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gambitzpro/gambutz | gambitzpro | 2025-04-01T10:49:15Z | 61 | 0 | [
"license:bigscience-bloom-rail-1.0",
"region:us"
] | [] | 2025-04-01T10:49:15Z | 0 | ---
license: bigscience-bloom-rail-1.0
---
|
psg777/blacktape_filtered1 | psg777 | 2025-06-17T15:09:33Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot"
] | [
"robotics"
] | 2025-06-17T15:09:13Z | 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": 58,
"total_frames": 39331,
"total_tasks": 1,
"total_videos": 116,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
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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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"names": [
"shoulder_pan.pos",
"shoulder_lift.pos",
"elbow_flex.pos",
"wrist_flex.pos",
"wrist_roll.pos",
"gripper.pos"
]
},
"observation.state": {
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"wrist_roll.pos",
"gripper.pos"
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480,
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3
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}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
shaznin/task4_fix_generation_less | shaznin | 2025-03-11T21:46:37Z | 15 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-03-11T21:40:09Z | 0 | ---
dataset_info:
features:
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dtype: string
- name: input
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- name: output
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dtype: int64
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download_size: 7778864
dataset_size: 17350930
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
MisterMango23/m18_Hellcat_tank_dataset | MisterMango23 | 2025-05-13T17:21:57Z | 0 | 0 | [
"license:artistic-2.0",
"size_categories:n<1K",
"format:text",
"modality:image",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | [] | 2025-05-13T17:21:32Z | 0 | ---
license: artistic-2.0
---
|
AI4Protein/DeepET_Topt | AI4Protein | 2025-04-12T09:53:12Z | 146 | 0 | [
"task_categories:text-classification",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2503.15438",
"region:us",
"protein",
"downstream task"
] | [
"text-classification"
] | 2025-01-30T09:08:58Z | 0 | ---
license: apache-2.0
task_categories:
- text-classification
tags:
- protein
- downstream task
---
# DeepET_Topt Dataset
- Description: protein optimum temperature.
- Number of labels: 1
- Problem Type: regression
- Columns:
- aa_seq: protein amino acid sequence
# Github
VenusFactory: A Unified Platform for Protein Engineering Data Retrieval and Language Model Fine-Tuning
https://github.com/ai4protein/VenusFactory
# Citation
Please cite our work if you use our dataset.
```
@article{tan2025venusfactory,
title={VenusFactory: A Unified Platform for Protein Engineering Data Retrieval and Language Model Fine-Tuning},
author={Tan, Yang and Liu, Chen and Gao, Jingyuan and Wu, Banghao and Li, Mingchen and Wang, Ruilin and Zhang, Lingrong and Yu, Huiqun and Fan, Guisheng and Hong, Liang and Zhou, Bingxin},
journal={arXiv preprint arXiv:2503.15438},
year={2025}
}
``` |
charleyong/so100_y_cube | charleyong | 2025-05-05T02:09:24Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"region:us",
"LeRobot",
"so100",
"yc_demo"
] | [
"robotics"
] | 2025-05-05T02:08:36Z | 0 | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so100
- yc_demo
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.1",
"robot_type": "so100",
"total_episodes": 50,
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"total_chunks": 1,
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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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],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.state": {
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"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
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"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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"task_index": {
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],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
DETAIM/parque-recreacional-datos | DETAIM | 2025-01-31T21:10:22Z | 37 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-01-31T21:10:16Z | 0 | ---
dataset_info:
features:
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dtype: string
splits:
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num_bytes: 6527
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- name: tarifas
num_bytes: 645
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- name: contacto
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num_examples: 4
- name: reservas
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download_size: 8118
dataset_size: 7791
configs:
- config_name: default
data_files:
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path: data/preguntas-*
- split: tarifas
path: data/tarifas-*
- split: contacto
path: data/contacto-*
- split: reservas
path: data/reservas-*
---
|
LogicNet-Subnet/Aristole | LogicNet-Subnet | 2025-01-18T17:03:42Z | 19 | 1 | [
"task_categories:question-answering",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
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"library:mlcroissant",
"library:polars",
"region:us"
] | [
"question-answering"
] | 2025-01-18T16:50:32Z | 0 | ---
dataset_info:
features:
- name: Instruction
dtype: string
- name: Input
dtype: string
- name: Response
dtype: string
splits:
- name: train
num_bytes: 634349269
num_examples: 303496
download_size: 149717506
dataset_size: 634349269
configs:
- config_name: default
data_files:
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path: data/train-*
license: apache-2.0
task_categories:
- question-answering
---
# Dataset Name
This dataset contains instructions, inputs, and responses formatted for training language models. It is designed to help models understand and generate responses based on given instructions and inputs.
## Dataset Structure
The dataset is structured with the following features:
- **Instruction**: A string containing the task description or question.
- **Input**: A string providing additional context or options.
- **Response**: A string with the expected answer or completion. |
abdulmannan-01/rag_combined_dataset_orca_and_openscholar_messages_format | abdulmannan-01 | 2025-01-09T14:02:12Z | 17 | 0 | [
"size_categories:100K<n<1M",
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] | [] | 2025-01-09T14:00:10Z | 0 | ---
dataset_info:
features:
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list:
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dtype: string
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dtype: string
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path: data/train-*
---
|
artao/x_dataset_158 | artao | 2024-12-03T01:01:55Z | 16 | 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",
"task_ids:extractive-qa",
"task_ids:news-articles-summarization",
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"format:parquet",
"modality:text",
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] | [
"text-classification",
"token-classification",
"question-answering",
"summarization",
"text-generation"
] | 2024-12-03T01:01:52Z | 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 X (Twitter) 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:** artao/x_dataset_158
- **Subnet:** Bittensor Subnet 13
- **Miner Hotkey:** 5G75HCGsuHpPdCfsKgPszqzMqV5cf2KyLmUcifb39g954AXk
### Dataset Summary
This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed data from X (formerly Twitter). The data is continuously updated by network miners, providing a real-time stream of tweets 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
- Trend Detection
- Content Analysis
- User Behavior Modeling
### 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 tweet with the following fields:
### Data Fields
- `text` (string): The main content of the tweet.
- `label` (string): Sentiment or topic category of the tweet.
- `tweet_hashtags` (list): A list of hashtags used in the tweet. May be empty if no hashtags are present.
- `datetime` (string): The date when the tweet was posted.
- `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 tweet. May be empty if no URLs are present.
### 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 tweets on X (Twitter), 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 X (Twitter) data, including demographic and content biases. This dataset reflects the content and opinions expressed on X and should not be considered a representative sample of the general population.
### Limitations
- Data quality may vary due to the decentralized nature of collection and preprocessing.
- 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 tweets and does not include private accounts or direct messages.
- Not all tweets contain hashtags or URLs.
## Additional Information
### Licensing Information
The dataset is released under the MIT license. The use of this dataset is also subject to X Terms of Use.
### Citation Information
If you use this dataset in your research, please cite it as follows:
```
@misc{artao2024datauniversex_dataset_158,
title={The Data Universe Datasets: The finest collection of social media data the web has to offer},
author={artao},
year={2024},
url={https://huggingface.co/datasets/artao/x_dataset_158},
}
```
### 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:** 5089
- **Date Range:** 2017-10-07T00:00:00Z to 2024-12-03T00:00:00Z
- **Last Updated:** 2024-12-03T01:01:54Z
### Data Distribution
- Tweets with hashtags: 9.61%
- Tweets without hashtags: 90.39%
### Top 10 Hashtags
For full statistics, please refer to the `stats.json` file in the repository.
| Rank | Topic | Total Count | Percentage |
|------|-------|-------------|-------------|
| 1 | #bitcoin | 123 | 25.15% |
| 2 | #btc | 79 | 16.16% |
| 3 | #crypto | 20 | 4.09% |
| 4 | #dogecoin | 16 | 3.27% |
| 5 | #blockchain | 12 | 2.45% |
| 6 | #thdtjsdn | 7 | 1.43% |
| 7 | #xrp | 7 | 1.43% |
| 8 | #entrepreneur | 4 | 0.82% |
| 9 | #swisstronik | 4 | 0.82% |
| 10 | #doge | 4 | 0.82% |
## Update History
| Date | New Instances | Total Instances |
|------|---------------|-----------------|
| 2024-12-03T01:01:51Z | 4600 | 4600 |
| 2024-12-03T01:01:54Z | 489 | 5089 |
|
paulrichmond/astro_gen0 | paulrichmond | 2024-11-20T14:55:27Z | 16 | 0 | [
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] | [] | 2024-11-20T08:07:28Z | 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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- name: comp_Llama-2-7b-hf
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configs:
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data_files:
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path: data/test-*
---
Generated with the following parameters
- max_new_tokens: 1024
- min_new_tokens: 1
- temperature: 0.8
- do_sample: true |
kothasuhas/qasper-eval | kothasuhas | 2024-10-17T21:35:34Z | 11 | 0 | [
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] | [] | 2024-10-17T21:35:30Z | 0 | ---
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---
|
Charlie911/tmmluplus_CKIP_segmented | Charlie911 | 2024-10-13T20:48:14Z | 1,435 | 0 | [
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configs:
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data_files:
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path: accounting/test-*
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path: accounting/validation-*
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path: accounting/train-*
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data_files:
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path: administrative_law/test-*
- split: validation
path: administrative_law/validation-*
- split: train
path: administrative_law/train-*
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data_files:
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path: advance_chemistry/test-*
- split: validation
path: advance_chemistry/validation-*
- split: train
path: advance_chemistry/train-*
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data_files:
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path: agriculture/test-*
- split: validation
path: agriculture/validation-*
- split: train
path: agriculture/train-*
- config_name: anti_money_laundering
data_files:
- split: test
path: anti_money_laundering/test-*
- split: validation
path: anti_money_laundering/validation-*
- split: train
path: anti_money_laundering/train-*
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data_files:
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path: auditing/test-*
- split: validation
path: auditing/validation-*
- split: train
path: auditing/train-*
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data_files:
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path: basic_medical_science/test-*
- split: validation
path: basic_medical_science/validation-*
- split: train
path: basic_medical_science/train-*
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data_files:
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path: business_management/test-*
- split: validation
path: business_management/validation-*
- split: train
path: business_management/train-*
- config_name: chinese_language_and_literature
data_files:
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path: chinese_language_and_literature/test-*
- split: validation
path: chinese_language_and_literature/validation-*
- split: train
path: chinese_language_and_literature/train-*
- config_name: clinical_psychology
data_files:
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path: clinical_psychology/test-*
- split: validation
path: clinical_psychology/validation-*
- split: train
path: clinical_psychology/train-*
- config_name: computer_science
data_files:
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path: computer_science/test-*
- split: validation
path: computer_science/validation-*
- split: train
path: computer_science/train-*
- config_name: culinary_skills
data_files:
- split: test
path: culinary_skills/test-*
- split: validation
path: culinary_skills/validation-*
- split: train
path: culinary_skills/train-*
- config_name: dentistry
data_files:
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path: dentistry/test-*
- split: validation
path: dentistry/validation-*
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path: dentistry/train-*
- config_name: economics
data_files:
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path: economics/test-*
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path: economics/validation-*
- split: train
path: economics/train-*
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data_files:
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path: education/test-*
- split: validation
path: education/validation-*
- split: train
path: education/train-*
- config_name: education_(profession_level)
data_files:
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path: education_(profession_level)/test-*
- split: validation
path: education_(profession_level)/validation-*
- split: train
path: education_(profession_level)/train-*
- config_name: educational_psychology
data_files:
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path: educational_psychology/test-*
- split: validation
path: educational_psychology/validation-*
- split: train
path: educational_psychology/train-*
- config_name: engineering_math
data_files:
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path: engineering_math/test-*
- split: validation
path: engineering_math/validation-*
- split: train
path: engineering_math/train-*
- config_name: finance_banking
data_files:
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path: finance_banking/test-*
- split: validation
path: finance_banking/validation-*
- split: train
path: finance_banking/train-*
- config_name: financial_analysis
data_files:
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path: financial_analysis/test-*
- split: validation
path: financial_analysis/validation-*
- split: train
path: financial_analysis/train-*
- config_name: fire_science
data_files:
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path: fire_science/test-*
- split: validation
path: fire_science/validation-*
- split: train
path: fire_science/train-*
- config_name: general_principles_of_law
data_files:
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path: general_principles_of_law/test-*
- split: validation
path: general_principles_of_law/validation-*
- split: train
path: general_principles_of_law/train-*
- config_name: geography_of_taiwan
data_files:
- split: test
path: geography_of_taiwan/test-*
- split: validation
path: geography_of_taiwan/validation-*
- split: train
path: geography_of_taiwan/train-*
- config_name: human_behavior
data_files:
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path: human_behavior/test-*
- split: validation
path: human_behavior/validation-*
- split: train
path: human_behavior/train-*
- config_name: insurance_studies
data_files:
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path: insurance_studies/test-*
- split: validation
path: insurance_studies/validation-*
- split: train
path: insurance_studies/train-*
- config_name: introduction_to_law
data_files:
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path: introduction_to_law/test-*
- split: validation
path: introduction_to_law/validation-*
- split: train
path: introduction_to_law/train-*
- config_name: jce_humanities
data_files:
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path: jce_humanities/test-*
- split: validation
path: jce_humanities/validation-*
- split: train
path: jce_humanities/train-*
- config_name: junior_chemistry
data_files:
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path: junior_chemistry/test-*
- split: validation
path: junior_chemistry/validation-*
- split: train
path: junior_chemistry/train-*
- config_name: junior_chinese_exam
data_files:
- split: test
path: junior_chinese_exam/test-*
- split: validation
path: junior_chinese_exam/validation-*
- split: train
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- config_name: junior_math_exam
data_files:
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- split: validation
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- split: train
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- config_name: junior_science_exam
data_files:
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path: junior_science_exam/test-*
- split: validation
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- split: train
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data_files:
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path: junior_social_studies/test-*
- split: validation
path: junior_social_studies/validation-*
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data_files:
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path: linear_algebra/test-*
- split: validation
path: linear_algebra/validation-*
- split: train
path: linear_algebra/train-*
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data_files:
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path: logic_reasoning/test-*
- split: validation
path: logic_reasoning/validation-*
- split: train
path: logic_reasoning/train-*
- config_name: macroeconomics
data_files:
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path: macroeconomics/test-*
- split: validation
path: macroeconomics/validation-*
- split: train
path: macroeconomics/train-*
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data_files:
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path: management_accounting/test-*
- split: validation
path: management_accounting/validation-*
- split: train
path: management_accounting/train-*
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data_files:
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path: marketing_management/test-*
- split: validation
path: marketing_management/validation-*
- split: train
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data_files:
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path: mechanical/test-*
- split: validation
path: mechanical/validation-*
- split: train
path: mechanical/train-*
- config_name: music
data_files:
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path: music/test-*
- split: validation
path: music/validation-*
- split: train
path: music/train-*
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data_files:
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path: national_protection/test-*
- split: validation
path: national_protection/validation-*
- split: train
path: national_protection/train-*
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data_files:
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path: nautical_science/test-*
- split: validation
path: nautical_science/validation-*
- split: train
path: nautical_science/train-*
- config_name: occupational_therapy_for_psychological_disorders
data_files:
- split: test
path: occupational_therapy_for_psychological_disorders/test-*
- split: validation
path: occupational_therapy_for_psychological_disorders/validation-*
- split: train
path: occupational_therapy_for_psychological_disorders/train-*
- config_name: official_document_management
data_files:
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path: official_document_management/test-*
- split: validation
path: official_document_management/validation-*
- split: train
path: official_document_management/train-*
- config_name: optometry
data_files:
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path: optometry/test-*
- split: validation
path: optometry/validation-*
- split: train
path: optometry/train-*
- config_name: organic_chemistry
data_files:
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path: organic_chemistry/test-*
- split: validation
path: organic_chemistry/validation-*
- split: train
path: organic_chemistry/train-*
- config_name: pharmacology
data_files:
- split: test
path: pharmacology/test-*
- split: validation
path: pharmacology/validation-*
- split: train
path: pharmacology/train-*
- config_name: pharmacy
data_files:
- split: test
path: pharmacy/test-*
- split: validation
path: pharmacy/validation-*
- split: train
path: pharmacy/train-*
- config_name: physical_education
data_files:
- split: test
path: physical_education/test-*
- split: validation
path: physical_education/validation-*
- split: train
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- config_name: physics
data_files:
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path: physics/test-*
- split: validation
path: physics/validation-*
- split: train
path: physics/train-*
- config_name: politic_science
data_files:
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path: politic_science/test-*
- split: validation
path: politic_science/validation-*
- split: train
path: politic_science/train-*
- config_name: real_estate
data_files:
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path: real_estate/test-*
- split: validation
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- split: train
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- config_name: secondary_physics
data_files:
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path: secondary_physics/test-*
- split: validation
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- config_name: statistics_and_machine_learning
data_files:
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path: statistics_and_machine_learning/test-*
- split: validation
path: statistics_and_machine_learning/validation-*
- split: train
path: statistics_and_machine_learning/train-*
- config_name: taiwanese_hokkien
data_files:
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path: taiwanese_hokkien/test-*
- split: validation
path: taiwanese_hokkien/validation-*
- split: train
path: taiwanese_hokkien/train-*
- config_name: taxation
data_files:
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path: taxation/test-*
- split: validation
path: taxation/validation-*
- split: train
path: taxation/train-*
- config_name: technical
data_files:
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path: technical/test-*
- split: validation
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- config_name: three_principles_of_people
data_files:
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- split: validation
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- split: train
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- config_name: trade
data_files:
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path: trade/validation-*
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path: trade/train-*
- config_name: traditional_chinese_medicine_clinical_medicine
data_files:
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path: traditional_chinese_medicine_clinical_medicine/test-*
- split: validation
path: traditional_chinese_medicine_clinical_medicine/validation-*
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- config_name: trust_practice
data_files:
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path: trust_practice/test-*
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- split: train
path: trust_practice/train-*
- config_name: ttqav2
data_files:
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path: ttqav2/test-*
- split: validation
path: ttqav2/validation-*
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- config_name: tve_chinese_language
data_files:
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path: tve_chinese_language/test-*
- split: validation
path: tve_chinese_language/validation-*
- split: train
path: tve_chinese_language/train-*
- config_name: tve_design
data_files:
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path: tve_design/test-*
- split: validation
path: tve_design/validation-*
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path: tve_design/train-*
- config_name: tve_mathematics
data_files:
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path: tve_mathematics/test-*
- split: validation
path: tve_mathematics/validation-*
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- config_name: tve_natural_sciences
data_files:
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path: tve_natural_sciences/test-*
- split: validation
path: tve_natural_sciences/validation-*
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path: tve_natural_sciences/train-*
- config_name: veterinary_pathology
data_files:
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path: veterinary_pathology/test-*
- split: validation
path: veterinary_pathology/validation-*
- split: train
path: veterinary_pathology/train-*
- config_name: veterinary_pharmacology
data_files:
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path: veterinary_pharmacology/test-*
- split: validation
path: veterinary_pharmacology/validation-*
- split: train
path: veterinary_pharmacology/train-*
---
|
sagar27kumar/ECG-XRAY-dataset | sagar27kumar | 2025-01-25T00:40:01Z | 72 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-01-25T00:29:38Z | 0 | ---
dataset_info:
features:
- name: image
dtype: string
- name: label
dtype: int64
- name: pixel_values
sequence:
sequence:
sequence: float32
splits:
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num_examples: 472
download_size: 647076823
dataset_size: 3278706062
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
|
Yuyeong/rw_cora_nbw_50_cycle | Yuyeong | 2025-04-22T01:25:43Z | 16 | 0 | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-04-22T01:22:25Z | 0 | ---
dataset_info:
features:
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dtype: string
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dtype:
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---
|
qfq/eidata_newscript_dev20241102_221115_iter2_5 | qfq | 2024-11-02T23:18:57Z | 16 | 0 | [
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alea-institute/kl3m-filter-data-dotgov-www.acf.hhs.gov | alea-institute | 2025-02-03T21:20:35Z | 16 | 0 | [
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---
|
ziyu3141/rf_newtrain_7_38 | ziyu3141 | 2025-02-07T10:51:15Z | 13 | 0 | [
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Archistrax/answers | Archistrax | 2025-05-11T14:36:51Z | 0 | 0 | [
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|
colabfit/mlearn_Mo_train | colabfit | 2025-04-23T18:14:17Z | 20 | 0 | [
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"molecular dynamics",
"mlip",
"interatomic potential"
] | [] | 2025-04-01T20:39:46Z | 0 | ---
configs:
- config_name: default
data_files: "main/*.parquet"
license: bsd-3-clause
tags:
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- mlip
- interatomic potential
pretty_name: mlearn Mo train
---
# Dataset
mlearn Mo train
### Description
A comprehensive DFT data set was generated for six elements - Li, Mo, Ni, Cu, Si, and Ge. These elements were chosen to span a variety of chemistries (main group metal, transition metal, and semiconductor), crystal structures (bcc, fcc, and diamond) and bonding types (metallic and covalent). This dataset comprises only the Mo configurations
<br>Additional details stored in dataset columns prepended with "dataset_".
### Dataset authors
Yunxing Zuo, Chi Chen, Xiangguo Li, Zhi Deng, Yiming Chen, Jörg Behler, Gábor Csányi, Alexander V. Shapeev, Aidan P. Thompson, Mitchell A. Wood, Shyue Ping Ong
### Publication
https://doi.org/10.1021/acs.jpca.9b08723
### Original data link
https://github.com/materialsvirtuallab/mlearn/tree/master/data
### License
BSD-3-Clause
### Number of unique molecular configurations
194
### Number of atoms
10087
### Elements included
Mo
### Properties included
energy, atomic forces, cauchy stress
### Cite this dataset
Zuo, Y., Chen, C., Li, X., Deng, Z., Chen, Y., Behler, J., Csányi, G., Shapeev, A. V., Thompson, A. P., Wood, M. A., and Ong, S. P. _mlearn Mo train_. ColabFit, 2023. https://doi.org/10.60732/3827e5e1 |
EuskadIA/Erregistro_Zibila_Hiztegia | EuskadIA | 2025-03-29T22:09:08Z | 17 | 0 | [
"task_categories:translation",
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"language:fra",
"language:eng",
"region:us"
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"translation"
] | 2025-01-14T17:52:42Z | 0 | ---
language:
- eu
- spa
- fra
- eng
task_categories:
- translation
---
> [!NOTE]
> Dataset origin: https://www.ivap.euskadi.eus/webivap00-a5app3/fr/ac36aEuskaltermWar/publiko/erakutsiBankuEdukia
|
TAUR-dev/qwen2.5_1.5B__2d_retries_eval_fixed__working__first_response_correct__training | TAUR-dev | 2025-05-28T00:31:06Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-28T00:31:04Z | 0 | ---
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---
|
haorandai/New_Mammal_1Samples_epsilon_0.1_alpha_0.005_With1Constraints | haorandai | 2024-10-03T02:56:08Z | 19 | 0 | [
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] | [] | 2024-10-03T02:15:13Z | 0 | ---
dataset_info:
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---
|
anthonyj/numina-deepseek-r1-qwen-7b | anthonyj | 2025-01-28T23:36:18Z | 53 | 0 | [
"size_categories:n<1K",
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"library:distilabel",
"region:us",
"synthetic",
"distilabel",
"rlaif"
] | [] | 2025-01-28T23:36:13Z | 0 | ---
size_categories: n<1K
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list:
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struct:
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list:
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dtype: string
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dtype: string
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struct:
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dtype: int64
- name: model_name
dtype: string
splits:
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num_bytes: 796104
num_examples: 40
download_size: 262284
dataset_size: 796104
configs:
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data_files:
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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 numina-deepseek-r1-qwen-7b
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/anthonyj/numina-deepseek-r1-qwen-7b/raw/main/pipeline.yaml"
```
or explore the configuration:
```console
distilabel pipeline info --config "https://huggingface.co/datasets/anthonyj/numina-deepseek-r1-qwen-7b/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": "You will be given a problem. Please reason step by step, and put your final answer within \boxed{}:\nWhat is the coefficient of $x^2y^6$ in the expansion of $\\left(\\frac{3}{5}x-\\frac{y}{2}\\right)^8$? Express your answer as a common fraction.",
"role": "user"
}
],
"raw_output_text_generation_0": "\u003cthink\u003e\nOkay, let me try to figure out the coefficient of \\(x^2 y^6\\) in the expansion of \\(\\left(\\frac{3}{5}x - \\frac{y}{2}\\right)^8\\). Hmm, I remember that when expanding a binomial like \\((a + b)^n\\), we can use the binomial theorem. The binomial theorem states that each term in the expansion is of the form \\(\\binom{n}{k} a^{n - k} b^{k}\\), where \\(k\\) ranges from 0 to \\(n\\).\n\nSo, in this problem, our binomial is \\(\\left(\\frac{3}{5}x - \\frac{y}{2}\\right)\\), and we\u0027re raising it to the 8th power. Let me denote \\(a = \\frac{3}{5}x\\) and \\(b = -\\frac{y}{2}\\). Then, the expansion will have terms of the form \\(\\binom{8}{k} \\left(\\frac{3}{5}x\\right)^{8 - k} \\left(-\\frac{y}{2}\\right)^k\\).\n\nWe need the term where the power of \\(x\\) is 2 and the power of \\(y\\) is 6. Since \\(x\\) is raised to the power of \\(8 - k\\) and \\(y\\) is raised to the power of \\(k\\), we can set up the equation \\(8 - k = 2\\). Solving for \\(k\\), we get \\(k = 6\\). So, the term we\u0027re interested in corresponds to \\(k = 6\\).\n\nNow, let\u0027s compute the coefficient for \\(k = 6\\). The binomial coefficient is \\(\\binom{8}{6}\\). I recall that \\(\\binom{n}{k} = \\binom{n}{n - k}\\), so \\(\\binom{8}{6} = \\binom{8}{2}\\). Calculating \\(\\binom{8}{2}\\), which is \\(\\frac{8 \\times 7}{2 \\times 1} = 28\\).\n\nNext, we need to compute \\(\\left(\\frac{3}{5}\\right)^{8 - 6} = \\left(\\frac{3}{5}\\right)^2\\). Let me calculate that: \\(\\left(\\frac{3}{5}\\right)^2 = \\frac{9}{25}\\).\n\nThen, we have \\(\\left(-\\frac{1}{2}\\right)^6\\). Hmm, raising a negative number to an even power gives a positive result. So, \\(\\left(-\\frac{1}{2}\\right)^6 = \\left(\\frac{1}{2}\\right)^6 = \\frac{1}{64}\\).\n\nNow, multiplying all these together: the coefficient is \\(\\binom{8}{6} \\times \\left(\\frac{3}{5}\\right)^2 \\times \\left(-\\frac{1}{2}\\right)^6 = 28 \\times \\frac{9}{25} \\times \\frac{1}{64}\\).\n\nLet me compute this step by step. First, multiply 28 and \\(\\frac{9}{25}\\). So, \\(28 \\times \\frac{9}{25}\\). Let\u0027s see, 28 divided by 25 is \\(\\frac{28}{25}\\), and then multiplied by 9 gives \\(\\frac{252}{25}\\). \n\nThen, we multiply this result by \\(\\frac{1}{64}\\): \\(\\frac{252}{25} \\times \\frac{1}{64}\\). Multiplying the numerators: 252 \u00d7 1 = 252, and denominators: 25 \u00d7 64 = 1600. So, the coefficient is \\(\\frac{252}{1600}\\).\n\nWait, can we simplify this fraction? Let\u0027s see. Both 252 and 1600 are divisible by 4. Dividing numerator and denominator by 4: 252 \u00f7 4 = 63, and 1600 \u00f7 4 = 400. So, now we have \\(\\frac{63}{400}\\). \n\nIs 63 and 400 divisible by any common number? Well, 63 is 7 \u00d7 9, and 400 is 16 \u00d7 25. They don\u0027t share any common factors besides 1, so \\(\\frac{63}{400}\\) is the simplified fraction.\n\nBut hold on, let me double-check my calculations to make sure I didn\u0027t make a mistake. So, binomial coefficient was 28, which is correct because \\(\\binom{8}{6}\\) is 28. Then, \\(\\left(\\frac{3}{5}\\right)^2 is indeed 9/25, and \\(\\left(-\\frac{1}{2}\\right)^6 is 1/64. Multiplying 28, 9/25, and 1/64:\n\n28 \u00d7 9 = 252, and 25 \u00d7 64 = 1600. So that\u0027s 252/1600. Then simplifying, dividing numerator and denominator by 4 gives 63/400. That seems correct.\n\nBut wait, I just realized that the term is \\(\\left(-\\frac{y}{2}\\right)^6\\), which is positive because 6 is even, so that\u0027s correct. So, the coefficient is positive 63/400. Hmm, that seems right.\n\nLet me think if there\u0027s another way to approach this. Maybe using the multinomial theorem? But no, in this case, since it\u0027s a binomial, the binomial theorem should suffice. Yeah, I think my approach is correct.\n\nAlternatively, I can think about the term in the expansion. The general term is:\n\n\\[\n\\binom{8}{k} \\left(\\frac{3}{5}x\\right)^{8 - k} \\left(-\\frac{y}{2}\\right)^k\n\\]\n\nWe found that when \\(k = 6\\), the term is:\n\n\\[\n\\binom{8}{6} \\left(\\frac{3}{5}\\right)^{2} \\left(-\\frac{1}{2}\\right)^6 x^2 y^6\n\\]\n\nWhich is exactly what we computed. So, yes, the coefficient is 28 \u00d7 (9/25) \u00d7 (1/64) = 63/400.\n\nWait, but let me make sure about the signs. Since the term is \\(\\left(-\\frac{y}{2}\\right)^6\\), which is positive because any negative number raised to an even power is positive. So, the coefficient is indeed positive 63/400.\n\nJust to be thorough, let me compute 28 \u00d7 9 first. 28 \u00d7 9: 20 \u00d7 9 = 180, 8 \u00d7 9 = 72, so 180 + 72 = 252. Then, 25 \u00d7 64: 25 \u00d7 60 = 1500, 25 \u00d7 4 = 100, so 1500 + 100 = 1600. So, 252/1600 simplifies to 63/400.\n\nAlright, so I think I\u0027m confident that 63/400 is the correct coefficient. I don\u0027t see any errors in my reasoning.\n\n**Final Answer**\nThe coefficient of \\(x^2 y^6\\) is \\boxed{\\dfrac{63}{400}}.\n\u003c/think\u003e\n\nTo find the coefficient of \\(x^2 y^6\\) in the expansion of \\(\\left(\\frac{3}{5}x - \\frac{y}{2}\\right)^8\\), we use the binomial theorem. The binomial theorem states that each term in the expansion is of the form \\(\\binom{8}{k} \\left(\\frac{3}{5}x\\right)^{8 - k} \\left(-\\frac{y}{2}\\right)^k\\).\n\nWe need the term where the power of \\(x\\) is 2 and the power of \\(y\\) is 6. This corresponds to \\(k = 6\\) because \\(8 - k = 2\\).\n\n1. Calculate the binomial coefficient \\(\\binom{8}{6}\\):\n \\[\n \\binom{8}{6} = \\binom{8}{2} = \\frac{8 \\times 7}{2 \\times 1} = 28\n \\]\n\n2. Compute \\(\\left(\\frac{3}{5}\\right)^2\\):\n \\[\n \\left(\\frac{3}{5}\\right)^2 = \\frac{9}{25}\n \\]\n\n3. Compute \\(\\left(-\\frac{1}{2}\\right)^6\\):\n \\[\n \\left(-\\frac{1}{2}\\right)^6 = \\left(\\frac{1}{2}\\right)^6 = \\frac{1}{64}\n \\]\n\n4. Multiply these results together:\n \\[\n 28 \\times \\frac{9}{25} \\times \\frac{1}{64} = 28 \\times \\frac{9}{25} \\times \\frac{1}{64} = \\frac{252}{1600}\n \\]\n\n5. Simplify the fraction \\(\\frac{252}{1600}\\) by dividing both the numerator and the denominator by 4:\n \\[\n \\frac{252}{1600} = \\frac{63}{400}\n \\]\n\nThus, the coefficient of \\(x^2 y^6\\) is \\(\\boxed{\\dfrac{63}{400}}\\).",
"statistics_text_generation_0": {
"input_tokens": 77,
"output_tokens": 2199
}
},
"generation": "\u003cthink\u003e\nOkay, let me try to figure out the coefficient of \\(x^2 y^6\\) in the expansion of \\(\\left(\\frac{3}{5}x - \\frac{y}{2}\\right)^8\\). Hmm, I remember that when expanding a binomial like \\((a + b)^n\\), we can use the binomial theorem. The binomial theorem states that each term in the expansion is of the form \\(\\binom{n}{k} a^{n - k} b^{k}\\), where \\(k\\) ranges from 0 to \\(n\\).\n\nSo, in this problem, our binomial is \\(\\left(\\frac{3}{5}x - \\frac{y}{2}\\right)\\), and we\u0027re raising it to the 8th power. Let me denote \\(a = \\frac{3}{5}x\\) and \\(b = -\\frac{y}{2}\\). Then, the expansion will have terms of the form \\(\\binom{8}{k} \\left(\\frac{3}{5}x\\right)^{8 - k} \\left(-\\frac{y}{2}\\right)^k\\).\n\nWe need the term where the power of \\(x\\) is 2 and the power of \\(y\\) is 6. Since \\(x\\) is raised to the power of \\(8 - k\\) and \\(y\\) is raised to the power of \\(k\\), we can set up the equation \\(8 - k = 2\\). Solving for \\(k\\), we get \\(k = 6\\). So, the term we\u0027re interested in corresponds to \\(k = 6\\).\n\nNow, let\u0027s compute the coefficient for \\(k = 6\\). The binomial coefficient is \\(\\binom{8}{6}\\). I recall that \\(\\binom{n}{k} = \\binom{n}{n - k}\\), so \\(\\binom{8}{6} = \\binom{8}{2}\\). Calculating \\(\\binom{8}{2}\\), which is \\(\\frac{8 \\times 7}{2 \\times 1} = 28\\).\n\nNext, we need to compute \\(\\left(\\frac{3}{5}\\right)^{8 - 6} = \\left(\\frac{3}{5}\\right)^2\\). Let me calculate that: \\(\\left(\\frac{3}{5}\\right)^2 = \\frac{9}{25}\\).\n\nThen, we have \\(\\left(-\\frac{1}{2}\\right)^6\\). Hmm, raising a negative number to an even power gives a positive result. So, \\(\\left(-\\frac{1}{2}\\right)^6 = \\left(\\frac{1}{2}\\right)^6 = \\frac{1}{64}\\).\n\nNow, multiplying all these together: the coefficient is \\(\\binom{8}{6} \\times \\left(\\frac{3}{5}\\right)^2 \\times \\left(-\\frac{1}{2}\\right)^6 = 28 \\times \\frac{9}{25} \\times \\frac{1}{64}\\).\n\nLet me compute this step by step. First, multiply 28 and \\(\\frac{9}{25}\\). So, \\(28 \\times \\frac{9}{25}\\). Let\u0027s see, 28 divided by 25 is \\(\\frac{28}{25}\\), and then multiplied by 9 gives \\(\\frac{252}{25}\\). \n\nThen, we multiply this result by \\(\\frac{1}{64}\\): \\(\\frac{252}{25} \\times \\frac{1}{64}\\). Multiplying the numerators: 252 \u00d7 1 = 252, and denominators: 25 \u00d7 64 = 1600. So, the coefficient is \\(\\frac{252}{1600}\\).\n\nWait, can we simplify this fraction? Let\u0027s see. Both 252 and 1600 are divisible by 4. Dividing numerator and denominator by 4: 252 \u00f7 4 = 63, and 1600 \u00f7 4 = 400. So, now we have \\(\\frac{63}{400}\\). \n\nIs 63 and 400 divisible by any common number? Well, 63 is 7 \u00d7 9, and 400 is 16 \u00d7 25. They don\u0027t share any common factors besides 1, so \\(\\frac{63}{400}\\) is the simplified fraction.\n\nBut hold on, let me double-check my calculations to make sure I didn\u0027t make a mistake. So, binomial coefficient was 28, which is correct because \\(\\binom{8}{6}\\) is 28. Then, \\(\\left(\\frac{3}{5}\\right)^2 is indeed 9/25, and \\(\\left(-\\frac{1}{2}\\right)^6 is 1/64. Multiplying 28, 9/25, and 1/64:\n\n28 \u00d7 9 = 252, and 25 \u00d7 64 = 1600. So that\u0027s 252/1600. Then simplifying, dividing numerator and denominator by 4 gives 63/400. That seems correct.\n\nBut wait, I just realized that the term is \\(\\left(-\\frac{y}{2}\\right)^6\\), which is positive because 6 is even, so that\u0027s correct. So, the coefficient is positive 63/400. Hmm, that seems right.\n\nLet me think if there\u0027s another way to approach this. Maybe using the multinomial theorem? But no, in this case, since it\u0027s a binomial, the binomial theorem should suffice. Yeah, I think my approach is correct.\n\nAlternatively, I can think about the term in the expansion. The general term is:\n\n\\[\n\\binom{8}{k} \\left(\\frac{3}{5}x\\right)^{8 - k} \\left(-\\frac{y}{2}\\right)^k\n\\]\n\nWe found that when \\(k = 6\\), the term is:\n\n\\[\n\\binom{8}{6} \\left(\\frac{3}{5}\\right)^{2} \\left(-\\frac{1}{2}\\right)^6 x^2 y^6\n\\]\n\nWhich is exactly what we computed. So, yes, the coefficient is 28 \u00d7 (9/25) \u00d7 (1/64) = 63/400.\n\nWait, but let me make sure about the signs. Since the term is \\(\\left(-\\frac{y}{2}\\right)^6\\), which is positive because any negative number raised to an even power is positive. So, the coefficient is indeed positive 63/400.\n\nJust to be thorough, let me compute 28 \u00d7 9 first. 28 \u00d7 9: 20 \u00d7 9 = 180, 8 \u00d7 9 = 72, so 180 + 72 = 252. Then, 25 \u00d7 64: 25 \u00d7 60 = 1500, 25 \u00d7 4 = 100, so 1500 + 100 = 1600. So, 252/1600 simplifies to 63/400.\n\nAlright, so I think I\u0027m confident that 63/400 is the correct coefficient. I don\u0027t see any errors in my reasoning.\n\n**Final Answer**\nThe coefficient of \\(x^2 y^6\\) is \\boxed{\\dfrac{63}{400}}.\n\u003c/think\u003e\n\nTo find the coefficient of \\(x^2 y^6\\) in the expansion of \\(\\left(\\frac{3}{5}x - \\frac{y}{2}\\right)^8\\), we use the binomial theorem. The binomial theorem states that each term in the expansion is of the form \\(\\binom{8}{k} \\left(\\frac{3}{5}x\\right)^{8 - k} \\left(-\\frac{y}{2}\\right)^k\\).\n\nWe need the term where the power of \\(x\\) is 2 and the power of \\(y\\) is 6. This corresponds to \\(k = 6\\) because \\(8 - k = 2\\).\n\n1. Calculate the binomial coefficient \\(\\binom{8}{6}\\):\n \\[\n \\binom{8}{6} = \\binom{8}{2} = \\frac{8 \\times 7}{2 \\times 1} = 28\n \\]\n\n2. Compute \\(\\left(\\frac{3}{5}\\right)^2\\):\n \\[\n \\left(\\frac{3}{5}\\right)^2 = \\frac{9}{25}\n \\]\n\n3. Compute \\(\\left(-\\frac{1}{2}\\right)^6\\):\n \\[\n \\left(-\\frac{1}{2}\\right)^6 = \\left(\\frac{1}{2}\\right)^6 = \\frac{1}{64}\n \\]\n\n4. Multiply these results together:\n \\[\n 28 \\times \\frac{9}{25} \\times \\frac{1}{64} = 28 \\times \\frac{9}{25} \\times \\frac{1}{64} = \\frac{252}{1600}\n \\]\n\n5. Simplify the fraction \\(\\frac{252}{1600}\\) by dividing both the numerator and the denominator by 4:\n \\[\n \\frac{252}{1600} = \\frac{63}{400}\n \\]\n\nThus, the coefficient of \\(x^2 y^6\\) is \\(\\boxed{\\dfrac{63}{400}}\\).",
"messages": [
{
"content": "What is the coefficient of $x^2y^6$ in the expansion of $\\left(\\frac{3}{5}x-\\frac{y}{2}\\right)^8$? Express your answer as a common fraction.",
"role": "user"
},
{
"content": "To determine the coefficient of \\(x^2y^6\\) in the expansion of \\(\\left(\\frac{3}{5}x - \\frac{y}{2}\\right)^8\\), we can use the binomial theorem.\n\nThe binomial theorem states:\n\\[\n(a + b)^n = \\sum_{k=0}^{n} \\binom{n}{k} a^{n-k} b^k\n\\]\n\nIn this case, \\(a = \\frac{3}{5}x\\), \\(b = -\\frac{y}{2}\\), and \\(n = 8\\).\n\nWe are interested in the term that contains \\(x^2y^6\\). In the general term of the binomial expansion:\n\\[\n\\binom{8}{k} \\left(\\frac{3}{5}x\\right)^{8-k} \\left(-\\frac{y}{2}\\right)^k\n\\]\n\nTo get \\(x^2\\), we need \\(8 - k = 2\\), thus \\(k = 6\\).\n\nSubstituting \\(k = 6\\) into the expression:\n\\[\n\\binom{8}{6} \\left(\\frac{3}{5}x\\right)^{8-6} \\left(-\\frac{y}{2}\\right)^6 = \\binom{8}{6} \\left(\\frac{3}{5}x\\right)^2 \\left(-\\frac{y}{2}\\right)^6\n\\]\n\nNow, we will compute each part of this expression.\n\n1. Calculate the binomial coefficient \\(\\binom{8}{6}\\).\n2. Compute \\(\\left(\\frac{3}{5}\\right)^2\\).\n3. Compute \\(\\left(-\\frac{y}{2}\\right)^6\\).\n4. Combine everything together to get the coefficient of \\(x^2y^6\\).\n\nLet\u0027s compute these in Python.\n```python\nfrom math import comb\n\n# Given values\nn = 8\nk = 6\n\n# Calculate the binomial coefficient\nbinom_coeff = comb(n, k)\n\n# Compute (3/5)^2\na_term = (3/5)**2\n\n# Compute (-1/2)^6\nb_term = (-1/2)**6\n\n# Combine terms to get the coefficient of x^2y^6\ncoefficient = binom_coeff * a_term * b_term\nprint(coefficient)\n```\n```output\n0.1575\n```\nThe coefficient of \\(x^2y^6\\) in the expansion of \\(\\left(\\frac{3}{5}x - \\frac{y}{2}\\right)^8\\) is \\(0.1575\\). To express this as a common fraction, we recognize that:\n\n\\[ 0.1575 = \\frac{1575}{10000} = \\frac{63}{400} \\]\n\nThus, the coefficient can be expressed as:\n\n\\[\n\\boxed{\\frac{63}{400}}\n\\]",
"role": "assistant"
}
],
"model_name": "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
"problem": "What is the coefficient of $x^2y^6$ in the expansion of $\\left(\\frac{3}{5}x-\\frac{y}{2}\\right)^8$? Express your answer as a common fraction.",
"solution": "To determine the coefficient of \\(x^2y^6\\) in the expansion of \\(\\left(\\frac{3}{5}x - \\frac{y}{2}\\right)^8\\), we can use the binomial theorem.\n\nThe binomial theorem states:\n\\[\n(a + b)^n = \\sum_{k=0}^{n} \\binom{n}{k} a^{n-k} b^k\n\\]\n\nIn this case, \\(a = \\frac{3}{5}x\\), \\(b = -\\frac{y}{2}\\), and \\(n = 8\\).\n\nWe are interested in the term that contains \\(x^2y^6\\). In the general term of the binomial expansion:\n\\[\n\\binom{8}{k} \\left(\\frac{3}{5}x\\right)^{8-k} \\left(-\\frac{y}{2}\\right)^k\n\\]\n\nTo get \\(x^2\\), we need \\(8 - k = 2\\), thus \\(k = 6\\).\n\nSubstituting \\(k = 6\\) into the expression:\n\\[\n\\binom{8}{6} \\left(\\frac{3}{5}x\\right)^{8-6} \\left(-\\frac{y}{2}\\right)^6 = \\binom{8}{6} \\left(\\frac{3}{5}x\\right)^2 \\left(-\\frac{y}{2}\\right)^6\n\\]\n\nNow, we will compute each part of this expression.\n\n1. Calculate the binomial coefficient \\(\\binom{8}{6}\\).\n2. Compute \\(\\left(\\frac{3}{5}\\right)^2\\).\n3. Compute \\(\\left(-\\frac{y}{2}\\right)^6\\).\n4. Combine everything together to get the coefficient of \\(x^2y^6\\).\n\nLet\u0027s compute these in Python.\n```python\nfrom math import comb\n\n# Given values\nn = 8\nk = 6\n\n# Calculate the binomial coefficient\nbinom_coeff = comb(n, k)\n\n# Compute (3/5)^2\na_term = (3/5)**2\n\n# Compute (-1/2)^6\nb_term = (-1/2)**6\n\n# Combine terms to get the coefficient of x^2y^6\ncoefficient = binom_coeff * a_term * b_term\nprint(coefficient)\n```\n```output\n0.1575\n```\nThe coefficient of \\(x^2y^6\\) in the expansion of \\(\\left(\\frac{3}{5}x - \\frac{y}{2}\\right)^8\\) is \\(0.1575\\). To express this as a common fraction, we recognize that:\n\n\\[ 0.1575 = \\frac{1575}{10000} = \\frac{63}{400} \\]\n\nThus, the coefficient can be expressed as:\n\n\\[\n\\boxed{\\frac{63}{400}}\n\\]"
}
```
This subset can be loaded as:
```python
from datasets import load_dataset
ds = load_dataset("anthonyj/numina-deepseek-r1-qwen-7b", "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("anthonyj/numina-deepseek-r1-qwen-7b")
```
</details>
|
demeguyessa/Dame | demeguyessa | 2025-06-24T16:49:11Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | [] | 2025-06-24T16:49:11Z | 0 | ---
license: apache-2.0
---
|
sghosts/rand-tezler-firstlast20-little-sample_alpFirst20pages2pdf | sghosts | 2025-03-08T13:35:45Z | 16 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-03-08T13:35:43Z | 0 | ---
dataset_info:
features:
- name: subdir
dtype: string
- name: pdf_path
dtype: string
- name: page_num
dtype: int64
- name: image
dtype: image
- name: alpFirst20pages2pdf
dtype: int64
splits:
- name: train
num_bytes: 6780515.0
num_examples: 80
download_size: 6761744
dataset_size: 6780515.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Ben10x/lvm_bird_spectrogram | Ben10x | 2025-04-08T20:31:43Z | 39 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-04-08T20:27:54Z | 0 | ---
dataset_info:
features:
- name: input_image
dtype: image
- name: output_image
dtype: binary
splits:
- name: train
num_bytes: 24982701187.125
num_examples: 61375
- name: test
num_bytes: 4471537621.0
num_examples: 10832
download_size: 4725229980
dataset_size: 29454238808.125
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
dive-lab/ShockCast | dive-lab | 2025-06-08T02:41:10Z | 0 | 0 | [
"license:cc-by-nc-sa-4.0",
"region:us"
] | [] | 2025-06-08T02:21:43Z | 0 | ---
license: cc-by-nc-sa-4.0
---
|
infinite-dataset-hub/PregnancyBotConvo | infinite-dataset-hub | 2025-02-05T18:00:16Z | 12 | 0 | [
"license:mit",
"size_categories:n<1K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"infinite-dataset-hub",
"synthetic"
] | [] | 2024-11-22T19:00:27Z | 0 | ---
license: mit
tags:
- infinite-dataset-hub
- synthetic
---
# PregnancyBotConvo
tags: ConversationalAI, Pregnancy, Healthcare
_Note: This is an AI-generated dataset so its content may be inaccurate or false_
**Dataset Description:**
The 'PregnancyBotConvo' dataset is designed for training conversational AI models that are focused on providing assistance and information to individuals who are expecting a baby. The dataset consists of various conversational excerpts that deal with common queries and concerns during pregnancy. Each row in the dataset represents a single interaction with a user seeking information or advice. The dataset is tagged with labels to categorize the type of inquiry or advice provided.
**CSV Content Preview:**
```csv
"dialogue_id","user_input","bot_response","label"
"001","What are some good exercises during pregnancy?","Certain exercises can help maintain fitness and prepare your body for labor. It's best to consult with your healthcare provider before starting any new exercise routine during pregnancy.","Exercise_Advice"
"002","I'm worried about morning sickness. Any tips?","There are several strategies to help manage morning sickness, such as eating small, frequent meals, avoiding strong odors, and ginger can be soothing. Always speak to your healthcare provider for personalized advice.","Morning_Sickness_Advice"
"003","What should I know about prenatal vitamins?","Prenatal vitamins are crucial for your baby's development and can help prevent certain birth defects. Make sure to choose a reputable brand and discuss with your healthcare provider to find the best option for you.","Prenatal_Vitamins_Info"
"004","How can I prepare for a natural birth?","Preparing for a natural birth involves understanding your options and working with your healthcare provider. You might consider attending childbirth education classes, creating a birth plan, and practicing relaxation techniques.","Birth_Preparation_Advice"
"005","I'm feeling anxious about childbirth. What should I do?","Feeling anxious about childbirth is common. It's important to communicate your feelings with your healthcare provider. They can provide support and refer you to counseling services if necessary.","Anxiety_Relief_Advice"
```
**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 '':
- **Dataset Generation Page**: https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub?q=&dataset=PregnancyBotConvo&tags=ConversationalAI,+Pregnancy,+Healthcare
- **Model**: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct
- **More Datasets**: https://huggingface.co/datasets?other=infinite-dataset-hub
|
Ehsanrs2/Forex_Factory_Calendar | Ehsanrs2 | 2025-04-14T11:03:04Z | 89 | 2 | [
"task_categories:time-series-forecasting",
"task_categories:tabular-classification",
"task_categories:tabular-regression",
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"forex",
"economic-calendar",
"financial-data",
"macro-economics",
"trading-strategy",
"time-series",
"market-events",
"selenium",
"pandas",
"scraper"
] | [
"time-series-forecasting",
"tabular-classification",
"tabular-regression"
] | 2025-04-14T10:29:10Z | 0 | ---
license: mit
language:
- en
task_categories:
- time-series-forecasting
- tabular-classification
- tabular-regression
tags:
- forex
- economic-calendar
- financial-data
- macro-economics
- trading-strategy
- time-series
- market-events
- selenium
- pandas
- scraper
pretty_name: Forex Factory Economic Calendar (2007–2025)
size_categories:
- 10K<n<100K
---
# 📅 Forex Factory Economic Calendar Dataset (2007-01-01 to 2025-04-07)
This dataset contains a comprehensive archive of macroeconomic calendar events sourced from [Forex Factory](https://www.forexfactory.com/calendar), spanning from **January 1, 2007** to **April 7, 2025**.
Each row captures a specific event with detailed metadata including currency, event type, market impact level, reported values, and descriptive context.
---
## 📦 Dataset Summary
- **Total timespan:** 2007-01-01 → 2025-04-07
- **Format:** CSV (UTF-8)
- **Timezone:** Asia/Tehran
- **Detailed fields included**
---
## 🧾 Columns
| Column | Description |
|-------------|-----------------------------------------------------------------------------|
| `DateTime` | Date and time of the event in ISO 8601 format |
| `Currency` | Currency affected by the event (e.g., USD, EUR, GBP) |
| `Impact` | Impact level: Low, Medium, or High Impact Expected |
| `Event` | Name of the economic event |
| `Actual` | Reported value of the indicator (if available) |
| `Forecast` | Forecasted value |
| `Previous` | Previously reported value |
| `Detail` | Rich description including source, frequency, and why it matters to traders |
---
## 🔍 Example
```csv
2007-01-01T04:30:00+03:30,CNY,High Impact Expected,Manufacturing PMI,54.8,,55.3,"Source: CFLP (latest release) | Measures: Level of a diffusion index based on surveyed purchasing managers..."
```
---
## 💼 Use Cases
- Financial time series forecasting
- Event-driven backtesting in algorithmic trading
- Economic policy impact analysis
- Macro indicators modeling for global trends
- Educational purposes in finance and economics
---
## ⚙️ How It Was Built
The data was collected using a custom Python scraper using:
- [Selenium](https://pypi.org/project/selenium/) (via `undetected-chromedriver`)
- [pandas](https://pandas.pydata.org/) for CSV export and manipulation
- Incremental scraping and detailed event parsing
- Timezone-aware timestamping
GitHub repository (scraper code): [🔗 Link to repository](https://github.com/ehsanrs2/forexfactory-scraper)
---
## 🛡 Disclaimer
This dataset is intended for **educational and research purposes only**.
Data was collected from [Forex Factory](https://www.forexfactory.com/) using publicly accessible web content.
This project is **not affiliated, endorsed, or sponsored by Forex Factory** in any way.
Please respect [Forex Factory’s Terms of Use](https://www.forexfactory.com/notices). Do not use this dataset for commercial purposes or redistribution without checking the original source's policies.
---
## 📚 Citation
If you use this dataset in your work, please cite it as:
```
@misc{forexcalendar2025,
title={Forex Factory Economic Calendar Dataset (2007–2025)},
author={Ehsan},
year={2025},
howpublished={\url{https://huggingface.co/datasets/yourusername/forex-factory-calendar}},
note={Accessed: YYYY-MM-DD}
}
```
---
## 🏷 Tags
`forex` `financial-data` `economic-calendar` `macro-economics`
`time-series` `trading-strategy` `pandas` `selenium` `market-events`
```
|
zpc2090/charades_8frames_new | zpc2090 | 2025-03-09T12:34:10Z | 12 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-03-09T12:31:48Z | 0 | ---
dataset_info:
features:
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sequence: image
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dtype: string
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dtype: string
splits:
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dataset_size: 1469334185.0
configs:
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data_files:
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path: data/train-*
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path: data/validation-*
- split: test
path: data/test-*
---
|
carminho/piqa-mt-pt | carminho | 2025-06-05T16:45:51Z | 0 | 0 | [
"language:pt",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-05T12:57:09Z | 0 | ---
language:
- pt
configs:
- config_name: default
data_files:
- split: train
path: piqa_train_pt.jsonl
- split: test
path: piqa_test_pt.jsonl
- split: validation
path: piqa_validation_pt.jsonl
--- |
1231czx/dstrain_orm_2e6_bz128_head_test_ms_math | 1231czx | 2024-12-04T10:17:01Z | 54 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-12-04T10:16:57Z | 0 | ---
dataset_info:
features:
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dtype: string
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sequence: float64
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num_examples: 500
download_size: 71900257
dataset_size: 318457011
configs:
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data_files:
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path: data/train-*
---
|
deutsche-telekom/Ger-RAG-eval | deutsche-telekom | 2024-08-23T11:10:52Z | 192 | 45 | [
"language:de",
"license:cc-by-sa-4.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-04-07T19:45:49Z | 2 | ---
license: cc-by-sa-4.0
language:
- de
size_categories:
- 1K<n<10K
configs:
- config_name: task1
data_files:
- split: test
path: "task1_test.parquet"
- config_name: task2
data_files:
- split: test
path: "task2_test.parquet"
- config_name: task3
data_files:
- split: test
path: "task3_test.parquet"
- config_name: task4
data_files:
- split: test
path: "task4_test.parquet"
---
# German RAG LLM Evaluation Dataset
This dataset is intended for the evaluation of German RAG (retrieval augmented generation) capabilities of LLM models.
It is based on the test set of the [deutsche-telekom/wikipedia-22-12-de-dpr](https://huggingface.co/datasets/deutsche-telekom/wikipedia-22-12-de-dpr)
data set (also see [wikipedia-22-12-de-dpr on GitHub](https://github.com/telekom/wikipedia-22-12-de-dpr)) and
consists of 4 subsets or tasks.
## Task Description
The dataset consists of 4 subsets for the following 4 tasks (each task with 1000 prompts):
### choose_context_by_question (subset task2)
Given is a question and 4 contexts. The task is to decide which context can answer the question.
Example:
```text
Auf Basis welcher der folgenden Kontexte (A oder B oder C oder D) lässt sich die Frage beantworten?
Frage: Wie wurde Saidi im Januar 2006 noch einmal deutscher Meister?
Kontexte:
A:
Ceferino Garcia (* 26. August 1906 in Naval, Biliran; † 1. Januar 1981 in San Diego, Kalifornien, Vereinigte Staaten) war ein philippinischer Boxer im Mittelgewicht. Der von den Philippinen stammende Garcia, der nach anderen Angaben bereits um 1903 geboren wurde, begann seine Karriere als Boxer 1923 zunächst im Weltergewicht und gewann am 2. Oktober 1939 den Weltmeistertitel der NYSAC im Mittelgewicht der "International Boxing Union" bei einem Kampf gegen Fred Apostoli in New York City, den er in den siebten Runde durch ein Knockout. Am 23. Dezember 1939 verteidigte er seinen Titel in Manila gegen Glen Lee durch ein technisches K.O. Sein Sieg im Mittelgewichtstitelkampf am 1. März 1940 gegen Henry Armstrong, gegen den er im Weltergewicht schon mal verloren hatte, gilt als Fehlurteil. 1945 beendete er seine Karriere nach 18 Jahren, wobei er 67 Mal durch KO gewann sowie weitere 24 Mal durch Punkteentscheidung. Garcia wurde besonders durch seinen Kampfstil bekannt und dem von ihm verwendeten sogenannten „Bolo Punch“, den er wie einen Aufwärtshaken anwendete. Einer seiner Coachs war Ray Arcel.
B:
Ernst Stimmel (* 23. März 1891 in Hamburg; † 28. März 1978 in Reichenau) war ein deutscher Schauspieler und Autor. Nach Abitur und Studium wurde Ernst Stimmel 1919 in München mit der Dissertation "Einfluß der Schopenhauerschen Philosophie auf Wilhelm Raabe" promoviert. In den 1930er und 1940er Jahren wirkte er in vielen Filmproduktionen als Darsteller überwiegend in Nebenrollen mit. Darunter befanden sich die nationalsozialistischen Propagandafilme "Jud Süß", "Die Rothschilds" und "Kampfgeschwader Lützow", die heute in Deutschland als Vorbehaltsfilme nur unter bestimmten Voraussetzungen aufgeführt werden können. Ernst Stimmel spielte aber auch in Unterhaltungs- und Historienfilmen wie "Der Gasmann" mit Heinz Rühmann, "Der große König" mit Otto Gebühr und "Die Entlassung" mit Emil Jannings. Zudem war er an dem Film "Zwischen Herz und Gewissen" beteiligt, der als Überläufer erst im Jahr 1951 uraufgeführt wurde, obwohl dieser kurz vor Ende des Zweiten Weltkriegs noch unter dem Titel "Das fremde Leben" fertiggestellt wurde.
C:
Saidis Laufbahn als Berufsboxer begann mit einem Kampf im November 1989, seinen letzten Kampf bestritt er im Dezember 2006. Im Mai 1990 gewann er gegen Andreas Schweiger die internationale deutsche Meisterschaft im Halbschwergewicht und wurde im Juni 1990 deutscher Schwergewichtsmeister. Im November 1992 wurde Saidi durch einen Sieg über Rund Kanika aus dem Kongo Afrikameister im Halbschwergewicht. Er musste den internationalen deutschen Meistertitel abgegeben, nachdem er im Februar 1993 gegen Dariusz Michalczewski verloren hatte. Saidi wurde im April 1994 Weltmeister im Halbschwergewicht nach Version der WBF. Er sicherte sich Ende Januar 1997 den deutschen Meistertitel im Halbschwergewicht, diesen verlor er im Dezember desselben Jahres wieder, als er gegen Sven Ottke verlor. Im Februar 1999 boxte Saidi wieder um die deutsche Meisterschaft im Halbschwergewicht, verlor aber gegen Thomas Ulrich. Anschließend legte er eine jahrelange Pause ein, im Mai 2005 kehrte Saidi in den Ring zurück. Noch einmal deutscher Meister, diesmal im Cruisergewicht, wurde er im Januar 2006 durch einen Sieg über Mario Stein.
D:
Uwe Boegelsack (* 2. Dezember 1939 in Gommern; † 28. Januar 2017) war ein deutscher Politiker der Sozialistischen Einheitspartei Deutschlands (SED) in der Deutschen Demokratischen Republik (DDR). Er war von 1984 bis 1987 stellvertretender Minister für Elektrotechnik und Elektronik und von 1987 bis 1990 Generaldirektor des "VEB Kombinat Rundfunk und Fernsehen Staßfurt". Boegelsack, Sohn eines Angestellten, arbeitete nach dem Abitur 1958 als Stanzer und Hilfsarbeiter und wurde 1961 Setzer und Feiler.
```
### choose_question_by_context (subset task1)
Given is a context and 4 questions. The task is to decide which question can be answered by the context.
Example:
```text
Welche der folgenden Fragen (A oder B oder C oder D) lässt sich anhand des Kontext beantworten?
Kontext:
Lsjbot ist ein von Lars Sverker Johansson (Akronym "Lsj") betriebener Bot, der aus digitalen Informationsquellen und Datenbanken kurze Wikipedia-Artikel („Stubs“) in schwedischer Sprache sowie in Cebuano und Wáray-Wáray, zwei auf den Philippinen gesprochenen Sprachen, generierte. Am 15. Juni 2013 überschritt die schwedischsprachige Wikipedia durch einen von Lsjbot erstellten Artikel über die Schmetterlingsart "Erysichton elaborata" die Schwelle von einer Million Artikeln. Zu diesem Zeitpunkt war rund die Hälfte des Artikelbestands der schwedischen Wikipedia botgeneriert. Etwa ein Drittel der von Lsjbot erstellten Artikel wurden für die schwedische Wikipedia erstellt. Im August 2013 erzeugte Lsjbot mit etwa täglich 7200 Artikeln für die schwedische Wikipedia die meisten Artikel pro Tag für eine Wikipedia. Laut "The Wall Street Journal" hatte Lsjbot im Juli 2014 bereits rund 2,7 Millionen Artikel in Wikipedia eingestellt, was zu dieser Zeit etwa 8,5 Prozent des gesamten Bestandes der Wikipedia entsprach. Für die Artikelproduktion griff Lsjbot auf Datenbanken wie den Catalogue of Life zu, wobei offenbar veraltete Offline-Kopien genutzt wurden.
Fragen:
A: Welche Schmetterlingsart wurde durch einen von Lsjbot erstellten Artikel bekannt?
B: Welche Partei stand der Hannoverschen Landeszeitung nahe?
C: In welchem Jahr wurde die Anwendungssoftware erstmals erstellt?
D: Wo werden die Server der Enciclopedia Libre Universal en Español betrieben?
```
### context_question_match (subset task4)
Given is a context and a question. The task is to decide whether the question can be answered by the context or not.
Example:
```text
Lässt sich die Frage mithilfe der Informationen aus dem Kontext beantworten? Antworte mit J für ja oder N für nein.
Kontext:
Oren Koules (* 31. Januar 1961 in La Grange, Illinois) ist ein ehemaliger US-amerikanischer Eishockeyspieler und jetziger -funktionär, sowie Filmproduzent. Bekannt wurde er vor allem durch die Filmreihe Saw, die von seiner Produktionsfirma produziert wird. Oren Koules begann seine Karriere als Eishockeyspieler in der kanadischen Juniorenliga Western Hockey League, in der er von 1979 bis 1982 für die Portland Winter Hawks, Great Falls Americans, Medicine Hat Tigers, Spokane Flyers, Calgary Wranglers und Brandon Wheat Kings aktiv war. Bei den Great Falls Americans, die vorzeitig in ihrer Premierensaison den Spielbetrieb einstellten, hält er mit neun Treffern den Rekord als bester Torschütze in der Franchise-Geschichte. Gegen Ende der Saison 1981/82 bestritt der Flügelspieler zudem ein Spiel für die Saginaw Gears in der International Hockey League.
Die Frage: Bei welchem Verein war Thomas Kleine zweieinhalb Jahre Kapitän?
```
### question_answer_match (subset task3)
Given is a question and an answer. The task is to decide whether the answer actualy answers the question.
Example:
```text
Beantwortet die Antwort wirklich die Frage? Antworte mit J für ja oder N für nein.
Die Frage: Mit welchem Unternehmen fusionierte die Adesso AG im Jahr 2006?
Die Antwort: Bruno Zumino erwarb sein Physik-Diplom an der Universität Rom im Jahr 1945.
```
## Usage
This evaluation task is implemented in [LightEval](https://github.com/huggingface/lighteval):
- <https://github.com/huggingface/lighteval/blob/main/community_tasks/german_rag_evals.py>
- <https://github.com/huggingface/lighteval/blob/main/examples/tasks/all_german_rag_evals.txt>
<span style="color:red;font-weight:bold">Important note:</span>
Unfortunately, after the implementation in LightEval,
we found another weakness in the prompt.
This was fixed by 23. April 2024 in PR
[Fix prompt format german rag community task #171](https://github.com/huggingface/lighteval/pull/171).
Each evaluation done before this date should be carried out again.
Unfortunately, LightEval does not yet support versioning of tasks.
This will hopefully be added later
(see also [Version of a task should be configurable. #172](https://github.com/huggingface/lighteval/issues/172)).
To run the tests on a multi GPU setup, you must first be in the LightEval root directory.
It can be run by:
```bash
# this is a hack to avoid a bug while loading the dataset
# see https://github.com/huggingface/lighteval/issues/278
export HF_DATASETS_TRUST_REMOTE_CODE=TRUE
export MODEL_NAME="DiscoResearch/DiscoLM_German_7b_v1"
accelerate launch --multi_gpu --num_processes=2 -m \
lighteval accelerate \
--model_args "pretrained=$MODEL_NAME,model_parallel=True,trust_remote_code=True" \
--use_chat_template \
--override_batch_size 1 \
--tasks "examples/tasks/all_german_rag_evals.txt" \
--custom_tasks "community_tasks/german_rag_evals.py" \
--output_dir="./evals/"
```
## Results
The evaluation was carried out with [LightEval](https://github.com/huggingface/lighteval) and
the `--use_chat_template --override_batch_size 1` options.
For details see [telekom/llm_evaluation_results](https://github.com/telekom/llm_evaluation_results).
| model name | choose context by question acc | choose question by context acc | context question match acc | question answer match acc | all acc | all acc stderr |
|:---------------------------------------------------------|---------------------------------:|---------------------------------:|-----------------------------:|----------------------------:|----------:|-----------------:|
| mistralai/Mixtral-8x22B-Instruct-v0.1 | 0.998 | 1 | 0.967 | 0.986 | 0.98775 | 0.00269564 |
| VAGOsolutions/Llama-3-SauerkrautLM-70b-Instruct | 0.998 | 1 | 0.973 | 0.949 | 0.98 | 0.0033755 |
| VAGOsolutions/SauerkrautLM-Mixtral-8x7B-Instruct | 0.953 | 0.998 | 0.975 | 0.974 | 0.975 | 0.00452096 |
| mistralai/Mixtral-8x7B-Instruct-v0.1 | 0.94 | 0.998 | 0.973 | 0.973 | 0.971 | 0.00479586 |
| meta-llama/Meta-Llama-3-70B-Instruct | 0.94 | 1 | 0.974 | 0.946 | 0.965 | 0.00492486 |
| microsoft/Phi-3-mini-4k-instruct | 0.847 | 0.998 | 0.965 | 0.964 | 0.9435 | 0.00612787 |
| VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct | 0.928 | 0.824 | 0.982 | 0.906 | 0.91 | 0.00841656 |
| meta-llama/Meta-Llama-3-8B-Instruct | 0.725 | 0.855 | 0.977 | 0.943 | 0.875 | 0.00933624 |
| DiscoResearch/DiscoLM_German_7b_v1 | 0.625 | 0.991 | 0.914 | 0.927 | 0.86425 | 0.0088514 |
| occiglot/occiglot-7b-de-en-instruct | 0.343 | 0.994 | 0.863 | 0.969 | 0.79225 | 0.00845623 |
| occiglot/occiglot-7b-eu5-instruct | 0.722 | 0.982 | 0.587 | 0.814 | 0.77625 | 0.0115674 |
| LeoLM/leo-mistral-hessianai-7b-chat | 0.865 | 0.949 | 0.735 | 0.52 | 0.76725 | 0.0118855 |
| occiglot/occiglot-7b-de-en | 0.453 | 0.698 | 0.501 | 0.5 | 0.538 | 0.0154785 |
| DiscoResearch/Llama3_DiscoLM_German_8b_v0.1_experimental | 0.303 | 0.28 | 0.751 | 0.594 | 0.482 | 0.0144911 |
| occiglot/occiglot-7b-eu5 | 0.327 | 0.582 | 0.5 | 0.5 | 0.47725 | 0.0155215 |
The evaluation results for the first (old) prompt version can be seen here:
<https://huggingface.co/datasets/deutsche-telekom/Ger-RAG-eval/blob/main/old-eval.md>
## Licensing
The Wikipedia texts are licensed under [CC BY-SA 4.0 Deed](https://creativecommons.org/licenses/by-sa/4.0/deed)
by the corresponding authors of the [German Wikipedia](https://de.wikipedia.org/).\
The questions and answers are copyright ([CC BY-SA 4.0 Deed](https://creativecommons.org/licenses/by-sa/4.0/deed)) by
[Philip May](https://philipmay.org), [Deutsche Telekom AG](https://www.telekom.de/).
|
cfrench/matcha_stir | cfrench | 2025-04-20T18:29:46Z | 72 | 0 | [
"task_categories:robotics",
"size_categories:n<1K",
"modality:video",
"library:datasets",
"library:mlcroissant",
"region:us",
"phosphobot",
"so100",
"phospho-dk"
] | [
"robotics"
] | 2025-04-20T15:44:47Z | 0 |
---
tags:
- phosphobot
- so100
- phospho-dk
task_categories:
- robotics
---
# matcha_stir
**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.
|
laolaorkk/stage1_sampled | laolaorkk | 2025-04-10T11:18:07Z | 13 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-04-10T11:11:48Z | 0 | ---
dataset_info:
features:
- name: problem
dtype: string
- name: answer
dtype: string
- name: solution
dtype: string
- name: difficulty
dtype: float64
splits:
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configs:
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data_files:
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path: data/train-*
---
|
Upstboy/2234234 | Upstboy | 2025-06-24T22:36:02Z | 0 | 0 | [
"task_categories:robotics",
"size_categories:n<1K",
"modality:video",
"library:datasets",
"library:mlcroissant",
"region:us",
"phosphobot",
"so100",
"phospho-dk"
] | [
"robotics"
] | 2025-06-24T21:41:02Z | 0 |
---
tags:
- phosphobot
- so100
- phospho-dk
task_categories:
- robotics
---
# 2234234
**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.
|
Majinuub/Resume_Parsing | Majinuub | 2025-03-03T18:09:16Z | 41 | 2 | [
"license:apache-2.0",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-03-03T15:23:03Z | 0 | ---
license: apache-2.0
---
|
AmarHelio/record-test13 | AmarHelio | 2025-06-15T03:17:44Z | 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"
] | 2025-06-15T03:14:49Z | 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": 3013,
"total_tasks": 1,
"total_videos": 2,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
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"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]
``` |
neuralnets/letter_bench | neuralnets | 2025-02-20T15:59:11Z | 22 | 4 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-02-20T15:58:14Z | 0 | ---
dataset_info:
features:
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dtype: string
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path: data/data-*
---
|
RyanYr/reflect_mini8bSFTt2_mini8BSFTt1_om2g8kom2AG40k_iPSDP_it1_t02_correct | RyanYr | 2025-03-26T01:52:42Z | 68 | 0 | [
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] | [] | 2024-12-30T20:34:22Z | 0 | ---
dataset_info:
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configs:
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path: data/train-*
---
|
dgambettavuw/D_gen0_run0_llama2-7b_sciabs_doc1000_real64_synt64_vuw | dgambettavuw | 2024-12-23T03:52:24Z | 16 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
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] | [] | 2024-12-23T03:52:21Z | 0 | ---
dataset_info:
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---
|
techiaith/macsen_intent_parsing | techiaith | 2025-03-31T11:41:00Z | 22 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
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] | [] | 2025-03-31T11:40:59Z | 0 | ---
dataset_info:
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---
|
ZixuanKe/fingpt_convfinqa_sup_sample_from_policy_v1.1_dpo_val_chunk_26 | ZixuanKe | 2024-11-23T01:27:25Z | 15 | 0 | [
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"library:pandas",
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] | [] | 2024-11-23T01:27:24Z | 0 | ---
dataset_info:
features:
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dtype: string
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dtype: string
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dtype: string
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path: data/train-*
---
|
mteb/multi-hatecheck | mteb | 2025-05-04T16:08:10Z | 33 | 0 | [
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"task_ids:sentiment-scoring",
"task_ids:sentiment-classification",
"task_ids:hate-speech-detection",
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"arxiv:2210.07316",
"region:us",
"mteb",
"text"
] | [
"text-classification"
] | 2024-04-19T20:47:19Z | 0 | ---
annotations_creators:
- expert-annotated
language:
- ara
- cmn
- deu
- eng
- fra
- hin
- ita
- nld
- pol
- por
- spa
license: cc-by-4.0
multilinguality: multilingual
size_categories:
- 10K<n<100K
task_categories:
- text-classification
task_ids:
- sentiment-analysis
- sentiment-scoring
- sentiment-classification
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configs:
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data_files:
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split: test
- config_name: hin
data_files:
- path: multi-hatecheck/test/hin.jsonl.gz
split: test
- config_name: spa
data_files:
- path: multi-hatecheck/test/spa.jsonl.gz
split: test
- config_name: pol
data_files:
- path: multi-hatecheck/test/pol.jsonl.gz
split: test
- config_name: eng
data_files:
- path: multi-hatecheck/test/eng.jsonl.gz
split: test
- config_name: fra
data_files:
- path: multi-hatecheck/test/fra.jsonl.gz
split: test
- config_name: nld
data_files:
- path: multi-hatecheck/test/nld.jsonl.gz
split: test
- config_name: ita
data_files:
- path: multi-hatecheck/test/ita.jsonl.gz
split: test
- config_name: deu
data_files:
- path: multi-hatecheck/test/deu.jsonl.gz
split: test
- config_name: ara
data_files:
- path: multi-hatecheck/test/ara.jsonl.gz
split: test
- config_name: por
data_files:
- path: multi-hatecheck/test/por.jsonl.gz
split: test
- config_name: cmn
data_files:
- path: multi-hatecheck/test/cmn.jsonl.gz
split: test
tags:
- mteb
- text
---
<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->
<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
<h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">MultiHateClassification</h1>
<div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div>
<div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
</div>
Hate speech detection dataset with binary
(hateful vs non-hateful) labels. Includes 25+ distinct types of hate
and challenging non-hate, and 11 languages.
| | |
|---------------|---------------------------------------------|
| Task category | t2c |
| Domains | Constructed, Written |
| Reference | https://aclanthology.org/2022.woah-1.15/ |
## How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
```python
import mteb
task = mteb.get_tasks(["MultiHateClassification"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
```
<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb).
## Citation
If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb).
```bibtex
@inproceedings{rottger-etal-2021-hatecheck,
abstract = {Detecting online hate is a difficult task that even state-of-the-art models struggle with. Typically, hate speech detection models are evaluated by measuring their performance on held-out test data using metrics such as accuracy and F1 score. However, this approach makes it difficult to identify specific model weak points. It also risks overestimating generalisable model performance due to increasingly well-evidenced systematic gaps and biases in hate speech datasets. To enable more targeted diagnostic insights, we introduce HateCheck, a suite of functional tests for hate speech detection models. We specify 29 model functionalities motivated by a review of previous research and a series of interviews with civil society stakeholders. We craft test cases for each functionality and validate their quality through a structured annotation process. To illustrate HateCheck{'}s utility, we test near-state-of-the-art transformer models as well as two popular commercial models, revealing critical model weaknesses.},
address = {Online},
author = {R{\"o}ttger, Paul and
Vidgen, Bertie and
Nguyen, Dong and
Waseem, Zeerak and
Margetts, Helen and
Pierrehumbert, Janet},
booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
doi = {10.18653/v1/2021.acl-long.4},
editor = {Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto},
month = aug,
pages = {41--58},
publisher = {Association for Computational Linguistics},
title = {{H}ate{C}heck: Functional Tests for Hate Speech Detection Models},
url = {https://aclanthology.org/2021.acl-long.4},
year = {2021},
}
@inproceedings{rottger-etal-2022-multilingual,
abstract = {Hate speech detection models are typically evaluated on held-out test sets. However, this risks painting an incomplete and potentially misleading picture of model performance because of increasingly well-documented systematic gaps and biases in hate speech datasets. To enable more targeted diagnostic insights, recent research has thus introduced functional tests for hate speech detection models. However, these tests currently only exist for English-language content, which means that they cannot support the development of more effective models in other languages spoken by billions across the world. To help address this issue, we introduce Multilingual HateCheck (MHC), a suite of functional tests for multilingual hate speech detection models. MHC covers 34 functionalities across ten languages, which is more languages than any other hate speech dataset. To illustrate MHC{'}s utility, we train and test a high-performing multilingual hate speech detection model, and reveal critical model weaknesses for monolingual and cross-lingual applications.},
address = {Seattle, Washington (Hybrid)},
author = {R{\"o}ttger, Paul and
Seelawi, Haitham and
Nozza, Debora and
Talat, Zeerak and
Vidgen, Bertie},
booktitle = {Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH)},
doi = {10.18653/v1/2022.woah-1.15},
editor = {Narang, Kanika and
Mostafazadeh Davani, Aida and
Mathias, Lambert and
Vidgen, Bertie and
Talat, Zeerak},
month = jul,
pages = {154--169},
publisher = {Association for Computational Linguistics},
title = {Multilingual {H}ate{C}heck: Functional Tests for Multilingual Hate Speech Detection Models},
url = {https://aclanthology.org/2022.woah-1.15},
year = {2022},
}
@article{enevoldsen2025mmtebmassivemultilingualtext,
title={MMTEB: Massive Multilingual Text Embedding Benchmark},
author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
publisher = {arXiv},
journal={arXiv preprint arXiv:2502.13595},
year={2025},
url={https://arxiv.org/abs/2502.13595},
doi = {10.48550/arXiv.2502.13595},
}
@article{muennighoff2022mteb,
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
url = {https://arxiv.org/abs/2210.07316},
doi = {10.48550/ARXIV.2210.07316},
}
```
# Dataset Statistics
<details>
<summary> Dataset Statistics</summary>
The following code contains the descriptive statistics from the task. These can also be obtained using:
```python
import mteb
task = mteb.get_task("MultiHateClassification")
desc_stats = task.metadata.descriptive_stats
```
```json
{
"test": {
"num_samples": 11000,
"number_of_characters": 502013,
"number_texts_intersect_with_train": 16,
"min_text_length": 1,
"average_text_length": 45.63754545454545,
"max_text_length": 135,
"unique_text": 10990,
"unique_labels": 2,
"labels": {
"0": {
"count": 7661
},
"1": {
"count": 3339
}
}
},
"train": {
"num_samples": 11000,
"number_of_characters": 505993,
"number_texts_intersect_with_train": null,
"min_text_length": 4,
"average_text_length": 45.99936363636364,
"max_text_length": 131,
"unique_text": 10993,
"unique_labels": 2,
"labels": {
"0": {
"count": 7659
},
"1": {
"count": 3341
}
}
}
}
```
</details>
---
*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)* |
Synthyra/StringDBSeqsv12 | Synthyra | 2024-12-05T20:24:48Z | 32 | 0 | [
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---
All the IDs and sequences in StringDB version 12
https://string-db.org/cgi/download
|
beyondgravityresearch/nlp_zh_dazhongdianping | beyondgravityresearch | 2025-01-16T03:15:38Z | 15 | 0 | [
"license:apache-2.0",
"region:us"
] | [] | 2025-01-16T03:15:38Z | 0 | ---
license: apache-2.0
---
|
jiuyal2/so100_marker_new_camera_pose_new | jiuyal2 | 2025-06-09T05:49:52Z | 0 | 0 | [
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] | [
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] | 2025-06-09T05:16:09Z | 0 | ---
license: apache-2.0
task_categories:
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tags:
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configs:
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data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
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}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
ayushsi42/reward-bench-mistral-7b-sft-beta-comprehensive | ayushsi42 | 2025-06-23T21:03:14Z | 0 | 0 | [
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] | [] | 2025-06-23T21:02:57Z | 0 | ---
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|
orionweller/c4-text | orionweller | 2024-10-31T15:45:11Z | 29 | 0 | [
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] | [] | 2024-10-31T15:20:56Z | 0 | ---
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---
This dataset contains two configurations: c4 and msmarco, both stored in gz format |
pclucas14/narrative_qa_rag_128_32overlap_12_30 | pclucas14 | 2024-10-17T20:04:52Z | 20 | 0 | [
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|
violetxi/NUMINA-V2-Clean-Blocks-9000_9500-0_500 | violetxi | 2024-11-30T18:55:51Z | 13 | 0 | [
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cfpark00/toy-multistep-nn_20-na_20-nab_120-seed_0 | cfpark00 | 2025-04-07T08:50:58Z | 15 | 0 | [
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---
|
uzair921/SKILLSPAN_LLM_CONTEXT_3541_25 | uzair921 | 2024-10-09T08:56:35Z | 20 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-10-08T16:17:04Z | 0 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-Skill
'2': I-Skill
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- name: validation
num_bytes: 715196
num_examples: 1397
- name: test
num_bytes: 758463
num_examples: 1523
download_size: 0
dataset_size: 2532469
---
# Dataset Card for "SKILLSPAN_LLM_CONTEXT_3541_25"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
HovorunB/example_dataset | HovorunB | 2025-06-14T19:06:21Z | 0 | 0 | [
"task_categories:robotics",
"size_categories:n<1K",
"modality:video",
"library:datasets",
"library:mlcroissant",
"region:us",
"phosphobot",
"so100",
"phospho-dk"
] | [
"robotics"
] | 2025-06-14T19:06:18Z | 0 |
---
tags:
- phosphobot
- so100
- phospho-dk
task_categories:
- robotics
---
# example_dataset
**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.
|
DanOgh07/train_icl_sample | DanOgh07 | 2025-03-11T19:37:33Z | 14 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-03-11T09:47:58Z | 0 | ---
dataset_info:
features:
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sequence: string
- name: target
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num_examples: 1497
download_size: 1074376
dataset_size: 104655883
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_8665861c-1fc5-4a62-9195-7634e450401e | argilla-internal-testing | 2024-10-21T16:26:40Z | 19 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-10-21T16:26:39Z | 0 | ---
dataset_info:
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- name: label
dtype:
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'0': positive
'1': negative
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num_examples: 3
download_size: 1454
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configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
artianand/bbq_deberta_v3_large_race_custom_loss_custom_dataset_bbq | artianand | 2025-04-15T14:13:41Z | 8 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-04-12T12:36:06Z | 0 | ---
dataset_info:
features:
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- name: example_id
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- name: question_index
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configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
infinite-dataset-hub/ElClasicoLiveSentiment | infinite-dataset-hub | 2025-01-09T16:03:52Z | 12 | 0 | [
"license:mit",
"size_categories:n<1K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"infinite-dataset-hub",
"synthetic"
] | [] | 2025-01-09T16:03:50Z | 0 | ---
license: mit
tags:
- infinite-dataset-hub
- synthetic
---
# ElClasicoLiveSentiment
tags: Classification, Spanish, Football Comments
_Note: This is an AI-generated dataset so its content may be inaccurate or false_
**Dataset Description:**
The 'ElClasicoLiveSentiment' dataset contains real-time comments from live broadcasts of the El Clasico football matches. Each comment has been translated into Spanish and analyzed for sentiment. The dataset is structured to facilitate sentiment classification, focusing on expressions related to football events like passes, goals, and sprints. Comments have been anonymized and tagged with either positive, neutral, or negative sentiment labels. The CSV file contains a selection of comments along with their respective sentiment labels, formatted for easy consumption by machine learning practitioners interested in analyzing the emotional tone of live football commentary.
**CSV Content Preview:**
```
CommentID,CommentText,Label
1,"¡Esto es una asombrosa jugada! El pase es inimaginable.",Positive
2,"Estoy muy confundido con la situación actual en el campo.",Neutral
3,"¡El gol fue abominable, solo noche sin fútbol!",Negative
4,"Parece que el equipo X tiene una increíble sensibilidad en los pases.",Positive
5,"¿Cuál fue el movimiento que llevó al segundo gol? Gracioso!",Positive
```
**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 'dataset of football game live comments in spanish for el clasico, positive/neutral/negative sentiment, quoted csv, comments on passes, goals, sprints etc.':
- **Dataset Generation Page**: https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub?q=dataset+of+football+game+live+comments+in+spanish+for+el+clasico,+positive/neutral/negative+sentiment,+quoted+csv,+comments+on+passes,+goals,+sprints+etc.&dataset=ElClasicoLiveSentiment&tags=Classification,+Spanish,+Football+Comments
- **Model**: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct
- **More Datasets**: https://huggingface.co/datasets?other=infinite-dataset-hub
|
MexIvanov/RAG-v1-ruen | MexIvanov | 2024-11-11T07:32:40Z | 82 | 1 | [
"language:en",
"language:ru",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"rag",
"synthetic",
"code"
] | [] | 2024-11-11T07:21:12Z | 0 | ---
license: apache-2.0
language:
- en
- ru
tags:
- rag
- synthetic
- code
---
A version of the glaiveai/RAG-v1 dataset extended with machine translation to Russian language for multilingual retrieval-augmented generation tasks.
Released under the same license as the original dataset, provided as is with research intent (but not limited), use/read at your own risk. |
ahmedheakl/plotly_pie_chart | ahmedheakl | 2025-02-03T18:00:11Z | 22 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"datadreamer",
"datadreamer-0.35.0",
"synthetic",
"gpt-4o"
] | [] | 2025-02-03T17:59:32Z | 0 | ---
dataset_info:
features:
- name: metadata
dtype: string
- name: topic
dtype: string
- name: data
dtype: string
- name: code
dtype: string
- name: image
dtype: image
splits:
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num_examples: 4
download_size: 305028
dataset_size: 312700.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
library_name: datadreamer
size_categories:
- n<1K
tags:
- datadreamer
- datadreamer-0.35.0
- synthetic
- gpt-4o
- gpt-4o
- gpt-4o
---
# Dataset Card
[Add more information here](https://huggingface.co/datasets/templates/dataset-card-example)
---
This dataset was produced with [DataDreamer 🤖💤](https://datadreamer.dev). The synthetic dataset card can be found [here](datadreamer.json). |
aklywtx/corpus_vi_sop | aklywtx | 2024-12-07T14:25:34Z | 14 | 0 | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-12-07T14:20:20Z | 0 | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
splits:
- name: train
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download_size: 1119754151
dataset_size: 2660889947
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
underctrl/handcamera_single_orange | underctrl | 2024-12-10T06:09:31Z | 84 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot"
] | [
"robotics"
] | 2024-12-10T03:36:32Z | 0 | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.0",
"robot_type": "unknown",
"total_episodes": 80,
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},
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"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"observation.state": {
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"shape": [
6
],
"names": {
"motors": [
"motor_0",
"motor_1",
"motor_2",
"motor_3",
"motor_4",
"motor_5"
]
}
},
"observation.images.android": {
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480,
640,
3
],
"names": [
"height",
"width",
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],
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},
"observation.images.webcam": {
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480,
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],
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],
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},
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],
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}
},
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],
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]
}
},
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}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
mlabonne/orca-agentinstruct-1M-v1-cleaned | mlabonne | 2025-01-25T16:01:27Z | 205 | 59 | [
"task_categories:question-answering",
"language:en",
"license:cdla-permissive-2.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"question-answering"
] | 2024-11-17T18:09:55Z | 0 | ---
language:
- en
license: cdla-permissive-2.0
size_categories:
- 1M<n<10M
task_categories:
- question-answering
dataset_info:
features:
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download_size: 2178041194
dataset_size: 4638101451
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# 🐋 Orca-AgentInstruct-1M-v1-cleaned
This is a cleaned version of the [microsoft/orca-agentinstruct-1M-v1](https://huggingface.co/datasets/microsoft/orca-agentinstruct-1M-v1) dataset released by Microsoft.
> orca-agentinstruct-1M-v1 is a fully synthetic dataset using only raw text publicly available on the web as seed data. It is a subset of the full AgentInstruct dataset (~25M samples) that created Orca-3-Mistral. Compared to Mistral 7B Instruct, the authors claim 40% improvement on AGIEval, 19% improvement on MMLU, 54% improvement on GSM8K, 38% improvement on BBH and 45% improvement on AlpacaEval.
Here's what I changed:
1. Splits are unified into one, with a new "split" column
2. Strings were converted into lists of dicts to ensure compatibility with most frameworks
3. Empty system prompts were removed so you don't get weird errors
Data categories in the dataset:
- creative_content
- text_modification
- struct2text_flow
- rc
- rag
- text_extraction
- mcq
- follow_up
- analytical_reasoning
- fermi
- fs_cot_flow
- code_
- brain_teaser
- text_classification
- open_domain_q |
ketchup123/TeleEval_ShareGPT | ketchup123 | 2025-03-18T17:00:49Z | 15 | 0 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-03-18T17:00:43Z | 0 | ---
dataset_info:
features:
- name: conversations
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download_size: 99217737
dataset_size: 260048447
configs:
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data_files:
- split: train
path: data/train-*
---
|
mlfoundations-dev/openthoughts2_science_1k | mlfoundations-dev | 2025-05-06T05:34:31Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-06T05:34:30Z | 0 | ---
dataset_info:
features:
- name: conversations
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dataset_size: 12487276.660572821
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Procit004/sentiment | Procit004 | 2025-03-24T06:59:13Z | 7 | 0 | [
"size_categories:10K<n<100K",
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"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-03-24T06:59:06Z | 0 | ---
dataset_info:
features:
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dataset_size: 18601833
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
gm168/GDPR_norms_extraction | gm168 | 2024-10-09T03:00:41Z | 25 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-10-04T00:39:58Z | 0 | ---
dataset_info:
features:
- name: gdpr_article_number
dtype: string
- name: cross_reference
dtype: string
- name: gdpr_article
dtype: string
- name: norms
dtype: string
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dataset_size: 781633.0
configs:
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data_files:
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path: data/train-*
- split: test
path: data/test-*
---
|
tenfoldpaper/pick-gummy4 | tenfoldpaper | 2025-06-14T16:38:00Z | 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"
] | 2025-06-14T16:37:54Z | 0 | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.1",
"robot_type": "so101_follower",
"total_episodes": 4,
"total_frames": 1778,
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"train": "0:4"
},
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"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
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],
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"shoulder_lift.pos",
"elbow_flex.pos",
"wrist_flex.pos",
"wrist_roll.pos",
"gripper.pos"
]
},
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],
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"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": {
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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
}
},
"observation.images.side": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
}
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"frame_index": {
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"shape": [
1
],
"names": null
},
"episode_index": {
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],
"names": null
},
"index": {
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"shape": [
1
],
"names": null
},
"task_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
Self-GRIT/open-hermes-2.5-sft-active-retrieval-instruct-linq-wikiv2-prefix-suffix | Self-GRIT | 2024-10-02T08:20:13Z | 19 | 0 | [
"size_categories:100K<n<1M",
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"modality:text",
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configs:
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---
|
kothasuhas/gold-37M-1.5M-gens-4-30 | kothasuhas | 2025-05-01T11:47:44Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-01T11:44:17Z | 0 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
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num_bytes: 3575804917
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- name: validation
num_bytes: 2451760
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dataset_size: 3578256677
configs:
- config_name: default
data_files:
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path: data/train-*
- split: validation
path: data/validation-*
---
|
smanni/train_so100_pick_place_double_purple_object | smanni | 2025-05-28T12:42:41Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot"
] | [
"robotics"
] | 2025-05-28T12:42:26Z | 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": "so100",
"total_episodes": 50,
"total_frames": 29850,
"total_tasks": 1,
"total_videos": 50,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:50"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"action": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.images.intel_realsense": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.fps": 30.0,
"video.height": 480,
"video.width": 640,
"video.channels": 3,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"frame_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"episode_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"index": {
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"shape": [
1
],
"names": null
},
"task_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
c-ho/2025-05-16_processed_dev_df_with_linsearch_num | c-ho | 2025-05-16T17:58:28Z | 0 | 0 | [
"size_categories:10K<n<100K",
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"library:pandas",
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"library:polars",
"region:us"
] | [] | 2025-05-16T14:48:34Z | 0 | ---
dataset_info:
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download_size: 0
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- config_name: default
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path: data/train-*
---
# Dataset Card for "2025-05-16_processed_dev_df_with_linsearch_num"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tmpmodelsave/llama3_70bcorr_type2_selftype1_beta01_500tmp10 | tmpmodelsave | 2025-01-20T15:56:53Z | 15 | 0 | [
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"region:us"
] | [] | 2025-01-20T15:56:31Z | 0 | ---
dataset_info:
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dtype: int64
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---
|
AyaKhaled/test_csv_data3 | AyaKhaled | 2025-03-30T10:24:46Z | 52 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-03-30T10:15:35Z | 0 | ---
dataset_info:
features:
- name: image_path
dtype: string
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dtype: string
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dtype: string
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dtype: image
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dataset_size: 100028841.0
configs:
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---
|
wyh1128/US-1M | wyh1128 | 2025-05-23T09:29:35Z | 299 | 0 | [
"task_categories:image-feature-extraction",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:imagefolder",
"modality:image",
"library:datasets",
"library:mlcroissant",
"region:us",
"medical"
] | [
"image-feature-extraction"
] | 2025-05-15T07:49:55Z | 0 | ---
license: apache-2.0
task_categories:
- image-feature-extraction
language:
- en
tags:
- medical
size_categories:
- n>1T
---
# Dataset Card for Dataset Name
US-1M establishes a new benchmark in medical imaging research, comprising 1.28 million rigorously annotated ultrasound images with precise anatomical structure delineations and comprehensive
demographic metadata
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).
## Uses
## Dataset Structure
|
tlpss/roboflow-garlic | tlpss | 2024-12-16T19:27:47Z | 26 | 0 | [
"size_categories:1K<n<10K",
"format:imagefolder",
"modality:image",
"library:datasets",
"library:mlcroissant",
"region:us",
"keypoint-detection"
] | [] | 2024-12-16T19:20:16Z | 0 | ---
size_categories:
- n<1K
tags:
- keypoint-detection
---
# Garlic Keypoint Detection dataset
This dataset contains 1000 images of a single garlic clove in a pressumably industrial setting. The annotations are coco-formatted and are composed of a bounding box and 2 keypoints: head and tail.
The dataset was taken from https://universe.roboflow.com/gesture-recognition-dsn2n/garlic_keypoint/dataset/1. Refer to the original repo for licensing questions.
The annotations json files were slightly modified (formatting, image base directory,..) w.r.t. the original files. |
ZixuanKe/flare_finqa_sup_sample_from_policy_v1.1_dpo_train_chunk_18 | ZixuanKe | 2024-11-23T21:36:43Z | 18 | 0 | [
"size_categories:1K<n<10K",
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"library:pandas",
"library:mlcroissant",
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"region:us"
] | [] | 2024-11-23T21:36:41Z | 0 | ---
dataset_info:
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- name: rejected
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- name: chosen
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download_size: 571451
dataset_size: 5389539
configs:
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---
|
test-gen/code_livecodebench_qwen2.5-7b_t1.0_n8_tests_livecodebench_qwen3-4b-easy-unique_t0.0_n1 | test-gen | 2025-05-21T17:19:15Z | 0 | 0 | [
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] | [] | 2025-05-21T17:19:12Z | 0 | ---
dataset_info:
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dtype: string
- name: generated_code
sequence: string
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sequence: float64
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sequence: float64
- name: verification_info
struct:
- name: language
dtype: string
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sequence: string
splits:
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num_bytes: 2199516
num_examples: 182
download_size: 874946
dataset_size: 2199516
configs:
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path: data/test-*
---
|
neelabh17/new_news_exploded_prompt_n_5_d_perc_60_num_gen_10_Qwen2.5-7B-Instruct | neelabh17 | 2025-05-15T15:12:35Z | 0 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-15T15:12:34Z | 0 | ---
dataset_info:
features:
- name: id
dtype: string
- name: name
dtype: string
- name: topic
dtype: string
- name: news
dtype: string
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dtype: string
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dtype: string
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dtype: int64
splits:
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configs:
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data_files:
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---
|
HumanoidTeam/demo-maltesers | HumanoidTeam | 2024-12-19T22:17:10Z | 28 | 0 | [
"task_categories:robotics",
"region:us",
"LeRobot"
] | [
"robotics"
] | 2024-12-19T22:15:33Z | 0 | ---
task_categories:
- robotics
tags:
- LeRobot
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
|
mamung/reddit_dataset_192 | mamung | 2025-02-28T07:58:02Z | 9 | 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",
"task_ids:extractive-qa",
"task_ids:news-articles-summarization",
"multilinguality:multilingual",
"source_datasets:original",
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"text-classification",
"token-classification",
"question-answering",
"summarization",
"text-generation"
] | 2025-02-26T20:54:42Z | 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:** mamung/reddit_dataset_192
- **Subnet:** Bittensor Subnet 13
- **Miner Hotkey:** 5DtfQfETXqrW9JAEBgqZcidaHaxogXgWSMrQ8Qotzth9GCsr
### 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{mamung2025datauniversereddit_dataset_192,
title={The Data Universe Datasets: The finest collection of social media data the web has to offer},
author={mamung},
year={2025},
url={https://huggingface.co/datasets/mamung/reddit_dataset_192},
}
```
### 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:** 17425
- **Date Range:** 2024-11-08T00:00:00Z to 2025-02-28T00:00:00Z
- **Last Updated:** 2025-02-28T07:58:01Z
### Data Distribution
- Posts: 24.25%
- Comments: 75.75%
### Top 10 Subreddits
For full statistics, please refer to the `stats.json` file in the repository.
| Rank | Topic | Total Count | Percentage |
|------|-------|-------------|-------------|
| 1 | r/teenagers | 2361 | 13.55% |
| 2 | r/NoStupidQuestions | 2295 | 13.17% |
| 3 | r/Advice | 2205 | 12.65% |
| 4 | r/Monopoly_GO | 1935 | 11.10% |
| 5 | r/dirtyr4r | 1709 | 9.81% |
| 6 | r/AITAH | 1629 | 9.35% |
| 7 | r/videogames | 1365 | 7.83% |
| 8 | r/AmIOverreacting | 1320 | 7.58% |
| 9 | r/PcBuild | 1296 | 7.44% |
| 10 | r/moviecritic | 1033 | 5.93% |
## Update History
| Date | New Instances | Total Instances |
|------|---------------|-----------------|
| 2025-02-26T20:54:44Z | 198 | 198 |
| 2025-02-27T14:34:13Z | 6080 | 6278 |
| 2025-02-28T07:58:01Z | 11147 | 17425 |
|
fatlonder/i24 | fatlonder | 2024-11-22T03:02:49Z | 49 | 0 | [
"license:other",
"size_categories:1M<n<10M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2024-11-21T23:08:22Z | 0 | ---
license: other
license_name: i24
license_link: LICENSE
---
Data use agreement
By acquiring and using the I-24 MOTION data, you agree to the following data use agreement (updated 19 September 2023).
You are free to use the data in academic and commercial work.
The dataset contains anonymous trajectories. Any activities to re-identify individuals in the dataset or activities that may cause harm to individuals in the dataset are prohibited.
When you use I-24 MOTION data in published academic work, you are required to include the following citation contents. This allows us to aggregate statistics on the data use in publications:
Gloudemans, D., Wang, Y., Ji, J., Zachar, G., Barbour, W., Hall, E., Cebelak, M., Smith, L. and Work, D.B., 2023. I-24 MOTION: An instrument for freeway traffic science. Transportation Research Part C: Emerging Technologies, 155, p.104311.
The bibtex version of the reference is:
@article{gloudemans202324,
title={I-24 MOTION: An instrument for freeway traffic science},
author={Gloudemans, Derek and Wang, Yanbing and Ji, Junyi and Zachar, Gergely and Barbour, William and Hall, Eric and Cebelak, Meredith and Smith, Lee and Work, Daniel B},
journal={Transportation Research Part C: Emerging Technologies},
volume={155},
pages={104311},
year={2023},
publisher={Elsevier}
}
You are free to create and share derivative products as long as you maintain the terms above.
The data is provided “As is.” We make no other warranties, express or implied, and hereby disclaim all implied warranties, including any warranty of merchantability and warranty of fitness for a particular purpose.
|
WPRM/preference_data_llama_factory_corrected_format_text_only | WPRM | 2025-04-21T07:18:25Z | 27 | 0 | [
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kornwtp/sts17-crosslingual-mya-sts | kornwtp | 2025-03-09T15:06:19Z | 21 | 0 | [
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] | [] | 2025-03-03T06:06:00Z | 0 | ---
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---
|
BIT-MJY/test_tube_pick | BIT-MJY | 2025-06-16T12:55:45Z | 32 | 0 | [
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] | [] | 2025-05-01T08:32:22Z | 0 | ---
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Yiheyihe/galaxea-r1-shelf-debug | Yiheyihe | 2025-02-26T21:04:35Z | 37 | 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"
] | 2025-02-14T12:11:17Z | 0 | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
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}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
binyoonan/common | binyoonan | 2025-04-13T02:14:07Z | 16 | 0 | [
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] | [] | 2025-04-13T02:09:36Z | 0 | ---
dataset_info:
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|
MayAlsofyani/balanced_manybugs_few1 | MayAlsofyani | 2025-01-28T20:02:37Z | 18 | 0 | [
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] | [] | 2025-01-28T20:02:36Z | 0 | ---
dataset_info:
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---
|
vGassen/EU-Parliament-Minutes-Dutch | vGassen | 2025-06-17T14:14:27Z | 0 | 0 | [
"license:cc-by-4.0",
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] | [] | 2025-06-17T13:51:31Z | 0 | ---
license: cc-by-4.0
configs:
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dataset_size: 7219277
---
Dutch-European-Parliament-Minutes
This dataset contains cleaned Dutch minutes from the plenary sessions of the European Parliament.
The texts are scraped directly from the official website using scraper.py in this repository.
Dataset Structure
Each record includes:
URL – the original source link to the XML file.
text – the cleaned minutes text with procedural content removed.
source – a fixed string identifying the data as “European Parliament Minutes”.
Example:
{
"URL": "https://www.europarl.europa.eu/doceo/document/PV-10-2025-06-16_NL.xml",
"text": "De vergadering wordt om 14.00 uur geopend. ...",
"source": "European Parliament Minutes"
}
How to Use
Load the dataset directly from the Hugging Face Hub:
from datasets import load_dataset
dataset = load_dataset("YOUR_USERNAME/Dutch-European-Parliament-Minutes", split="train")
You can then iterate over dataset or convert it to other formats supported by the datasets library.
Source and Collection
The scraper starts from a given “table of contents” page (e.g. PV‑10‑2025‑06‑16‑TOC_NL.html) and follows the “Vorige” link to work backwards through parliamentary terms, currently stopping at term 6. Each corresponding XML minutes file is downloaded, cleaned, and converted into the dataset format.
The script excludes the “PRESENTIELIJST” section and other procedural notes so that each record focuses on the actual debate text.
License
The compiled dataset is released under CC-BY‑4.0.
According to the European Parliament website, older minutes use an equivalent open license (e.g. CC-BY‑SA or another permissive license). Please verify individual documents if you have specific reuse requirements.
When using this dataset, please credit “European Parliament” and, if applicable, link back to the Hugging Face dataset page. |
Alok2304/Indian_Property_Law | Alok2304 | 2025-03-18T17:41:22Z | 14 | 0 | [
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"region:us"
] | [] | 2025-03-18T17:41:17Z | 0 | ---
dataset_info:
features:
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list:
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dtype: string
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dtype: string
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download_size: 7341
dataset_size: 108550
configs:
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data_files:
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path: data/train-*
---
|
BarryFutureman/vpt_data_8xx_shard0110 | BarryFutureman | 2025-06-11T01:33:25Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:10K<n<100K",
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"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot"
] | [
"robotics"
] | 2025-06-11T01:31:27Z | 0 | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.1",
"robot_type": null,
"total_episodes": 10,
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"train": "0:10"
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"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"observation.image": {
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3,
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"channel",
"height",
"width"
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},
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1
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1
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}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
CodeSoft/BeatSaberLM-Dataset | CodeSoft | 2025-04-23T23:30:08Z | 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",
"beatsaber"
] | [
"question-answering"
] | 2025-04-23T23:18:58Z | 0 | ---
license: mit
task_categories:
- question-answering
language:
- en
tags:
- beatsaber
pretty_name: BeatSaberLM
size_categories:
- n<1K
---
# BeatSaberLM
BeatSaberLM is a conversational QA dataset built for the virtual reality video game Beat Saber. It includes prompts and detailed completions derived from community experience, guides, and technical modding instructions. Data was auto-generated and manually filtered for quality, but some inaccuracies may still be present.
## Features
- Covers gameplay, modding, difficulty levels, saber types, and more
- Focused on PC and Quest platforms
- Structured as prompt-completion pairs for language model fine-tuning or inference
- Continuously expanding based on community input and updates to Beat Saber
## Usage
BeatSaberLM is ideal for training or evaluating models on:
- Beat Saber-specific virtual assistant tasks like modding, mapping, or gameplay
## Limitations
- Dataset was filtered manually, but some answers may still be outdated or partially incorrect
- Some answers may reflect opinions from the community or common practices rather than official sources
## Updates
This dataset will be randomly updated.
## Licensing
This dataset is released under the MIT License.
## How to Cite
If you use this dataset in your research or projects, please cite it as follows:
```bibtex
@misc{codesoft2025beatsaberlm,
author = "CodeSoft",
title = "BeatSaberLM Dataset",
year = 2025,
note = "https://huggingface.co/datasets/CodeSoft/BeatSaberLM-Dataset"
}
```
---
For contributions or suggestions, reach out to me in the community tab. |
GitBag/Qwen2.5-7B-tldr-val | GitBag | 2025-04-10T12:15:30Z | 8 | 0 | [
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-04-10T06:43:12Z | 0 | ---
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path: data/validation-*
---
|
ZHIYII/successful_finite_inf_1000_all_parts | ZHIYII | 2025-04-08T07:40:19Z | 7 | 0 | [
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"library:dask",
"library:mlcroissant",
"library:polars",
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] | [] | 2025-04-08T06:14:07Z | 0 | ---
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data_files:
- split: train
path: train/train-*
---
|
sylvan54/Bean_Captioning_Dataset_1 | sylvan54 | 2025-01-21T10:50:34Z | 14 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-01-21T10:50:22Z | 0 | ---
dataset_info:
features:
- name: Image
sequence:
sequence:
sequence: uint8
- name: plant_description
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 271556338
num_examples: 771
download_size: 117703764
dataset_size: 271556338
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
oakwood/efe_br-35 | oakwood | 2024-10-13T02:40:22Z | 15 | 0 | [
"task_categories:robotics",
"region:us",
"LeRobot"
] | [
"robotics"
] | 2024-10-13T02:40:14Z | 0 | ---
task_categories:
- robotics
tags:
- LeRobot
---
This dataset was created using [🤗 LeRobot](https://github.com/huggingface/lerobot).
|
umang122104/RM-Bench-code-Mistral-7B-Instruct-v0.3-yesno | umang122104 | 2025-06-23T21:48:12Z | 0 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-23T21:48:10Z | 0 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: prompt
dtype: string
- name: subset
dtype: string
- name: language
dtype: string
- name: task_id
dtype: string
- name: chosen_1
dtype: string
- name: chosen_2
dtype: string
- name: chosen_3
dtype: string
- name: rejected_1
dtype: string
- name: rejected_2
dtype: string
- name: rejected_3
dtype: string
- name: chosen_1_yes_prob
dtype: float64
- name: chosen_1_no_prob
dtype: float64
- name: rejected_1_yes_prob
dtype: float64
- name: rejected_1_no_prob
dtype: float64
- name: chosen_2_yes_prob
dtype: float64
- name: chosen_2_no_prob
dtype: float64
- name: rejected_2_yes_prob
dtype: float64
- name: rejected_2_no_prob
dtype: float64
- name: chosen_3_yes_prob
dtype: float64
- name: chosen_3_no_prob
dtype: float64
- name: rejected_3_yes_prob
dtype: float64
- name: rejected_3_no_prob
dtype: float64
splits:
- name: train
num_bytes: 2576722
num_examples: 228
download_size: 1100364
dataset_size: 2576722
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
nimashoghi/matbench_mp_gap_fold3 | nimashoghi | 2025-03-12T20:52:23Z | 65 | 0 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-03-12T20:52:19Z | 0 | ---
dataset_info:
features:
- name: orig_idx
dtype: int64
- name: positions
sequence:
sequence: float32
- name: atomic_numbers
sequence: int64
- name: natoms
dtype: int64
- name: tags
sequence: int64
- name: fixed
sequence: bool
- name: cell
sequence:
sequence:
sequence: float32
- name: pbc
sequence:
sequence: bool
- name: y
dtype: float64
splits:
- name: train
num_bytes: 81772005
num_examples: 76401
- name: val
num_bytes: 8912098
num_examples: 8490
- name: test
num_bytes: 22709105
num_examples: 21222
download_size: 57276627
dataset_size: 113393208
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
---
|
yasimed/Cifar100_test | yasimed | 2025-02-19T02:32:01Z | 38 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-02-19T02:31:58Z | 0 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': apple
'1': aquarium_fish
'2': baby
'3': bear
'4': beaver
'5': bed
'6': bee
'7': beetle
'8': bicycle
'9': bottle
'10': bowl
'11': boy
'12': bridge
'13': bus
'14': butterfly
'15': camel
'16': can
'17': castle
'18': caterpillar
'19': cattle
'20': chair
'21': chimpanzee
'22': clock
'23': cloud
'24': cockroach
'25': couch
'26': crab
'27': crocodile
'28': cup
'29': dinosaur
'30': dolphin
'31': elephant
'32': flatfish
'33': forest
'34': fox
'35': girl
'36': hamster
'37': house
'38': kangaroo
'39': keyboard
'40': lamp
'41': lawn_mower
'42': leopard
'43': lion
'44': lizard
'45': lobster
'46': man
'47': maple_tree
'48': motorcycle
'49': mountain
'50': mouse
'51': mushroom
'52': oak_tree
'53': orange
'54': orchid
'55': otter
'56': palm_tree
'57': pear
'58': pickup_truck
'59': pine_tree
'60': plain
'61': plate
'62': poppy
'63': porcupine
'64': possum
'65': rabbit
'66': raccoon
'67': ray
'68': road
'69': rocket
'70': rose
'71': sea
'72': seal
'73': shark
'74': shrew
'75': skunk
'76': skyscraper
'77': snail
'78': snake
'79': spider
'80': squirrel
'81': streetcar
'82': sunflower
'83': sweet_pepper
'84': table
'85': tank
'86': telephone
'87': television
'88': tiger
'89': tractor
'90': train
'91': trout
'92': tulip
'93': turtle
'94': wardrobe
'95': whale
'96': willow_tree
'97': wolf
'98': woman
'99': worm
splits:
- name: train
num_bytes: 22484261.0
num_examples: 10000
download_size: 22551810
dataset_size: 22484261.0
---
# Dataset Card for "Cifar100_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
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