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Asap7772/omnimath-hint-generator-qwen3-4b-filtered-lr5e5 | Asap7772 | 2025-05-06T03:38:13Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-06T03:38:08Z | null | ---
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---
|
Asap7772/omnimath-hint-generator-qwen3-4b-filtered-lr1e6 | Asap7772 | 2025-05-06T03:37:54Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-06T03:37:48Z | null | ---
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|
milashkaarshif/MoeGirlPedia_wikitext_raw_archive | milashkaarshif | 2025-05-06T03:24:48Z | 261 | 29 | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"language:zh",
"language:ja",
"language:en",
"license:cc-by-nc-sa-3.0",
"size_categories:1M<n<10M",
"region:us",
"wiki",
"wikitext",
"anime",
"comic",
"game",
"archive",
"art",
"music",
"pedia",
"MGP",
"萌娘百科",
"萌百",
"百科",
"维基"
] | [
"text-generation",
"text2text-generation"
] | 2023-05-03T14:07:17Z | null | ---
configs:
- config_name: default
data_files:
- split: train
path: "mgp_archive_2505.tar.gz"
license: cc-by-nc-sa-3.0
task_categories:
- text-generation
- text2text-generation
language:
- zh
- ja
- en
tags:
- wiki
- wikitext
- anime
- comic
- game
- archive
- art
- music
- pedia
- MGP
- 萌娘百科
- 萌百
- 百科
- 维基
size_categories:
- 1M<n<10M
---
Glad to see models and datasets were inspired from this dataset, thanks to all who are using this dataset in their training materials.
Feel free to re-upload the contents to places like the Internet Archive (Please follow the license and keep these files as-is) to help preserve this digital asset.
Looking forward to see more models and synthetic datasets trained from this raw archive, good luck!
Note: Due to the content censorship system introduced by MGP on 2024/03/29, it is unclear that how future backups will be conducted. mgp_archive_240329.tar.gz is the last dataset before content censorship. |
flyingbugs/OpenR1-Math-220k-pruned-middle-random-perturbation | flyingbugs | 2025-05-06T03:24:24Z | 0 | 0 | [
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|
kaiwenw/distill-r1-qwen-1.5b-hmmt-feb-25-4096-with-old-prm-indices_38400_46080 | kaiwenw | 2025-05-06T03:19:25Z | 0 | 0 | [
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] | [] | 2025-05-06T03:19:11Z | null | ---
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---
|
flyingbugs/OpenR1-Math-220k-pruned-tail-random-perturbation | flyingbugs | 2025-05-06T03:18:47Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-06T03:17:35Z | null | ---
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---
|
deployedApps/logs | deployedApps | 2025-05-06T03:18:36Z | 0 | 0 | [
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trqcbf/merged_par0 | trqcbf | 2025-05-06T03:14:03Z | 0 | 0 | [
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HungVu2003/opt-350m_beta_0.5_alpha_0.2_num-company_3_dataset_1_for_gen_3_v2 | HungVu2003 | 2025-05-06T03:07:27Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-06T03:07:25Z | null | ---
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kaiwenw/distill-r1-qwen-1.5b-hmmt-feb-25-4096-with-labels-prm-indices_38400_46080 | kaiwenw | 2025-05-06T03:07:14Z | 0 | 0 | [
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|
ashikshaffi08/reddit_dataset_250 | ashikshaffi08 | 2025-05-06T02:56:18Z | 174 | 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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"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"text-classification",
"token-classification",
"question-answering",
"summarization",
"text-generation"
] | 2025-03-26T21:49:34Z | 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 Reddit Dataset
<center>
<img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/bittensor.png" alt="Data-universe: The finest collection of social media data the web has to offer">
</center>
<center>
<img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/macrocosmos-black.png" alt="Data-universe: The finest collection of social media data the web has to offer">
</center>
## Dataset Description
- **Repository:** ashikshaffi08/reddit_dataset_250
- **Subnet:** Bittensor Subnet 13
- **Miner Hotkey:** 5F9HhkadjnEgvCwMqDpD3eS3jeaHmj9WNM9KRYia9PAdqBjS
### 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 Reddit data. The data is continuously updated by network miners, providing a real-time stream of Reddit content for various analytical and machine learning tasks.
For more information about the dataset, please visit the [official repository](https://github.com/macrocosm-os/data-universe).
### Supported Tasks
The versatility of this dataset allows researchers and data scientists to explore various aspects of social media dynamics and develop innovative applications. Users are encouraged to leverage this data creatively for their specific research or business needs.
For example:
- Sentiment Analysis
- Topic Modeling
- Community Analysis
- Content Categorization
### Languages
Primary language: Datasets are mostly English, but can be multilingual due to decentralized ways of creation.
## Dataset Structure
### Data Instances
Each instance represents a single Reddit post or comment with the following fields:
### Data Fields
- `text` (string): The main content of the Reddit post or comment.
- `label` (string): Sentiment or topic category of the content.
- `dataType` (string): Indicates whether the entry is a post or a comment.
- `communityName` (string): The name of the subreddit where the content was posted.
- `datetime` (string): The date when the content was posted or commented.
- `username_encoded` (string): An encoded version of the username to maintain user privacy.
- `url_encoded` (string): An encoded version of any URLs included in the content.
### Data Splits
This dataset is continuously updated and does not have fixed splits. Users should create their own splits based on their requirements and the data's timestamp.
## Dataset Creation
### Source Data
Data is collected from public posts and comments on Reddit, adhering to the platform's terms of service and API usage guidelines.
### Personal and Sensitive Information
All usernames and URLs are encoded to protect user privacy. The dataset does not intentionally include personal or sensitive information.
## Considerations for Using the Data
### Social Impact and Biases
Users should be aware of potential biases inherent in Reddit data, including demographic and content biases. This dataset reflects the content and opinions expressed on Reddit and should not be considered a representative sample of the general population.
### Limitations
- Data quality may vary due to the nature of media sources.
- The dataset may contain noise, spam, or irrelevant content typical of social media platforms.
- Temporal biases may exist due to real-time collection methods.
- The dataset is limited to public subreddits and does not include private or restricted communities.
## Additional Information
### Licensing Information
The dataset is released under the MIT license. The use of this dataset is also subject to Reddit Terms of Use.
### Citation Information
If you use this dataset in your research, please cite it as follows:
```
@misc{ashikshaffi082025datauniversereddit_dataset_250,
title={The Data Universe Datasets: The finest collection of social media data the web has to offer},
author={ashikshaffi08},
year={2025},
url={https://huggingface.co/datasets/ashikshaffi08/reddit_dataset_250},
}
```
### 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:** 4482985
- **Date Range:** 2009-06-19T00:00:00Z to 2025-05-06T00:00:00Z
- **Last Updated:** 2025-05-06T02:56:16Z
### Data Distribution
- Posts: 12.35%
- Comments: 87.65%
### Top 10 Subreddits
For full statistics, please refer to the `stats.json` file in the repository.
| Rank | Topic | Total Count | Percentage |
|------|-------|-------------|-------------|
| 1 | r/AskReddit | 124281 | 2.77% |
| 2 | r/wallstreetbets | 89717 | 2.00% |
| 3 | r/politics | 86937 | 1.94% |
| 4 | r/worldnews | 63162 | 1.41% |
| 5 | r/news | 28455 | 0.63% |
| 6 | r/gaming | 28186 | 0.63% |
| 7 | r/nba | 24429 | 0.54% |
| 8 | r/pics | 24399 | 0.54% |
| 9 | r/relationship_advice | 21964 | 0.49% |
| 10 | r/todayilearned | 21703 | 0.48% |
## Update History
| Date | New Instances | Total Instances |
|------|---------------|-----------------|
| 2025-03-26T21:50:11Z | 430042 | 430042 |
| 2025-03-27T15:51:31Z | 985643 | 1415685 |
| 2025-04-22T08:53:11Z | 5141 | 1420826 |
| 2025-04-23T02:50:50Z | 2 | 1420828 |
| 2025-04-23T20:04:19Z | 2 | 1420830 |
| 2025-04-24T14:04:32Z | 2 | 1420832 |
| 2025-04-25T08:20:26Z | 2 | 1420834 |
| 2025-05-01T18:23:13Z | 257841 | 1678675 |
| 2025-05-02T12:22:07Z | 318080 | 1996755 |
| 2025-05-03T06:29:53Z | 808673 | 2805428 |
| 2025-05-06T02:56:16Z | 1677557 | 4482985 |
|
pranavsaroha/so100_foldtowel_0505_01 | pranavsaroha | 2025-05-06T02:52:50Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"region:us",
"LeRobot",
"so100",
"fold_towel"
] | [
"robotics"
] | 2025-05-06T02:44:59Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so100
- fold_towel
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": 8,
"total_frames": 11746,
"total_tasks": 1,
"total_videos": 32,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:8"
},
"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.left_wrist": {
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"shape": [
480,
640,
3
],
"names": [
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"width",
"channels"
],
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"video.channels": 3,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
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},
"observation.images.overhead": {
"dtype": "video",
"shape": [
480,
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"names": [
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],
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"observation.images.side_camera": {
"dtype": "video",
"shape": [
720,
1280,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.fps": 30.0,
"video.height": 720,
"video.width": 1280,
"video.channels": 3,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"frame_index": {
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1
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"task_index": {
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1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
kaiwenw/distill-r1-qwen-1.5b-hmmt-feb-25-4096-with-labels-prm-indices_69120_76800 | kaiwenw | 2025-05-06T02:52:13Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-06T02:51:48Z | null | ---
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kaiwenw/distill-r1-qwen-1.5b-hmmt-feb-25-4096-with-old-prm-indices_69120_76800 | kaiwenw | 2025-05-06T02:48:54Z | 0 | 0 | [
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|
PhanithLIM/whisper-small-khmer-pre | PhanithLIM | 2025-05-06T02:46:38Z | 0 | 0 | [
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] | [] | 2025-05-06T02:46:38Z | null | ---
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---
|
kaiwenw/distill-r1-qwen-1.5b-hmmt-feb-25-4096-with-old-prm-indices_7680_15360 | kaiwenw | 2025-05-06T02:40:18Z | 0 | 0 | [
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AlignmentResearch/DoNotAnswer | AlignmentResearch | 2025-05-06T02:18:25Z | 0 | 0 | [
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|
HungVu2003/opt-350m_beta_0.5_alpha_0.2_num-company_2_dataset_0_for_gen_2_v2 | HungVu2003 | 2025-05-06T01:24:48Z | 0 | 0 | [
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|
Asap7772/Omni-MATH-20-per-source | Asap7772 | 2025-05-06T01:24:10Z | 8 | 0 | [
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grimjim/role_meta_info_multilingual | grimjim | 2025-05-06T01:23:54Z | 0 | 0 | [
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] | [] | 2025-05-06T01:11:54Z | null | ---
language:
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---
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["Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignment" by Keming Lu, Bowen Yu, Chang Zhou, and Jingren Zhou](https://arxiv.org/abs/2401.12474)
and the associated [GitHub repository OFA-Sys/Ditto](https://github.com/OFA-Sys/Ditto).
The contents of said repo were declared public domain; in that spirit, the original and derived ChatML-formatted jsonl files have also been released as public domain. |
reasoning-proj/_judged_math_traces_original_DeepSeek-R1-Distill-Qwen-7B | reasoning-proj | 2025-05-06T01:11:07Z | 0 | 0 | [
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reasoning-proj/_judged_math_traces_original_DeepSeek-R1-Distill-Qwen-14B | reasoning-proj | 2025-05-06T01:09:35Z | 0 | 0 | [
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kothasuhas/ctx16-4-epochs-1.6Mv4-5-5 | kothasuhas | 2025-05-06T01:05:27Z | 0 | 0 | [
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|
HungVu2003/opt-350m_beta_0.5_alpha_0.0_num-company_3_dataset_0_for_gen_19_v2 | HungVu2003 | 2025-05-06T00:58:17Z | 0 | 0 | [
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|
HungVu2003/opt-350m_beta_0.5_alpha_0.0_num-company_3_dataset_1_for_gen_18_v2 | HungVu2003 | 2025-05-06T00:56:37Z | 0 | 0 | [
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HungVu2003/opt-350m_beta_0.5_alpha_0.0_num-company_3_dataset_0_for_gen_18_v2 | HungVu2003 | 2025-05-06T00:56:35Z | 0 | 0 | [
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|
zijiewang/qwen2.7b.pretrained.negated.anion_CondaQA | zijiewang | 2025-05-06T00:56:14Z | 0 | 0 | [
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|
HungVu2003/opt-350m_beta_0.5_alpha_0.0_num-company_3_dataset_1_for_gen_17_v2 | HungVu2003 | 2025-05-06T00:54:51Z | 0 | 0 | [
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reasoning-proj/_judged_math_traces_original_DeepSeek-R1-Distill | reasoning-proj | 2025-05-06T00:49:02Z | 0 | 0 | [
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|
HungVu2003/opt-350m_beta_1.0_alpha_0.2_num-company_3_dataset_1_for_gen_3_v2 | HungVu2003 | 2025-05-06T00:47:39Z | 0 | 0 | [
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|
HungVu2003/opt-350m_beta_0.5_alpha_0.0_num-company_3_dataset_0_for_gen_11_v2 | HungVu2003 | 2025-05-06T00:44:32Z | 0 | 0 | [
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|
HungVu2003/opt-350m_beta_0.5_alpha_0.0_num-company_3_dataset_1_for_gen_10_v2 | HungVu2003 | 2025-05-06T00:42:56Z | 0 | 0 | [
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HungVu2003/opt-350m_beta_1.0_alpha_0.4_num-company_2_dataset_0_for_gen_9_v2 | HungVu2003 | 2025-05-06T00:42:46Z | 0 | 0 | [
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|
ssundaram/Qwen2.5-Math-PRM-7B_gsm8k_n7_seed47639 | ssundaram | 2025-05-06T00:41:56Z | 0 | 0 | [
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ssundaram/Qwen2.5-Math-PRM-7B_gsm8k_n3_seed1234 | ssundaram | 2025-05-06T00:41:46Z | 0 | 0 | [
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ssundaram/Qwen2.5-Math-PRM-7B_gsm8k_n2_seed1234 | ssundaram | 2025-05-06T00:41:43Z | 0 | 0 | [
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|
HungVu2003/opt-350m_beta_0.5_alpha_0.0_num-company_3_dataset_1_for_gen_7_v2 | HungVu2003 | 2025-05-06T00:37:50Z | 0 | 0 | [
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HungVu2003/opt-350m_beta_0.0_alpha_0.2_num-company_3_dataset_2_for_gen_2_v2 | HungVu2003 | 2025-05-06T00:06:44Z | 0 | 0 | [
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kothasuhas/ctx16-4-epochs-1.6Mv3-5-5 | kothasuhas | 2025-05-06T00:00:21Z | 0 | 0 | [
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mhr2004/nevir-original-mhr2004-roberta-large-stsb-lr2e-05-bs32-pred | mhr2004 | 2025-05-05T23:53:39Z | 0 | 0 | [
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mhr2004/nev-original-cross-encoder-stsb-roberta-large-bs8-lr2e-05-pred | mhr2004 | 2025-05-05T23:44:21Z | 14 | 0 | [
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guluthemonster/M3VIR | guluthemonster | 2025-05-05T23:31:22Z | 220 | 0 | [
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] | [
"image-to-3d"
] | 2025-04-16T22:40:54Z | null | ---
license: mit
language:
- en
pretty_name: M³VIR
size_categories:
- 100B<n<1T
task_categories:
- image-to-3d
---
# M³VIR
In the field of restoration and 3D reconstruction, particularly for rendered content such as gaming environments, the lack of sufficient ground-truth training data presents a significant challenge. While these techniques are extensively studied in real-world applications, their adaptation to virtual or synthetic environments remains relatively underexplored. This is largely due to the distinct characteristics of rendered content, which differ from natural scenes in terms of texture, lighting, and geometry. The absence of high-quality, annotated datasets tailored for virtual content restoration and reconstruction has hindered progress and limited the development of effective methods in this domain.
To address this gap, we introduce a large-scale, high-quality dataset specifically designed for rendered environments. This dataset aims to support a wide range of tasks, including image restoration, 3D reconstruction, novel view synthesis, and content manipulation, thereby facilitating research and development of generative AI algorithms for virtual content.
### Dataset Sources
- **Repository:** [M3VIR](https://huggingface.co/datasets/guluthemonster/M3VIR)
- **Paper:** [More Information Needed]
## Dataset Details
M³VIR provids 8 categories: Churches-And-Temples, Hiking-Trails, Hotels-And-Restaurants, Mountains, Parks-And-Recreation-Areas, Residential-Areas, School-Universities, and Urban-Street-Views. For each category, we collected three types of scenes:
- MovingCameraDynamicScene
- MovingCameraStaticScene
- StaticCameraDynamicScene
For each scene type, we collect 10 distinct video sets featuring varying scene content. Each set includes different resolutions and visual styles: a photo-realistic style available in 960×540, 1920×1080, and 2880×1620 resolutions (Realistic_960x540_1024sample, Realistic_1920x1080_1024sample, Realistic_2880x1620_1024sample); a cartoon style in 1920×1080 resolution (Cartoon_1920x1080_1024sample); and a metalized style also in 1920×1080 resolution (Metalize_1920x1080_1024sample). Corresponding segmentation maps are provided as ID_images. Since Realistic_1920x1080_1024sample, Cartoon_1920x1080_1024sample, and Metalize_1920x1080_1024sample share the same segmentation annotations, we include the ID_images only once to conserve storage.
The dataset is split into 80% for training (64 sets) and 20% for testing (16 sets). To support the four challenge tracks, the full M³VIR dataset is divided into two subsets: M³VIR_MR and M³VIR_MS. Due to the large size of the dataset, a small-scale mini training set will also be provided for Track 1 to facilitate quick experimentation and baseline development.
Each video sample—defined by a specific style and resolution (e.g., realistic style at 1920×1080 resolution)—includes six temporally synchronized camera views with a shared camera center. These views are captured from different perspectives: Back, Front, Left60, Left120, Right60, and Right120, providing diverse angular coverage of the scene. Each video sequence is 2 seconds long, recorded at 15 frames per second, resulting in a total of 30 image frames per view.
### M³VIR-Tracks
| Dataset | Rate|Scene Types|Resolution Styles|Data Path|
| :----------- | :-----------: | :-------: | :-------: | :-------: |
| M³VIR_MR | 5% |MovingCamDyn/MovingCamStatic/StaticCamDyn |Real_960x540/Real_1920x1080/Real_2880x1620|Track1|
| | Full |MovingCamStatic|Real_1920x1080|Track2|
| | Full |MovingCamStatic|Real_960x540/Real_1920x1080/Real_2880x1620|Track3|
| M³VIR_MS | Full |MovingCamDyn/MovingCamStatic/StaticCamDyn |Cartoon_1920x1080/Metal_1920x1080/Real_1920x1080|Track4|
For more details about the datasets and challenge tracks, please refer to the official challenge page:
https://richmediagai.github.io/challenges.html
## How to Download
[Use Hugging Face Command Line Interface (CLI)](https://huggingface.co/docs/huggingface_hub/guides/cli#huggingface-cli-download)
```
Download Entire Dataset
$ huggingface-cli download guluthemonster/M3VIR --repo-type dataset --local-dir .
Download Specified Folder
$ huggingface-cli download guluthemonster/M3VIR --repo-type dataset --include TRACKS/* --local-dir .
```
[Use Git](https://huggingface.co/docs/hub/datasets-downloading#using-git)
```
$ git clone https://huggingface.co/datasets/guluthemonster/M3VIR
```
After download the dataset, you can use following codes to extract the files in each subfolder (take the TRACKS/Track1/Batch1 as an example)：
```
$ python Scripts/extract_track1.py --input_path TRACKS/Track1/Batch1 --output_path /path/to/your/folder
```
# Citation |
rasdani/swe-fixer-debug-DeepSeek-R1 | rasdani | 2025-05-05T23:31:07Z | 100 | 0 | [
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] | [] | 2025-04-26T12:20:51Z | null | ---
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justus27/s2-bigmath | justus27 | 2025-05-05T23:23:29Z | 0 | 0 | [
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HungVu2003/opt-350m_beta_0.0_alpha_0.2_num-company_3_dataset_0_for_gen_2_v2 | HungVu2003 | 2025-05-05T23:23:19Z | 0 | 0 | [
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konwoo/dbs2-fs1-np1-3e-07-wd0.0-token-37M | konwoo | 2025-05-05T23:18:42Z | 0 | 0 | [
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HungVu2003/opt-350m_beta_1.0_alpha_0.2_num-company_3_dataset_2_for_gen_1_v2 | HungVu2003 | 2025-05-05T23:03:05Z | 0 | 0 | [
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mlfoundations-dev/am_1000k | mlfoundations-dev | 2025-05-05T22:45:33Z | 0 | 0 | [
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Solmazp/synthetic_data_67k | Solmazp | 2025-05-05T22:45:02Z | 0 | 0 | [
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|
littleGuagua/x_dataset_11627 | littleGuagua | 2025-05-05T22:40:39Z | 1,545 | 0 | [
"task_categories:text-classification",
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] | [
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] | 2025-01-26T13:13:48Z | null | ---
license: mit
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---
# 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:** littleGuagua/x_dataset_11627
- **Subnet:** Bittensor Subnet 13
- **Miner Hotkey:** 5FUByNzgdM2eukk6SwetFsZ4EPTxRqaV4YNEhNcusS1SxRVX
### 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{littleGuagua2025datauniversex_dataset_11627,
title={The Data Universe Datasets: The finest collection of social media data the web has to offer},
author={littleGuagua},
year={2025},
url={https://huggingface.co/datasets/littleGuagua/x_dataset_11627},
}
```
### 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:** 149000631
- **Date Range:** 2025-01-21T00:00:00Z to 2025-02-10T00:00:00Z
- **Last Updated:** 2025-02-18T20:47:28Z
### Data Distribution
- Tweets with hashtags: 42.62%
- Tweets without hashtags: 57.38%
### Top 10 Hashtags
For full statistics, please refer to the `stats.json` file in the repository.
| Rank | Topic | Total Count | Percentage |
|------|-------|-------------|-------------|
| 1 | NULL | 85489881 | 57.38% |
| 2 | #riyadh | 1033096 | 0.69% |
| 3 | #zelena | 790108 | 0.53% |
| 4 | #tiktok | 618215 | 0.41% |
| 5 | #bbb25 | 362232 | 0.24% |
| 6 | #ad | 356819 | 0.24% |
| 7 | #jhope_at_galadespiècesjaunes | 234343 | 0.16% |
| 8 | #bbmzansi | 207541 | 0.14% |
| 9 | #pr | 188395 | 0.13% |
| 10 | #yahooニュース | 178958 | 0.12% |
## Update History
| Date | New Instances | Total Instances |
|------|---------------|-----------------|
| 2025-01-26T13:14:32Z | 2274090 | 2274090 |
| 2025-01-30T01:26:02Z | 29523249 | 31797339 |
| 2025-02-02T13:36:10Z | 29333848 | 61131187 |
| 2025-02-06T01:47:05Z | 28740147 | 89871334 |
| 2025-02-09T14:00:59Z | 29293177 | 119164511 |
| 2025-02-13T02:15:32Z | 28379764 | 147544275 |
| 2025-02-18T05:45:25Z | 808939 | 148353214 |
| 2025-02-18T20:47:28Z | 647417 | 149000631 |
|
mlfoundations-dev/am_300k | mlfoundations-dev | 2025-05-05T22:33:58Z | 0 | 0 | [
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HungVu2003/opt-350m_beta_1.0_alpha_0.2_num-company_3_dataset_0_for_gen_1_v2 | HungVu2003 | 2025-05-05T22:28:11Z | 0 | 0 | [
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saurabh5/tulu-3-personas-code-rlvr | saurabh5 | 2025-05-05T22:26:22Z | 140 | 0 | [
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ajagota71/ajagota71_pythia-70m-detox-epoch-20_2000_samples_detoxified | ajagota71 | 2025-05-05T22:24:07Z | 0 | 0 | [
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mlfoundations-dev/limo_0.3k | mlfoundations-dev | 2025-05-05T22:20:28Z | 0 | 0 | [
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PeggyPeiyao/SentimentAnalysis | PeggyPeiyao | 2025-05-05T22:20:24Z | 0 | 0 | [
"license:apache-2.0",
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license: apache-2.0
---
|
HungVu2003/opt-350m_beta_1.0_alpha_0.0_num-company_3_dataset_0_for_gen_19_v2 | HungVu2003 | 2025-05-05T22:06:27Z | 0 | 0 | [
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HungVu2003/opt-350m_beta_0.0_alpha_0.0_num-company_3_dataset_1_for_gen_19_v2 | HungVu2003 | 2025-05-05T22:05:10Z | 0 | 0 | [
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HungVu2003/opt-350m_beta_1.0_alpha_0.0_num-company_3_dataset_2_for_gen_18_v2 | HungVu2003 | 2025-05-05T22:04:20Z | 0 | 0 | [
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HungVu2003/opt-350m_beta_0.0_alpha_0.0_num-company_3_dataset_2_for_gen_15_v2 | HungVu2003 | 2025-05-05T21:57:49Z | 0 | 0 | [
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HungVu2003/opt-350m_beta_1.0_alpha_0.0_num-company_3_dataset_2_for_gen_12_v2 | HungVu2003 | 2025-05-05T21:52:34Z | 0 | 0 | [
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HungVu2003/opt-350m_beta_0.0_alpha_0.0_num-company_3_dataset_2_for_gen_12_v2 | HungVu2003 | 2025-05-05T21:51:56Z | 0 | 0 | [
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HungVu2003/opt-350m_beta_1.0_alpha_0.0_num-company_3_dataset_2_for_gen_10_v2 | HungVu2003 | 2025-05-05T21:48:42Z | 0 | 0 | [
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HungVu2003/opt-350m_beta_1.0_alpha_0.0_num-company_3_dataset_1_for_gen_10_v2 | HungVu2003 | 2025-05-05T21:48:40Z | 0 | 0 | [
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HungVu2003/opt-350m_beta_0.0_alpha_0.0_num-company_3_dataset_1_for_gen_10_v2 | HungVu2003 | 2025-05-05T21:48:07Z | 0 | 0 | [
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HungVu2003/opt-350m_beta_0.0_alpha_0.0_num-company_3_dataset_1_for_gen_8_v2 | HungVu2003 | 2025-05-05T21:44:16Z | 0 | 0 | [
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HungVu2003/opt-350m_beta_0.0_alpha_0.0_num-company_3_dataset_0_for_gen_8_v2 | HungVu2003 | 2025-05-05T21:44:14Z | 0 | 0 | [
"region:us"
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HungVu2003/opt-350m_beta_1.0_alpha_0.0_num-company_3_dataset_2_for_gen_6_v2 | HungVu2003 | 2025-05-05T21:41:00Z | 0 | 0 | [
"region:us"
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Setayeshabiazi/Train_Test_Split_LLM_Project | Setayeshabiazi | 2025-05-05T21:40:01Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-05T21:37:09Z | null | ---
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---
|
icedwind/x_dataset_12552 | icedwind | 2025-05-05T21:38:46Z | 1,251 | 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",
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"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"text-classification",
"token-classification",
"question-answering",
"summarization",
"text-generation"
] | 2025-01-27T09:06:40Z | 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:** icedwind/x_dataset_12552
- **Subnet:** Bittensor Subnet 13
- **Miner Hotkey:** 5EsgiG2PjgxDgxGHe8sqdeADbznL53ScJSG2UMRozvuDHJW7
### 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{icedwind2025datauniversex_dataset_12552,
title={The Data Universe Datasets: The finest collection of social media data the web has to offer},
author={icedwind},
year={2025},
url={https://huggingface.co/datasets/icedwind/x_dataset_12552},
}
```
### 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:** 55275936
- **Date Range:** 2025-01-21T00:00:00Z to 2025-02-11T00:00:00Z
- **Last Updated:** 2025-02-18T19:50:04Z
### Data Distribution
- Tweets with hashtags: 44.05%
- Tweets without hashtags: 55.95%
### Top 10 Hashtags
For full statistics, please refer to the `stats.json` file in the repository.
| Rank | Topic | Total Count | Percentage |
|------|-------|-------------|-------------|
| 1 | NULL | 30924129 | 55.95% |
| 2 | #riyadh | 390293 | 0.71% |
| 3 | #zelena | 311850 | 0.56% |
| 4 | #tiktok | 238579 | 0.43% |
| 5 | #bbb25 | 161226 | 0.29% |
| 6 | #ad | 134995 | 0.24% |
| 7 | #jhope_at_galadespiècesjaunes | 89186 | 0.16% |
| 8 | #grammys | 79177 | 0.14% |
| 9 | #bbmzansi | 74293 | 0.13% |
| 10 | #pr | 73757 | 0.13% |
## Update History
| Date | New Instances | Total Instances |
|------|---------------|-----------------|
| 2025-01-27T09:07:36Z | 2955867 | 2955867 |
| 2025-01-30T21:10:30Z | 9690897 | 12646764 |
| 2025-02-03T09:14:40Z | 11584067 | 24230831 |
| 2025-02-06T21:18:44Z | 9766486 | 33997317 |
| 2025-02-10T09:23:04Z | 9007442 | 43004759 |
| 2025-02-13T21:27:37Z | 10986716 | 53991475 |
| 2025-02-18T04:48:46Z | 650061 | 54641536 |
| 2025-02-18T19:50:04Z | 634400 | 55275936 |
|
HungVu2003/opt-350m_beta_0.0_alpha_0.0_num-company_3_dataset_2_for_gen_5_v2 | HungVu2003 | 2025-05-05T21:38:45Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-05T21:38:44Z | null | ---
dataset_info:
features:
- name: question
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splits:
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num_examples: 12500
download_size: 557156
dataset_size: 807175
configs:
- config_name: default
data_files:
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path: data/train-*
---
|
HungVu2003/opt-350m_beta_0.0_alpha_0.0_num-company_3_dataset_1_for_gen_5_v2 | HungVu2003 | 2025-05-05T21:38:44Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-05T21:38:43Z | null | ---
dataset_info:
features:
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download_size: 561184
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---
|
HungVu2003/opt-350m_beta_1.0_alpha_0.0_num-company_3_dataset_1_for_gen_4_v2 | HungVu2003 | 2025-05-05T21:37:12Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-05T21:37:11Z | null | ---
dataset_info:
features:
- name: question
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splits:
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num_examples: 12500
download_size: 3355823
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---
|
HungVu2003/opt-350m_beta_0.0_alpha_0.0_num-company_3_dataset_0_for_gen_3_v2 | HungVu2003 | 2025-05-05T21:35:12Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-05T21:35:08Z | null | ---
dataset_info:
features:
- name: question
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splits:
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download_size: 575037
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|
HungVu2003/opt-350m_beta_0.0_alpha_0.0_num-company_3_dataset_2_for_gen_2_v2 | HungVu2003 | 2025-05-05T21:33:27Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-05T21:33:26Z | null | ---
dataset_info:
features:
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|
HungVu2003/opt-350m_beta_0.0_alpha_0.0_num-company_3_dataset_0_for_gen_2_v2 | HungVu2003 | 2025-05-05T21:33:23Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-05T21:33:21Z | null | ---
dataset_info:
features:
- name: question
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configs:
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---
|
ieuniversity/group_4_submission | ieuniversity | 2025-05-05T21:32:45Z | 249 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-04-23T21:10:03Z | null | ---
dataset_info:
features:
- name: ID
dtype: string
- name: CLASE
dtype: string
splits:
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configs:
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---
|
uzairrj/MNIST-Numpy-Dump | uzairrj | 2025-05-05T21:26:30Z | 0 | 0 | [
"license:apache-2.0",
"size_categories:10K<n<100K",
"region:us"
] | [] | 2025-05-05T21:07:29Z | null | ---
license: apache-2.0
pretty_name: MNIST numpy dump
size_categories:
- 10K<n<100K
--- |
littleGuagua/x_dataset_8140 | littleGuagua | 2025-05-05T21:07:51Z | 1,049 | 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"
] | 2025-01-26T13:25:56Z | 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:** littleGuagua/x_dataset_8140
- **Subnet:** Bittensor Subnet 13
- **Miner Hotkey:** 5HasdyDaczLXYaiykhuuszTMWS65QmAgo72UpwABUi3czyeu
### 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{littleGuagua2025datauniversex_dataset_8140,
title={The Data Universe Datasets: The finest collection of social media data the web has to offer},
author={littleGuagua},
year={2025},
url={https://huggingface.co/datasets/littleGuagua/x_dataset_8140},
}
```
### 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:** 50376997
- **Date Range:** 2025-01-21T00:00:00Z to 2025-02-10T00:00:00Z
- **Last Updated:** 2025-02-18T20:55:45Z
### Data Distribution
- Tweets with hashtags: 39.81%
- Tweets without hashtags: 60.19%
### Top 10 Hashtags
For full statistics, please refer to the `stats.json` file in the repository.
| Rank | Topic | Total Count | Percentage |
|------|-------|-------------|-------------|
| 1 | NULL | 30319553 | 60.19% |
| 2 | #riyadh | 310085 | 0.62% |
| 3 | #zelena | 215655 | 0.43% |
| 4 | #tiktok | 192806 | 0.38% |
| 5 | #ad | 112205 | 0.22% |
| 6 | #bbb25 | 110854 | 0.22% |
| 7 | #grammys | 82659 | 0.16% |
| 8 | #jhope_at_galadespiècesjaunes | 70215 | 0.14% |
| 9 | #bbmzansi | 66978 | 0.13% |
| 10 | #sixtonesann | 65126 | 0.13% |
## Update History
| Date | New Instances | Total Instances |
|------|---------------|-----------------|
| 2025-01-26T13:26:49Z | 2721817 | 2721817 |
| 2025-01-30T01:43:17Z | 9702324 | 12424141 |
| 2025-02-02T13:47:13Z | 12507356 | 24931497 |
| 2025-02-06T01:50:29Z | 8691717 | 33623214 |
| 2025-02-09T13:54:19Z | 8748247 | 42371461 |
| 2025-02-13T02:21:42Z | 6726572 | 49098033 |
| 2025-02-18T05:54:36Z | 648154 | 49746187 |
| 2025-02-18T20:55:45Z | 630810 | 50376997 |
|
XiaoZhang98/OntoBench | XiaoZhang98 | 2025-05-05T21:02:06Z | 291 | 0 | [
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"library:polars",
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] | [] | 2025-04-30T22:11:20Z | null | ---
license: apache-2.0
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path: data/3_5_ontology_alignment-*
---
|
semran1/calibration_test3 | semran1 | 2025-05-05T21:00:41Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-05T21:00:13Z | null | ---
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---
|
Enpas/Sabian-2.0 | Enpas | 2025-05-05T20:54:46Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-05T20:44:06Z | null | ---
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|
doublesizebed/malay-tts-tags | doublesizebed | 2025-05-05T20:47:53Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-05T19:01:09Z | null | ---
dataset_info:
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|
HungVu2003/opt-350m_beta_1.0_alpha_0.4_num-company_2_dataset_0_for_gen_8_v2 | HungVu2003 | 2025-05-05T20:46:10Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-05T20:46:08Z | null | ---
dataset_info:
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---
|
icedwind/x_dataset_7114 | icedwind | 2025-05-05T20:40:18Z | 1,045 | 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",
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] | [
"text-classification",
"token-classification",
"question-answering",
"summarization",
"text-generation"
] | 2025-01-29T04:56:37Z | null | ---
license: mit
multilinguality:
- multilingual
source_datasets:
- original
task_categories:
- text-classification
- token-classification
- question-answering
- summarization
- text-generation
task_ids:
- sentiment-analysis
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- 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:** icedwind/x_dataset_7114
- **Subnet:** Bittensor Subnet 13
- **Miner Hotkey:** 5HWXQSVqjd4pY525MupNTU7NaEb7r35ppxXfgeDWPgpfBuhm
### 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{icedwind2025datauniversex_dataset_7114,
title={The Data Universe Datasets: The finest collection of social media data the web has to offer},
author={icedwind},
year={2025},
url={https://huggingface.co/datasets/icedwind/x_dataset_7114},
}
```
### 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:** 38590587
- **Date Range:** 2025-01-22T00:00:00Z to 2025-02-12T00:00:00Z
- **Last Updated:** 2025-02-18T21:48:51Z
### Data Distribution
- Tweets with hashtags: 48.74%
- Tweets without hashtags: 51.26%
### Top 10 Hashtags
For full statistics, please refer to the `stats.json` file in the repository.
| Rank | Topic | Total Count | Percentage |
|------|-------|-------------|-------------|
| 1 | NULL | 19781385 | 51.26% |
| 2 | #riyadh | 289177 | 0.75% |
| 3 | #zelena | 233455 | 0.60% |
| 4 | #tiktok | 182562 | 0.47% |
| 5 | #ad | 111797 | 0.29% |
| 6 | #bbb25 | 107360 | 0.28% |
| 7 | #jhope_at_galadespiècesjaunes | 73362 | 0.19% |
| 8 | #pr | 58834 | 0.15% |
| 9 | #yahooニュース | 56344 | 0.15% |
| 10 | #theheartkillersep11 | 55012 | 0.14% |
## Update History
| Date | New Instances | Total Instances |
|------|---------------|-----------------|
| 2025-01-29T04:57:34Z | 3402840 | 3402840 |
| 2025-02-01T16:59:53Z | 7356908 | 10759748 |
| 2025-02-05T05:03:04Z | 9386957 | 20146705 |
| 2025-02-08T17:06:06Z | 7524854 | 27671559 |
| 2025-02-12T05:13:11Z | 9621743 | 37293302 |
| 2025-02-18T06:47:29Z | 636505 | 37929807 |
| 2025-02-18T21:48:51Z | 660780 | 38590587 |
|
weqweasdas/qw_grpo_new_test_minerva_math | weqweasdas | 2025-05-05T20:33:06Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-05T20:29:59Z | null | ---
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configs:
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path: data/train-*
---
|
Evangelinejy/DeepScaleR_with_solution_external_difficulty | Evangelinejy | 2025-05-05T20:32:13Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-05T20:32:12Z | null | ---
dataset_info:
features:
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dtype: string
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---
|
ai-chem/Chelate_metal_complexes | ai-chem | 2025-05-05T20:32:11Z | 0 | 0 | [
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"library:mlcroissant",
"library:polars",
"region:us",
"metal-complexes",
"radiometals",
"coordination-chemistry"
] | [] | 2025-05-05T17:47:49Z | null | ---
dataset_info:
features:
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dtype: string
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dtype: int64
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dtype: int64
- name: compound_id
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- name: smiles
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- name: metal
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configs:
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data_files:
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path: data/train-*
tags:
- metal-complexes
- radiometals
- coordination-chemistry
---
# Dataset Card for Complexes
This dataset includes information about **metal-containing chemical complexes**, particularly those involving radiometals like gallium. It contains chemical structures, target values, and references to the source literature.
## Dataset Summary
- **Number of rows**: 907
- **Number of columns**: 20
- **Data type**: CSV
## Column Examples
- `smiles`: Chemical structure
- `metal`: Metal involved (e.g., Ga)
- `target`: Property of interest
- `doi`, `publisher`, `title`: Source article
## Potential Uses
- Coordination chemistry studies
- Metal–ligand interaction modeling
- Radiopharmaceutical design
## License
MIT
|
ai-chem/Cytotoxicity | ai-chem | 2025-05-05T20:30:19Z | 0 | 0 | [
"size_categories:1K<n<10K",
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"modality:tabular",
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"library:mlcroissant",
"library:polars",
"region:us",
"cytotoxicity",
"nanomaterials",
"toxicology",
"bio-nano-interactions"
] | [] | 2025-05-05T17:47:51Z | null | ---
dataset_info:
features:
- name: sn
dtype: int64
- name: Material
dtype: string
- name: Shape
dtype: string
- name: Coat/Functional group
dtype: string
- name: Synthesis method
dtype: string
- name: Surface charge
dtype: string
- name: Size in medium (nm)
dtype: float64
- name: Zeta in medium (mV)
dtype: float64
- name: no. of cells (cells/well)
dtype: float64
- name: Human/Animal
dtype: string
- name: Cell source
dtype: string
- name: Cell tissue
dtype: string
- name: Cell morphology
dtype: string
- name: Cell age
dtype: string
- name: Time (hr)
dtype: int64
- name: Concentration (µg/ml)
dtype: float64
- name: Test
dtype: string
- name: Test indicator
dtype: string
- name: Viability (%)
dtype: float64
- name: doi
dtype: string
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dtype: int64
- name: Core size (nm)
dtype: float64
- name: Hydrodynamic diameter (nm)
dtype: float64
- name: Zeta potential (mV)
dtype: float64
- name: Cell type
dtype: string
- name: journal_name
dtype: string
- name: publisher
dtype: string
- name: year
dtype: float64
- name: title
dtype: string
- name: journal_is_oa
dtype: bool
- name: is_oa
dtype: string
- name: oa_status
dtype: string
- name: pdf
dtype: string
- name: access
dtype: int64
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dataset_size: 2594352
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- cytotoxicity
- nanomaterials
- toxicology
- bio-nano-interactions
---
# Dataset Card for cytox_NeurIPS_updated_data
This dataset reports **cytotoxicity data** for various nanomaterials tested on different cell types. It includes nanomaterial characteristics, experimental conditions, and cell viability outcomes.
## Dataset Summary
- **Number of rows**: 5476
- **Number of columns**: 32
- **Data format**: CSV
## Column Examples
- `material`, `shape`, `coat/functional group`: Nanomaterial descriptors
- `cell type`, `cell tissue`, `human/animal`: Biological models used
- `viability (%)`: Measured cytotoxicity
- `doi`, `publisher`, `title`: Source references
## Potential Uses
- Nanotoxicology studies
- Predictive modeling of nanomaterial–cell interactions
- Safe-by-design nanomaterial development
## License
MIT
|
ai-chem/Eye_drops | ai-chem | 2025-05-05T20:24:38Z | 0 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"eye-drops",
"permeability",
"drug-delivery"
] | [] | 2025-05-05T17:47:56Z | null | ---
dataset_info:
features:
- name: smiles
dtype: string
- name: name
dtype: string
- name: perm (cm/s)
dtype: string
- name: logP
dtype: float64
- name: doi
dtype: string
- name: PMID
dtype: float64
- name: title
dtype: string
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dtype: string
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dtype: int64
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dtype: int64
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dtype: float64
- name: origin
dtype: string
splits:
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num_examples: 163
download_size: 14233
dataset_size: 45633
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- eye-drops
- permeability
- drug-delivery
---
# Dataset Card for Eye Drops
This dataset contains data on **ocular drug candidates** and their **corneal permeability**. It includes SMILES, physicochemical properties, and literature references.
## Dataset Summary
- **Number of rows**: 163
- **Number of columns**: 12
- **Data type**: CSV
## Column Examples
- `smiles`: Molecular structure
- `perm (cm/s)`: Permeability value
- `logP`: Lipophilicity
- `PMID`, `title`, `publisher`: Source info
## Potential Uses
- Drug delivery modeling
- QSAR for corneal absorption
- Eye drop formulation research
## License
MIT
|
TheRealPilot638/Llama-3.2-1B-beam-search_16_no_chunking_H200 | TheRealPilot638 | 2025-05-05T20:11:23Z | 2 | 0 | [
"region:us"
] | [] | 2025-05-04T17:39:19Z | null | ---
dataset_info:
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---
|
polygraf-ai/dpo-dataset-v1 | polygraf-ai | 2025-05-05T20:07:45Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-05T20:07:39Z | null | ---
dataset_info:
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---
|
osama24sy/llama3.1-8b-it-10k-qwen-singleturn-onesolution-r64-24-v0.3 | osama24sy | 2025-05-05T19:58:17Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-05T19:58:13Z | null | ---
dataset_info:
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---
|
HungVu2003/opt-350m_beta_0.5_alpha_0.0_num-company_2_dataset_1_for_gen_17_v2 | HungVu2003 | 2025-05-05T19:58:04Z | 0 | 0 | [
"region:us"
] | [] | 2025-05-05T19:58:03Z | null | ---
dataset_info:
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data_files:
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---
|
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