Hub Card Data
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Updated
•
4
Datasets:
Dataset Viewer
datasetId
large_stringlengths 6
116
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42
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large_stringdate 2021-04-29 15:34:29
2025-06-04 04:15:01
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3.97M
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7.74k
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large_stringdate 2022-03-02 23:29:22
2025-06-04 04:13:01
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|---|---|---|---|---|---|---|---|---|---|
ChavyvAkvar/synthetic-trades-XRP-batch-37 | ChavyvAkvar | 2025-06-03T22:05:30Z | 0 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-03T22:04:33Z | null | ---
dataset_info:
features:
- name: scenario_id
dtype: string
- name: final_pnl_ratio
dtype: float64
- name: max_drawdown
dtype: float64
- name: total_trades
dtype: int64
- name: synthetic_ohlc_open
sequence: float64
- name: synthetic_ohlc_high
sequence: float64
- name: synthetic_ohlc_low
sequence: float64
- name: synthetic_ohlc_close
sequence: float64
- name: garch_params_used_for_sim_str
dtype: string
- name: strategy_params_str
dtype: string
- name: strategy_exit_rules_str
dtype: string
splits:
- name: train
num_bytes: 923448393
num_examples: 1000
download_size: 924485504
dataset_size: 923448393
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
john-1111/x_dataset_060232 | john-1111 | 2025-06-03T16:43:33Z | 1,046 | 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:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"text-classification",
"token-classification",
"question-answering",
"summarization",
"text-generation"
] | 2025-01-27T06:45:15Z | null | ---
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:** john-1111/x_dataset_060232
- **Subnet:** Bittensor Subnet 13
- **Miner Hotkey:** 5CMJFHrRUZjZ3pD41DmrrLPXwqqre8RYVLHYiPTFUQaukL3a
### Miner Data Compliance Agreement
In uploading this dataset, I am agreeing to the [Macrocosmos Miner Data Compliance Policy](https://github.com/macrocosm-os/data-universe/blob/add-miner-policy/docs/miner_policy.md).
### 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{john-11112025datauniversex_dataset_060232,
title={The Data Universe Datasets: The finest collection of social media data the web has to offer},
author={john-1111},
year={2025},
url={https://huggingface.co/datasets/john-1111/x_dataset_060232},
}
```
### 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:** 1300883
- **Date Range:** 2025-01-02T00:00:00Z to 2025-05-24T00:00:00Z
- **Last Updated:** 2025-06-03T16:43:32Z
### Data Distribution
- Tweets with hashtags: 18.29%
- Tweets without hashtags: 81.71%
### Top 10 Hashtags
For full statistics, please refer to the `stats.json` file in the repository.
| Rank | Topic | Total Count | Percentage |
|------|-------|-------------|-------------|
| 1 | NULL | 1062956 | 81.71% |
| 2 | #thenextprinceep4 | 10897 | 0.84% |
| 3 | #箱根駅伝 | 8147 | 0.63% |
| 4 | #tiktok | 8034 | 0.62% |
| 5 | #thameposeriesep9 | 7605 | 0.58% |
| 6 | #riyadh | 6755 | 0.52% |
| 7 | #اااااعلانك_ترند_oち32ち9111ち | 5162 | 0.40% |
| 8 | #zelena | 4878 | 0.37% |
| 9 | #smackdown | 4844 | 0.37% |
| 10 | #कबीर_परमेश्वर_निर्वाण_दिवस | 4843 | 0.37% |
## Update History
| Date | New Instances | Total Instances |
|------|---------------|-----------------|
| 2025-01-27T06:45:45Z | 471976 | 471976 |
| 2025-02-18T03:42:10Z | 506494 | 978470 |
| 2025-06-03T16:43:32Z | 322413 | 1300883 |
|
james-1111/x_dataset_0306116 | james-1111 | 2025-06-03T16:12:06Z | 1,211 | 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:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"text-classification",
"token-classification",
"question-answering",
"summarization",
"text-generation"
] | 2025-01-25T07:09:54Z | null | ---
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:** james-1111/x_dataset_0306116
- **Subnet:** Bittensor Subnet 13
- **Miner Hotkey:** 5CBBUJwfT1ygAPTFaoEQ35qTuqwM5LyHxR2sjZ8isW6B9njQ
### Miner Data Compliance Agreement
In uploading this dataset, I am agreeing to the [Macrocosmos Miner Data Compliance Policy](https://github.com/macrocosm-os/data-universe/blob/add-miner-policy/docs/miner_policy.md).
### 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{james-11112025datauniversex_dataset_0306116,
title={The Data Universe Datasets: The finest collection of social media data the web has to offer},
author={james-1111},
year={2025},
url={https://huggingface.co/datasets/james-1111/x_dataset_0306116},
}
```
### 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:** 3933757
- **Date Range:** 2025-01-02T00:00:00Z to 2025-05-24T00:00:00Z
- **Last Updated:** 2025-06-03T16:12:05Z
### Data Distribution
- Tweets with hashtags: 4.31%
- Tweets without hashtags: 95.69%
### Top 10 Hashtags
For full statistics, please refer to the `stats.json` file in the repository.
| Rank | Topic | Total Count | Percentage |
|------|-------|-------------|-------------|
| 1 | NULL | 1062956 | 86.24% |
| 2 | #thenextprinceep4 | 10897 | 0.88% |
| 3 | #箱根駅伝 | 8147 | 0.66% |
| 4 | #tiktok | 8034 | 0.65% |
| 5 | #riyadh | 7740 | 0.63% |
| 6 | #thameposeriesep9 | 7605 | 0.62% |
| 7 | #اااااعلانك_ترند_oち32ち9111ち | 5162 | 0.42% |
| 8 | #zelena | 4878 | 0.40% |
| 9 | #smackdown | 4844 | 0.39% |
| 10 | #कबीर_परमेश्वर_निर्वाण_दिवस | 4843 | 0.39% |
## Update History
| Date | New Instances | Total Instances |
|------|---------------|-----------------|
| 2025-01-25T07:07:31Z | 453526 | 453526 |
| 2025-01-25T07:07:59Z | 453526 | 907052 |
| 2025-01-25T07:08:28Z | 453526 | 1360578 |
| 2025-01-25T07:08:56Z | 446896 | 1807474 |
| 2025-01-25T07:09:24Z | 446896 | 2254370 |
| 2025-01-25T07:09:52Z | 446896 | 2701266 |
| 2025-01-25T07:10:21Z | 446896 | 3148162 |
| 2025-02-18T03:39:54Z | 467290 | 3615452 |
| 2025-06-03T16:12:05Z | 318305 | 3933757 |
|
william-1111/x_dataset_010718 | william-1111 | 2025-06-03T15:30:24Z | 1,062 | 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",
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"license:mit",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"text-classification",
"token-classification",
"question-answering",
"summarization",
"text-generation"
] | 2025-01-25T07:05:51Z | null | ---
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:** william-1111/x_dataset_010718
- **Subnet:** Bittensor Subnet 13
- **Miner Hotkey:** 5EkBRcprHEuZXqNKYg3BaEfSeLqiEQGre1AhHWGtckeS8F36
### Miner Data Compliance Agreement
In uploading this dataset, I am agreeing to the [Macrocosmos Miner Data Compliance Policy](https://github.com/macrocosm-os/data-universe/blob/add-miner-policy/docs/miner_policy.md).
### 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{william-11112025datauniversex_dataset_010718,
title={The Data Universe Datasets: The finest collection of social media data the web has to offer},
author={william-1111},
year={2025},
url={https://huggingface.co/datasets/william-1111/x_dataset_010718},
}
```
### 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:** 2655106
- **Date Range:** 2025-01-02T00:00:00Z to 2025-05-24T00:00:00Z
- **Last Updated:** 2025-06-03T15:30:23Z
### Data Distribution
- Tweets with hashtags: 9.45%
- Tweets without hashtags: 90.55%
### Top 10 Hashtags
For full statistics, please refer to the `stats.json` file in the repository.
| Rank | Topic | Total Count | Percentage |
|------|-------|-------------|-------------|
| 1 | NULL | 1063379 | 80.90% |
| 2 | #sixtonesann | 44706 | 3.40% |
| 3 | #thenextprinceep4 | 17391 | 1.32% |
| 4 | #mono | 9336 | 0.71% |
| 5 | #अध्यात्म_का_बेड़ा_गर्क_करदिया | 8897 | 0.68% |
| 6 | #tiktok | 8173 | 0.62% |
| 7 | #箱根駅伝 | 8147 | 0.62% |
| 8 | #thameposeriesep9 | 7605 | 0.58% |
| 9 | #ملعببببب_الإنماء | 7302 | 0.56% |
| 10 | #ミッドナイト屋台 | 7210 | 0.55% |
## Update History
| Date | New Instances | Total Instances |
|------|---------------|-----------------|
| 2025-01-25T07:04:53Z | 446896 | 446896 |
| 2025-01-25T07:05:21Z | 446896 | 893792 |
| 2025-01-25T07:05:50Z | 446896 | 1340688 |
| 2025-01-25T07:06:18Z | 446896 | 1787584 |
| 2025-02-18T03:35:39Z | 467290 | 2254874 |
| 2025-06-03T15:30:23Z | 400232 | 2655106 |
|
Kyleyee/train_data_Helpful_drdpo_preference | Kyleyee | 2025-06-03T13:36:05Z | 80 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-03-17T16:07:59Z | null | ---
dataset_info:
features:
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: prompt
list:
- name: content
dtype: string
- name: role
dtype: string
- name: a_1
list:
- name: content
dtype: string
- name: role
dtype: string
- name: a_2
list:
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dtype: string
- name: role
dtype: string
- name: chosen_preference
dtype: float64
- name: rejected_preference
dtype: float64
- name: a_1_preference
dtype: float64
- name: a_2_preference
dtype: float64
splits:
- name: train
num_bytes: 69438428
num_examples: 43835
- name: test
num_bytes: 3812201
num_examples: 2354
download_size: 42617495
dataset_size: 73250629
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
ManTang034/so101_test | ManTang034 | 2025-06-03T13:32:28Z | 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",
"so101",
"tutorial"
] | [
"robotics"
] | 2025-06-03T13:32:11Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so101
- tutorial
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.1",
"robot_type": "so101",
"total_episodes": 10,
"total_frames": 5960,
"total_tasks": 1,
"total_videos": 10,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:10"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"action": {
"dtype": "float32",
"shape": [
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.wrist": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
}
},
"timestamp": {
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],
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},
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},
"episode_index": {
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},
"index": {
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},
"task_index": {
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"shape": [
1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
QuanHoangNgoc/cmp_dataset_dev | QuanHoangNgoc | 2025-06-03T13:26:28Z | 0 | 0 | [
"region:us"
] | [] | 2025-06-03T13:21:44Z | null | ---
dataset_info:
features:
- name: text
dtype: string
- name: audio_file
dtype: string
- name: audio_array16
sequence: float32
splits:
- name: dev
num_bytes: 2365130410
num_examples: 1900
download_size: 2365317554
dataset_size: 2365130410
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
---
|
youssefbelghmi/MNLP_M3_mcqa_dataset_2 | youssefbelghmi | 2025-06-03T13:16:38Z | 44 | 0 | [
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"multiple-choice"
] | 2025-06-03T11:12:01Z | null | ---
annotations_creators:
- expert-generated
language:
- en
license: mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
task_categories:
- multiple-choice
task_ids:
- multiple-choice-qa
pretty_name: MNLP M3 MCQA Dataset
---
# MNLP M3 MCQA Dataset
The **MNLP M3 MCQA Dataset** is a carefully curated collection of **Multiple-Choice Question Answering (MCQA)** examples, unified from several academic and benchmark datasets.
Developed as part of the *CS-552: Modern NLP* course at EPFL (Spring 2025), this dataset is designed for training and evaluating models on multiple-choice QA tasks, particularly in the **STEM** and general knowledge domains.
## Key Features
- ~30,000 MCQA questions
- 6 diverse sources: `SciQ`, `OpenBookQA`, `MathQA`, `ARC-Easy`, `ARC-Challenge`, and `MedMCQA`
- Each question has exactly 4 options (A–D) and one correct answer
- Covers a wide range of topics: science, technology, engineering, mathematics, and general knowledge
## Dataset Structure
Each example is a dictionary with the following fields:
| Field | Type | Description |
|-----------|----------|---------------------------------------------------|
| `dataset` | `string` | Source dataset (`sciq`, `openbookqa`, etc.) |
| `id` | `string` | Unique identifier for the question |
| `question`| `string` | The question text |
| `choices` | `list` | List of 4 answer options (corresponding to A–D) |
| `answer` | `string` | The correct option, as a letter: `"A"`, `"B"`, `"C"`, or `"D"` |
```markdown
Example:
```json
{
"dataset": "sciq",
"id": "sciq_01_00042",
"question": "What does a seismograph measure?",
"choices": ["Earthquakes", "Rainfall", "Sunlight", "Temperature"],
"answer": "A"
}
```
## Source Datasets
This dataset combines multiple high-quality MCQA sources to support research and fine-tuning in STEM education and reasoning. The full corpus contains **29,870 multiple-choice questions** from the following sources:
| Source (Hugging Face) | Name | Size | Description & Role in the Dataset |
| ------------------------------------------- | ------------------- | ------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `allenai/sciq` | **SciQ** | 11,679 | **Science questions** (Physics, Chemistry, Biology, Earth science). Crowdsourced with 4 answer choices and optional supporting evidence. Used to provide **well-balanced, factual STEM questions** at a middle/high-school level. |
| `allenai/openbookqa` | **OpenBookQA** | 4,957 | Science exam-style questions requiring **multi-step reasoning** and use of **commonsense or external knowledge**. Contributes more **challenging** and **inference-based** questions. |
| `allenai/math_qa` | **MathQA** | 5,000 | Subsample of quantitative math word problems derived from AQuA-RAT, annotated with structured answer options. Introduces **numerical reasoning** and **problem-solving** components into the dataset. |
| `allenai/ai2_arc` (config: `ARC-Easy`) | **ARC-Easy** | 2,140 | Science questions at the middle school level. Useful for testing **basic STEM understanding** and **factual recall**. Filtered to retain only valid 4-choice entries. |
| `allenai/ai2_arc` (config: `ARC-Challenge`) | **ARC-Challenge** | 1,094 | More difficult science questions requiring **reasoning and inference**. Widely used as a benchmark for evaluating LLMs. Also filtered for clean MCQA format compatibility. |
| `openlifescienceai/medmcqa` | **MedMCQA** | 5,000 | A subsample of multiple-choice questions on **medical topics** from various exams, filtered for a single-choice format. Contains real-world and domain-specific **clinical reasoning** questions covering various medical disciplines. |
## Intended Applications and Structure
This dataset is split into three parts:
- `train` (~70%) — for training MCQA models
- `validation` (~15%) — for tuning and monitoring performance during training
- `test` (~15%) — for final evaluation on unseen questions
It is suitable for multiple-choice question answering tasks, especially in the **STEM** domain (Science, Technology, Engineering, Mathematics).
## Author
This dataset was created and published by [Youssef Belghmi](https://huggingface.co/youssefbelghmi) as part of the *CS-552: Modern NLP* course at EPFL (Spring 2025).
|
haraouikouceil/n | haraouikouceil | 2025-06-03T11:35:30Z | 0 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-03T11:35:27Z | null | ---
dataset_info:
features:
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 31117380
num_examples: 71452
download_size: 2843773
dataset_size: 31117380
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
chenxing1234567890/eval_koch_test3 | chenxing1234567890 | 2025-06-03T10:49:37Z | 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",
"tutorial"
] | [
"robotics"
] | 2025-06-03T10:48:21Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- tutorial
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.1",
"robot_type": "koch",
"total_episodes": 10,
"total_frames": 7066,
"total_tasks": 1,
"total_videos": 30,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:10"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"action": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.images.laptop": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
}
},
"observation.images.phone": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
}
},
"observation.images.top": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
}
},
"timestamp": {
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],
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1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
StormKing99/x_dataset_55139 | StormKing99 | 2025-06-03T10:22:59Z | 1,094 | 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:100M<n<1B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"text-classification",
"token-classification",
"question-answering",
"summarization",
"text-generation"
] | 2025-01-29T01:07:24Z | null | ---
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:** StormKing99/x_dataset_55139
- **Subnet:** Bittensor Subnet 13
- **Miner Hotkey:** 5E4y9kJmMS6XaitQbdhfBRkUGEvCCD6rW32iwj3dm4NiQjbb
### 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{StormKing992025datauniversex_dataset_55139,
title={The Data Universe Datasets: The finest collection of social media data the web has to offer},
author={StormKing99},
year={2025},
url={https://huggingface.co/datasets/StormKing99/x_dataset_55139},
}
```
### 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:** 45546252
- **Date Range:** 2025-01-23T00:00:00Z to 2025-02-12T00:00:00Z
- **Last Updated:** 2025-02-18T21:02:59Z
### Data Distribution
- Tweets with hashtags: 42.66%
- Tweets without hashtags: 57.34%
### Top 10 Hashtags
For full statistics, please refer to the `stats.json` file in the repository.
| Rank | Topic | Total Count | Percentage |
|------|-------|-------------|-------------|
| 1 | NULL | 26117517 | 57.34% |
| 2 | #riyadh | 286662 | 0.63% |
| 3 | #zelena | 252537 | 0.55% |
| 4 | #tiktok | 184851 | 0.41% |
| 5 | #ad | 101282 | 0.22% |
| 6 | #bbb25 | 99746 | 0.22% |
| 7 | #theheartkillersep11 | 67143 | 0.15% |
| 8 | #transferlerlebirliktezafere | 64621 | 0.14% |
| 9 | #bbmzansi | 61074 | 0.13% |
| 10 | #แจกจริง | 55533 | 0.12% |
## Update History
| Date | New Instances | Total Instances |
|------|---------------|-----------------|
| 2025-01-29T01:07:37Z | 399713 | 399713 |
| 2025-02-01T13:11:17Z | 11997555 | 12397268 |
| 2025-02-05T01:15:13Z | 10941377 | 23338645 |
| 2025-02-08T13:19:17Z | 10005707 | 33344352 |
| 2025-02-12T01:24:07Z | 10705327 | 44049679 |
| 2025-02-18T06:01:52Z | 696224 | 44745903 |
| 2025-02-18T21:02:59Z | 800349 | 45546252 |
|
davanstrien/dataset_cards_with_metadata | davanstrien | 2025-06-03T10:20:08Z | 422 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-04-17T09:48:47Z | null | ---
dataset_info:
features:
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dtype: large_string
- name: author
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dtype: large_string
splits:
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num_examples: 32315
download_size: 30124925
dataset_size: 110530629
configs:
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data_files:
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path: data/train-*
---
|
daniel-dona/sparql-dataset-reasoning-test3 | daniel-dona | 2025-06-03T10:13:56Z | 0 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-03T10:13:51Z | null | ---
dataset_info:
features:
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dtype: string
- name: lang
dtype: string
- name: nlq
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splits:
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download_size: 961054
dataset_size: 11712015
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
athrv/Embedded13 | athrv | 2025-06-03T10:11:56Z | 131 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-22T09:43:34Z | null | ---
dataset_info:
features:
- name: ID
dtype: string
- name: Language
dtype: string
- name: Repository Name
dtype: string
- name: Base File Name
dtype: string
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dtype: string
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splits:
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num_examples: 1
download_size: 41534
dataset_size: 8504
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Embedded13"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
athrv/Embedded12 | athrv | 2025-06-03T10:10:47Z | 88 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-22T09:17:39Z | null | ---
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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dtype: string
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splits:
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num_bytes: 48719
num_examples: 1
download_size: 22445
dataset_size: 48719
configs:
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data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Embedded12"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
love3165303/so100_train1 | love3165303 | 2025-06-03T09:39:10Z | 0 | 0 | [
"task_categories:robotics",
"size_categories:n<1K",
"modality:video",
"library:datasets",
"library:mlcroissant",
"region:us",
"phosphobot",
"so100",
"phospho-dk"
] | [
"robotics"
] | 2025-06-03T09:37:03Z | null |
---
tags:
- phosphobot
- so100
- phospho-dk
task_categories:
- robotics
---
# so100_train1
**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.
|
gisako/multiwoz-chat | gisako | 2025-06-03T09:28:19Z | 0 | 0 | [
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"region:us"
] | [
"text-generation"
] | 2025-06-03T09:23:31Z | null | ---
license: mit
task_categories:
- text-generation
language:
- en
pretty_name: multiwoz-chat-llama-gpt
size_categories:
- 1K<n<10K
--- |
ustc-zyt/time-r1-data | ustc-zyt | 2025-06-03T09:13:46Z | 0 | 0 | [
"task_categories:time-series-forecasting",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"time-series-forecasting"
] | 2025-06-03T09:01:15Z | null | ---
license: apache-2.0
task_categories:
- time-series-forecasting
language:
- en
pretty_name: a
size_categories:
- 1K<n<10K
---
# 📊 Time-R1 RL Training Dataset
This dataset is used in the **Reinforcement Learning (RL)** phase of the paper:
**"Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs"**.
---
## 📁 Data Format Overview
The dataset is stored in **Parquet** format. Each sample includes:
| Field | Type | Description |
| -------------- | ------------ | ---------------------------------------------------------------------------- |
| `prompt` | `list[dict]` | Natural language instruction including 96-step historical input sequence. |
| `reward_model` | `dict` | Contains the `ground_truth` field – the target values for the next 96 steps. |
| `data_source` | `string` | Dataset name (e.g., `"ETTh1"`). |
| `ability` | `string` | Task type – here always `"TimeSeriesForecasting"`. |
| `extra_info` | `dict` | Metadata including sample `index` and data `split` (e.g., `"train"`). |
---
## 🧾 Example Sample
```json
{
"prompt": [
{
"content": "Here is the High Useful Load data of the transformer. (dataset is ETTh1)..."
}
],
"data_source": "ETTh1",
"ability": "TimeSeriesForecasting",
"reward_model": {
"ground_truth": "date HUFL\n2016-07-05 00:00:00 11.989\n2016-07-05 01:00:00 12.525\n..."
},
"extra_info": {
"index": 0,
"split": "train"
}
}
```
Each prompt contains structured temporal input (96 steps) in a language-style format.
The `ground_truth` contains corresponding 96-step future targets with timestamps and values. |
Nitish906099/nitish | Nitish906099 | 2025-06-03T09:07:57Z | 0 | 0 | [
"license:intel-research",
"region:us"
] | [] | 2025-06-03T09:07:57Z | null | ---
license: intel-research
---
|
Nitish906099/dream11-eng-wi-___ | Nitish906099 | 2025-06-03T08:58:32Z | 0 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-03T08:58:31Z | null | ---
dataset_info:
features:
- name: Player
dtype: string
- name: Avg Fpts
dtype: float64
- name: Runs
dtype: int64
- name: WK
dtype: int64
- name: RR1
dtype: int64
- name: RR2
dtype: int64
- name: RR3
dtype: int64
- name: RR4
dtype: int64
- name: RR5
dtype: int64
- name: RW1
dtype: int64
- name: RW2
dtype: int64
- name: RW3
dtype: int64
- name: RW4
dtype: int64
- name: RW5
dtype: int64
splits:
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num_bytes: 622
num_examples: 5
download_size: 5893
dataset_size: 622
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dream11-eng-wi-___"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EQX55/voice_test | EQX55 | 2025-06-03T08:28:55Z | 0 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-03T08:28:51Z | null | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: text
dtype: string
splits:
- name: train
num_bytes: 19030645.0
num_examples: 61
download_size: 18992096
dataset_size: 19030645.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
LLM360/guru_RL | LLM360 | 2025-06-03T08:26:44Z | 0 | 0 | [
"task_categories:text2text-generation",
"task_categories:text-generation",
"task_categories:table-question-answering",
"task_categories:question-answering",
"language:aa",
"license:cc-by-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"code",
"math",
"reasoning",
"logic",
"tabular"
] | [
"text2text-generation",
"text-generation",
"table-question-answering",
"question-answering"
] | 2025-06-03T04:39:38Z | null | ---
license: cc-by-2.0
task_categories:
- text2text-generation
- text-generation
- table-question-answering
- question-answering
language:
- aa
tags:
- code
- math
- reasoning
- logic
- tabular
pretty_name: >-
GURU: Incentivizing General Reasoning Skills with a Curated Open Reinforcement
Learning Dataset
size_categories:
- 10K<n<100K
---
# GURU: Incentivizing General Reasoning Skills with a Curated Open Reinforcement Learning Dataset
## Dataset Description
**GURU** is a meticulously curated cross-domain dataset specifically designed for training large language models on complex reasoning tasks. The dataset contains 91.9K high-quality samples spanning six diverse reasoning-intensive domains, processed through a comprehensive five-stage curation pipeline to ensure both domain diversity and reward verifiability.
### Dataset Summary
GURU addresses the critical need for robust cross-domain reasoning capabilities in LLMs by providing a carefully balanced collection of problems across mathematics, coding, science, logic, simulation, and tabular reasoning. Each sample has been filtered for quality and equipped with automated verification mechanisms, making it ideal for reinforcement learning applications.
### Key Features
- **Cross-Domain Coverage**: Six distinct reasoning domains ensuring comprehensive skill development
- **Quality Assurance**: Five-stage curation pipeline with deduplication and heuristic filtering
- **Automated Verification**: Domain-specific reward functions for reliable evaluation
- **Difficulty Calibration**: Samples filtered to maintain appropriate challenge levels
- **RL-Ready**: Binary reward system compatible with reinforcement learning frameworks
## Dataset Structure
### Domains and Statistics
| Domain | Datasets Included | Final Sample Count | Key Focus Areas |
|--------|------------------|-------------------|-----------------|
| **Math** | OR1, DAPO, DeepScaler | 54.4K | Competition problems, symbolic reasoning |
| **Code** | LeetCode, TACO-Verified, PrimeIntellect, LiveCodeBench | 18.1K | Programming challenges, algorithm design |
| **Science** | WebInstruct-Verified | 3.6K | University/PhD-level physics, chemistry, biology |
| **Logic** | ARC-AGI, BARC, Custom puzzles | 6.3K | Symbolic reasoning, constraint satisfaction |
| **Simulation** | Code I/O (PyEdu) | 3.7K | Code behavior prediction without execution |
| **Tabular** | HiTab, MultiHierTT | 6.1K | Single and multi-table reasoning |
**Total Samples**: 91.9K (filtered from 684.3K raw samples)
## Citation
If you use this dataset in your research, please cite:
```bibtex
```
*This dataset card follows the Hugging Face dataset card template and provides comprehensive information about the GURU dataset structure, creation process, and intended use cases.* |
TessWOfficial/pixtral_finetune_ham10000 | TessWOfficial | 2025-06-03T08:15:12Z | 0 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-02T12:37:44Z | null | ---
dataset_info:
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dtype: image
- name: text
dtype: string
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num_bytes: 205974104.0
num_examples: 725
download_size: 205962930
dataset_size: 205974104.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "pixtral_finetune_ham10000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pch11/final02 | pch11 | 2025-06-03T08:03:25Z | 0 | 0 | [
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-03T07:49:45Z | null | ---
license: apache-2.0
dataset_info:
features:
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dtype: string
- name: image
dtype: image
- name: caption_flux
dtype: string
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num_examples: 47
download_size: 8745947
dataset_size: 8768080.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
mteb/results | mteb | 2025-06-03T07:58:16Z | 10,416 | 1 | [
"benchmark:mteb",
"region:us"
] | [] | 2024-07-06T20:19:19Z | null | ---
benchmark: mteb
type: evaluation
submission_name: MTEB
---
> [!NOTE]
> Previously, it was possible to submit model results to MTEB by adding them to the metadata of the model card on huggingface. However, this is no longer possible as we want to ensure that we can match the results with the model implementation. If you want to add your model, please follow the [guide](https://github.com/embeddings-benchmark/mteb/blob/main/docs/adding_a_model.md) on how to do so.
This repository contains the results of the embedding benchmark evaluated using the package `mteb`.
| Reference | |
| ------------------- | ---------------------------------------------------------------------------------------- |
| 🦾 **[Leaderboard]** | An up to date leaderboard of embedding models |
| 📚 **[mteb]** | Guides and instructions on how to use `mteb`, including running, submitting scores, etc. |
| 🙋 **[Questions]** | Questions about the results |
| 🙋 **[Issues]** | Issues or bugs you have found |
[Leaderboard]: https://huggingface.co/spaces/mteb/leaderboard
[mteb]: https://github.com/embeddings-benchmark/mteb
[Questions]: https://github.com/embeddings-benchmark/mteb/discussions
[Issues]: https://github.com/embeddings-benchmark/mteb/issues
|
macrocosm-os/macrobench-bittensor-01 | macrocosm-os | 2025-06-03T07:05:31Z | 2,634 | 2 | [
"license:mit",
"region:us"
] | [] | 2025-02-05T11:18:22Z | null | ---
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--- |
DIaac/m23k-subproblem-analysis_sieved_sft_ready-0603_1459 | DIaac | 2025-06-03T07:00:23Z | 0 | 0 | [
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willcb/V3-wordle-test | willcb | 2025-06-03T06:16:39Z | 0 | 0 | [
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configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
romban38/x_dataset_51 | romban38 | 2025-06-03T05:52:22Z | 760 | 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:100M<n<1B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"text-classification",
"token-classification",
"question-answering",
"summarization",
"text-generation"
] | 2025-04-29T16:27:55Z | null | ---
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:** romban38/x_dataset_51
- **Subnet:** Bittensor Subnet 13
- **Miner Hotkey:** 5DyCJ6P43VwGTYC3gqYB2S7wEBSno5jrV4QbnyszXRwJpEqm
### Miner Data Compliance Agreement
In uploading this dataset, I am agreeing to the [Macrocosmos Miner Data Compliance Policy](https://github.com/macrocosm-os/data-universe/blob/add-miner-policy/docs/miner_policy.md).
### 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{romban382025datauniversex_dataset_51,
title={The Data Universe Datasets: The finest collection of social media data the web has to offer},
author={romban38},
year={2025},
url={https://huggingface.co/datasets/romban38/x_dataset_51},
}
```
### 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:** 14628693350
- **Date Range:** 2025-04-05T00:00:00Z to 2025-05-23T00:00:00Z
- **Last Updated:** 2025-06-03T05:52:18Z
### Data Distribution
- Tweets with hashtags: 2.19%
- Tweets without hashtags: 97.81%
### Top 10 Hashtags
For full statistics, please refer to the `stats.json` file in the repository.
| Rank | Topic | Total Count | Percentage |
|------|-------|-------------|-------------|
| 1 | NULL | 14308697202 | 97.81% |
| 2 | #lolfanfest2025d1 | 30146934 | 0.21% |
| 3 | #lolfanfest2025d2 | 22551791 | 0.15% |
| 4 | #riyadh | 10694211 | 0.07% |
| 5 | #goスト | 4159373 | 0.03% |
| 6 | #eurovisionrtve | 1374060 | 0.01% |
| 7 | #eurovision2025 | 1288784 | 0.01% |
| 8 | #eurovision | 1259207 | 0.01% |
| 9 | #cometobesiktasronaldo | 1100082 | 0.01% |
| 10 | #mygoldenbloodep10 | 888209 | 0.01% |
## Update History
| Date | New Instances | Total Instances |
|------|---------------|-----------------|
| 2025-04-29T16:29:24Z | 1 | 1 |
| 2025-04-30T09:30:42Z | 1 | 2 |
| 2025-05-01T02:32:13Z | 1 | 3 |
| 2025-05-01T19:33:40Z | 1 | 4 |
| 2025-05-02T12:35:02Z | 1 | 5 |
| 2025-05-03T05:36:19Z | 1 | 6 |
| 2025-05-03T22:37:26Z | 1 | 7 |
| 2025-05-04T15:38:35Z | 1 | 8 |
| 2025-05-05T08:41:01Z | 1 | 9 |
| 2025-05-05T09:43:22Z | 1 | 10 |
| 2025-05-06T02:44:35Z | 1 | 11 |
| 2025-05-06T19:46:13Z | 1 | 12 |
| 2025-05-07T12:47:19Z | 1 | 13 |
| 2025-05-07T13:49:22Z | 1 | 14 |
| 2025-05-08T06:50:45Z | 1 | 15 |
| 2025-05-08T23:51:54Z | 1 | 16 |
| 2025-05-09T16:53:11Z | 1 | 17 |
| 2025-05-10T09:54:21Z | 1 | 18 |
| 2025-05-11T02:55:26Z | 1 | 19 |
| 2025-05-11T19:57:08Z | 1 | 20 |
| 2025-05-11T20:59:25Z | 1 | 21 |
| 2025-05-12T14:00:52Z | 1 | 22 |
| 2025-05-13T07:02:41Z | 1 | 23 |
| 2025-05-14T00:04:03Z | 1 | 24 |
| 2025-05-14T17:05:39Z | 1 | 25 |
| 2025-05-15T10:08:02Z | 1 | 26 |
| 2025-05-31T19:26:08Z | 819137 | 819163 |
| 2025-05-31T19:29:47Z | 1609813 | 2428976 |
| 2025-05-31T19:33:29Z | 2401448 | 4830424 |
| 2025-05-31T19:37:22Z | 3185279 | 8015703 |
| 2025-05-31T19:48:36Z | 3960220 | 11975923 |
| 2025-05-31T19:53:25Z | 4724018 | 16699941 |
| 2025-05-31T20:00:33Z | 5479948 | 22179889 |
| 2025-05-31T20:04:08Z | 6235521 | 28415410 |
| 2025-05-31T20:07:52Z | 7002509 | 35417919 |
| 2025-05-31T20:14:10Z | 7775558 | 43193477 |
| 2025-05-31T20:26:32Z | 8555573 | 51749050 |
| 2025-05-31T20:32:31Z | 9344100 | 61093150 |
| 2025-05-31T20:36:31Z | 10130317 | 71223467 |
| 2025-05-31T20:41:53Z | 10898209 | 82121676 |
| 2025-05-31T20:50:20Z | 11671337 | 93793013 |
| 2025-05-31T20:54:01Z | 12444488 | 106237501 |
| 2025-05-31T21:00:08Z | 13208624 | 119446125 |
| 2025-05-31T21:07:04Z | 13963498 | 133409623 |
| 2025-05-31T21:10:53Z | 14699155 | 148108778 |
| 2025-05-31T21:15:28Z | 15447021 | 163555799 |
| 2025-05-31T21:19:01Z | 16203507 | 179759306 |
| 2025-05-31T21:22:54Z | 16969576 | 196728882 |
| 2025-05-31T21:26:51Z | 17759968 | 214488850 |
| 2025-05-31T21:33:44Z | 18538015 | 233026865 |
| 2025-05-31T21:38:53Z | 19325385 | 252352250 |
| 2025-05-31T21:48:23Z | 20106485 | 272458735 |
| 2025-05-31T21:52:04Z | 20888119 | 293346854 |
| 2025-05-31T21:56:46Z | 21680626 | 315027480 |
| 2025-05-31T22:00:53Z | 22454225 | 337481705 |
| 2025-05-31T22:04:38Z | 23218937 | 360700642 |
| 2025-05-31T22:12:04Z | 23976440 | 384677082 |
| 2025-05-31T22:18:24Z | 24733111 | 409410193 |
| 2025-05-31T22:25:19Z | 25499977 | 434910170 |
| 2025-05-31T22:29:26Z | 26284963 | 461195133 |
| 2025-05-31T22:40:01Z | 27086771 | 488281904 |
| 2025-05-31T22:44:07Z | 27894206 | 516176110 |
| 2025-05-31T22:51:31Z | 28712300 | 544888410 |
| 2025-05-31T22:55:29Z | 29503809 | 574392219 |
| 2025-05-31T22:59:17Z | 30293616 | 604685835 |
| 2025-05-31T23:07:09Z | 31066257 | 635752092 |
| 2025-05-31T23:11:00Z | 31831464 | 667583556 |
| 2025-05-31T23:16:14Z | 32588297 | 700171853 |
| 2025-05-31T23:22:17Z | 33346613 | 733518466 |
| 2025-05-31T23:26:10Z | 34106190 | 767624656 |
| 2025-05-31T23:30:46Z | 34874999 | 802499655 |
| 2025-05-31T23:34:39Z | 35649767 | 838149422 |
| 2025-05-31T23:46:23Z | 36430220 | 874579642 |
| 2025-05-31T23:50:07Z | 37216456 | 911796098 |
| 2025-05-31T23:54:38Z | 38015149 | 949811247 |
| 2025-06-01T00:00:58Z | 38833805 | 988645052 |
| 2025-06-01T00:05:04Z | 39634133 | 1028279185 |
| 2025-06-01T00:13:12Z | 40395737 | 1068674922 |
| 2025-06-01T00:16:52Z | 41149114 | 1109824036 |
| 2025-06-01T00:21:44Z | 41896170 | 1151720206 |
| 2025-06-01T00:31:14Z | 42633936 | 1194354142 |
| 2025-06-01T00:40:33Z | 43374794 | 1237728936 |
| 2025-06-01T00:46:49Z | 44133534 | 1281862470 |
| 2025-06-01T00:53:55Z | 44899792 | 1326762262 |
| 2025-06-01T01:03:31Z | 45678925 | 1372441187 |
| 2025-06-01T01:07:24Z | 46486639 | 1418927826 |
| 2025-06-01T01:11:29Z | 47328802 | 1466256628 |
| 2025-06-01T01:17:56Z | 48118154 | 1514374782 |
| 2025-06-01T01:21:42Z | 48896689 | 1563271471 |
| 2025-06-01T01:25:39Z | 49669203 | 1612940674 |
| 2025-06-01T01:32:29Z | 50422070 | 1663362744 |
| 2025-06-01T01:36:11Z | 51174925 | 1714537669 |
| 2025-06-01T01:40:08Z | 51920571 | 1766458240 |
| 2025-06-01T01:52:28Z | 52676569 | 1819134809 |
| 2025-06-01T01:58:12Z | 53434237 | 1872569046 |
| 2025-06-01T02:06:05Z | 54197152 | 1926766198 |
| 2025-06-01T02:12:41Z | 54975028 | 1981741226 |
| 2025-06-01T02:16:53Z | 55773522 | 2037514748 |
| 2025-06-01T02:23:51Z | 56560985 | 2094075733 |
| 2025-06-01T02:27:43Z | 57341387 | 2151417120 |
| 2025-06-01T02:32:38Z | 58252437 | 2209669557 |
| 2025-06-01T02:40:23Z | 59117168 | 2268786725 |
| 2025-06-01T02:44:51Z | 59971152 | 2328757877 |
| 2025-06-01T02:52:01Z | 60808453 | 2389566330 |
| 2025-06-01T02:56:05Z | 61627940 | 2451194270 |
| 2025-06-01T03:00:21Z | 62440982 | 2513635252 |
| 2025-06-01T03:07:21Z | 63261054 | 2576896306 |
| 2025-06-01T03:17:04Z | 64100639 | 2640996945 |
| 2025-06-01T03:21:30Z | 65003673 | 2706000618 |
| 2025-06-01T03:29:59Z | 65911819 | 2771912437 |
| 2025-06-01T03:34:53Z | 66819481 | 2838731918 |
| 2025-06-01T03:41:22Z | 67702510 | 2906434428 |
| 2025-06-01T03:48:06Z | 68594992 | 2975029420 |
| 2025-06-01T03:52:26Z | 69459690 | 3044489110 |
| 2025-06-01T04:03:45Z | 70315144 | 3114804254 |
| 2025-06-01T04:09:17Z | 71151213 | 3185955467 |
| 2025-06-01T04:13:29Z | 71968879 | 3257924346 |
| 2025-06-01T04:20:39Z | 72790119 | 3330714465 |
| 2025-06-01T04:30:10Z | 73620731 | 3404335196 |
| 2025-06-01T04:37:36Z | 74457069 | 3478792265 |
| 2025-06-01T04:41:53Z | 75325869 | 3554118134 |
| 2025-06-01T04:46:32Z | 76212492 | 3630330626 |
| 2025-06-01T04:55:13Z | 77097629 | 3707428255 |
| 2025-06-01T05:01:46Z | 77958308 | 3785386563 |
| 2025-06-01T05:06:30Z | 78813390 | 3864199953 |
| 2025-06-01T05:11:23Z | 79658265 | 3943858218 |
| 2025-06-01T05:16:05Z | 80503428 | 4024361646 |
| 2025-06-01T05:24:08Z | 81332863 | 4105694509 |
| 2025-06-01T05:28:21Z | 82164562 | 4187859071 |
| 2025-06-01T05:35:48Z | 82996608 | 4270855679 |
| 2025-06-01T05:40:51Z | 83806958 | 4354662637 |
| 2025-06-01T05:50:18Z | 84643709 | 4439306346 |
| 2025-06-01T05:56:07Z | 85516039 | 4524822385 |
| 2025-06-01T06:00:36Z | 86421122 | 4611243507 |
| 2025-06-01T06:05:15Z | 87328220 | 4698571727 |
| 2025-06-01T06:10:34Z | 88218428 | 4786790155 |
| 2025-06-01T06:16:54Z | 89092983 | 4875883138 |
| 2025-06-01T06:21:20Z | 89950598 | 4965833736 |
| 2025-06-01T06:25:54Z | 90818066 | 5056651802 |
| 2025-06-01T06:31:40Z | 91658010 | 5148309812 |
| 2025-06-01T06:40:25Z | 92487284 | 5240797096 |
| 2025-06-01T06:47:44Z | 93308923 | 5334106019 |
| 2025-06-01T06:51:46Z | 94120367 | 5428226386 |
| 2025-06-01T06:56:10Z | 94981968 | 5523208354 |
| 2025-06-01T07:06:00Z | 95866211 | 5619074565 |
| 2025-06-01T07:10:30Z | 96762381 | 5715836946 |
| 2025-06-01T07:15:04Z | 97669684 | 5813506630 |
| 2025-06-01T07:19:25Z | 98558453 | 5912065083 |
| 2025-06-01T07:23:48Z | 99438475 | 6011503558 |
| 2025-06-01T07:28:08Z | 100280809 | 6111784367 |
| 2025-06-01T07:37:32Z | 101124982 | 6212909349 |
| 2025-06-01T07:42:55Z | 101950482 | 6314859831 |
| 2025-06-01T07:47:06Z | 102781557 | 6417641388 |
| 2025-06-01T07:51:15Z | 103604366 | 6521245754 |
| 2025-06-01T07:55:34Z | 104425801 | 6625671555 |
| 2025-06-01T08:04:19Z | 105263345 | 6730934900 |
| 2025-06-01T08:08:50Z | 106130946 | 6837065846 |
| 2025-06-01T08:13:26Z | 107024366 | 6944090212 |
| 2025-06-01T08:17:51Z | 107921050 | 7052011262 |
| 2025-06-01T08:22:10Z | 108800298 | 7160811560 |
| 2025-06-01T08:26:57Z | 109680407 | 7270491967 |
| 2025-06-01T08:33:20Z | 110529049 | 7381021016 |
| 2025-06-01T08:37:32Z | 111370819 | 7492391835 |
| 2025-06-01T08:41:40Z | 112186046 | 7604577881 |
| 2025-06-01T08:45:44Z | 112994181 | 7717572062 |
| 2025-06-01T08:49:44Z | 113804531 | 7831376593 |
| 2025-06-01T08:54:20Z | 114618695 | 7945995288 |
| 2025-06-01T09:02:54Z | 115456687 | 8061451975 |
| 2025-06-01T09:07:21Z | 116326296 | 8177778271 |
| 2025-06-01T09:11:44Z | 117231532 | 8295009803 |
| 2025-06-01T09:16:17Z | 118127454 | 8413137257 |
| 2025-06-01T09:20:51Z | 118987582 | 8532124839 |
| 2025-06-01T09:29:11Z | 119863415 | 8651988254 |
| 2025-06-01T09:34:37Z | 120725935 | 8772714189 |
| 2025-06-01T09:38:49Z | 121577540 | 8894291729 |
| 2025-06-01T09:42:55Z | 122422046 | 9016713775 |
| 2025-06-01T09:48:46Z | 123243035 | 9139956810 |
| 2025-06-01T09:55:10Z | 124072684 | 9264029494 |
| 2025-06-01T09:59:15Z | 124902815 | 9388932309 |
| 2025-06-01T10:03:29Z | 125738896 | 9514671205 |
| 2025-06-01T10:09:43Z | 126609885 | 9641281090 |
| 2025-06-01T10:14:12Z | 127524630 | 9768805720 |
| 2025-06-01T10:22:38Z | 128430143 | 9897235863 |
| 2025-06-01T10:26:55Z | 129310475 | 10026546338 |
| 2025-06-01T10:31:19Z | 130218521 | 10156764859 |
| 2025-06-01T10:35:32Z | 131095948 | 10287860807 |
| 2025-06-01T10:39:45Z | 131950389 | 10419811196 |
| 2025-06-01T10:44:17Z | 132797300 | 10552608496 |
| 2025-06-01T10:54:10Z | 133640653 | 10686249149 |
| 2025-06-01T10:58:19Z | 134480700 | 10820729849 |
| 2025-06-01T11:02:32Z | 135336165 | 10956066014 |
| 2025-06-01T11:06:49Z | 136234632 | 11092300646 |
| 2025-06-01T11:11:32Z | 137165840 | 11229466486 |
| 2025-06-01T11:17:32Z | 138124317 | 11367590803 |
| 2025-06-01T11:22:07Z | 139088025 | 11506678828 |
| 2025-06-01T11:26:28Z | 140005124 | 11646683952 |
| 2025-06-01T11:30:55Z | 140925335 | 11787609287 |
| 2025-06-01T11:35:10Z | 141803316 | 11929412603 |
| 2025-06-01T11:45:01Z | 142701228 | 12072113831 |
| 2025-06-01T11:49:17Z | 143597883 | 12215711714 |
| 2025-06-01T11:53:23Z | 144440412 | 12360152126 |
| 2025-06-01T11:57:41Z | 145276617 | 12505428743 |
| 2025-06-01T12:01:48Z | 146129107 | 12651557850 |
| 2025-06-01T12:09:25Z | 147008048 | 12798565898 |
| 2025-06-01T12:13:52Z | 147911543 | 12946477441 |
| 2025-06-01T12:24:21Z | 933291 | 12947410732 |
| 2025-06-01T12:26:50Z | 1875241 | 12949285973 |
| 2025-06-01T12:28:59Z | 2785485 | 12952071458 |
| 2025-06-01T12:31:02Z | 3681998 | 12955753456 |
| 2025-06-01T12:33:02Z | 4589844 | 12960343300 |
| 2025-06-01T12:35:05Z | 5495591 | 12965838891 |
| 2025-06-01T12:37:19Z | 6430853 | 12972269744 |
| 2025-06-01T12:38:48Z | 7034896 | 12979304640 |
| 2025-06-01T12:40:50Z | 7940372 | 12987245012 |
| 2025-06-01T12:42:58Z | 8863328 | 12996108340 |
| 2025-06-01T12:45:07Z | 9799297 | 13005907637 |
| 2025-06-01T12:47:25Z | 10741705 | 13016649342 |
| 2025-06-01T12:49:39Z | 11690649 | 13028339991 |
| 2025-06-01T12:51:22Z | 12384659 | 13040724650 |
| 2025-06-01T12:52:38Z | 12875396 | 13053600046 |
| 2025-06-01T12:54:19Z | 13611437 | 13067211483 |
| 2025-06-01T12:55:30Z | 14098691 | 13081310174 |
| 2025-06-01T12:57:47Z | 15026414 | 13096336588 |
| 2025-06-01T12:59:54Z | 15913171 | 13112249759 |
| 2025-06-01T13:02:08Z | 16798327 | 13129048086 |
| 2025-06-01T13:04:20Z | 17683854 | 13146731940 |
| 2025-06-01T13:06:33Z | 18615685 | 13165347625 |
| 2025-06-01T13:09:30Z | 19529359 | 13184876984 |
| 2025-06-01T13:12:01Z | 20493302 | 13205370286 |
| 2025-06-01T13:14:17Z | 21432407 | 13226802693 |
| 2025-06-01T13:16:32Z | 22374219 | 13249176912 |
| 2025-06-01T13:18:49Z | 23310360 | 13272487272 |
| 2025-06-01T13:21:06Z | 24252912 | 13296740184 |
| 2025-06-01T13:23:23Z | 25192434 | 13321932618 |
| 2025-06-01T13:25:28Z | 25969175 | 13347901793 |
| 2025-06-01T13:27:20Z | 26587383 | 13374489176 |
| 2025-06-01T13:29:01Z | 27138395 | 13401627571 |
| 2025-06-01T13:30:45Z | 27686692 | 13429314263 |
| 2025-06-01T13:32:30Z | 28208656 | 13457522919 |
| 2025-06-01T13:34:13Z | 28740540 | 13486263459 |
| 2025-06-01T13:36:02Z | 29293100 | 13515556559 |
| 2025-06-01T13:37:52Z | 29873530 | 13545430089 |
| 2025-06-01T13:39:40Z | 30491744 | 13575921833 |
| 2025-06-01T13:41:30Z | 31142418 | 13607064251 |
| 2025-06-01T13:43:29Z | 31812918 | 13638877169 |
| 2025-06-01T13:45:15Z | 32448635 | 13671325804 |
| 2025-06-01T13:47:17Z | 33067426 | 13704393230 |
| 2025-06-01T13:49:03Z | 33682537 | 13738075767 |
| 2025-06-01T13:50:36Z | 34243905 | 13772319672 |
| 2025-06-01T13:52:11Z | 34791417 | 13807111089 |
| 2025-06-01T13:53:59Z | 35315121 | 13842426210 |
| 2025-06-01T13:55:46Z | 35847276 | 13878273486 |
| 2025-06-01T13:57:22Z | 36382923 | 13914656409 |
| 2025-06-01T13:58:57Z | 36949273 | 13951605682 |
| 2025-06-01T14:00:37Z | 37561219 | 13989166901 |
| 2025-06-01T14:02:20Z | 38210180 | 14027377081 |
| 2025-06-01T14:04:13Z | 38861358 | 14066238439 |
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| 2025-06-01T14:12:39Z | 42002001 | 14270058096 |
| 2025-06-01T14:14:08Z | 42574099 | 14312632195 |
| 2025-06-01T14:16:39Z | 43168704 | 14355800899 |
| 2025-06-01T14:18:22Z | 43786835 | 14399587734 |
| 2025-06-01T14:20:15Z | 44435173 | 14444022907 |
| 2025-06-01T14:21:57Z | 45099800 | 14489122707 |
| 2025-06-01T14:23:44Z | 45790918 | 14534913625 |
| 2025-06-01T14:25:35Z | 46532582 | 14581446207 |
| 2025-06-01T14:27:24Z | 47247104 | 14628693311 |
| 2025-06-01T15:51:48Z | 1 | 14628693312 |
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| 2025-06-01T19:16:42Z | 1 | 14628693316 |
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| 2025-06-01T21:18:38Z | 1 | 14628693318 |
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|
ixelszy/lena | ixelszy | 2025-06-03T05:48:39Z | 0 | 0 | [
"license:creativeml-openrail-m",
"region:us"
] | [] | 2025-06-03T04:48:13Z | null | ---
license: creativeml-openrail-m
---
|
allenai/sciriff-yesno | allenai | 2025-06-03T04:54:09Z | 0 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-03T04:50:10Z | null | ---
dataset_info:
features:
- name: id
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: metadata
struct:
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sequence: string
- name: input_context
dtype: string
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dtype: string
- name: source_type
dtype: string
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dtype: string
splits:
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num_bytes: 3713449
num_examples: 1582
- name: validation
num_bytes: 267088
num_examples: 130
- name: test
num_bytes: 881291
num_examples: 531
download_size: 2326429
dataset_size: 4861828
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
Thanarit/Thai-Voice-Test-Main-Final | Thanarit | 2025-06-03T04:52:09Z | 0 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-03T04:27:12Z | null | ---
dataset_info:
features:
- name: ID
dtype: string
- name: speaker_id
dtype: string
- name: Language
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcript
dtype: string
- name: length
dtype: float32
- name: dataset_name
dtype: string
- name: confidence_score
dtype: float64
splits:
- name: train
num_examples: 20
download_size: 0
dataset_size: 0
configs:
- config_name: default
data_files:
- split: train
path: data/train/*.parquet
---
# Thanarit/Thai-Voice
Combined Thai audio dataset from multiple sources
## Dataset Details
- **Total samples**: 20
- **Total duration**: 0.02 hours
- **Language**: Thai (th)
- **Audio format**: 16kHz mono WAV
- **Volume normalization**: -20dB
## Sources
Processed 1 datasets in streaming mode
## Source Datasets
1. **GigaSpeech2**: Large-scale multilingual speech corpus
## Usage
```python
from datasets import load_dataset
# Load with streaming to avoid downloading everything
dataset = load_dataset("Thanarit/Thai-Voice-Test-Main-Final", streaming=True)
# Iterate through samples
for sample in dataset['train']:
print(sample['ID'], sample['transcript'][:50])
# Process audio: sample['audio']
break
```
## Schema
- `ID`: Unique identifier (S1, S2, S3, ...)
- `speaker_id`: Speaker identifier (SPK_00001, SPK_00002, ...)
- `Language`: Language code (always "th" for Thai)
- `audio`: Audio data with 16kHz sampling rate
- `transcript`: Text transcript of the audio
- `length`: Duration in seconds
- `dataset_name`: Source dataset name (e.g., "GigaSpeech2", "ProcessedVoiceTH", "MozillaCommonVoice")
- `confidence_score`: Confidence score of the transcript (0.0-1.0)
- 1.0: Original transcript from source dataset
- <1.0: STT-generated transcript
- 0.0: Fallback transcript (e.g., [NO_TRANSCRIPT])
## Processing Details
This dataset was created using streaming processing to handle large-scale data without requiring full downloads.
Audio has been standardized to 16kHz mono with -20dB volume normalization.
|
tatung/hybrid_gripper_paper_pickup | tatung | 2025-06-03T04:14:10Z | 0 | 0 | [
"task_categories:robotics",
"size_categories:n<1K",
"modality:video",
"library:datasets",
"library:mlcroissant",
"region:us",
"phosphobot",
"so100",
"phospho-dk"
] | [
"robotics"
] | 2025-06-03T03:34:19Z | null |
---
tags:
- phosphobot
- so100
- phospho-dk
task_categories:
- robotics
---
# hybrid_gripper_paper_pickup
**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.
|
CohenQu/deepscalar_RL_hard_1_verl | CohenQu | 2025-06-03T03:21:42Z | 0 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-03T03:21:40Z | null | ---
dataset_info:
features:
- name: data_source
dtype: 'null'
- name: prompt
list:
- name: content
dtype: string
- name: role
dtype: string
- name: ability
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- name: reward_model
struct:
- name: ground_truth
dtype: string
- name: style
dtype: string
- name: extra_info
struct:
- name: index
dtype: int64
- name: no_hint_prompt
dtype: bool
- name: problem
dtype: string
- name: split
dtype: string
splits:
- name: train
num_bytes: 1567875
num_examples: 3000
- name: test
num_bytes: 191369
num_examples: 300
download_size: 151914
dataset_size: 1759244
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
RebecaLeyva/UNAM_ParraPostPartum_dataset | RebecaLeyva | 2025-06-03T03:05:07Z | 0 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-03T02:56:03Z | null | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 778221838.92
num_examples: 3024
- name: test
num_bytes: 138775756.0
num_examples: 534
download_size: 937487644
dataset_size: 916997594.92
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
mothnaZl/seq_dis_T0.6-Qwen2.5-7B-best_of_n-VLLM-Skywork-o1-Open-PRM-Qwen-2.5-7B-completions | mothnaZl | 2025-06-03T02:41:33Z | 86 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-13T08:08:11Z | null | ---
dataset_info:
config_name: mothnaZl_minerva_math--T-0.8--top_p-1.0--n-128--seed-0--agg_strategy-last--merged--evals
features:
- name: n
dtype: int64
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- name: Bigrams
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- name: Trigrams
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- name: Fourgrams
dtype: float64
- name: pass_tag
sequence: 'null'
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dtype: int64
- name: pred_entropy
dtype: float64
splits:
- name: train
num_bytes: 928
num_examples: 8
download_size: 7123
dataset_size: 928
configs:
- config_name: mothnaZl_minerva_math--T-0.8--top_p-1.0--n-128--seed-0--agg_strategy-last--merged--evals
data_files:
- split: train
path: mothnaZl_minerva_math--T-0.8--top_p-1.0--n-128--seed-0--agg_strategy-last--merged--evals/train-*
---
|
ShuoHsuan/grasp_0603 | ShuoHsuan | 2025-06-03T02:40:54Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot",
"so100",
"collect"
] | [
"robotics"
] | 2025-06-03T02:40:32Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so100
- collect
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": 20,
"total_frames": 5302,
"total_tasks": 1,
"total_videos": 40,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:20"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"action": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.images.laptop": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
}
},
"observation.images.phone": {
"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]
``` |
hamidkaloorazi/so100_bi_towel_hadi | hamidkaloorazi | 2025-06-03T02:35:20Z | 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",
"so100_bi"
] | [
"robotics"
] | 2025-06-03T02:21:46Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so100_bi
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_bimanual",
"total_episodes": 101,
"total_frames": 32489,
"total_tasks": 1,
"total_videos": 303,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 15,
"splits": {
"train": "0:101"
},
"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": [
12
],
"names": [
"left_shoulder_pan",
"left_shoulder_lift",
"left_elbow_flex",
"left_wrist_flex",
"left_wrist_roll",
"left_gripper",
"right_shoulder_pan",
"right_shoulder_lift",
"right_elbow_flex",
"right_wrist_flex",
"right_wrist_roll",
"right_gripper"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
12
],
"names": [
"left_shoulder_pan",
"left_shoulder_lift",
"left_elbow_flex",
"left_wrist_flex",
"left_wrist_roll",
"left_gripper",
"right_shoulder_pan",
"right_shoulder_lift",
"right_elbow_flex",
"right_wrist_flex",
"right_wrist_roll",
"right_gripper"
]
},
"observation.images.right": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "h264",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 15,
"video.channels": 3,
"has_audio": false
}
},
"observation.images.left": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "h264",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 15,
"video.channels": 3,
"has_audio": false
}
},
"observation.images.top": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "h264",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 15,
"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]
``` |
DanqingZ/so100_test_3_cameras | DanqingZ | 2025-06-03T02:21:48Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot",
"so100",
"tutorial"
] | [
"robotics"
] | 2025-06-03T02:21:36Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so100
- tutorial
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.1",
"robot_type": "so100",
"total_episodes": 2,
"total_frames": 1189,
"total_tasks": 1,
"total_videos": 6,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:2"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"action": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.images.logitech": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
}
},
"observation.images.on_robot": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
}
},
"observation.images.phone": {
"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]
``` |
Darkhn/WOFRP_V2_All_Good_Stories | Darkhn | 2025-06-03T00:30:40Z | 0 | 0 | [
"license:apache-2.0",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-03T00:30:27Z | null | ---
license: apache-2.0
---
|
jlbaker361/clip-league_captioned_splash | jlbaker361 | 2025-06-03T00:08:18Z | 122 | 0 | [
"size_categories:1K<n<10K",
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-23T15:07:58Z | null | ---
dataset_info:
features:
- name: image
dtype: image
- name: embedding
sequence:
sequence:
sequence: float32
- name: text
sequence:
sequence:
sequence: float16
- name: prompt
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download_size: 397404716
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configs:
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data_files:
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path: data/train-*
---
|
jlbaker361/clip-league_captioned_tile | jlbaker361 | 2025-06-03T00:07:46Z | 124 | 0 | [
"size_categories:1K<n<10K",
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"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-23T15:07:56Z | null | ---
dataset_info:
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sequence:
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configs:
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data_files:
- split: train
path: data/train-*
---
|
jlbaker361/dino-art_coco_captioned-50 | jlbaker361 | 2025-06-02T23:57:12Z | 0 | 0 | [
"size_categories:n<1K",
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"library:pandas",
"library:mlcroissant",
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] | [] | 2025-06-02T23:57:10Z | null | ---
dataset_info:
features:
- name: image
dtype: image
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sequence:
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path: data/train-*
---
|
VisualSphinx/VisualSphinx-V1-Rules | VisualSphinx | 2025-06-02T23:55:18Z | 55 | 0 | [
"language:en",
"language:zh",
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"library:mlcroissant",
"library:polars",
"arxiv:2505.23977",
"region:us"
] | [] | 2025-05-12T21:27:53Z | null | ---
license: cc-by-nc-4.0
dataset_info:
features:
- name: id
dtype: int32
- name: rule_content
sequence: string
- name: generation
dtype: int32
- name: parents
sequence: int32
- name: mutated
dtype: bool
- name: question_type
dtype: string
- name: knowledge_point
dtype: string
- name: times_used
dtype: int32
- name: creation_method
dtype: string
- name: format_score
dtype: int32
- name: content_quality_score
dtype: int32
- name: feasibility_score
dtype: int32
splits:
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num_bytes: 60383953
num_examples: 60339
- name: rules_filted
num_bytes: 40152007
num_examples: 41287
download_size: 48914349
dataset_size: 100535960
configs:
- config_name: default
data_files:
- split: synthetic_rules
path: data/synthetic_rules-*
- split: rules_filted
path: data/rules_filted-*
language:
- en
- zh
---
# 🦁 VisualSphinx: Large-Scale Synthetic Vision Logic Puzzles for RL
VisualSphinx is the largest fully-synthetic open-source dataset providing vision logic puzzles. It consists of over **660K** automatically generated logical visual puzzles. Each logical puzzle is grounded with an interpretable rule and accompanied by both correct answers and plausible distractors.
- 🌐 [Project Website](https://visualsphinx.github.io/) - Learn more about VisualSphinx
- 📖 [Technical Report](https://arxiv.org/abs/2505.23977) - Discover the methodology and technical details behind VisualSphinx
- 🔧 [Github Repo](https://github.com/VisualSphinx/VisualSphinx) - Access the complete pipeline used to produce VisualSphinx-V1
- 🤗 HF Datasets:
- [VisualSphinx-V1 (Raw)](https://huggingface.co/datasets/VisualSphinx/VisualSphinx-V1-Raw);
- [VisualSphinx-V1 (For RL)](https://huggingface.co/datasets/VisualSphinx/VisualSphinx-V1-RL-20K);
- [VisualSphinx-V1 (Benchmark)](https://huggingface.co/datasets/VisualSphinx/VisualSphinx-V1-Benchmark);
- [VisualSphinx (Seeds)](https://huggingface.co/datasets/VisualSphinx/VisualSphinx-Seeds);
- [VisualSphinx (Rules)](https://huggingface.co/datasets/VisualSphinx/VisualSphinx-V1-Rules). [📍| You are here!]
## 📊 Dataset Details
### 🎯 Purpose
This dataset contains the **synthetic logical rules** that power the next step VisualSphinx generation. These rules represent the core logical patterns and constraints used to automatically generate thousands of coherent visual logic puzzles with interpretable reasoning paths.
### 📈 Dataset Splits
- **`synthetic_rules`**: Contains all generated synthetic rules with complete metadata
- **`rules_filted`**: Contains only high-quality rules that passed filtering criteria
### 🏗️ Dataset Structure
Each rule in the dataset contains the following fields:
| Field | Type | Description |
|-------|------|-------------|
| `id` | `int32` | Unique identifier for each rule |
| `rule_content` | `Sequence[string]` | List of logical statements defining the rule |
| `generation` | `int32` | Generation number in the evolutionary process |
| `parents` | `Sequence[int32]` | IDs of parent rules (for rule evolution tracking) |
| `mutated` | `bool` | Whether this rule was created through mutation |
| `question_type` | `string` | Category of questions this rule generates |
| `knowledge_point` | `string` | Associated knowledge domain or concept |
| `times_used` | `int32` | Number of times this rule was used to generate puzzles |
| `creation_method` | `string` | Method used to create this rule (e.g., manual, genetic, hybrid) |
| `format_score` | `int32` | Structural formatting quality score (1-10 scale) |
| `content_quality_score` | `int32` | Logical coherence and clarity score (1-10 scale) |
| `feasibility_score` | `int32` | Practical applicability score (1-10 scale) |
### 📏 Dataset Statistics
- **Total Rules**: Comprehensive collection of synthetic logical rules
- **Rule Evolution**: Multi-generational rule development with parent-child relationships
- **Quality Control**: Triple-scored validation (format, content, feasibility)
- **Usage Tracking**: Statistics on rule effectiveness and popularity
### 🧬 Rule Evolution Process
The dataset captures a complete evolutionary process:
- **Inheritance**: Child rules inherit characteristics from parent rules
- **Mutation**: Systematic variations create new rule variants
- **Selection**: Quality scores determine rule survival and usage
- **Genealogy**: Full family trees of rule development preserved
## 🔧 Other Information
**License**: Please follow [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/deed.en).
**Contact**: Please contact [Yichen](mailto:yfeng42@uw.edu) by email.
## 📚 Citation
If you find the data or code useful, please cite:
```
@misc{feng2025visualsphinx,
title={VisualSphinx: Large-Scale Synthetic Vision Logic Puzzles for RL},
author={Yichen Feng and Zhangchen Xu and Fengqing Jiang and Yuetai Li and Bhaskar Ramasubramanian and Luyao Niu and Bill Yuchen Lin and Radha Poovendran},
year={2025},
eprint={2505.23977},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2505.23977},
}
``` |
jlbaker361/clip-league_captioned_splash-50 | jlbaker361 | 2025-06-02T23:50:50Z | 0 | 0 | [
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-06-02T19:48:58Z | null | ---
dataset_info:
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---
---
dataset_info:
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path: data/train-*
---
|
jlbaker361/siglip2-celeb_captioned-50 | jlbaker361 | 2025-06-02T23:46:18Z | 0 | 0 | [
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] | [] | 2025-06-02T19:47:24Z | null | ---
dataset_info:
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---
|
jlbaker361/dino-league_captioned_splash-50 | jlbaker361 | 2025-06-02T23:16:05Z | 0 | 0 | [
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dataset_info:
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---
|
kothasuhas/residual-teacher-6-2-iter-0-ctx16-12800000 | kothasuhas | 2025-06-02T23:11:55Z | 0 | 0 | [
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dataset_info:
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dtype: string
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---
|
daniel-dona/sparql-dataset-reasoning-test1 | daniel-dona | 2025-06-02T22:12:41Z | 0 | 0 | [
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path: data/train-*
---
|
kuzheren/geometry-dash-levels-tensors-2 | kuzheren | 2025-06-02T22:12:11Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | [] | 2025-06-02T22:03:01Z | null | ---
license: apache-2.0
---
# Geometry Dash Chunks HDF5 Dataset
## Описание
Этот датасет создан для обучения нейросетевых моделей (автоэнкодер, DiT, diffusion и др.) на реальных уровнях из Geometry Dash. Он хранится в форматах HDF5 (каждый файл содержит не более 5000 уровней) и подготовлен для максимально быстрой и эффективной работы с последовательностями чанков уровней.
## Структура данных
Каждый HDF5-файл содержит:
- Датасет: chunk_data — тензоры с чанками уровней.
- Датасет: valid_mask — булева маска валидных чанков в каждом уровне.
- Атрибут: metadata_json_list — JSON-список метаданных уровней из оригинальных .jsonl-файлов (кроме level_string и неважных служебных полей).
- Другие атрибуты описывают размерности тензоров и смысл признаков.
### chunk_data
Размерность: ```num_levels, max_seq_len, chunk_h, chunk_w, num_block_features```
**Значения: int32**
- num_levels — количество уровней в файле
- max_seq_len — максимальное число чанков среди всех уровней в этом файле
- chunk_h — высота чанка в сеточных "пикселях" (например, 32)
- chunk_w — ширина чанка (например, 128)
- num_block_features — количество признаков на ячейку
### valid_mask
Размерность: ```num_levels, max_seq_len```
**Тип: bool**
- Показывает, какие чанки в каждом уровне содержат реальные данные (True), а какие — добавлены паддингом (False).
### metadata_json_list (атрибут)
Это JSON-список метаданных каждого уровня в файле. Пример содержимого одного entry:
```
{
"level_id": 123456,
"level_name": "My Level",
"difficulty_stars": 5,
"length_code": 2,
"downloads": 1234,
"likes": 56,
"num_chunks_generated": 12
}
```
level_string (и похожие служебные поля) не сохраняются для экономии места и скорости доступа.
## Содержимое одного чанка
- Каждый чанк — это сетка PPPchunk_h x chunk_wPPP.
- В каждой ячейке хранится массив из PPPnum_block_featuresPPP чисел:
1. block_id — целое, идентификатор блока Geometry Dash (0 = пусто)
2. x_rel — индекс ячейки (0 .. chunk_w-1)
3. y_rel — индекс ячейки (0 .. chunk_h-1)
4. rotation_index — 0–3 (соответствует 0°/90°/180°/270°)
5. flip_combined — код флипа: 0=нет, 1=flip_y, 2=flip_x, 3=flip_x+flip_y
Пустая ячейка содержит block_id=0 и все остальные значения ноль.
## Как читать датасет
Пример на Python с использованием библиотеки h5py и numpy:
```
import h5py
import numpy as np
filename = "gd_dataset_chunked_part_1.h5"
with h5py.File(filename, "r") as hf:
chunk_data = hf["chunk_data"] # Размер: (num_levels, max_seq_len, chunk_h, chunk_w, num_block_features)
valid_mask = hf["valid_mask"] # Размер: (num_levels, max_seq_len)
meta_json = hf.attrs["metadata_json_list"]
metadata = json.loads(meta_json)
# Пример: получить все чанки первого уровня:
idx = 0
real_len = valid_mask[idx].sum()
level_chunks = chunk_data[idx, :real_len] # (real_len, chunk_h, chunk_w, num_block_features)
# Декодировать первый чанк в уровень:
chunk = level_chunks[0] # (chunk_h, chunk_w, num_block_features)
block_ids = chunk[:,:,0] # карта блоков
x_coords = chunk[:,:,1]
y_coords = chunk[:,:,2]
rotation_idxs = chunk[:,:,3]
flip_combined = chunk[:,:,4]
```
## Как использовать в DataLoader
- Для обучения transformer/DiT моделей: формируйте батчи из уровней (последовательностей чанков), используйте valid_mask для attention mask и masking в loss.
- Для автоэнкодера: берите отдельные чанки, считывайте PPPchunk_h x chunk_w x num_block_featuresPPP тензоры, пустые блоки можно игнорировать или паддить.
## Описание признаков
| Индекс | Название | Описание |
|--------|----------------|---------------------------------------------------------------|
| 0 | block_id | GD ID блока. 0 — пусто |
| 1 | x_rel | X (столбец) внутри чанка, 0 .. chunk_w-1 |
| 2 | y_rel | Y (строка) внутри чанка, 0 .. chunk_h-1 |
| 3 | rotation_index | Индекс поворота: 0=0°, 1=90°, 2=180°, 3=270° |
| 4 | flip_combined | 0=нет; 1=flip_y; 2=flip_x; 3=оба |
## Пример визуализации чанка
```
import matplotlib.pyplot as plt
plt.imshow(block_ids, cmap="tab20") # или cmap="nipy_spectral"
plt.title("Карта ID блоков в чанке")
plt.show()
```
## Лицензия и источник
- Данные Levels: из open Geometry Dash (2013-2025).
- Код парсера и структуры: Kuzheren (actually, not quite), 2025.
- Используйте свободно для ML-исследований и геймдев-прототипирования!
## Обратная связь
Вопросы, предложения и баги — в Issues HuggingFace или на [github.com/kuzheren/gdparse](https://github.com/kuzheren/gdparse)
|
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Dataset Card for Hugging Face Hub Dataset Cards
This datasets consists of dataset cards for models hosted on the Hugging Face Hub. The dataset cards are created by the community and provide information about datasets hosted on the Hugging Face Hub. This dataset is updated on a daily basis and includes publicly available datasets on the Hugging Face Hub.
This dataset is made available to help support users wanting to work with a large number of Dataset Cards from the Hub. We hope that this dataset will help support research in the area of Dataset Cards and their use but the format of this dataset may not be useful for all use cases. If there are other features that you would like to see included in this dataset, please open a new discussion.
Dataset Details
Uses
There are a number of potential uses for this dataset including:
- text mining to find common themes in dataset cards
- analysis of the dataset card format/content
- topic modelling of dataset cards
- training language models on the dataset cards
Out-of-Scope Use
[More Information Needed]
Dataset Structure
This dataset has a single split.
Dataset Creation
Curation Rationale
The dataset was created to assist people in working with dataset cards. In particular it was created to support research in the area of dataset cards and their use. It is possible to use the Hugging Face Hub API or client library to download dataset cards and this option may be preferable if you have a very specific use case or require a different format.
Source Data
The source data is README.md files for datasets hosted on the Hugging Face Hub. We do not include any other supplementary files that may be included in the dataset directory.
Data Collection and Processing
The data is downloaded using a CRON job on a daily basis.
Who are the source data producers?
The source data producers are the creators of the dataset cards on the Hugging Face Hub. This includes a broad variety of people from the community ranging from large companies to individual researchers. We do not gather any information about who created the dataset card in this repository although this information can be gathered from the Hugging Face Hub API.
Annotations [optional]
There are no additional annotations in this dataset beyond the dataset card content.
Annotation process
N/A
Who are the annotators?
N/A
Personal and Sensitive Information
We make no effort to anonymize the data. Whilst we don't expect the majority of dataset cards to contain personal or sensitive information, it is possible that some dataset cards may contain this information. Dataset cards may also link to websites or email addresses.
Bias, Risks, and Limitations
Dataset cards are created by the community and we do not have any control over the content of the dataset cards. We do not review the content of the dataset cards and we do not make any claims about the accuracy of the information in the dataset cards. Some dataset cards will themselves discuss bias and sometimes this is done by providing examples of bias in either the training data or the responses provided by the dataset. As a result this dataset may contain examples of bias.
Whilst we do not directly download any images linked to in the dataset cards, some dataset cards may include images. Some of these images may not be suitable for all audiences.
Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
Citation
No formal citation is required for this dataset but if you use this dataset in your work, please include a link to this dataset page.
Dataset Card Authors
Dataset Card Contact
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Curated by:
Daniel van Strien
Language(s) (NLP):
Dataset cards on the Hugging Face Hub are predominantly in English but may include other languages.
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105 MB
Number of rows:
142,991