilabel">
<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
</a>
</p>
# Dataset Card for my-distiset-737c164c
This dataset has been created with [distilabel](https://distilabel.argilla.io/).
## Dataset Summary
This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:
```console
distilabel pipeline run --config "https://huggingface.co/datasets/FerronHooi/my-distiset-737c164c/raw/main/pipeline.yaml"
```
or explore the configuration:
```console
distilabel pipeline info --config "https://huggingface.co/datasets/FerronHooi/my-distiset-737c164c/raw/main/pipeline.yaml"
```
## Dataset structure
The examples have the following structure per configuration:
<details><summary> Configuration: default </summary><hr>
```json
{
"label": 2,
"text": "The new wireless charging pad is a game-changer for my busy lifestyle. I can simply place my phone on it and it starts charging instantly. No more tangled cables or worrying about running out of battery."
}
```
This subset can be loaded as:
```python
from datasets import load_dataset
ds = load_dataset("FerronHooi/my-distiset-737c164c", "default")
```
Or simply as it follows, since there's only one configuration and is named `default`:
```python
from datasets import load_dataset
ds = load_dataset("FerronHooi/my-distiset-737c164c")
```
</details>
|
HungVu2003/opt-350m_beta_1.0_alpha_0.4_num-company_2_dataset_1_for_gen_12 | HungVu2003 | 2025-04-17T10:02:14Z | 17 | 0 | [
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---
|
klo1/seq_monkey | klo1 | 2025-01-27T13:37:03Z | 130 | 0 | [
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] | [] | 2025-01-27T12:45:25Z | 0 | ---
license: apache-2.0
---
|
emilbiju/Shortened-Plan-Execution-Data-Math | emilbiju | 2025-04-28T08:16:11Z | 20 | 0 | [
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path: math_eval/train-*
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path: math_eval/test-*
---
|
christinakopi/M2_preference_pairs_final | christinakopi | 2025-05-27T14:50:41Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-27T14:50:32Z | 0 | ---
dataset_info:
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---
|
hcurbelo/Kichwa_ES_001 | hcurbelo | 2025-03-04T17:37:40Z | 15 | 0 | [
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] | [] | 2025-03-04T17:35:53Z | 0 | ---
license: apache-2.0
---
|
math-extraction-comp/0-hero__Matter-0.2-7B-DPO | math-extraction-comp | 2025-01-25T21:10:53Z | 9 | 0 | [
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---
|
ytan-ucr/mu_llava_bruce_lee | ytan-ucr | 2025-01-23T17:02:03Z | 16 | 0 | [
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|
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|
stalaei/realmath_2025-2025-05 | stalaei | 2025-06-24T01:47:07Z | 0 | 0 | [
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|
SayantanJoker/tts-rj-hi-karya-44100hz-part-33-quality | SayantanJoker | 2025-03-30T20:28:08Z | 16 | 0 | [
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|
supergoose/flan_combined_task902_deceptive_opinion_spam_classification | supergoose | 2025-03-05T21:56:51Z | 17 | 0 | [
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-03-05T21:56:49Z | 0 | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: _template_idx
dtype: int64
- name: _task_source
dtype: string
- name: _task_name
dtype: string
- name: _template_type
dtype: string
splits:
- name: train
num_bytes: 8965760
num_examples: 4757
download_size: 4171503
dataset_size: 8965760
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
kowndinya23/flan2021-held-out-v11 | kowndinya23 | 2025-05-11T07:28:57Z | 1 | 0 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-11T07:28:49Z | 0 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: task_source
dtype: string
- name: task_name
dtype: string
- name: template_type
dtype: string
splits:
- name: train
num_bytes: 167613225.59223446
num_examples: 100000
- name: validation
num_bytes: 16761322.559223447
num_examples: 10000
download_size: 78640611
dataset_size: 184374548.1514579
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
sagarhv001/gamelore | sagarhv001 | 2025-04-10T05:15:19Z | 13 | 0 | [
"license:apache-2.0",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-04-10T05:13:35Z | 0 | ---
license: apache-2.0
---
|
zjrwtxtechstudio/o1data66 | zjrwtxtechstudio | 2024-12-27T11:05:47Z | 16 | 0 | [
"task_categories:question-answering",
"language:en",
"license:mit",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"camel",
"question-answering"
] | [
"question-answering"
] | 2024-12-27T11:05:45Z | 0 | ---
authors:
- zjrwtxtechstudio
description: Question-Answer dataset generated by CAMEL O1DataGene
language:
- en
license: mit
pretty_name: zjrwtxtechstudio/o1data66
size_categories: <1MB
tags:
- camel
- question-answering
task_categories:
- question-answering
version: 0.1.0
--- |
jokoum/record-image-test | jokoum | 2025-06-15T10:37:50Z | 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"
] | 2025-06-14T09:31:46Z | 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": 3600,
"total_tasks": 1,
"total_videos": 2,
"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
}
},
"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]
``` |
SatsukiVie/ECHO | SatsukiVie | 2025-05-12T09:43:41Z | 0 | 0 | [
"task_categories:question-answering",
"license:cc-by-nc-4.0",
"size_categories:10M<n<100M",
"region:us"
] | [
"question-answering"
] | 2025-05-12T09:42:44Z | 0 | ---
license: cc-by-nc-4.0
task_categories:
- question-answering
size_categories:
- 10M<n<100M
--- |
uonyeka/uplimit-model-pooling | uonyeka | 2025-03-17T12:03:19Z | 8 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"library:distilabel",
"region:us",
"synthetic",
"distilabel",
"rlaif"
] | [] | 2025-03-17T12:03:09Z | 0 | ---
size_categories: n<1K
dataset_info:
features:
- name: instruction
dtype: string
- name: grouped_generation
sequence: string
- name: model_name
dtype: string
- name: distilabel_metadata
struct:
- name: raw_input_text_generation_0
list:
- name: content
dtype: string
- name: role
dtype: string
- name: raw_input_text_generation_1
list:
- name: content
dtype: string
- name: role
dtype: string
- name: raw_output_text_generation_0
dtype: string
- name: raw_output_text_generation_1
dtype: string
- name: statistics_text_generation_0
struct:
- name: input_tokens
dtype: int64
- name: output_tokens
dtype: int64
- name: statistics_text_generation_1
struct:
- name: input_tokens
dtype: int64
- name: output_tokens
dtype: int64
splits:
- name: train
num_bytes: 8959
num_examples: 3
download_size: 22160
dataset_size: 8959
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
---
<p align="left">
<a href="https://github.com/argilla-io/distilabel">
<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
</a>
</p>
# Dataset Card for uplimit-model-pooling
This dataset has been created with [distilabel](https://distilabel.argilla.io/).
## Dataset Summary
This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:
```console
distilabel pipeline run --config "https://huggingface.co/datasets/uonyeka/uplimit-model-pooling/raw/main/pipeline.yaml"
```
or explore the configuration:
```console
distilabel pipeline info --config "https://huggingface.co/datasets/uonyeka/uplimit-model-pooling/raw/main/pipeline.yaml"
```
## Dataset structure
The examples have the following structure per configuration:
<details><summary> Configuration: default </summary><hr>
```json
{
"distilabel_metadata": {
"raw_input_text_generation_0": [
{
"content": "Explain the concept of reinforcement learning.",
"role": "user"
}
],
"raw_input_text_generation_1": [
{
"content": "Explain the concept of reinforcement learning.",
"role": "user"
}
],
"raw_output_text_generation_0": "Sure, I\u0027d be happy to explain!\n\nReinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions in its environment and receiving rewards or penalties for those actions. The goal of the agent is to learn a policy that maximizes the cumulative reward over time. \n\nHere\u0027s how it works:\n\n1. **Environment**: The environment is the world in which the agent operates. It can be anything from a game board to a real-world robot navigating through a room.\n\n2. **Agent**: The agent is the entity that interacts with the environment. It takes",
"raw_output_text_generation_1": "Reinforcement Learning (RL) is an area of machine learning that focuses on how software agents should take actions in an environment to maximize some notion of cumulative reward. The goal of RL is to learn policies - algorithms that map states to actions - through trial and error interactions with the environment.\n\nIn reinforcement learning, an agent interacts with its environment over time, receiving feedback in the form of rewards or penalties for each action it takes. The objective is to find a policy that maximizes the total expected cumulative reward over time. This process involves several key concepts:\n\n1. **State**: The current situation or context of the agent.\n2. **",
"statistics_text_generation_0": {
"input_tokens": 7,
"output_tokens": 128
},
"statistics_text_generation_1": {
"input_tokens": 8,
"output_tokens": 128
}
},
"grouped_generation": [
"Sure, I\u0027d be happy to explain!\n\nReinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions in its environment and receiving rewards or penalties for those actions. The goal of the agent is to learn a policy that maximizes the cumulative reward over time. \n\nHere\u0027s how it works:\n\n1. **Environment**: The environment is the world in which the agent operates. It can be anything from a game board to a real-world robot navigating through a room.\n\n2. **Agent**: The agent is the entity that interacts with the environment. It takes",
"Reinforcement Learning (RL) is an area of machine learning that focuses on how software agents should take actions in an environment to maximize some notion of cumulative reward. The goal of RL is to learn policies - algorithms that map states to actions - through trial and error interactions with the environment.\n\nIn reinforcement learning, an agent interacts with its environment over time, receiving feedback in the form of rewards or penalties for each action it takes. The objective is to find a policy that maximizes the total expected cumulative reward over time. This process involves several key concepts:\n\n1. **State**: The current situation or context of the agent.\n2. **"
],
"instruction": "Explain the concept of reinforcement learning.",
"model_name": "Qwen/Qwen2.5-1.5B-Instruct"
}
```
This subset can be loaded as:
```python
from datasets import load_dataset
ds = load_dataset("uonyeka/uplimit-model-pooling", "default")
```
Or simply as it follows, since there's only one configuration and is named `default`:
```python
from datasets import load_dataset
ds = load_dataset("uonyeka/uplimit-model-pooling")
```
</details>
|
MatthewsFace/diagnosticar-es_train | MatthewsFace | 2025-02-24T17:15:07Z | 56 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-02-18T17:23:36Z | 0 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 54751
num_examples: 255
download_size: 23425
dataset_size: 54751
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
dmeldrum6/c-sharp-linq-dataset | dmeldrum6 | 2024-12-21T13:11:46Z | 15 | 0 | [
"language:en",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"library:distilabel",
"region:us",
"synthetic",
"distilabel",
"rlaif",
"datacraft"
] | [] | 2024-12-19T21:08:21Z | 0 | ---
language:
- en
size_categories: n<1K
dataset_info:
features:
- name: text
dtype: string
- name: labels
sequence:
class_label:
names:
'0': useful
'1': not-useful
splits:
- name: train
num_bytes: 28874
num_examples: 100
download_size: 14087
dataset_size: 28874
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
- datacraft
---
<p align="left">
<a href="https://github.com/argilla-io/distilabel">
<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
</a>
</p>
# Dataset Card for c-sharp-linq-dataset
This dataset has been created with [distilabel](https://distilabel.argilla.io/).
## Dataset Summary
This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:
```console
distilabel pipeline run --config "https://huggingface.co/datasets/dmeldrum6/c-sharp-linq-dataset/raw/main/pipeline.yaml"
```
or explore the configuration:
```console
distilabel pipeline info --config "https://huggingface.co/datasets/dmeldrum6/c-sharp-linq-dataset/raw/main/pipeline.yaml"
```
## Dataset structure
The examples have the following structure per configuration:
<details><summary> Configuration: default </summary><hr>
```json
{
"labels": [
0,
1
],
"text": "The following C# LINQ query utilizes the GroupBy method to aggregate a list of employee records by department, then projects the result into a new anonymous type containing the department name and the average salary of employees within that department."
}
```
This subset can be loaded as:
```python
from datasets import load_dataset
ds = load_dataset("dmeldrum6/c-sharp-linq-dataset", "default")
```
Or simply as it follows, since there's only one configuration and is named `default`:
```python
from datasets import load_dataset
ds = load_dataset("dmeldrum6/c-sharp-linq-dataset")
```
</details>
|
LucasChen/VAD-extracted-features | LucasChen | 2025-06-19T03:05:19Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | [] | 2025-06-19T03:05:19Z | 0 | ---
license: apache-2.0
---
|
babs/OpenSLR-Nigerian-Accented-English | babs | 2024-10-16T20:23:37Z | 5 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-10-16T20:20:19Z | 0 | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: text
dtype: string
splits:
- name: train
num_bytes: 2138528084.6
num_examples: 3358
download_size: 1509463898
dataset_size: 2138528084.6
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
About this resource:
This data set contains transcribed high-quality audio of Nigerian English sentences recorded by volunteers, in Lagos Nigerian and in London. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file.
The data set has been manually quality checked, but there might still be errors.
Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues
See LICENSE file for license information.
Copyright 2018, 2019 Google, Inc.
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
fady21/fady-llama2-1k | fady21 | 2025-04-23T16:35:36Z | 23 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-04-23T16:35:30Z | 0 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: Question
dtype: string
- name: Context
dtype: string
- name: Topic
dtype: string
- name: Answer
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 1485574
num_examples: 1000
download_size: 673273
dataset_size: 1485574
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
YanNeu/RePOPE | YanNeu | 2025-06-06T14:59:37Z | 0 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-06T14:59:35Z | 0 | ---
dataset_info:
features:
- name: question_id
dtype: int64
- name: category
dtype: string
- name: id
dtype: int64
- name: question
dtype: string
- name: answer
dtype: string
- name: image_source
dtype: string
splits:
- name: train
num_bytes: 809281
num_examples: 8185
download_size: 133693
dataset_size: 809281
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
introvoyz041/wills_trusts_and_estates | introvoyz041 | 2025-04-11T10:18:50Z | 16 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-04-11T10:18:49Z | 0 | ---
dataset_info:
features:
- name: text
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 1714844
num_examples: 577
download_size: 779238
dataset_size: 1714844
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Exmind2/Exmind2 | Exmind2 | 2025-02-20T23:17:21Z | 47 | 0 | [
"license:apache-2.0",
"region:us"
] | [] | 2025-02-20T23:17:21Z | 0 | ---
license: apache-2.0
---
|
brandonyang/square_d1 | brandonyang | 2025-05-01T04:17:52Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:image",
"modality:timeseries",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot"
] | [
"robotics"
] | 2025-05-01T04:16:50Z | 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": 1000,
"total_frames": 152400,
"total_tasks": 1,
"total_videos": 0,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 10,
"splits": {
"train": "0:1000"
},
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```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
SanskritVoyager/Sanskrit-Literature-Raw | SanskritVoyager | 2025-06-16T12:55:10Z | 0 | 0 | [
"license:cc-by-nc-4.0",
"region:us"
] | [] | 2025-06-16T06:27:45Z | 0 | ---
license: cc-by-nc-4.0
---
|
relai-ai/angular-reasoning | relai-ai | 2025-04-21T16:07:52Z | 20 | 0 | [
"task_categories:question-answering",
"language:en",
"license:cc-by-4.0",
"size_categories:1K<n<10K",
"format:csv",
"modality:text",
"library:datasets",
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"library:mlcroissant",
"library:polars",
"region:us"
] | [
"question-answering"
] | 2025-04-21T16:06:32Z | 0 | ---
license: cc-by-4.0
task_categories:
- question-answering
language:
- en
---
Samples in this benchmark were generated by RELAI using the following data source(s):
Data Source Name: Angular 17 Documentation
Data Source Link: https://v17.angular.io/docs
Data Source License: https://github.com/angular/angular/blob/main/LICENSE
Data Source Authors: Google
AI Benchmarks by Data Agents © 2025 RELAI.AI · Licensed under CC BY 4.0. Source: https://relai.ai |
PGLearn/PGLearn-Medium-2869_pegase | PGLearn | 2025-05-04T01:03:29Z | 0 | 0 | [
"task_categories:tabular-regression",
"license:cc-by-sa-4.0",
"region:us",
"energy",
"optimization",
"optimal_power_flow",
"power_grid"
] | [
"tabular-regression"
] | 2025-05-03T20:45:57Z | 0 | ---
license: cc-by-sa-4.0
tags:
- energy
- optimization
- optimal_power_flow
- power_grid
pretty_name: PGLearn Optimal Power Flow (2869_pegase)
task_categories:
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|
GitBag/block-q-sharp_ds-distilled-qwen-1.5b-ppo-kl-1e-4-ec-0.001-good-1_hmmt-feb-24_eval_new_256 | GitBag | 2025-05-12T13:13:22Z | 0 | 0 | [
"size_categories:1K<n<10K",
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-12T13:13:19Z | 0 | ---
dataset_info:
features:
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---
|
SayantanJoker/tts-rj-hi-karya-44100hz-part-33-quality-metadata | SayantanJoker | 2025-03-30T20:28:45Z | 17 | 0 | [
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] | [] | 2025-03-30T20:28:38Z | 0 | ---
dataset_info:
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---
|
chaiamy/reddit_dataset_197 | chaiamy | 2025-03-25T13:26:44Z | 98 | 0 | [
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:question-answering",
"task_categories:summarization",
"task_categories:text-generation",
"task_ids:sentiment-analysis",
"task_ids:topic-classification",
"task_ids:named-entity-recognition",
"task_ids:language-modeling",
"task_ids:text-scoring",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
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] | [
"text-classification",
"token-classification",
"question-answering",
"summarization",
"text-generation"
] | 2025-02-10T19:44:03Z | 0 | ---
license: mit
multilinguality:
- multilingual
source_datasets:
- original
task_categories:
- text-classification
- token-classification
- question-answering
- summarization
- text-generation
task_ids:
- sentiment-analysis
- topic-classification
- named-entity-recognition
- language-modeling
- text-scoring
- multi-class-classification
- multi-label-classification
- extractive-qa
- news-articles-summarization
---
# Bittensor Subnet 13 Reddit Dataset
<center>
<img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/bittensor.png" alt="Data-universe: The finest collection of social media data the web has to offer">
</center>
<center>
<img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/macrocosmos-black.png" alt="Data-universe: The finest collection of social media data the web has to offer">
</center>
## Dataset Description
- **Repository:** chaiamy/reddit_dataset_197
- **Subnet:** Bittensor Subnet 13
- **Miner Hotkey:** 5HSmU8zVqHRhVskecoyh2JWPD5erGuDRT6B6TBNtUwqJyopG
### Dataset Summary
This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed Reddit data. The data is continuously updated by network miners, providing a real-time stream of Reddit content for various analytical and machine learning tasks.
For more information about the dataset, please visit the [official repository](https://github.com/macrocosm-os/data-universe).
### Supported Tasks
The versatility of this dataset allows researchers and data scientists to explore various aspects of social media dynamics and develop innovative applications. Users are encouraged to leverage this data creatively for their specific research or business needs.
For example:
- Sentiment Analysis
- Topic Modeling
- Community Analysis
- Content Categorization
### Languages
Primary language: Datasets are mostly English, but can be multilingual due to decentralized ways of creation.
## Dataset Structure
### Data Instances
Each instance represents a single Reddit post or comment with the following fields:
### Data Fields
- `text` (string): The main content of the Reddit post or comment.
- `label` (string): Sentiment or topic category of the content.
- `dataType` (string): Indicates whether the entry is a post or a comment.
- `communityName` (string): The name of the subreddit where the content was posted.
- `datetime` (string): The date when the content was posted or commented.
- `username_encoded` (string): An encoded version of the username to maintain user privacy.
- `url_encoded` (string): An encoded version of any URLs included in the content.
### Data Splits
This dataset is continuously updated and does not have fixed splits. Users should create their own splits based on their requirements and the data's timestamp.
## Dataset Creation
### Source Data
Data is collected from public posts and comments on Reddit, adhering to the platform's terms of service and API usage guidelines.
### Personal and Sensitive Information
All usernames and URLs are encoded to protect user privacy. The dataset does not intentionally include personal or sensitive information.
## Considerations for Using the Data
### Social Impact and Biases
Users should be aware of potential biases inherent in Reddit data, including demographic and content biases. This dataset reflects the content and opinions expressed on Reddit and should not be considered a representative sample of the general population.
### Limitations
- Data quality may vary due to the nature of media sources.
- The dataset may contain noise, spam, or irrelevant content typical of social media platforms.
- Temporal biases may exist due to real-time collection methods.
- The dataset is limited to public subreddits and does not include private or restricted communities.
## Additional Information
### Licensing Information
The dataset is released under the MIT license. The use of this dataset is also subject to Reddit Terms of Use.
### Citation Information
If you use this dataset in your research, please cite it as follows:
```
@misc{chaiamy2025datauniversereddit_dataset_197,
title={The Data Universe Datasets: The finest collection of social media data the web has to offer},
author={chaiamy},
year={2025},
url={https://huggingface.co/datasets/chaiamy/reddit_dataset_197},
}
```
### Contributions
To report issues or contribute to the dataset, please contact the miner or use the Bittensor Subnet 13 governance mechanisms.
## Dataset Statistics
[This section is automatically updated]
- **Total Instances:** 1043896
- **Date Range:** 2019-07-24T00:00:00Z to 2025-03-25T00:00:00Z
- **Last Updated:** 2025-03-25T13:26:44Z
### Data Distribution
- Posts: 2.43%
- Comments: 97.57%
### Top 10 Subreddits
For full statistics, please refer to the `stats.json` file in the repository.
| Rank | Topic | Total Count | Percentage |
|------|-------|-------------|-------------|
| 1 | r/wallstreetbets | 298843 | 28.63% |
| 2 | r/Bitcoin | 188943 | 18.10% |
| 3 | r/CryptoCurrency | 173168 | 16.59% |
| 4 | r/solana | 87910 | 8.42% |
| 5 | r/CryptoMarkets | 77526 | 7.43% |
| 6 | r/ethtrader | 71839 | 6.88% |
| 7 | r/leagueoflegends | 42181 | 4.04% |
| 8 | r/balatro | 32235 | 3.09% |
| 9 | r/pokemon | 27081 | 2.59% |
| 10 | r/btc | 18807 | 1.80% |
## Update History
| Date | New Instances | Total Instances |
|------|---------------|-----------------|
| 2025-02-10T19:45:26Z | 772395 | 772395 |
| 2025-02-14T07:08:28Z | 47300 | 819695 |
| 2025-02-17T19:08:39Z | 43473 | 863168 |
| 2025-02-27T16:36:36Z | 41093 | 904261 |
| 2025-03-13T14:38:00Z | 1431 | 905692 |
| 2025-03-14T08:38:07Z | 9781 | 915473 |
| 2025-03-15T02:38:15Z | 11402 | 926875 |
| 2025-03-15T20:38:23Z | 8293 | 935168 |
| 2025-03-16T14:38:30Z | 7677 | 942845 |
| 2025-03-17T08:38:36Z | 9781 | 952626 |
| 2025-03-18T02:26:22Z | 9838 | 962464 |
| 2025-03-18T20:26:28Z | 9052 | 971516 |
| 2025-03-19T14:26:35Z | 9232 | 980748 |
| 2025-03-19T15:56:41Z | 980 | 981728 |
| 2025-03-19T17:26:46Z | 893 | 982621 |
| 2025-03-19T18:56:51Z | 969 | 983590 |
| 2025-03-19T20:26:55Z | 997 | 984587 |
| 2025-03-19T21:57:01Z | 966 | 985553 |
| 2025-03-19T23:27:07Z | 605 | 986158 |
| 2025-03-20T00:57:14Z | 654 | 986812 |
| 2025-03-20T02:27:21Z | 851 | 987663 |
| 2025-03-20T03:57:32Z | 168 | 987831 |
| 2025-03-20T05:27:46Z | 600 | 988431 |
| 2025-03-20T06:57:51Z | 450 | 988881 |
| 2025-03-20T08:27:58Z | 493 | 989374 |
| 2025-03-20T09:58:02Z | 484 | 989858 |
| 2025-03-20T11:28:07Z | 693 | 990551 |
| 2025-03-20T12:58:17Z | 641 | 991192 |
| 2025-03-20T14:28:35Z | 657 | 991849 |
| 2025-03-20T15:58:41Z | 872 | 992721 |
| 2025-03-20T17:29:00Z | 954 | 993675 |
| 2025-03-20T18:59:07Z | 693 | 994368 |
| 2025-03-20T20:29:15Z | 704 | 995072 |
| 2025-03-20T21:59:36Z | 752 | 995824 |
| 2025-03-20T23:29:45Z | 773 | 996597 |
| 2025-03-21T00:59:52Z | 869 | 997466 |
| 2025-03-21T02:29:56Z | 518 | 997984 |
| 2025-03-21T04:00:01Z | 620 | 998604 |
| 2025-03-21T05:30:06Z | 427 | 999031 |
| 2025-03-21T07:00:11Z | 468 | 999499 |
| 2025-03-21T08:30:17Z | 267 | 999766 |
| 2025-03-21T10:00:26Z | 177 | 999943 |
| 2025-03-21T11:30:43Z | 672 | 1000615 |
| 2025-03-21T13:00:48Z | 750 | 1001365 |
| 2025-03-21T14:30:58Z | 454 | 1001819 |
| 2025-03-21T16:01:11Z | 590 | 1002409 |
| 2025-03-21T19:25:32Z | 203 | 1002612 |
| 2025-03-22T13:26:15Z | 6930 | 1009542 |
| 2025-03-23T07:26:23Z | 8551 | 1018093 |
| 2025-03-24T01:26:31Z | 9212 | 1027305 |
| 2025-03-24T19:26:38Z | 8629 | 1035934 |
| 2025-03-25T13:26:44Z | 7962 | 1043896 |
|
jkcho/QA-Dataset-mini | jkcho | 2025-03-23T10:59:06Z | 16 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-03-23T10:28:14Z | 0 | ---
dataset_info:
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splits:
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configs:
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---
|
test-gen/code_mbpp_qwen2.5-3b_t0.1_n8_tests_mbpp_qwen3-8b_t0.6_n1_think | test-gen | 2025-05-16T16:11:30Z | 0 | 0 | [
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---
|
korbih/ui-sensei-iteration-0-20250408_114450-complete | korbih | 2025-04-08T09:54:24Z | 15 | 0 | [
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---
|
Self-GRIT/open-hermes-2.5-sft-active-retrieval-sample-300k-retrieval-llama3-infer-query-ref | Self-GRIT | 2024-10-04T02:20:28Z | 19 | 0 | [
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dataset_info:
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---
|
deepcopy/text_recognition_en_zh_small_250k | deepcopy | 2025-06-20T12:26:50Z | 0 | 0 | [
"region:us"
] | [] | 2025-06-20T12:23:19Z | 0 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
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num_bytes: 1954585583.0
num_examples: 250000
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num_examples: 2894
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num_examples: 2910
download_size: 2799453221
dataset_size: 2018248341.4060001
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: val
path: data/val-*
---
# Dataset Card for "text_recognition_en_zh_small_250k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_0e54ffda-419c-4cac-b017-82089ffb0db7 | argilla-internal-testing | 2025-01-21T08:38:16Z | 17 | 0 | [
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---
|
HungVu2003/opt-350m_beta_0.5_alpha_0.2_num-company_3_dataset_1_for_gen_4 | HungVu2003 | 2025-04-29T17:55:18Z | 20 | 0 | [
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---
|
zekeZZ/medmcqa-gen-by-zephyr-ft-gpqa-all | zekeZZ | 2025-01-14T02:00:47Z | 14 | 0 | [
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---
|
reasoning-proj/j_c_dfiltered_DeepSeek-R1-Distill-Llama-8B_madversarial_continue_with_wrong_reasoning_t50 | reasoning-proj | 2025-05-11T10:08:41Z | 0 | 0 | [
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] | [] | 2025-05-11T10:08:35Z | 0 | ---
dataset_info:
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---
|
PRINCEEMMANUEL/AMAZON_FFR_TVT | PRINCEEMMANUEL | 2025-03-24T16:36:02Z | 16 | 0 | [
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"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-03-17T18:16:28Z | 0 | ---
dataset_info:
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- name: Summary_CL
dtype: string
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|
Samll/math_early_answering_data_v2 | Samll | 2025-01-20T11:08:51Z | 16 | 0 | [
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|
AmarHelio/record-test19 | AmarHelio | 2025-06-15T05:48:10Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
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"library:polars",
"region:us",
"LeRobot"
] | [
"robotics"
] | 2025-06-15T05:47:19Z | 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",
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"action": {
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]
},
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6
],
"names": [
"shoulder_pan.pos",
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},
"observation.images.front": {
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"shape": [
480,
640,
3
],
"names": [
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"width",
"channels"
],
"info": {
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"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": {
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"shape": [
1
],
"names": null
},
"episode_index": {
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"shape": [
1
],
"names": null
},
"index": {
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1
],
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},
"task_index": {
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"shape": [
1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
infinite-dataset-hub/OvarianUltrasoundFeatureExtraction | infinite-dataset-hub | 2024-11-16T10:38:43Z | 14 | 1 | [
"license:mit",
"size_categories:n<1K",
"format:csv",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"infinite-dataset-hub",
"synthetic"
] | [] | 2024-11-16T10:38:42Z | 0 | ---
license: mit
tags:
- infinite-dataset-hub
- synthetic
---
# OvarianUltrasoundFeatureExtraction
tags: Regression, Feature Learning, Gynecological Imaging
_Note: This is an AI-generated dataset so its content may be inaccurate or false_
**Dataset Description:**
The 'OvarianUltrasoundFeatureExtraction' dataset comprises high-resolution ultrasound images of the ovaries from various gynecological imaging sources. Each image is annotated with key features and corresponding labels, such as follicular cysts, corpus luteum, and endometriomas. This dataset serves as a basis for regression and feature learning models aimed at assisting radiologists and machine learning practitioners in the diagnosis and treatment planning for ovarian pathologies. The annotations are performed by expert radiologists and are used for supervised learning tasks in gynecological healthcare.
**CSV Content Preview:**
```
ImageID,ImageURL,Labels,Feature1,Feature2,Feature3
Ultrasound001,http://example.com/images/ultrasound001.jpg,["FollicularCyst"],3.2,14.8,0.5
Ultrasound002,http://example.com/images/ultrasound002.jpg,["CorpusLuteum"],5.1,12.3,0.2
Ultrasound003,http://example.com/images/ultrasound003.jpg,["Endometrioma"],4.6,13.5,0.8
Ultrasound004,http://example.com/images/ultrasound004.jpg,["Normal"],2.9,10.0,0.1
Ultrasound005,http://example.com/images/ultrasound005.jpg,["PolycysticOvary"],6.2,15.2,0.3
```
In this dataset, the 'ImageURL' column links to the ultrasound images, the 'Labels' column contains the list of identified features, and the 'Feature1', 'Feature2', and 'Feature3' columns represent extracted features from the images that could be used for machine learning tasks. The feature values are indicative of certain characteristics of the ultrasound images, such as the size and number of cysts, which are important for classification and regression tasks in ovarian ultrasound analysis.
**Source of the data:**
The dataset was generated using the [Infinite Dataset Hub](https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub) and microsoft/Phi-3-mini-4k-instruct using the query 'ultrasound':
- **Dataset Generation Page**: https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub?q=ultrasound&dataset=OvarianUltrasoundFeatureExtraction&tags=Regression,+Feature+Learning,+Gynecological+Imaging
- **Model**: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct
- **More Datasets**: https://huggingface.co/datasets?other=infinite-dataset-hub
|
omercann/twinup | omercann | 2024-12-30T14:20:55Z | 15 | 0 | [
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"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-12-30T14:20:27Z | 0 | ---
dataset_info:
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dtype: string
- name: input
dtype: string
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dtype: string
splits:
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num_examples: 7
- name: test
num_bytes: 484
num_examples: 1
download_size: 11210
dataset_size: 3726.75
configs:
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data_files:
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path: data/train-*
- split: test
path: data/test-*
---
|
obiwan96/obiwan96owm_raw_v3__100000_120000 | obiwan96 | 2025-02-26T19:39:10Z | 14 | 0 | [
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] | [] | 2025-02-26T15:16:21Z | 0 | ---
dataset_info:
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---
|
ashraq/fashion-product-images-small | ashraq | 2022-11-01T20:25:52Z | 1,194 | 31 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2022-11-01T20:22:50Z | 1 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: gender
dtype: string
- name: masterCategory
dtype: string
- name: subCategory
dtype: string
- name: articleType
dtype: string
- name: baseColour
dtype: string
- name: season
dtype: string
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dtype: float64
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dtype: string
- name: productDisplayName
dtype: string
- name: image
dtype: image
splits:
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num_bytes: 546202015.44
num_examples: 44072
download_size: 271496441
dataset_size: 546202015.44
---
# Dataset Card for "fashion-product-images-small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Data was obtained from [here](https://www.kaggle.com/datasets/paramaggarwal/fashion-product-images-small) |
FlippyDora/numia_prompt_reward_iter1_0-2000 | FlippyDora | 2025-03-11T16:23:34Z | 16 | 0 | [
"size_categories:1K<n<10K",
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-03-11T16:23:33Z | 0 | ---
dataset_info:
features:
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dtype: string
- name: responses
sequence: string
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dtype: string
- name: problem
dtype: string
- name: rewards
sequence: float64
splits:
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num_examples: 2000
download_size: 10490498
dataset_size: 28393319
configs:
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data_files:
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path: data/train-*
---
|
hhhFuture/class_stand | hhhFuture | 2024-12-04T06:50:17Z | 15 | 0 | [
"license:apache-2.0",
"region:us"
] | [] | 2024-12-04T06:50:17Z | 0 | ---
license: apache-2.0
---
|
facebook/feint6k | facebook | 2024-10-09T21:07:02Z | 16 | 0 | [
"license:cc-by-nc-4.0",
"region:us"
] | [] | 2024-10-09T21:07:02Z | 0 | ---
license: cc-by-nc-4.0
---
|
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