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zkpbeats/reddit_ds_159877	zkpbeats	2025-05-13T04:08:37Z	1,682	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: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" ]	2025-04-03T12:21:28Z	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: zkpbeats/reddit_ds_159877 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5FAGEj2CU8cF6QV5iUijZu5kZDg9m7z9v6HtQy3nBLStZx6M ### 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{zkpbeats2025datauniversereddit_ds_159877, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={zkpbeats}, year={2025}, url={https://huggingface.co/datasets/zkpbeats/reddit_ds_159877}, } ``` ### 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: 6949050 - Date Range: 2025-03-05T00:00:00Z to 2025-05-13T00:00:00Z - Last Updated: 2025-05-13T04:08:34Z ### Data Distribution - Posts: 1.47% - Comments: 34.40% ### Top 10 Subreddits For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| r/politics \| 197486 \| 7.92% \| \| 2 \| r/facepalm \| 117485 \| 4.71% \| \| 3 \| r/wallstreetbets \| 65483 \| 2.63% \| \| 4 \| r/MapPorn \| 64839 \| 2.60% \| \| 5 \| r/PokemonTCG \| 61017 \| 2.45% \| \| 6 \| r/Fauxmoi \| 55953 \| 2.24% \| \| 7 \| r/animequestions \| 43740 \| 1.75% \| \| 8 \| r/GoodAssSub \| 35293 \| 1.42% \| \| 9 \| r/Costco \| 34874 \| 1.40% \| \| 10 \| r/GTA \| 31485 \| 1.26% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-04-03T11:50:44Z \| 210554 \| 210554 \| \| 2025-04-03T11:52:19Z \| 206249 \| 416803 \| \| 2025-04-03T11:53:58Z \| 204074 \| 620877 \| \| 2025-04-03T11:55:36Z \| 210761 \| 831638 \| \| 2025-04-03T11:57:11Z \| 202795 \| 1034433 \| \| 2025-04-03T11:58:47Z \| 228184 \| 1262617 \| \| 2025-04-03T12:00:22Z \| 210899 \| 1473516 \| \| 2025-04-03T12:01:57Z \| 204861 \| 1678377 \| \| 2025-04-03T12:03:35Z \| 219572 \| 1897949 \| \| 2025-04-03T12:05:11Z \| 216640 \| 2114589 \| \| 2025-04-03T12:06:39Z \| 160498 \| 2275087 \| \| 2025-04-03T12:08:09Z \| 166653 \| 2441740 \| \| 2025-04-03T12:09:37Z \| 167136 \| 2608876 \| \| 2025-04-03T12:11:04Z \| 166162 \| 2775038 \| \| 2025-04-03T12:13:03Z \| 380438 \| 3155476 \| \| 2025-04-03T12:15:00Z \| 375975 \| 3531451 \| \| 2025-04-03T12:16:59Z \| 387961 \| 3919412 \| \| 2025-04-03T12:18:27Z \| 162910 \| 4082322 \| \| 2025-04-03T12:20:25Z \| 374112 \| 4456434 \| \| 2025-04-03T12:22:46Z \| 406709 \| 4863143 \| \| 2025-04-03T14:45:01Z \| 6628 \| 4869771 \| \| 2025-04-03T17:07:16Z \| 6208 \| 4875979 \| \| 2025-04-03T19:29:23Z \| 5661 \| 4881640 \| \| 2025-04-03T21:51:59Z \| 7870 \| 4889510 \| \| 2025-04-04T00:14:22Z \| 6741 \| 4896251 \| \| 2025-04-04T02:37:44Z \| 6081 \| 4902332 \| \| 2025-04-04T04:59:53Z \| 5195 \| 4907527 \| \| 2025-04-04T07:21:53Z \| 4283 \| 4911810 \| \| 2025-04-04T09:43:50Z \| 2561 \| 4914371 \| \| 2025-04-04T12:06:04Z \| 4633 \| 4919004 \| \| 2025-04-04T14:28:55Z \| 5986 \| 4924990 \| \| 2025-04-04T16:51:18Z \| 6560 \| 4931550 \| \| 2025-04-04T19:13:57Z \| 5694 \| 4937244 \| \| 2025-04-04T21:36:44Z \| 5051 \| 4942295 \| \| 2025-04-04T23:59:35Z \| 6560 \| 4948855 \| \| 2025-04-05T02:21:48Z \| 6841 \| 4955696 \| \| 2025-04-05T04:43:44Z \| 3631 \| 4959327 \| \| 2025-04-05T07:05:55Z \| 3952 \| 4963279 \| \| 2025-04-05T09:28:02Z \| 2985 \| 4966264 \| \| 2025-04-05T11:50:33Z \| 2570 \| 4968834 \| \| 2025-04-05T14:13:04Z \| 5366 \| 4974200 \| \| 2025-04-05T16:35:34Z \| 5071 \| 4979271 \| \| 2025-04-05T18:58:13Z \| 6385 \| 4985656 \| \| 2025-04-05T21:20:43Z \| 5576 \| 4991232 \| \| 2025-04-05T23:43:37Z \| 5918 \| 4997150 \| \| 2025-04-06T02:05:59Z \| 5171 \| 5002321 \| \| 2025-04-06T04:28:13Z \| 6052 \| 5008373 \| \| 2025-04-06T06:50:33Z \| 3534 \| 5011907 \| \| 2025-04-06T09:12:59Z \| 2979 \| 5014886 \| \| 2025-04-06T11:35:25Z \| 3426 \| 5018312 \| \| 2025-04-06T13:57:58Z \| 5561 \| 5023873 \| \| 2025-04-06T16:20:33Z \| 5173 \| 5029046 \| \| 2025-04-06T18:43:12Z \| 5951 \| 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\| 5765 \| 5850194 \| \| 2025-04-24T05:54:10Z \| 2886 \| 5853080 \| \| 2025-04-24T08:16:58Z \| 2818 \| 5855898 \| \| 2025-04-24T10:39:49Z \| 3837 \| 5859735 \| \| 2025-04-24T13:01:59Z \| 6157 \| 5865892 \| \| 2025-04-24T15:24:33Z \| 8579 \| 5874471 \| \| 2025-04-24T17:47:30Z \| 7821 \| 5882292 \| \| 2025-04-24T20:11:32Z \| 7178 \| 5889470 \| \| 2025-04-24T22:34:31Z \| 8990 \| 5898460 \| \| 2025-04-25T00:57:23Z \| 6398 \| 5904858 \| \| 2025-04-25T03:20:07Z \| 4010 \| 5908868 \| \| 2025-04-25T05:44:20Z \| 4411 \| 5913279 \| \| 2025-04-25T08:07:15Z \| 3943 \| 5917222 \| \| 2025-04-25T10:30:36Z \| 4093 \| 5921315 \| \| 2025-04-25T12:53:11Z \| 3971 \| 5925286 \| \| 2025-04-25T15:15:36Z \| 5891 \| 5931177 \| \| 2025-04-25T17:38:16Z \| 9135 \| 5940312 \| \| 2025-04-25T20:01:49Z \| 6563 \| 5946875 \| \| 2025-04-25T22:24:38Z \| 4969 \| 5951844 \| \| 2025-04-26T00:47:12Z \| 6044 \| 5957888 \| \| 2025-04-26T03:09:28Z \| 6018 \| 5963906 \| \| 2025-04-26T05:31:29Z \| 4559 \| 5968465 \| \| 2025-04-26T07:53:31Z \| 3974 \| 5972439 \| \| 2025-04-26T10:15:11Z \| 2692 \| 5975131 \| \| 2025-04-26T12:38:56Z \| 3696 \| 5978827 \| \| 2025-04-26T15:03:57Z \| 4808 \| 5983635 \| \| 2025-04-26T17:26:12Z \| 7177 \| 5990812 \| \| 2025-04-26T19:50:55Z \| 7815 \| 5998627 \| \| 2025-04-26T22:14:18Z \| 6231 \| 6004858 \| \| 2025-04-27T00:37:03Z \| 5347 \| 6010205 \| \| 2025-04-27T02:59:42Z \| 5466 \| 6015671 \| \| 2025-04-27T05:21:41Z \| 3597 \| 6019268 \| \| 2025-04-27T07:44:08Z \| 3784 \| 6023052 \| \| 2025-04-27T10:06:36Z \| 3134 \| 6026186 \| \| 2025-04-27T12:28:47Z \| 4285 \| 6030471 \| \| 2025-04-27T14:51:41Z \| 4818 \| 6035289 \| \| 2025-04-27T17:17:14Z \| 5973 \| 6041262 \| \| 2025-04-27T19:39:33Z \| 6327 \| 6047589 \| \| 2025-04-27T22:01:53Z \| 8255 \| 6055844 \| \| 2025-04-28T00:25:20Z \| 5993 \| 6061837 \| \| 2025-04-28T02:48:24Z \| 5212 \| 6067049 \| \| 2025-04-28T05:10:54Z \| 4052 \| 6071101 \| \| 2025-04-28T07:33:00Z \| 3616 \| 6074717 \| \| 2025-04-28T09:55:54Z \| 2873 \| 6077590 \| \| 2025-04-28T12:18:58Z \| 3504 \| 6081094 \| \| 2025-04-28T14:42:00Z \| 6660 \| 6087754 \| \| 2025-04-28T17:04:24Z \| 8769 \| 6096523 \| \| 2025-04-28T19:27:22Z \| 6204 \| 6102727 \| \| 2025-04-28T21:50:31Z \| 8413 \| 6111140 \| \| 2025-04-29T00:12:58Z \| 5810 \| 6116950 \| \| 2025-04-29T02:35:50Z \| 7220 \| 6124170 \| \| 2025-04-29T04:58:44Z \| 3697 \| 6127867 \| \| 2025-04-29T07:20:45Z \| 3245 \| 6131112 \| \| 2025-04-29T09:44:23Z \| 3361 \| 6134473 \| \| 2025-04-29T12:07:37Z \| 4572 \| 6139045 \| \| 2025-04-29T14:29:57Z \| 5704 \| 6144749 \| \| 2025-04-29T16:52:12Z \| 7865 \| 6152614 \| \| 2025-04-29T19:15:31Z \| 8052 \| 6160666 \| \| 2025-04-29T21:38:27Z \| 7652 \| 6168318 \| \| 2025-04-30T00:00:46Z \| 7743 \| 6176061 \| \| 2025-04-30T02:23:11Z \| 8592 \| 6184653 \| \| 2025-04-30T04:45:31Z \| 6616 \| 6191269 \| \| 2025-04-30T07:08:30Z \| 5719 \| 6196988 \| \| 2025-04-30T09:31:29Z \| 4662 \| 6201650 \| \| 2025-04-30T11:53:30Z \| 2892 \| 6204542 \| \| 2025-04-30T14:15:47Z \| 7379 \| 6211921 \| \| 2025-04-30T16:38:03Z \| 10053 \| 6221974 \| \| 2025-04-30T19:02:19Z \| 7697 \| 6229671 \| \| 2025-04-30T21:24:25Z \| 7698 \| 6237369 \| \| 2025-04-30T23:46:45Z \| 8588 \| 6245957 \| \| 2025-05-01T02:10:46Z \| 5921 \| 6251878 \| \| 2025-05-01T04:33:33Z \| 5501 \| 6257379 \| \| 2025-05-01T06:58:54Z \| 4537 \| 6261916 \| \| 2025-05-01T09:21:14Z \| 3900 \| 6265816 \| \| 2025-05-01T11:43:38Z \| 3201 \| 6269017 \| \| 2025-05-01T14:06:16Z \| 5730 \| 6274747 \| \| 2025-05-01T16:29:23Z \| 7486 \| 6282233 \| \| 2025-05-01T18:52:06Z \| 10059 \| 6292292 \| \| 2025-05-01T21:16:20Z \| 6209 \| 6298501 \| \| 2025-05-01T23:38:49Z \| 6509 \| 6305010 \| \| 2025-05-02T02:01:13Z \| 7313 \| 6312323 \| \| 2025-05-02T04:24:01Z \| 4767 \| 6317090 \| \| 2025-05-02T06:46:28Z \| 4247 \| 6321337 \| \| 2025-05-02T09:09:07Z \| 2924 \| 6324261 \| \| 2025-05-02T11:31:13Z \| 2657 \| 6326918 \| \| 2025-05-02T13:55:32Z \| 5545 \| 6332463 \| \| 2025-05-02T16:17:51Z \| 9639 \| 6342102 \| \| 2025-05-02T18:40:00Z \| 6546 \| 6348648 \| \| 2025-05-02T21:02:33Z \| 8599 \| 6357247 \| \| 2025-05-02T23:25:06Z \| 7164 \| 6364411 \| \| 2025-05-03T01:47:45Z \| 6593 \| 6371004 \| \| 2025-05-03T04:10:19Z \| 4058 \| 6375062 \| \| 2025-05-03T06:33:28Z \| 4467 \| 6379529 \| \| 2025-05-03T08:55:33Z \| 4528 \| 6384057 \| \| 2025-05-03T11:17:57Z \| 3694 \| 6387751 \| \| 2025-05-03T13:40:24Z \| 3431 \| 6391182 \| \| 2025-05-03T16:02:58Z \| 6216 \| 6397398 \| \| 2025-05-03T18:25:30Z \| 7470 \| 6404868 \| \| 2025-05-03T20:47:56Z \| 5781 \| 6410649 \| \| 2025-05-03T23:10:13Z \| 5086 \| 6415735 \| \| 2025-05-04T01:33:09Z \| 5205 \| 6420940 \| \| 2025-05-04T03:55:07Z \| 5842 \| 6426782 \| \| 2025-05-04T06:17:04Z \| 3098 \| 6429880 \| \| 2025-05-04T08:39:09Z \| 3111 \| 6432991 \| \| 2025-05-04T11:01:35Z \| 2387 \| 6435378 \| \| 2025-05-04T13:24:00Z \| 6168 \| 6441546 \| \| 2025-05-04T15:46:32Z \| 5266 \| 6446812 \| \| 2025-05-04T18:09:04Z \| 11107 \| 6457919 \| \| 2025-05-04T20:31:25Z \| 7679 \| 6465598 \| \| 2025-05-04T22:53:48Z \| 7878 \| 6473476 \| \| 2025-05-05T01:16:18Z \| 5242 \| 6478718 \| \| 2025-05-05T03:38:37Z \| 5488 \| 6484206 \| \| 2025-05-05T06:00:53Z \| 4255 \| 6488461 \| \| 2025-05-05T08:23:00Z \| 2418 \| 6490879 \| \| 2025-05-05T10:45:36Z \| 1931 \| 6492810 \| \| 2025-05-05T13:07:49Z \| 5449 \| 6498259 \| \| 2025-05-05T15:30:13Z \| 7627 \| 6505886 \| \| 2025-05-05T17:52:53Z \| 8852 \| 6514738 \| \| 2025-05-05T20:15:26Z \| 7356 \| 6522094 \| \| 2025-05-05T22:37:53Z \| 7496 \| 6529590 \| \| 2025-05-06T01:00:33Z \| 7248 \| 6536838 \| \| 2025-05-06T03:22:44Z \| 5382 \| 6542220 \| \| 2025-05-06T05:45:22Z \| 5350 \| 6547570 \| \| 2025-05-06T08:07:46Z \| 3441 \| 6551011 \| \| 2025-05-06T10:29:59Z \| 3601 \| 6554612 \| \| 2025-05-06T12:52:09Z \| 4890 \| 6559502 \| \| 2025-05-06T15:14:55Z \| 10228 \| 6569730 \| \| 2025-05-06T17:37:18Z \| 6137 \| 6575867 \| \| 2025-05-06T20:00:30Z \| 7357 \| 6583224 \| \| 2025-05-06T22:23:45Z \| 8721 \| 6591945 \| \| 2025-05-07T00:46:55Z \| 9295 \| 6601240 \| \| 2025-05-07T03:09:24Z \| 4949 \| 6606189 \| \| 2025-05-07T05:31:45Z \| 3940 \| 6610129 \| \| 2025-05-07T07:53:51Z \| 4211 \| 6614340 \| \| 2025-05-07T10:16:00Z \| 3192 \| 6617532 \| \| 2025-05-07T12:38:02Z \| 3867 \| 6621399 \| \| 2025-05-07T15:00:37Z \| 8210 \| 6629609 \| \| 2025-05-07T17:23:08Z \| 8978 \| 6638587 \| \| 2025-05-07T19:45:50Z \| 8028 \| 6646615 \| \| 2025-05-07T22:08:30Z \| 8813 \| 6655428 \| \| 2025-05-08T00:31:02Z \| 6855 \| 6662283 \| \| 2025-05-08T02:53:36Z \| 6238 \| 6668521 \| \| 2025-05-08T05:15:56Z \| 5098 \| 6673619 \| \| 2025-05-08T07:38:12Z \| 4394 \| 6678013 \| \| 2025-05-08T10:00:34Z \| 2663 \| 6680676 \| \| 2025-05-08T12:23:44Z \| 3719 \| 6684395 \| \| 2025-05-08T14:46:33Z \| 6453 \| 6690848 \| \| 2025-05-08T17:09:11Z \| 9057 \| 6699905 \| \| 2025-05-08T19:31:44Z \| 10143 \| 6710048 \| \| 2025-05-08T21:55:09Z \| 7493 \| 6717541 \| \| 2025-05-09T00:17:36Z \| 6752 \| 6724293 \| \| 2025-05-09T02:40:09Z \| 6938 \| 6731231 \| \| 2025-05-09T05:02:31Z \| 6331 \| 6737562 \| \| 2025-05-09T07:25:00Z \| 4920 \| 6742482 \| \| 2025-05-09T09:47:34Z \| 3173 \| 6745655 \| \| 2025-05-09T12:10:01Z \| 4756 \| 6750411 \| \| 2025-05-09T14:33:41Z \| 7389 \| 6757800 \| \| 2025-05-09T16:56:38Z \| 7838 \| 6765638 \| \| 2025-05-09T19:19:06Z \| 8042 \| 6773680 \| \| 2025-05-09T21:41:39Z \| 6907 \| 6780587 \| \| 2025-05-10T00:04:20Z \| 6118 \| 6786705 \| \| 2025-05-10T02:26:48Z \| 5481 \| 6792186 \| \| 2025-05-10T04:49:08Z \| 4291 \| 6796477 \| \| 2025-05-10T07:11:26Z \| 2665 \| 6799142 \| \| 2025-05-10T09:33:41Z \| 3370 \| 6802512 \| \| 2025-05-10T11:56:06Z \| 2740 \| 6805252 \| \| 2025-05-10T14:18:08Z \| 4099 \| 6809351 \| \| 2025-05-10T16:40:14Z \| 5805 \| 6815156 \| \| 2025-05-10T19:02:17Z \| 7538 \| 6822694 \| \| 2025-05-10T21:24:37Z \| 5940 \| 6828634 \| \| 2025-05-10T23:47:57Z \| 4960 \| 6833594 \| \| 2025-05-11T02:10:21Z \| 5385 \| 6838979 \| \| 2025-05-11T04:32:36Z \| 5138 \| 6844117 \| \| 2025-05-11T06:55:47Z \| 4544 \| 6848661 \| \| 2025-05-11T09:18:01Z \| 2452 \| 6851113 \| \| 2025-05-11T11:40:56Z \| 3374 \| 6854487 \| \| 2025-05-11T14:03:09Z \| 3782 \| 6858269 \| \| 2025-05-11T16:25:37Z \| 8660 \| 6866929 \| \| 2025-05-11T18:48:39Z \| 6693 \| 6873622 \| \| 2025-05-11T21:12:47Z \| 7468 \| 6881090 \| \| 2025-05-11T23:35:33Z \| 5943 \| 6887033 \| \| 2025-05-12T01:57:58Z \| 6441 \| 6893474 \| \| 2025-05-12T04:20:03Z \| 5329 \| 6898803 \| \| 2025-05-12T06:42:35Z \| 3468 \| 6902271 \| \| 2025-05-12T09:06:31Z \| 3562 \| 6905833 \| \| 2025-05-12T11:28:55Z \| 2785 \| 6908618 \| \| 2025-05-12T13:51:26Z \| 5396 \| 6914014 \| \| 2025-05-12T16:14:08Z \| 7082 \| 6921096 \| \| 2025-05-12T18:38:02Z \| 5818 \| 6926914 \| \| 2025-05-12T21:00:38Z \| 6101 \| 6933015 \| \| 2025-05-12T23:23:15Z \| 6026 \| 6939041 \| \| 2025-05-13T01:46:31Z \| 4697 \| 6943738 \| \| 2025-05-13T04:08:34Z \| 5312 \| 6949050 \|
Peizhen/X2C	Peizhen	2025-05-13T03:56:18Z	36	0	[ "task_categories:text-classification", "language:en", "license:cc-by-4.0", "size_categories:100K<n<1M", "modality:image", "library:datasets", "library:mlcroissant", "library:pandas", "region:us", "image", "datasets", "croissant", "pandas", "json" ]	[ "text-classification" ]	2025-03-27T20:25:12Z	null	--- license: cc-by-4.0 size_categories: - 100K<n<1M task_categories: - text-classification language: - en tags: - image - datasets - croissant - pandas - json pretty_name: X2C --- --- # Dataset Card for X2C <!-- Provide a quick summary of the dataset. --> X2C (Anything to Control) is a dataset featuring nuanced robotic facial expressions for realistic humanoid imitation and facial expression generation. It contains 100,000 images of a robot displaying diverse facial expressions, each annotated with 30 control values that can be applied to the physical robot to recreate the corresponding expression. ## Dataset Details - For more details, please refer to our paper: X2C: A Dataset Featuring Nuanced Facial Expressions for Realistic Humanoid Imitation - Source code is available at: [Mimetician](https://github.com/lipzh5/Mimetician) ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: This project was primarily conducted and received ethics approval via Macquarie University. - Language(s): English - License: Creative Commons Attribution 4.0 (CC BY 4.0) ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Dataset Card Authors [email protected] ## Dataset Card Contact For any comments or questions, please email [Peizhen Li](mailto:[email protected])
james-1111/x_dataset_0304209	james-1111	2025-05-13T03:44:59Z	396	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" ]	2025-01-25T07:10:53Z	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_0304209 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5GbEMnowhg3WhxDJvGtC2FyfFrnhKmcwBid4wFSpsBLrc6sn ### 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_0304209, 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_0304209}, } ``` ### 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: 4928728 - Date Range: 2025-01-02T00:00:00Z to 2025-05-03T00:00:00Z - Last Updated: 2025-05-13T03:44:59Z ### Data Distribution - Tweets with hashtags: 2.58% - Tweets without hashtags: 97.42% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| NULL \| 1206324 \| 90.45% \| \| 2 \| #箱根駅伝 \| 8147 \| 0.61% \| \| 3 \| #thameposeriesep9 \| 7605 \| 0.57% \| \| 4 \| #tiktok \| 6768 \| 0.51% \| \| 5 \| #ad \| 5088 \| 0.38% \| \| 6 \| #zelena \| 4878 \| 0.37% \| \| 7 \| #smackdown \| 4844 \| 0.36% \| \| 8 \| #कबीर_परमेश्वर_निर्वाण_दिवस \| 4843 \| 0.36% \| \| 9 \| #夜の屋上 \| 4580 \| 0.34% \| \| 10 \| #riyadh \| 4169 \| 0.31% \| ## 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 \| \| 2025-01-25T07:10:51Z \| 446896 \| 3595058 \| \| 2025-01-25T07:11:21Z \| 446896 \| 4041954 \| \| 2025-02-18T03:40:55Z \| 467290 \| 4509244 \| \| 2025-05-13T03:44:59Z \| 419484 \| 4928728 \|
zkpbeats/reddit_ds_295492	zkpbeats	2025-05-13T03:26:28Z	1,685	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: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" ]	2025-04-03T11:55:04Z	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: zkpbeats/reddit_ds_295492 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5GZPqLJQbXDEFDAHV1sMdshbED2qRnacKKamKB5qikdSqDqv ### 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{zkpbeats2025datauniversereddit_ds_295492, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={zkpbeats}, year={2025}, url={https://huggingface.co/datasets/zkpbeats/reddit_ds_295492}, } ``` ### 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: 1542416 - Date Range: 2025-04-14T00:00:00Z to 2025-05-13T00:00:00Z - Last Updated: 2025-05-13T03:26:27Z ### Data Distribution - Posts: 2.22% - Comments: 97.78% ### Top 10 Subreddits For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| r/pics \| 73947 \| 4.79% \| \| 2 \| r/memes \| 69295 \| 4.49% \| \| 3 \| r/television \| 64729 \| 4.20% \| \| 4 \| r/golf \| 60917 \| 3.95% \| \| 5 \| r/MadeMeSmile \| 50944 \| 3.30% \| \| 6 \| r/BoomersBeingFools \| 37986 \| 2.46% \| \| 7 \| r/wallstreetbets \| 34474 \| 2.24% \| \| 8 \| r/MovieSuggestions \| 34249 \| 2.22% \| \| 9 \| r/GTA6 \| 34091 \| 2.21% \| \| 10 \| r/Finanzen \| 33807 \| 2.19% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-04-14T14:31:44Z \| 6673 \| 6673 \| \| 2025-04-14T16:55:09Z \| 4860 \| 11533 \| \| 2025-04-14T19:18:06Z \| 7671 \| 19204 \| \| 2025-04-14T21:40:58Z \| 7777 \| 26981 \| \| 2025-04-15T00:03:11Z \| 5650 \| 32631 \| \| 2025-04-15T02:25:48Z \| 5824 \| 38455 \| \| 2025-04-15T04:48:49Z \| 4725 \| 43180 \| \| 2025-04-15T07:17:26Z \| 4394 \| 47574 \| \| 2025-04-15T10:55:49Z \| 762 \| 48336 \| \| 2025-04-15T13:18:30Z \| 700 \| 49036 \| \| 2025-04-15T15:19:37Z \| 742 \| 49778 \| \| 2025-04-15T17:42:49Z \| 1025 \| 50803 \| \| 2025-04-15T20:09:15Z \| 765 \| 51568 \| \| 2025-04-15T22:34:13Z \| 890 \| 52458 \| \| 2025-04-16T00:56:32Z \| 732 \| 53190 \| \| 2025-04-16T03:23:04Z \| 763 \| 53953 \| \| 2025-04-16T05:45:29Z \| 562 \| 54515 \| \| 2025-04-16T08:07:21Z \| 340 \| 54855 \| \| 2025-04-16T10:39:23Z \| 513 \| 55368 \| \| 2025-04-16T13:01:23Z \| 654 \| 56022 \| \| 2025-04-16T15:23:39Z \| 863 \| 56885 \| \| 2025-04-16T17:48:05Z \| 873 \| 57758 \| \| 2025-04-16T20:10:49Z \| 922 \| 58680 \| \| 2025-04-16T22:33:52Z \| 906 \| 59586 \| \| 2025-04-17T00:55:49Z \| 642 \| 60228 \| \| 2025-04-17T03:19:05Z \| 737 \| 60965 \| \| 2025-04-17T05:42:15Z \| 916 \| 61881 \| \| 2025-04-17T08:04:40Z \| 2864 \| 64745 \| \| 2025-04-17T10:27:05Z \| 3615 \| 68360 \| \| 2025-04-17T12:51:48Z \| 5274 \| 73634 \| \| 2025-04-17T15:14:35Z \| 5143 \| 78777 \| \| 2025-04-17T17:39:01Z \| 8639 \| 87416 \| \| 2025-04-17T20:03:32Z \| 8693 \| 96109 \| \| 2025-04-17T22:30:58Z \| 8411 \| 104520 \| \| 2025-04-18T00:56:14Z \| 7014 \| 111534 \| \| 2025-04-18T03:20:31Z \| 5450 \| 116984 \| \| 2025-04-18T05:44:52Z \| 4172 \| 121156 \| \| 2025-04-18T08:09:56Z \| 3172 \| 124328 \| \| 2025-04-18T10:37:03Z \| 3823 \| 128151 \| \| 2025-04-18T13:00:39Z \| 4983 \| 133134 \| \| 2025-04-18T15:23:35Z \| 5738 \| 138872 \| \| 2025-04-18T17:49:43Z \| 6860 \| 145732 \| \| 2025-04-18T20:13:49Z \| 6139 \| 151871 \| \| 2025-04-18T22:38:49Z \| 6670 \| 158541 \| \| 2025-04-19T01:01:52Z \| 4764 \| 163305 \| \| 2025-04-19T03:25:36Z \| 5860 \| 169165 \| \| 2025-04-19T05:52:11Z \| 3633 \| 172798 \| \| 2025-04-19T08:18:42Z \| 3741 \| 176539 \| \| 2025-04-19T10:42:47Z \| 3621 \| 180160 \| \| 2025-04-19T13:05:04Z \| 3790 \| 183950 \| \| 2025-04-19T15:27:43Z \| 4729 \| 188679 \| \| 2025-04-19T17:50:30Z \| 8422 \| 197101 \| \| 2025-04-19T20:14:33Z \| 4801 \| 201902 \| \| 2025-04-19T22:36:52Z \| 6786 \| 208688 \| \| 2025-04-20T01:00:50Z \| 5371 \| 214059 \| \| 2025-04-20T03:25:04Z \| 5260 \| 219319 \| \| 2025-04-20T05:48:22Z \| 4051 \| 223370 \| \| 2025-04-20T08:10:52Z \| 1909 \| 225279 \| \| 2025-04-20T10:32:41Z \| 3258 \| 228537 \| \| 2025-04-20T12:54:37Z \| 3377 \| 231914 \| \| 2025-04-20T15:17:49Z \| 4753 \| 236667 \| \| 2025-04-20T17:40:18Z \| 5278 \| 241945 \| \| 2025-04-20T20:03:14Z \| 7395 \| 249340 \| \| 2025-04-20T22:26:55Z \| 7079 \| 256419 \| \| 2025-04-21T00:49:16Z \| 4997 \| 261416 \| \| 2025-04-21T03:12:04Z \| 5480 \| 266896 \| \| 2025-04-21T05:36:48Z \| 3973 \| 270869 \| \| 2025-04-21T07:59:42Z \| 2685 \| 273554 \| \| 2025-04-21T10:22:53Z \| 3376 \| 276930 \| \| 2025-04-21T12:46:05Z \| 6334 \| 283264 \| \| 2025-04-21T15:10:00Z \| 6761 \| 290025 \| \| 2025-04-21T17:32:45Z \| 5606 \| 295631 \| \| 2025-04-21T19:56:00Z \| 7899 \| 303530 \| \| 2025-04-21T22:19:38Z \| 5927 \| 309457 \| \| 2025-04-22T00:43:44Z \| 4494 \| 313951 \| \| 2025-04-22T03:06:11Z \| 5684 \| 319635 \| \| 2025-04-22T05:29:00Z \| 5056 \| 324691 \| \| 2025-04-22T07:55:11Z \| 3028 \| 327719 \| \| 2025-04-22T10:18:13Z \| 4481 \| 332200 \| \| 2025-04-22T12:41:04Z \| 4042 \| 336242 \| \| 2025-04-22T15:03:38Z \| 7039 \| 343281 \| \| 2025-04-22T17:26:45Z \| 7700 \| 350981 \| \| 2025-04-22T19:50:04Z \| 8138 \| 359119 \| \| 2025-04-22T22:12:41Z \| 6542 \| 365661 \| \| 2025-04-23T00:35:37Z \| 7896 \| 373557 \| \| 2025-04-23T02:58:48Z \| 5146 \| 378703 \| \| 2025-04-23T05:21:20Z \| 5117 \| 383820 \| \| 2025-04-23T07:43:54Z \| 2479 \| 386299 \| \| 2025-04-23T10:07:34Z \| 2888 \| 389187 \| \| 2025-04-23T12:30:45Z \| 4509 \| 393696 \| \| 2025-04-23T14:53:07Z \| 4848 \| 398544 \| \| 2025-04-23T17:16:25Z \| 6114 \| 404658 \| \| 2025-04-23T19:39:15Z \| 7625 \| 412283 \| \| 2025-04-23T22:02:28Z \| 6517 \| 418800 \| \| 2025-04-24T00:24:48Z \| 6740 \| 425540 \| \| 2025-04-24T02:47:36Z \| 5694 \| 431234 \| \| 2025-04-24T05:09:53Z \| 4369 \| 435603 \| \| 2025-04-24T07:31:54Z \| 3086 \| 438689 \| \| 2025-04-24T09:54:40Z \| 3472 \| 442161 \| \| 2025-04-24T12:17:33Z \| 4192 \| 446353 \| \| 2025-04-24T14:39:57Z \| 6847 \| 453200 \| \| 2025-04-24T17:02:22Z \| 7525 \| 460725 \| \| 2025-04-24T19:25:27Z \| 7796 \| 468521 \| \| 2025-04-24T21:49:38Z \| 7763 \| 476284 \| \| 2025-04-25T00:12:29Z \| 6099 \| 482383 \| \| 2025-04-25T02:35:28Z \| 5364 \| 487747 \| \| 2025-04-25T04:58:37Z \| 5073 \| 492820 \| \| 2025-04-25T07:22:20Z \| 3674 \| 496494 \| \| 2025-04-25T09:45:45Z \| 2863 \| 499357 \| \| 2025-04-25T12:08:43Z \| 4583 \| 503940 \| \| 2025-04-25T14:30:54Z \| 5120 \| 509060 \| \| 2025-04-25T16:53:34Z \| 8051 \| 517111 \| \| 2025-04-25T19:16:15Z \| 7039 \| 524150 \| \| 2025-04-25T21:39:38Z \| 7399 \| 531549 \| \| 2025-04-26T00:02:46Z \| 8292 \| 539841 \| \| 2025-04-26T02:25:25Z \| 6781 \| 546622 \| \| 2025-04-26T04:47:06Z \| 6167 \| 552789 \| \| 2025-04-26T07:09:04Z \| 4409 \| 557198 \| \| 2025-04-26T09:31:08Z \| 3206 \| 560404 \| \| 2025-04-26T11:52:53Z \| 3654 \| 564058 \| \| 2025-04-26T14:17:51Z \| 7368 \| 571426 \| \| 2025-04-26T16:43:58Z \| 7795 \| 579221 \| \| 2025-04-26T19:08:36Z \| 6466 \| 585687 \| \| 2025-04-26T21:31:56Z \| 5941 \| 591628 \| \| 2025-04-26T23:54:22Z \| 5779 \| 597407 \| \| 2025-04-27T02:17:03Z \| 6672 \| 604079 \| \| 2025-04-27T04:39:38Z \| 6016 \| 610095 \| \| 2025-04-27T07:01:40Z \| 5397 \| 615492 \| \| 2025-04-27T09:24:05Z \| 3711 \| 619203 \| \| 2025-04-27T11:46:44Z \| 3263 \| 622466 \| \| 2025-04-27T14:09:10Z \| 7346 \| 629812 \| \| 2025-04-27T16:32:37Z \| 5958 \| 635770 \| \| 2025-04-27T18:57:15Z \| 7453 \| 643223 \| \| 2025-04-27T21:19:36Z \| 6681 \| 649904 \| \| 2025-04-27T23:43:01Z \| 5461 \| 655365 \| \| 2025-04-28T02:05:27Z \| 7020 \| 662385 \| \| 2025-04-28T04:28:26Z \| 4807 \| 667192 \| \| 2025-04-28T06:51:02Z \| 3494 \| 670686 \| \| 2025-04-28T09:13:55Z \| 3727 \| 674413 \| \| 2025-04-28T11:36:28Z \| 3877 \| 678290 \| \| 2025-04-28T13:59:09Z \| 7008 \| 685298 \| \| 2025-04-28T16:22:16Z \| 6085 \| 691383 \| \| 2025-04-28T18:44:59Z \| 6087 \| 697470 \| \| 2025-04-28T21:07:32Z \| 7916 \| 705386 \| \| 2025-04-28T23:30:36Z \| 8499 \| 713885 \| \| 2025-04-29T01:53:42Z \| 5808 \| 719693 \| \| 2025-04-29T04:16:00Z \| 5375 \| 725068 \| \| 2025-04-29T06:38:43Z \| 4036 \| 729104 \| \| 2025-04-29T09:01:22Z \| 3704 \| 732808 \| \| 2025-04-29T11:24:59Z \| 3713 \| 736521 \| \| 2025-04-29T13:47:59Z \| 5308 \| 741829 \| \| 2025-04-29T16:09:51Z \| 7905 \| 749734 \| \| 2025-04-29T18:33:13Z \| 9591 \| 759325 \| \| 2025-04-29T20:55:57Z \| 7236 \| 766561 \| \| 2025-04-29T23:18:28Z \| 7834 \| 774395 \| \| 2025-04-30T01:41:06Z \| 5985 \| 780380 \| \| 2025-04-30T04:03:21Z \| 6132 \| 786512 \| \| 2025-04-30T06:25:42Z \| 4648 \| 791160 \| \| 2025-04-30T08:49:13Z \| 3987 \| 795147 \| \| 2025-04-30T11:11:28Z \| 2665 \| 797812 \| \| 2025-04-30T13:33:22Z \| 5822 \| 803634 \| \| 2025-04-30T15:55:51Z \| 9064 \| 812698 \| \| 2025-04-30T18:20:08Z \| 7099 \| 819797 \| \| 2025-04-30T20:42:15Z \| 6469 \| 826266 \| \| 2025-04-30T23:04:35Z \| 5404 \| 831670 \| \| 2025-05-01T01:28:14Z \| 6511 \| 838181 \| \| 2025-05-01T03:51:04Z \| 6119 \| 844300 \| \| 2025-05-01T06:14:14Z \| 4847 \| 849147 \| \| 2025-05-01T08:39:04Z \| 2786 \| 851933 \| \| 2025-05-01T11:01:27Z \| 2311 \| 854244 \| \| 2025-05-01T13:24:05Z \| 5613 \| 859857 \| \| 2025-05-01T15:47:06Z \| 6602 \| 866459 \| \| 2025-05-01T18:09:29Z \| 7313 \| 873772 \| \| 2025-05-01T20:33:50Z \| 9689 \| 883461 \| \| 2025-05-01T22:56:28Z \| 6673 \| 890134 \| \| 2025-05-02T01:18:54Z \| 4914 \| 895048 \| \| 2025-05-02T03:41:18Z \| 6526 \| 901574 \| \| 2025-05-02T06:04:11Z \| 4177 \| 905751 \| \| 2025-05-02T08:26:31Z \| 3708 \| 909459 \| \| 2025-05-02T10:49:09Z \| 4408 \| 913867 \| \| 2025-05-02T13:12:24Z \| 6749 \| 920616 \| \| 2025-05-02T15:35:37Z \| 7704 \| 928320 \| \| 2025-05-02T17:57:51Z \| 8960 \| 937280 \| \| 2025-05-02T20:20:14Z \| 8292 \| 945572 \| \| 2025-05-02T22:42:42Z \| 8041 \| 953613 \| \| 2025-05-03T01:05:16Z \| 5516 \| 959129 \| \| 2025-05-03T03:27:40Z \| 4915 \| 964044 \| \| 2025-05-03T05:51:14Z \| 5377 \| 969421 \| \| 2025-05-03T08:13:23Z \| 4009 \| 973430 \| \| 2025-05-03T10:35:33Z \| 3917 \| 977347 \| \| 2025-05-03T12:58:06Z \| 5983 \| 983330 \| \| 2025-05-03T15:20:33Z \| 6144 \| 989474 \| \| 2025-05-03T17:43:19Z \| 7274 \| 996748 \| \| 2025-05-03T20:05:43Z \| 6516 \| 1003264 \| \| 2025-05-03T22:27:57Z \| 6386 \| 1009650 \| \| 2025-05-04T00:50:17Z \| 5717 \| 1015367 \| \| 2025-05-04T03:13:02Z \| 5571 \| 1020938 \| \| 2025-05-04T05:34:59Z \| 4198 \| 1025136 \| \| 2025-05-04T07:56:59Z \| 3116 \| 1028252 \| \| 2025-05-04T10:19:08Z \| 3235 \| 1031487 \| \| 2025-05-04T12:41:30Z \| 3964 \| 1035451 \| \| 2025-05-04T15:04:19Z \| 6701 \| 1042152 \| \| 2025-05-04T17:26:45Z \| 6301 \| 1048453 \| \| 2025-05-04T19:49:12Z \| 6616 \| 1055069 \| \| 2025-05-04T22:11:30Z \| 7442 \| 1062511 \| \| 2025-05-05T00:33:56Z \| 6061 \| 1068572 \| \| 2025-05-05T02:56:16Z \| 6136 \| 1074708 \| \| 2025-05-05T05:18:38Z \| 4600 \| 1079308 \| \| 2025-05-05T07:40:50Z \| 4061 \| 1083369 \| \| 2025-05-05T10:03:25Z \| 4580 \| 1087949 \| \| 2025-05-05T12:25:33Z \| 4213 \| 1092162 \| \| 2025-05-05T14:47:49Z \| 5478 \| 1097640 \| \| 2025-05-05T17:10:30Z \| 8934 \| 1106574 \| \| 2025-05-05T19:33:02Z \| 8213 \| 1114787 \| \| 2025-05-05T21:55:34Z \| 7214 \| 1122001 \| \| 2025-05-06T00:18:03Z \| 8177 \| 1130178 \| \| 2025-05-06T02:40:37Z \| 5955 \| 1136133 \| \| 2025-05-06T05:02:40Z \| 5116 \| 1141249 \| \| 2025-05-06T07:25:22Z \| 3744 \| 1144993 \| \| 2025-05-06T09:47:41Z \| 3070 \| 1148063 \| \| 2025-05-06T12:10:01Z \| 4720 \| 1152783 \| \| 2025-05-06T14:32:12Z \| 7654 \| 1160437 \| \| 2025-05-06T16:55:00Z \| 8033 \| 1168470 \| \| 2025-05-06T19:17:40Z \| 10475 \| 1178945 \| \| 2025-05-06T21:40:59Z \| 9051 \| 1187996 \| \| 2025-05-07T00:04:12Z \| 6984 \| 1194980 \| \| 2025-05-07T02:27:16Z \| 4915 \| 1199895 \| \| 2025-05-07T04:49:28Z \| 8032 \| 1207927 \| \| 2025-05-07T07:11:45Z \| 2981 \| 1210908 \| \| 2025-05-07T09:33:50Z \| 3535 \| 1214443 \| \| 2025-05-07T11:55:58Z \| 4552 \| 1218995 \| \| 2025-05-07T14:18:16Z \| 7591 \| 1226586 \| \| 2025-05-07T16:40:48Z \| 7490 \| 1234076 \| \| 2025-05-07T19:03:31Z \| 7697 \| 1241773 \| \| 2025-05-07T21:25:58Z \| 7464 \| 1249237 \| \| 2025-05-07T23:48:36Z \| 6012 \| 1255249 \| \| 2025-05-08T02:11:22Z \| 5626 \| 1260875 \| \| 2025-05-08T04:33:36Z \| 4904 \| 1265779 \| \| 2025-05-08T06:55:57Z \| 3071 \| 1268850 \| \| 2025-05-08T09:18:19Z \| 4384 \| 1273234 \| \| 2025-05-08T11:41:21Z \| 3480 \| 1276714 \| \| 2025-05-08T14:03:46Z \| 4772 \| 1281486 \| \| 2025-05-08T16:26:36Z \| 8377 \| 1289863 \| \| 2025-05-08T18:49:23Z \| 7134 \| 1296997 \| \| 2025-05-08T21:12:25Z \| 7794 \| 1304791 \| \| 2025-05-08T23:35:16Z \| 7596 \| 1312387 \| \| 2025-05-09T01:57:49Z \| 5227 \| 1317614 \| \| 2025-05-09T04:20:21Z \| 3323 \| 1320937 \| \| 2025-05-09T06:42:32Z \| 4122 \| 1325059 \| \| 2025-05-09T09:05:22Z \| 3633 \| 1328692 \| \| 2025-05-09T11:27:40Z \| 3043 \| 1331735 \| \| 2025-05-09T13:50:13Z \| 4396 \| 1336131 \| \| 2025-05-09T16:13:55Z \| 8101 \| 1344232 \| \| 2025-05-09T18:36:49Z \| 9015 \| 1353247 \| \| 2025-05-09T20:59:13Z \| 7561 \| 1360808 \| \| 2025-05-09T23:21:48Z \| 5723 \| 1366531 \| \| 2025-05-10T01:44:40Z \| 5822 \| 1372353 \| \| 2025-05-10T04:07:02Z \| 6011 \| 1378364 \| \| 2025-05-10T06:29:19Z \| 3930 \| 1382294 \| \| 2025-05-10T08:51:18Z \| 3727 \| 1386021 \| \| 2025-05-10T11:13:59Z \| 2783 \| 1388804 \| \| 2025-05-10T13:36:02Z \| 4079 \| 1392883 \| \| 2025-05-10T15:58:06Z \| 8078 \| 1400961 \| \| 2025-05-10T18:20:10Z \| 6748 \| 1407709 \| \| 2025-05-10T20:42:20Z \| 6048 \| 1413757 \| \| 2025-05-10T23:04:37Z \| 5751 \| 1419508 \| \| 2025-05-11T01:28:02Z \| 4612 \| 1424120 \| \| 2025-05-11T03:50:27Z \| 4873 \| 1428993 \| \| 2025-05-11T06:12:29Z \| 4297 \| 1433290 \| \| 2025-05-11T08:35:44Z \| 3128 \| 1436418 \| \| 2025-05-11T10:58:20Z \| 3217 \| 1439635 \| \| 2025-05-11T13:20:52Z \| 5358 \| 1444993 \| \| 2025-05-11T15:43:15Z \| 6346 \| 1451339 \| \| 2025-05-11T18:06:20Z \| 9622 \| 1460961 \| \| 2025-05-11T20:29:06Z \| 5681 \| 1466642 \| \| 2025-05-11T22:53:18Z \| 7301 \| 1473943 \| \| 2025-05-12T01:15:49Z \| 5646 \| 1479589 \| \| 2025-05-12T03:37:54Z \| 6078 \| 1485667 \| \| 2025-05-12T06:00:04Z \| 3686 \| 1489353 \| \| 2025-05-12T08:23:35Z \| 3445 \| 1492798 \| \| 2025-05-12T10:46:47Z \| 3175 \| 1495973 \| \| 2025-05-12T13:08:56Z \| 4743 \| 1500716 \| \| 2025-05-12T15:31:41Z \| 8333 \| 1509049 \| \| 2025-05-12T17:54:28Z \| 9727 \| 1518776 \| \| 2025-05-12T20:18:21Z \| 7380 \| 1526156 \| \| 2025-05-12T22:40:49Z \| 7010 \| 1533166 \| \| 2025-05-13T01:04:05Z \| 4426 \| 1537592 \| \| 2025-05-13T03:26:27Z \| 4824 \| 1542416 \|
zkpbeats/reddit_ds_734775	zkpbeats	2025-05-13T03:25:23Z	1,645	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: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" ]	2025-04-03T11:53:22Z	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: zkpbeats/reddit_ds_734775 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5FP1ZiBjTh9Rd7QE4UbnUH8uGyqj9ULjjPb19Rn4T5Rki8mk ### 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{zkpbeats2025datauniversereddit_ds_734775, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={zkpbeats}, year={2025}, url={https://huggingface.co/datasets/zkpbeats/reddit_ds_734775}, } ``` ### 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: 2478329 - Date Range: 2025-03-05T00:00:00Z to 2025-05-13T00:00:00Z - Last Updated: 2025-05-13T03:25:22Z ### Data Distribution - Posts: 3.00% - Comments: 80.18% ### Top 10 Subreddits For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| r/cats \| 104047 \| 5.05% \| \| 2 \| r/Genshin_Impact \| 85479 \| 4.15% \| \| 3 \| r/nottheonion \| 73405 \| 3.56% \| \| 4 \| r/tattooadvice \| 63780 \| 3.09% \| \| 5 \| r/askgaybros \| 57715 \| 2.80% \| \| 6 \| r/wallstreetbets \| 56315 \| 2.73% \| \| 7 \| r/self \| 54679 \| 2.65% \| \| 8 \| r/Cooking \| 52887 \| 2.57% \| \| 9 \| r/CFB \| 50297 \| 2.44% \| \| 10 \| r/EDH \| 47992 \| 2.33% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-04-03T11:50:44Z \| 210554 \| 210554 \| \| 2025-04-03T11:52:19Z \| 206249 \| 416803 \| \| 2025-04-03T11:53:58Z \| 204074 \| 620877 \| \| 2025-04-03T13:56:03Z \| 3300 \| 624177 \| \| 2025-04-03T16:18:22Z \| 6520 \| 630697 \| \| 2025-04-03T18:40:36Z \| 6526 \| 637223 \| \| 2025-04-03T21:02:51Z \| 6004 \| 643227 \| \| 2025-04-03T23:25:22Z \| 4942 \| 648169 \| \| 2025-04-04T01:47:45Z \| 5230 \| 653399 \| \| 2025-04-04T04:11:11Z \| 3508 \| 656907 \| \| 2025-04-04T06:33:15Z \| 3747 \| 660654 \| \| 2025-04-04T08:55:11Z \| 3318 \| 663972 \| \| 2025-04-04T11:17:08Z \| 2173 \| 666145 \| \| 2025-04-04T13:39:40Z \| 4890 \| 671035 \| \| 2025-04-04T16:02:23Z \| 5497 \| 676532 \| \| 2025-04-04T18:24:40Z \| 7277 \| 683809 \| \| 2025-04-04T20:47:26Z \| 4959 \| 688768 \| \| 2025-04-04T23:10:13Z \| 5008 \| 693776 \| \| 2025-04-05T01:32:55Z \| 4001 \| 697777 \| \| 2025-04-05T03:55:03Z \| 3526 \| 701303 \| \| 2025-04-05T06:17:05Z \| 3758 \| 705061 \| \| 2025-04-05T08:39:21Z \| 2582 \| 707643 \| \| 2025-04-05T11:01:51Z \| 2507 \| 710150 \| \| 2025-04-05T13:23:55Z \| 3403 \| 713553 \| \| 2025-04-05T15:46:36Z \| 5230 \| 718783 \| \| 2025-04-05T18:08:58Z \| 5671 \| 724454 \| \| 2025-04-05T20:31:34Z \| 4486 \| 728940 \| \| 2025-04-05T22:54:03Z \| 4268 \| 733208 \| \| 2025-04-06T01:16:58Z \| 5051 \| 738259 \| \| 2025-04-06T03:39:17Z \| 4478 \| 742737 \| \| 2025-04-06T06:01:31Z \| 2980 \| 745717 \| \| 2025-04-06T08:23:51Z \| 2344 \| 748061 \| \| 2025-04-06T10:46:25Z \| 2341 \| 750402 \| \| 2025-04-06T13:08:45Z \| 3373 \| 753775 \| \| 2025-04-06T15:31:16Z \| 4982 \| 758757 \| \| 2025-04-06T17:54:03Z \| 5701 \| 764458 \| \| 2025-04-06T20:16:51Z \| 6712 \| 771170 \| \| 2025-04-06T22:39:12Z \| 4403 \| 775573 \| \| 2025-04-07T01:01:45Z \| 5119 \| 780692 \| \| 2025-04-07T03:24:34Z \| 3878 \| 784570 \| \| 2025-04-07T05:46:55Z \| 4354 \| 788924 \| \| 2025-04-07T08:09:20Z \| 2347 \| 791271 \| \| 2025-04-07T10:32:43Z \| 2570 \| 793841 \| \| 2025-04-07T12:56:49Z \| 3835 \| 797676 \| \| 2025-04-07T15:27:04Z \| 6482 \| 804158 \| \| 2025-04-07T17:51:14Z \| 5207 \| 809365 \| \| 2025-04-07T20:13:52Z \| 5151 \| 814516 \| \| 2025-04-07T22:36:21Z \| 5493 \| 820009 \| \| 2025-04-08T01:02:51Z \| 5289 \| 825298 \| \| 2025-04-08T03:30:19Z \| 5656 \| 830954 \| \| 2025-04-08T05:53:04Z \| 5427 \| 836381 \| \| 2025-04-08T07:57:22Z \| 2108 \| 838489 \| \| 2025-04-08T10:22:49Z \| 2133 \| 840622 \| \| 2025-04-08T12:45:24Z \| 3214 \| 843836 \| \| 2025-04-08T15:07:48Z \| 3175 \| 847011 \| \| 2025-04-08T17:30:37Z \| 6412 \| 853423 \| \| 2025-04-08T19:54:33Z \| 5536 \| 858959 \| \| 2025-04-08T22:17:35Z \| 6714 \| 865673 \| \| 2025-04-09T00:40:17Z \| 4727 \| 870400 \| \| 2025-04-09T03:03:39Z \| 6401 \| 876801 \| \| 2025-04-09T05:26:11Z \| 4795 \| 881596 \| \| 2025-04-09T07:48:47Z \| 3104 \| 884700 \| \| 2025-04-09T10:11:06Z \| 3027 \| 887727 \| \| 2025-04-09T12:34:39Z \| 3648 \| 891375 \| \| 2025-04-09T14:57:22Z \| 4693 \| 896068 \| \| 2025-04-09T17:20:15Z \| 7094 \| 903162 \| \| 2025-04-09T19:43:51Z \| 6769 \| 909931 \| \| 2025-04-09T22:06:17Z \| 6947 \| 916878 \| \| 2025-04-10T00:29:01Z \| 3523 \| 920401 \| \| 2025-04-10T02:51:38Z \| 4722 \| 925123 \| \| 2025-04-10T05:13:44Z \| 3043 \| 928166 \| \| 2025-04-10T07:36:09Z \| 4316 \| 932482 \| \| 2025-04-10T09:58:21Z \| 2957 \| 935439 \| \| 2025-04-10T12:20:44Z \| 2645 \| 938084 \| \| 2025-04-10T14:43:17Z \| 5711 \| 943795 \| \| 2025-04-10T17:05:43Z \| 6243 \| 950038 \| \| 2025-04-10T19:28:24Z \| 6424 \| 956462 \| \| 2025-04-10T21:50:46Z \| 6449 \| 962911 \| \| 2025-04-11T00:13:03Z \| 5021 \| 967932 \| \| 2025-04-11T02:35:32Z \| 5546 \| 973478 \| \| 2025-04-11T04:57:59Z \| 5253 \| 978731 \| \| 2025-04-11T07:20:38Z \| 4368 \| 983099 \| \| 2025-04-11T09:43:10Z \| 3771 \| 986870 \| \| 2025-04-11T12:05:22Z \| 2848 \| 989718 \| \| 2025-04-11T14:27:39Z \| 5518 \| 995236 \| \| 2025-04-11T16:50:32Z \| 5021 \| 1000257 \| \| 2025-04-11T19:13:10Z \| 7433 \| 1007690 \| \| 2025-04-11T21:36:18Z \| 6508 \| 1014198 \| \| 2025-04-11T23:58:46Z \| 4563 \| 1018761 \| \| 2025-04-12T02:21:01Z \| 4436 \| 1023197 \| \| 2025-04-12T04:43:24Z \| 3902 \| 1027099 \| \| 2025-04-12T07:05:29Z \| 2048 \| 1029147 \| \| 2025-04-12T09:27:51Z \| 2844 \| 1031991 \| \| 2025-04-12T11:50:10Z \| 2193 \| 1034184 \| \| 2025-04-12T14:12:15Z \| 2612 \| 1036796 \| \| 2025-04-12T16:34:47Z \| 5932 \| 1042728 \| \| 2025-04-12T18:57:20Z \| 5317 \| 1048045 \| \| 2025-04-12T21:19:59Z \| 5445 \| 1053490 \| \| 2025-04-12T23:42:49Z \| 5187 \| 1058677 \| \| 2025-04-13T02:05:11Z \| 2803 \| 1061480 \| \| 2025-04-13T04:28:06Z \| 5438 \| 1066918 \| \| 2025-04-13T06:50:18Z \| 3893 \| 1070811 \| \| 2025-04-13T09:12:26Z \| 2835 \| 1073646 \| \| 2025-04-13T11:35:12Z \| 1927 \| 1075573 \| \| 2025-04-13T13:58:24Z \| 3452 \| 1079025 \| \| 2025-04-13T16:21:04Z \| 4445 \| 1083470 \| \| 2025-04-13T18:44:05Z \| 5432 \| 1088902 \| \| 2025-04-13T21:09:16Z \| 5479 \| 1094381 \| \| 2025-04-13T23:32:16Z \| 4168 \| 1098549 \| \| 2025-04-14T01:54:48Z \| 4858 \| 1103407 \| \| 2025-04-14T04:17:00Z \| 4925 \| 1108332 \| \| 2025-04-14T06:42:20Z \| 3302 \| 1111634 \| \| 2025-04-14T09:44:57Z \| 4925 \| 1116559 \| \| 2025-04-14T12:07:48Z \| 2957 \| 1119516 \| \| 2025-04-14T14:30:27Z \| 4151 \| 1123667 \| \| 2025-04-14T16:54:03Z \| 7925 \| 1131592 \| \| 2025-04-14T19:16:59Z \| 7480 \| 1139072 \| \| 2025-04-14T21:39:51Z \| 6817 \| 1145889 \| \| 2025-04-15T00:02:04Z \| 5813 \| 1151702 \| \| 2025-04-15T02:24:42Z \| 4754 \| 1156456 \| \| 2025-04-15T04:47:40Z \| 5508 \| 1161964 \| \| 2025-04-15T07:16:20Z \| 3478 \| 1165442 \| \| 2025-04-15T10:54:41Z \| 756 \| 1166198 \| \| 2025-04-15T13:17:14Z \| 670 \| 1166868 \| \| 2025-04-15T15:18:26Z \| 762 \| 1167630 \| \| 2025-04-15T17:41:35Z \| 967 \| 1168597 \| \| 2025-04-15T20:08:10Z \| 786 \| 1169383 \| \| 2025-04-15T22:32:32Z \| 917 \| 1170300 \| \| 2025-04-16T00:55:26Z \| 853 \| 1171153 \| \| 2025-04-16T03:21:58Z \| 732 \| 1171885 \| \| 2025-04-16T05:44:25Z \| 544 \| 1172429 \| \| 2025-04-16T08:06:15Z \| 357 \| 1172786 \| \| 2025-04-16T10:37:49Z \| 465 \| 1173251 \| \| 2025-04-16T13:00:09Z \| 639 \| 1173890 \| \| 2025-04-16T15:22:33Z \| 821 \| 1174711 \| \| 2025-04-16T17:46:58Z \| 784 \| 1175495 \| \| 2025-04-16T20:09:43Z \| 904 \| 1176399 \| \| 2025-04-16T22:32:46Z \| 930 \| 1177329 \| \| 2025-04-17T00:54:40Z \| 639 \| 1177968 \| \| 2025-04-17T03:17:59Z \| 734 \| 1178702 \| \| 2025-04-17T05:41:11Z \| 1750 \| 1180452 \| \| 2025-04-17T08:03:34Z \| 2865 \| 1183317 \| \| 2025-04-17T10:25:46Z \| 2123 \| 1185440 \| \| 2025-04-17T12:50:29Z \| 3709 \| 1189149 \| \| 2025-04-17T15:13:28Z \| 5778 \| 1194927 \| \| 2025-04-17T17:37:38Z \| 7607 \| 1202534 \| \| 2025-04-17T20:02:20Z \| 6100 \| 1208634 \| \| 2025-04-17T22:29:41Z \| 5241 \| 1213875 \| \| 2025-04-18T00:54:48Z \| 5971 \| 1219846 \| \| 2025-04-18T03:19:23Z \| 5227 \| 1225073 \| \| 2025-04-18T05:43:45Z \| 3257 \| 1228330 \| \| 2025-04-18T08:08:16Z \| 4199 \| 1232529 \| \| 2025-04-18T10:35:56Z \| 3849 \| 1236378 \| \| 2025-04-18T12:59:08Z \| 4412 \| 1240790 \| \| 2025-04-18T15:22:26Z \| 6059 \| 1246849 \| \| 2025-04-18T17:48:26Z \| 4276 \| 1251125 \| \| 2025-04-18T20:12:34Z \| 5601 \| 1256726 \| \| 2025-04-18T22:37:22Z \| 4656 \| 1261382 \| \| 2025-04-19T01:00:46Z \| 4381 \| 1265763 \| \| 2025-04-19T03:24:27Z \| 4257 \| 1270020 \| \| 2025-04-19T05:51:04Z \| 4987 \| 1275007 \| \| 2025-04-19T08:17:25Z \| 4100 \| 1279107 \| \| 2025-04-19T10:41:34Z \| 3340 \| 1282447 \| \| 2025-04-19T13:03:58Z \| 3314 \| 1285761 \| \| 2025-04-19T15:26:36Z \| 6106 \| 1291867 \| \| 2025-04-19T17:49:23Z \| 6479 \| 1298346 \| \| 2025-04-19T20:13:28Z \| 7219 \| 1305565 \| \| 2025-04-19T22:35:43Z \| 5831 \| 1311396 \| \| 2025-04-20T00:59:44Z \| 4723 \| 1316119 \| \| 2025-04-20T03:23:57Z \| 3842 \| 1319961 \| \| 2025-04-20T05:47:16Z \| 3836 \| 1323797 \| \| 2025-04-20T08:09:47Z \| 3327 \| 1327124 \| \| 2025-04-20T10:31:36Z \| 2346 \| 1329470 \| \| 2025-04-20T12:53:31Z \| 2780 \| 1332250 \| \| 2025-04-20T15:16:36Z \| 3932 \| 1336182 \| \| 2025-04-20T17:39:11Z \| 7060 \| 1343242 \| \| 2025-04-20T20:02:07Z \| 4646 \| 1347888 \| \| 2025-04-20T22:25:49Z \| 4266 \| 1352154 \| \| 2025-04-21T00:48:10Z \| 5833 \| 1357987 \| \| 2025-04-21T03:10:57Z \| 4340 \| 1362327 \| \| 2025-04-21T05:35:42Z \| 4254 \| 1366581 \| \| 2025-04-21T07:58:37Z \| 2224 \| 1368805 \| \| 2025-04-21T10:21:47Z \| 4188 \| 1372993 \| \| 2025-04-21T12:44:55Z \| 3299 \| 1376292 \| \| 2025-04-21T15:08:47Z \| 5558 \| 1381850 \| \| 2025-04-21T17:31:38Z \| 5456 \| 1387306 \| \| 2025-04-21T19:54:53Z \| 7883 \| 1395189 \| \| 2025-04-21T22:18:30Z \| 7928 \| 1403117 \| \| 2025-04-22T00:42:32Z \| 5402 \| 1408519 \| \| 2025-04-22T03:05:05Z \| 7573 \| 1416092 \| \| 2025-04-22T05:27:47Z \| 2929 \| 1419021 \| \| 2025-04-22T07:53:56Z \| 3067 \| 1422088 \| \| 2025-04-22T10:17:06Z \| 2349 \| 1424437 \| \| 2025-04-22T12:39:57Z \| 3092 \| 1427529 \| \| 2025-04-22T15:02:31Z \| 5254 \| 1432783 \| \| 2025-04-22T17:25:31Z \| 5087 \| 1437870 \| \| 2025-04-22T19:48:56Z \| 5413 \| 1443283 \| \| 2025-04-22T22:11:35Z \| 6276 \| 1449559 \| \| 2025-04-23T00:34:30Z \| 6191 \| 1455750 \| \| 2025-04-23T02:57:42Z \| 5140 \| 1460890 \| \| 2025-04-23T05:20:15Z \| 3340 \| 1464230 \| \| 2025-04-23T07:42:49Z \| 3005 \| 1467235 \| \| 2025-04-23T10:06:27Z \| 2466 \| 1469701 \| \| 2025-04-23T12:29:24Z \| 3068 \| 1472769 \| \| 2025-04-23T14:51:50Z \| 4450 \| 1477219 \| \| 2025-04-23T17:15:18Z \| 6683 \| 1483902 \| \| 2025-04-23T19:38:08Z \| 6255 \| 1490157 \| \| 2025-04-23T22:01:12Z \| 7720 \| 1497877 \| \| 2025-04-24T00:23:42Z \| 4605 \| 1502482 \| \| 2025-04-24T02:46:29Z \| 4669 \| 1507151 \| \| 2025-04-24T05:08:48Z \| 3300 \| 1510451 \| \| 2025-04-24T07:30:45Z \| 3313 \| 1513764 \| \| 2025-04-24T09:53:34Z \| 1992 \| 1515756 \| \| 2025-04-24T12:16:27Z \| 4360 \| 1520116 \| \| 2025-04-24T14:38:51Z \| 5798 \| 1525914 \| \| 2025-04-24T17:01:16Z \| 7418 \| 1533332 \| \| 2025-04-24T19:24:15Z \| 8749 \| 1542081 \| \| 2025-04-24T21:48:31Z \| 5725 \| 1547806 \| \| 2025-04-25T00:11:22Z \| 6260 \| 1554066 \| \| 2025-04-25T02:34:21Z \| 7192 \| 1561258 \| \| 2025-04-25T04:57:31Z \| 6186 \| 1567444 \| \| 2025-04-25T07:21:13Z \| 4323 \| 1571767 \| \| 2025-04-25T09:44:22Z \| 3228 \| 1574995 \| \| 2025-04-25T12:07:19Z \| 2717 \| 1577712 \| \| 2025-04-25T14:29:49Z \| 5122 \| 1582834 \| \| 2025-04-25T16:52:26Z \| 7597 \| 1590431 \| \| 2025-04-25T19:15:01Z \| 8139 \| 1598570 \| \| 2025-04-25T21:38:32Z \| 8062 \| 1606632 \| \| 2025-04-26T00:01:30Z \| 6622 \| 1613254 \| \| 2025-04-26T02:24:19Z \| 6608 \| 1619862 \| \| 2025-04-26T04:46:01Z \| 4773 \| 1624635 \| \| 2025-04-26T07:07:59Z \| 3421 \| 1628056 \| \| 2025-04-26T09:30:02Z \| 2770 \| 1630826 \| \| 2025-04-26T11:51:46Z \| 2741 \| 1633567 \| \| 2025-04-26T14:16:45Z \| 4371 \| 1637938 \| \| 2025-04-26T16:42:52Z \| 6504 \| 1644442 \| \| 2025-04-26T19:07:30Z \| 6204 \| 1650646 \| \| 2025-04-26T21:30:38Z \| 7061 \| 1657707 \| \| 2025-04-26T23:53:16Z \| 5442 \| 1663149 \| \| 2025-04-27T02:15:57Z \| 6639 \| 1669788 \| \| 2025-04-27T04:38:32Z \| 5185 \| 1674973 \| \| 2025-04-27T07:00:34Z \| 3138 \| 1678111 \| \| 2025-04-27T09:22:59Z \| 3273 \| 1681384 \| \| 2025-04-27T11:45:38Z \| 3464 \| 1684848 \| \| 2025-04-27T14:07:58Z \| 4790 \| 1689638 \| \| 2025-04-27T16:31:30Z \| 6427 \| 1696065 \| \| 2025-04-27T18:56:09Z \| 6085 \| 1702150 \| \| 2025-04-27T21:18:29Z \| 8553 \| 1710703 \| \| 2025-04-27T23:41:53Z \| 5377 \| 1716080 \| \| 2025-04-28T02:04:20Z \| 6208 \| 1722288 \| \| 2025-04-28T04:27:21Z \| 5781 \| 1728069 \| \| 2025-04-28T06:49:56Z \| 3093 \| 1731162 \| \| 2025-04-28T09:12:49Z \| 2090 \| 1733252 \| \| 2025-04-28T11:35:19Z \| 3744 \| 1736996 \| \| 2025-04-28T13:58:03Z \| 4419 \| 1741415 \| \| 2025-04-28T16:21:01Z \| 5226 \| 1746641 \| \| 2025-04-28T18:43:52Z \| 5639 \| 1752280 \| \| 2025-04-28T21:06:26Z \| 7590 \| 1759870 \| \| 2025-04-28T23:29:28Z \| 5469 \| 1765339 \| \| 2025-04-29T01:52:35Z \| 5673 \| 1771012 \| \| 2025-04-29T04:14:46Z \| 3678 \| 1774690 \| \| 2025-04-29T06:37:37Z \| 3144 \| 1777834 \| \| 2025-04-29T09:00:17Z \| 2753 \| 1780587 \| \| 2025-04-29T11:23:52Z \| 2801 \| 1783388 \| \| 2025-04-29T13:46:51Z \| 4395 \| 1787783 \| \| 2025-04-29T16:08:45Z \| 6775 \| 1794558 \| \| 2025-04-29T18:32:04Z \| 7632 \| 1802190 \| \| 2025-04-29T20:54:51Z \| 4705 \| 1806895 \| \| 2025-04-29T23:17:22Z \| 4333 \| 1811228 \| \| 2025-04-30T01:39:59Z \| 5659 \| 1816887 \| \| 2025-04-30T04:02:16Z \| 6127 \| 1823014 \| \| 2025-04-30T06:24:36Z \| 3476 \| 1826490 \| \| 2025-04-30T08:48:08Z \| 3883 \| 1830373 \| \| 2025-04-30T11:10:23Z \| 2635 \| 1833008 \| \| 2025-04-30T13:32:17Z \| 4808 \| 1837816 \| \| 2025-04-30T15:54:40Z \| 6435 \| 1844251 \| \| 2025-04-30T18:19:01Z \| 6002 \| 1850253 \| \| 2025-04-30T20:41:09Z \| 5543 \| 1855796 \| \| 2025-04-30T23:03:29Z \| 6318 \| 1862114 \| \| 2025-05-01T01:27:07Z \| 6140 \| 1868254 \| \| 2025-05-01T03:49:46Z \| 6044 \| 1874298 \| \| 2025-05-01T06:13:07Z \| 3766 \| 1878064 \| \| 2025-05-01T08:37:46Z \| 3020 \| 1881084 \| \| 2025-05-01T11:00:21Z \| 3544 \| 1884628 \| \| 2025-05-01T13:22:41Z \| 4703 \| 1889331 \| \| 2025-05-01T15:46:00Z \| 6037 \| 1895368 \| \| 2025-05-01T18:08:22Z \| 7278 \| 1902646 \| \| 2025-05-01T20:32:42Z \| 7381 \| 1910027 \| \| 2025-05-01T22:55:21Z \| 7500 \| 1917527 \| \| 2025-05-02T01:17:47Z \| 6542 \| 1924069 \| \| 2025-05-02T03:40:11Z \| 5245 \| 1929314 \| \| 2025-05-02T06:03:00Z \| 3761 \| 1933075 \| \| 2025-05-02T08:25:24Z \| 2753 \| 1935828 \| \| 2025-05-02T10:47:57Z \| 3285 \| 1939113 \| \| 2025-05-02T13:11:08Z \| 3242 \| 1942355 \| \| 2025-05-02T15:34:30Z \| 5534 \| 1947889 \| \| 2025-05-02T17:56:45Z \| 7419 \| 1955308 \| \| 2025-05-02T20:19:06Z \| 5081 \| 1960389 \| \| 2025-05-02T22:41:35Z \| 7457 \| 1967846 \| \| 2025-05-03T01:04:08Z \| 8273 \| 1976119 \| \| 2025-05-03T03:26:34Z \| 6101 \| 1982220 \| \| 2025-05-03T05:50:07Z \| 4149 \| 1986369 \| \| 2025-05-03T08:12:15Z \| 3599 \| 1989968 \| \| 2025-05-03T10:34:26Z \| 3181 \| 1993149 \| \| 2025-05-03T12:57:00Z \| 3242 \| 1996391 \| \| 2025-05-03T15:19:26Z \| 5511 \| 2001902 \| \| 2025-05-03T17:42:08Z \| 4470 \| 2006372 \| \| 2025-05-03T20:04:37Z \| 6172 \| 2012544 \| \| 2025-05-03T22:26:51Z \| 6111 \| 2018655 \| \| 2025-05-04T00:49:10Z \| 4219 \| 2022874 \| \| 2025-05-04T03:11:56Z \| 4660 \| 2027534 \| \| 2025-05-04T05:33:53Z \| 4333 \| 2031867 \| \| 2025-05-04T07:55:53Z \| 2737 \| 2034604 \| \| 2025-05-04T10:18:02Z \| 2512 \| 2037116 \| \| 2025-05-04T12:40:23Z \| 3041 \| 2040157 \| \| 2025-05-04T15:03:07Z \| 4775 \| 2044932 \| \| 2025-05-04T17:25:38Z \| 7353 \| 2052285 \| \| 2025-05-04T19:48:05Z \| 6168 \| 2058453 \| \| 2025-05-04T22:10:23Z \| 5201 \| 2063654 \| \| 2025-05-05T00:32:49Z \| 5162 \| 2068816 \| \| 2025-05-05T02:55:10Z \| 5095 \| 2073911 \| \| 2025-05-05T05:17:32Z \| 3793 \| 2077704 \| \| 2025-05-05T07:39:44Z \| 3602 \| 2081306 \| \| 2025-05-05T10:02:18Z \| 3229 \| 2084535 \| \| 2025-05-05T12:24:26Z \| 3262 \| 2087797 \| \| 2025-05-05T14:46:41Z \| 5207 \| 2093004 \| \| 2025-05-05T17:09:23Z \| 7999 \| 2101003 \| \| 2025-05-05T19:31:55Z \| 7323 \| 2108326 \| \| 2025-05-05T21:54:27Z \| 6199 \| 2114525 \| \| 2025-05-06T00:16:56Z \| 6071 \| 2120596 \| \| 2025-05-06T02:39:30Z \| 4227 \| 2124823 \| \| 2025-05-06T05:01:34Z \| 3973 \| 2128796 \| \| 2025-05-06T07:24:16Z \| 2555 \| 2131351 \| \| 2025-05-06T09:46:34Z \| 2266 \| 2133617 \| \| 2025-05-06T12:08:54Z \| 3945 \| 2137562 \| \| 2025-05-06T14:31:05Z \| 4811 \| 2142373 \| \| 2025-05-06T16:53:53Z \| 7939 \| 2150312 \| \| 2025-05-06T19:16:32Z \| 6258 \| 2156570 \| \| 2025-05-06T21:39:50Z \| 5978 \| 2162548 \| \| 2025-05-07T00:03:05Z \| 4848 \| 2167396 \| \| 2025-05-07T02:26:10Z \| 6589 \| 2173985 \| \| 2025-05-07T04:48:21Z \| 6470 \| 2180455 \| \| 2025-05-07T07:10:39Z \| 4330 \| 2184785 \| \| 2025-05-07T09:32:45Z \| 3304 \| 2188089 \| \| 2025-05-07T11:54:51Z \| 4074 \| 2192163 \| \| 2025-05-07T14:17:10Z \| 4575 \| 2196738 \| \| 2025-05-07T16:39:38Z \| 8174 \| 2204912 \| \| 2025-05-07T19:02:24Z \| 9706 \| 2214618 \| \| 2025-05-07T21:24:50Z \| 7912 \| 2222530 \| \| 2025-05-07T23:47:29Z \| 5486 \| 2228016 \| \| 2025-05-08T02:10:11Z \| 6357 \| 2234373 \| \| 2025-05-08T04:32:29Z \| 4520 \| 2238893 \| \| 2025-05-08T06:54:50Z \| 2878 \| 2241771 \| \| 2025-05-08T09:17:12Z \| 1982 \| 2243753 \| \| 2025-05-08T11:40:04Z \| 3718 \| 2247471 \| \| 2025-05-08T14:02:39Z \| 4514 \| 2251985 \| \| 2025-05-08T16:25:29Z \| 5153 \| 2257138 \| \| 2025-05-08T18:48:16Z \| 6700 \| 2263838 \| \| 2025-05-08T21:11:17Z \| 7488 \| 2271326 \| \| 2025-05-08T23:34:09Z \| 4875 \| 2276201 \| \| 2025-05-09T01:56:42Z \| 4590 \| 2280791 \| \| 2025-05-09T04:19:15Z \| 5853 \| 2286644 \| \| 2025-05-09T06:41:26Z \| 4989 \| 2291633 \| \| 2025-05-09T09:04:16Z \| 3207 \| 2294840 \| \| 2025-05-09T11:26:29Z \| 3343 \| 2298183 \| \| 2025-05-09T13:49:05Z \| 5372 \| 2303555 \| \| 2025-05-09T16:12:44Z \| 7883 \| 2311438 \| \| 2025-05-09T18:35:41Z \| 5433 \| 2316871 \| \| 2025-05-09T20:58:06Z \| 5036 \| 2321907 \| \| 2025-05-09T23:20:39Z \| 5369 \| 2327276 \| \| 2025-05-10T01:43:28Z \| 5404 \| 2332680 \| \| 2025-05-10T04:05:55Z \| 6035 \| 2338715 \| \| 2025-05-10T06:28:09Z \| 4953 \| 2343668 \| \| 2025-05-10T08:50:13Z \| 2697 \| 2346365 \| \| 2025-05-10T11:12:54Z \| 2250 \| 2348615 \| \| 2025-05-10T13:34:57Z \| 3964 \| 2352579 \| \| 2025-05-10T15:56:59Z \| 4476 \| 2357055 \| \| 2025-05-10T18:19:04Z \| 5843 \| 2362898 \| \| 2025-05-10T20:41:14Z \| 5037 \| 2367935 \| \| 2025-05-10T23:03:30Z \| 3731 \| 2371666 \| \| 2025-05-11T01:26:55Z \| 4873 \| 2376539 \| \| 2025-05-11T03:49:12Z \| 4450 \| 2380989 \| \| 2025-05-11T06:11:24Z \| 3072 \| 2384061 \| \| 2025-05-11T08:34:37Z \| 2606 \| 2386667 \| \| 2025-05-11T10:57:01Z \| 2583 \| 2389250 \| \| 2025-05-11T13:19:45Z \| 2877 \| 2392127 \| \| 2025-05-11T15:42:08Z \| 6053 \| 2398180 \| \| 2025-05-11T18:04:46Z \| 5750 \| 2403930 \| \| 2025-05-11T20:28:00Z \| 6373 \| 2410303 \| \| 2025-05-11T22:52:10Z \| 5489 \| 2415792 \| \| 2025-05-12T01:14:42Z \| 6058 \| 2421850 \| \| 2025-05-12T03:36:47Z \| 4821 \| 2426671 \| \| 2025-05-12T05:58:58Z \| 3532 \| 2430203 \| \| 2025-05-12T08:22:12Z \| 2190 \| 2432393 \| \| 2025-05-12T10:45:41Z \| 3435 \| 2435828 \| \| 2025-05-12T13:07:49Z \| 3612 \| 2439440 \| \| 2025-05-12T15:30:33Z \| 6496 \| 2445936 \| \| 2025-05-12T17:53:21Z \| 6379 \| 2452315 \| \| 2025-05-12T20:17:11Z \| 7695 \| 2460010 \| \| 2025-05-12T22:39:41Z \| 6369 \| 2466379 \| \| 2025-05-13T01:02:58Z \| 5804 \| 2472183 \| \| 2025-05-13T03:25:22Z \| 6146 \| 2478329 \|
PLAN-Lab/Hallu	PLAN-Lab	2025-05-13T02:59:17Z	0	0	[ "language:en", "license:osl-3.0", "modality:image", "region:us" ]	[]	2025-05-13T02:11:01Z	null	--- license: osl-3.0 language: - en ---
MirrorUser/UserMirrorer-eval	MirrorUser	2025-05-13T02:07:09Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-13T02:07:03Z	null	--- dataset_info: features: - name: text struct: - name: action_list sequence: string - name: history dtype: string - name: profile dtype: string - name: user_id dtype: string - name: item_id dtype: string - name: timestamp dtype: timestamp[ns] - name: item_pos dtype: int64 - name: choice_cnt dtype: int64 - name: dataset dtype: string - name: impression_list sequence: string splits: - name: test num_bytes: 14699116 num_examples: 6400 download_size: 5555631 dataset_size: 14699116 configs: - config_name: default data_files: - split: test path: data/test-* ---
Askio/mmrag_benchmark	Askio	2025-05-13T01:38:35Z	0	0	[ "task_categories:question-answering", "language:en", "license:apache-2.0", "doi:10.57967/hf/5405", "region:us" ]	[ "question-answering" ]	2025-05-12T14:49:38Z	null	--- license: apache-2.0 task_categories: - question-answering language: - en --- # 📚 mmrag benchmark ## 📁 Files Overview - `mmrag_train.json`: Training set for model training. - `mmrag_dev.json`: Validation set for hyperparameter tuning and development. - `mmrag_test.json`: Test set for evaluation. - `processed_documents.json`: The document pool used for document retrieval. --- ## 🔍 Query Datasets: `mmrag_train.json`, `mmrag_dev.json`, `mmrag_test.json` The three files are all lists of dictionaries. Each dictionary contains the following fields: ### 🔑 `id` - Description: Unique query identifier, structured as `dataset_queryID`. - Example: `ott_144`. ### ❓ `query` - Description: Text of the question. - Example: `"What is the capital of France?"` ### ✅ `answer` - Description: The gold-standard answer corresponding to the query. - Example: `"Paris"` ### 📑 `relevant_chunks` - Description: Dictionary of relevant document IDs and their corresponding relevance scores. The document IDs are structured as `dataset_documentID_chunkIndex`, equivalent to `dataset_queryID_chunkIndex` - Example: `{"ott_23573_2": 1}` ### 📖 `ori_context` - Description: A list of the original document IDs related to the query. - Example: `["ott_6104"]` ### 📜 `dataset_score` - Description: The relevance score of all datasets regarding this query. - Example: `{"tat": 0, "triviaqa": 2, "ott": 4, "kg": 1, "nq": 0}` --- ## 📚 Knowledge Base: `processed_documents.json` This file is a list of documents used for document retrieval, which contains the following fields: ### 🔑 `id` - Description: Unique document identifier, structured as `dataset_documentID_chunkIndex`, equivalent to `dataset_queryID_chunkIndex` - example: `ott_8075_0` ### 📄 `text` - Description: Text of the document. - Example: `A molecule editor is a computer program for creating and modifying representations of chemical structures.` --- ## 📄 License This dataset is licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
SAA-Lab/test_jan25-cwv-genrm_cot_qwen7b-ckptglobal_step_324	SAA-Lab	2025-05-13T00:37:16Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-13T00:37:14Z	null	--- dataset_info: features: - name: post_id dtype: int64 - name: chosen_body dtype: string - name: rejected_body dtype: string - name: chosen_upvotes dtype: int64 - name: rejected_upvotes dtype: int64 - name: chosen_length dtype: int64 - name: rejected_length dtype: int64 - name: chosen_username dtype: string - name: rejected_username dtype: string - name: chosen_timestamp dtype: timestamp[us] - name: rejected_timestamp dtype: timestamp[us] - name: post_title dtype: string - name: time_diff dtype: float64 - name: __index_level_0__ dtype: int64 - name: prompt list: - name: content dtype: string - name: role dtype: string - name: answer dtype: string - name: model_response dtype: string - name: reasoning dtype: string - name: preferred dtype: string - name: is_correct dtype: bool splits: - name: train num_bytes: 2364929 num_examples: 155 download_size: 1281579 dataset_size: 2364929 configs: - config_name: default data_files: - split: train path: data/train-* ---
SAA-Lab/test-jan24-cwv-genrm_cot_qwen3b-ckptglobal_step_324	SAA-Lab	2025-05-13T00:07:43Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-13T00:07:41Z	null	--- dataset_info: features: - name: post_id dtype: int64 - name: chosen_body dtype: string - name: rejected_body dtype: string - name: chosen_upvotes dtype: int64 - name: rejected_upvotes dtype: int64 - name: chosen_length dtype: int64 - name: rejected_length dtype: int64 - name: chosen_username dtype: string - name: rejected_username dtype: string - name: chosen_timestamp dtype: timestamp[us] - name: rejected_timestamp dtype: timestamp[us] - name: post_title dtype: string - name: time_diff dtype: float64 - name: __index_level_0__ dtype: int64 - name: prompt list: - name: content dtype: string - name: role dtype: string - name: answer dtype: string - name: model_response dtype: string - name: reasoning dtype: string - name: preferred dtype: string - name: is_correct dtype: bool splits: - name: train num_bytes: 12899940 num_examples: 796 download_size: 7252688 dataset_size: 12899940 configs: - config_name: default data_files: - split: train path: data/train-* ---
ilhamiuturkkan/response-to-dev-generator-hub	ilhamiuturkkan	2025-05-12T23:18:06Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T23:18:05Z	null	--- dataset_info: features: - name: response dtype: string splits: - name: train num_bytes: 904947 num_examples: 5792 download_size: 96249 dataset_size: 904947 configs: - config_name: default data_files: - split: train path: data/train-* ---
louisbrulenaudet/code-famille-aide-sociale	louisbrulenaudet	2025-05-12T23:13:34Z	460	0	[ "task_categories:text-generation", "task_categories:table-question-answering", "task_categories:summarization", "task_categories:text-retrieval", "task_categories:question-answering", "task_categories:text-classification", "multilinguality:monolingual", "source_datasets:original", "language:fr", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "finetuning", "legal", "french law", "droit français", "Code de la famille et de l'aide sociale" ]	[ "text-generation", "table-question-answering", "summarization", "text-retrieval", "question-answering", "text-classification" ]	2024-03-25T21:48:43Z	null	--- license: apache-2.0 language: - fr multilinguality: - monolingual tags: - finetuning - legal - french law - droit français - Code de la famille et de l'aide sociale source_datasets: - original pretty_name: Code de la famille et de l'aide sociale task_categories: - text-generation - table-question-answering - summarization - text-retrieval - question-answering - text-classification size_categories: - 1K<n<10K --- # Code de la famille et de l'aide sociale, non-instruct (2025-05-12) The objective of this project is to provide researchers, professionals and law students with simplified, up-to-date access to all French legal texts, enriched with a wealth of data to facilitate their integration into Community and European projects. Normally, the data is refreshed daily on all legal codes, and aims to simplify the production of training sets and labeling pipelines for the development of free, open-source language models based on open data accessible to all. ## Concurrent reading of the LegalKit [<img src="https://raw.githubusercontent.com/louisbrulenaudet/ragoon/main/assets/badge.svg" alt="Built with RAGoon" width="200" height="32"/>](https://github.com/louisbrulenaudet/ragoon) To use all the legal data published on LegalKit, you can use RAGoon: ```bash pip3 install ragoon ``` Then, you can load multiple datasets using this code snippet: ```python # -- coding: utf-8 -- from ragoon import load_datasets req = [ "louisbrulenaudet/code-artisanat", "louisbrulenaudet/code-action-sociale-familles", # ... ] datasets_list = load_datasets( req=req, streaming=False ) dataset = datasets.concatenate_datasets( datasets_list ) ``` ### Data Structure for Article Information This section provides a detailed overview of the elements contained within the `item` dictionary. Each key represents a specific attribute of the legal article, with its associated value providing detailed information. 1. Basic Information - `ref` (string): Reference - A reference to the article, combining the title_main and the article `number` (e.g., "Code Général des Impôts, art. 123"). - `texte` (string): Text Content - The textual content of the article. - `dateDebut` (string): Start Date - The date when the article came into effect. - `dateFin` (string): End Date - The date when the article was terminated or superseded. - `num` (string): Article Number - The number assigned to the article. - `id` (string): Article ID - Unique identifier for the article. - `cid` (string): Chronical ID - Chronical identifier for the article. - `type` (string): Type - The type or classification of the document (e.g., "AUTONOME"). - `etat` (string): Legal Status - The current legal status of the article (e.g., "MODIFIE_MORT_NE"). 2. Content and Notes - `nota` (string): Notes - Additional notes or remarks associated with the article. - `version_article` (string): Article Version - The version number of the article. - `ordre` (integer): Order Number - A numerical value used to sort articles within their parent section. 3. Additional Metadata - `conditionDiffere` (string): Deferred Condition - Specific conditions related to collective agreements. - `infosComplementaires` (string): Additional Information - Extra information pertinent to the article. - `surtitre` (string): Subtitle - A subtitle or additional title information related to collective agreements. - `nature` (string): Nature - The nature or category of the document (e.g., "Article"). - `texteHtml` (string): HTML Content - The article's content in HTML format. 4. Versioning and Extensions - `dateFinExtension` (string): End Date of Extension - The end date if the article has an extension. - `versionPrecedente` (string): Previous Version - Identifier for the previous version of the article. - `refInjection` (string): Injection Reference - Technical reference to identify the date of injection. - `idTexte` (string): Text ID - Identifier for the legal text to which the article belongs. - `idTechInjection` (string): Technical Injection ID - Technical identifier for the injected element. 5. Origin and Relationships - `origine` (string): Origin - The origin of the document (e.g., "LEGI"). - `dateDebutExtension` (string): Start Date of Extension - The start date if the article has an extension. - `idEliAlias` (string): ELI Alias - Alias for the European Legislation Identifier (ELI). - `cidTexte` (string): Text Chronical ID - Chronical identifier of the text. 6. Hierarchical Relationships - `sectionParentId` (string): Parent Section ID - Technical identifier of the parent section. - `multipleVersions` (boolean): Multiple Versions - Indicates if the article has multiple versions. - `comporteLiensSP` (boolean): Contains Public Service Links - Indicates if the article contains links to public services. - `sectionParentTitre` (string): Parent Section Title - Title of the parent section (e.g., "I : Revenu imposable"). - `infosRestructurationBranche` (string): Branch Restructuring Information - Information about branch restructuring. - `idEli` (string): ELI ID - European Legislation Identifier (ELI) for the article. - `sectionParentCid` (string): Parent Section Chronical ID - Chronical identifier of the parent section. 7. Additional Content and History - `numeroBo` (string): Official Bulletin Number - Number of the official bulletin where the article was published. - `infosRestructurationBrancheHtml` (string): Branch Restructuring Information (HTML) - Branch restructuring information in HTML format. - `historique` (string): History - Historical context or changes specific to collective agreements. - `infosComplementairesHtml` (string): Additional Information (HTML) - Additional information in HTML format. - `renvoi` (string): Reference - References to content within the article (e.g., "(1)"). - `fullSectionsTitre` (string): Full Section Titles - Concatenation of all titles in the parent chain. - `notaHtml` (string): Notes (HTML) - Additional notes or remarks in HTML format. - `inap` (string): INAP - A placeholder for INAP-specific information. ## Feedback If you have any feedback, please reach out at [[email protected]](mailto:[email protected]).
louisbrulenaudet/code-electoral	louisbrulenaudet	2025-05-12T23:13:33Z	419	0	[ "task_categories:text-generation", "task_categories:table-question-answering", "task_categories:summarization", "task_categories:text-retrieval", "task_categories:question-answering", "task_categories:text-classification", "multilinguality:monolingual", "source_datasets:original", "language:fr", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "finetuning", "legal", "french law", "droit français", "Code électoral" ]	[ "text-generation", "table-question-answering", "summarization", "text-retrieval", "question-answering", "text-classification" ]	2024-03-25T21:02:10Z	null	--- license: apache-2.0 language: - fr multilinguality: - monolingual tags: - finetuning - legal - french law - droit français - Code électoral source_datasets: - original pretty_name: Code électoral task_categories: - text-generation - table-question-answering - summarization - text-retrieval - question-answering - text-classification size_categories: - 1K<n<10K --- # Code électoral, non-instruct (2025-05-12) The objective of this project is to provide researchers, professionals and law students with simplified, up-to-date access to all French legal texts, enriched with a wealth of data to facilitate their integration into Community and European projects. Normally, the data is refreshed daily on all legal codes, and aims to simplify the production of training sets and labeling pipelines for the development of free, open-source language models based on open data accessible to all. ## Concurrent reading of the LegalKit [<img src="https://raw.githubusercontent.com/louisbrulenaudet/ragoon/main/assets/badge.svg" alt="Built with RAGoon" width="200" height="32"/>](https://github.com/louisbrulenaudet/ragoon) To use all the legal data published on LegalKit, you can use RAGoon: ```bash pip3 install ragoon ``` Then, you can load multiple datasets using this code snippet: ```python # -- coding: utf-8 -- from ragoon import load_datasets req = [ "louisbrulenaudet/code-artisanat", "louisbrulenaudet/code-action-sociale-familles", # ... ] datasets_list = load_datasets( req=req, streaming=False ) dataset = datasets.concatenate_datasets( datasets_list ) ``` ### Data Structure for Article Information This section provides a detailed overview of the elements contained within the `item` dictionary. Each key represents a specific attribute of the legal article, with its associated value providing detailed information. 1. Basic Information - `ref` (string): Reference - A reference to the article, combining the title_main and the article `number` (e.g., "Code Général des Impôts, art. 123"). - `texte` (string): Text Content - The textual content of the article. - `dateDebut` (string): Start Date - The date when the article came into effect. - `dateFin` (string): End Date - The date when the article was terminated or superseded. - `num` (string): Article Number - The number assigned to the article. - `id` (string): Article ID - Unique identifier for the article. - `cid` (string): Chronical ID - Chronical identifier for the article. - `type` (string): Type - The type or classification of the document (e.g., "AUTONOME"). - `etat` (string): Legal Status - The current legal status of the article (e.g., "MODIFIE_MORT_NE"). 2. Content and Notes - `nota` (string): Notes - Additional notes or remarks associated with the article. - `version_article` (string): Article Version - The version number of the article. - `ordre` (integer): Order Number - A numerical value used to sort articles within their parent section. 3. Additional Metadata - `conditionDiffere` (string): Deferred Condition - Specific conditions related to collective agreements. - `infosComplementaires` (string): Additional Information - Extra information pertinent to the article. - `surtitre` (string): Subtitle - A subtitle or additional title information related to collective agreements. - `nature` (string): Nature - The nature or category of the document (e.g., "Article"). - `texteHtml` (string): HTML Content - The article's content in HTML format. 4. Versioning and Extensions - `dateFinExtension` (string): End Date of Extension - The end date if the article has an extension. - `versionPrecedente` (string): Previous Version - Identifier for the previous version of the article. - `refInjection` (string): Injection Reference - Technical reference to identify the date of injection. - `idTexte` (string): Text ID - Identifier for the legal text to which the article belongs. - `idTechInjection` (string): Technical Injection ID - Technical identifier for the injected element. 5. Origin and Relationships - `origine` (string): Origin - The origin of the document (e.g., "LEGI"). - `dateDebutExtension` (string): Start Date of Extension - The start date if the article has an extension. - `idEliAlias` (string): ELI Alias - Alias for the European Legislation Identifier (ELI). - `cidTexte` (string): Text Chronical ID - Chronical identifier of the text. 6. Hierarchical Relationships - `sectionParentId` (string): Parent Section ID - Technical identifier of the parent section. - `multipleVersions` (boolean): Multiple Versions - Indicates if the article has multiple versions. - `comporteLiensSP` (boolean): Contains Public Service Links - Indicates if the article contains links to public services. - `sectionParentTitre` (string): Parent Section Title - Title of the parent section (e.g., "I : Revenu imposable"). - `infosRestructurationBranche` (string): Branch Restructuring Information - Information about branch restructuring. - `idEli` (string): ELI ID - European Legislation Identifier (ELI) for the article. - `sectionParentCid` (string): Parent Section Chronical ID - Chronical identifier of the parent section. 7. Additional Content and History - `numeroBo` (string): Official Bulletin Number - Number of the official bulletin where the article was published. - `infosRestructurationBrancheHtml` (string): Branch Restructuring Information (HTML) - Branch restructuring information in HTML format. - `historique` (string): History - Historical context or changes specific to collective agreements. - `infosComplementairesHtml` (string): Additional Information (HTML) - Additional information in HTML format. - `renvoi` (string): Reference - References to content within the article (e.g., "(1)"). - `fullSectionsTitre` (string): Full Section Titles - Concatenation of all titles in the parent chain. - `notaHtml` (string): Notes (HTML) - Additional notes or remarks in HTML format. - `inap` (string): INAP - A placeholder for INAP-specific information. ## Feedback If you have any feedback, please reach out at [[email protected]](mailto:[email protected]).
louisbrulenaudet/code-defense	louisbrulenaudet	2025-05-12T23:13:27Z	464	0	[ "task_categories:text-generation", "task_categories:table-question-answering", "task_categories:summarization", "task_categories:text-retrieval", "task_categories:question-answering", "task_categories:text-classification", "multilinguality:monolingual", "source_datasets:original", "language:fr", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "finetuning", "legal", "french law", "droit français", "Code de la défense" ]	[ "text-generation", "table-question-answering", "summarization", "text-retrieval", "question-answering", "text-classification" ]	2024-03-25T20:35:32Z	null	--- license: apache-2.0 language: - fr multilinguality: - monolingual tags: - finetuning - legal - french law - droit français - Code de la défense source_datasets: - original pretty_name: Code de la défense task_categories: - text-generation - table-question-answering - summarization - text-retrieval - question-answering - text-classification size_categories: - 1K<n<10K --- # Code de la défense, non-instruct (2025-05-12) The objective of this project is to provide researchers, professionals and law students with simplified, up-to-date access to all French legal texts, enriched with a wealth of data to facilitate their integration into Community and European projects. Normally, the data is refreshed daily on all legal codes, and aims to simplify the production of training sets and labeling pipelines for the development of free, open-source language models based on open data accessible to all. ## Concurrent reading of the LegalKit [<img src="https://raw.githubusercontent.com/louisbrulenaudet/ragoon/main/assets/badge.svg" alt="Built with RAGoon" width="200" height="32"/>](https://github.com/louisbrulenaudet/ragoon) To use all the legal data published on LegalKit, you can use RAGoon: ```bash pip3 install ragoon ``` Then, you can load multiple datasets using this code snippet: ```python # -- coding: utf-8 -- from ragoon import load_datasets req = [ "louisbrulenaudet/code-artisanat", "louisbrulenaudet/code-action-sociale-familles", # ... ] datasets_list = load_datasets( req=req, streaming=False ) dataset = datasets.concatenate_datasets( datasets_list ) ``` ### Data Structure for Article Information This section provides a detailed overview of the elements contained within the `item` dictionary. Each key represents a specific attribute of the legal article, with its associated value providing detailed information. 1. Basic Information - `ref` (string): Reference - A reference to the article, combining the title_main and the article `number` (e.g., "Code Général des Impôts, art. 123"). - `texte` (string): Text Content - The textual content of the article. - `dateDebut` (string): Start Date - The date when the article came into effect. - `dateFin` (string): End Date - The date when the article was terminated or superseded. - `num` (string): Article Number - The number assigned to the article. - `id` (string): Article ID - Unique identifier for the article. - `cid` (string): Chronical ID - Chronical identifier for the article. - `type` (string): Type - The type or classification of the document (e.g., "AUTONOME"). - `etat` (string): Legal Status - The current legal status of the article (e.g., "MODIFIE_MORT_NE"). 2. Content and Notes - `nota` (string): Notes - Additional notes or remarks associated with the article. - `version_article` (string): Article Version - The version number of the article. - `ordre` (integer): Order Number - A numerical value used to sort articles within their parent section. 3. Additional Metadata - `conditionDiffere` (string): Deferred Condition - Specific conditions related to collective agreements. - `infosComplementaires` (string): Additional Information - Extra information pertinent to the article. - `surtitre` (string): Subtitle - A subtitle or additional title information related to collective agreements. - `nature` (string): Nature - The nature or category of the document (e.g., "Article"). - `texteHtml` (string): HTML Content - The article's content in HTML format. 4. Versioning and Extensions - `dateFinExtension` (string): End Date of Extension - The end date if the article has an extension. - `versionPrecedente` (string): Previous Version - Identifier for the previous version of the article. - `refInjection` (string): Injection Reference - Technical reference to identify the date of injection. - `idTexte` (string): Text ID - Identifier for the legal text to which the article belongs. - `idTechInjection` (string): Technical Injection ID - Technical identifier for the injected element. 5. Origin and Relationships - `origine` (string): Origin - The origin of the document (e.g., "LEGI"). - `dateDebutExtension` (string): Start Date of Extension - The start date if the article has an extension. - `idEliAlias` (string): ELI Alias - Alias for the European Legislation Identifier (ELI). - `cidTexte` (string): Text Chronical ID - Chronical identifier of the text. 6. Hierarchical Relationships - `sectionParentId` (string): Parent Section ID - Technical identifier of the parent section. - `multipleVersions` (boolean): Multiple Versions - Indicates if the article has multiple versions. - `comporteLiensSP` (boolean): Contains Public Service Links - Indicates if the article contains links to public services. - `sectionParentTitre` (string): Parent Section Title - Title of the parent section (e.g., "I : Revenu imposable"). - `infosRestructurationBranche` (string): Branch Restructuring Information - Information about branch restructuring. - `idEli` (string): ELI ID - European Legislation Identifier (ELI) for the article. - `sectionParentCid` (string): Parent Section Chronical ID - Chronical identifier of the parent section. 7. Additional Content and History - `numeroBo` (string): Official Bulletin Number - Number of the official bulletin where the article was published. - `infosRestructurationBrancheHtml` (string): Branch Restructuring Information (HTML) - Branch restructuring information in HTML format. - `historique` (string): History - Historical context or changes specific to collective agreements. - `infosComplementairesHtml` (string): Additional Information (HTML) - Additional information in HTML format. - `renvoi` (string): Reference - References to content within the article (e.g., "(1)"). - `fullSectionsTitre` (string): Full Section Titles - Concatenation of all titles in the parent chain. - `notaHtml` (string): Notes (HTML) - Additional notes or remarks in HTML format. - `inap` (string): INAP - A placeholder for INAP-specific information. ## Feedback If you have any feedback, please reach out at [[email protected]](mailto:[email protected]).
Laudando-Associates-LLC/pucks	Laudando-Associates-LLC	2025-05-12T22:43:17Z	48	0	[ "task_categories:object-detection", "annotations_creators:machine-generated", "language_creators:found", "source_datasets:original", "language:en", "license:other", "size_categories:n<1K", "format:parquet", "modality:image", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "AgTech" ]	[ "object-detection" ]	2025-05-10T01:08:10Z	null	--- language: - en pretty_name: Pucks surrogate Laserweeder license: other license_name: laser-dataset-replication-license-v1.0 license_link: LICENSE tags: - AgTech annotations_creators: - machine-generated language_creators: - found size_categories: - n<1K source_datasets: - original task_categories: - object-detection configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: image_id dtype: int32 - name: image dtype: image - name: annotated_image dtype: image - name: resolution sequence: int32 length: 2 - name: bboxes sequence: sequence: float32 length: 4 - name: category_ids sequence: int32 splits: - name: train num_bytes: 316722721.0 num_examples: 118 - name: validation num_bytes: 48332168.0 num_examples: 18 - name: test num_bytes: 24279368.0 num_examples: 9 download_size: 389352498 dataset_size: 389334257.0 --- <h1 align="center"><strong>Pucks dataset</strong></h1> ## Dataset Overview <div align="justify"> The Pucks Dataset is part of the L&Aser surrogate target recognition pipeline developed by Laudando & Associates LLC. This dataset contains high-resolution field images annotated with bounding boxes around small circular surrogate targets (“pucks”) used to simulate real weed targets for the L&Aser weeding system. </div> ## Contents - Raw RGB images collected in field conditions using a NVIDIA Jetson and monocular camera - Bounding boxes in COCO-style format generated using a neural network - Annotated visual overlays for quick inspection ## Preview Format <div align="justify"> This dataset is optimized for visual inspection on the Hugging Face Hub, with an additional ```annotated_image``` column for rapid validation. The raw COCO JSON and images are included in their original form for reproducibility and training. </div> ## Example Use ```python from datasets import load_dataset ds = load_dataset("Laudando-Associates-LLC/pucks", split="train") img = ds[0]["image"] ann = ds[0]["bboxes"] ``` ## Recording Date <div align="justify"> The data was originally collected in ROS 2 bag format in April 2024 during controlled field trials conducted by Laudando & Associates LLC. This dataset is a stripped-down version containing only the extracted RGB images and their corresponding object detection annotations. </div> ## License <div align="justify"> This dataset is provided under the L&Aser Dataset Replication License (Version 1.0). See [LICENSE](LICENSE) for full terms. </div>
test-gen/livecodebench_qwen-0.5b-easy_t0.0_n1_generated_tests	test-gen	2025-05-12T21:57:08Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T21:57:07Z	null	--- dataset_info: features: - name: question_title dtype: string - name: question_content dtype: string - name: question_id dtype: string - name: contest_id dtype: string - name: test_id dtype: int64 - name: contest_date dtype: timestamp[ns] - name: starter_code dtype: string - name: function_name dtype: string - name: difficulty dtype: string - name: test dtype: string - name: verification_info struct: - name: language dtype: string - name: test_cases sequence: string splits: - name: test num_bytes: 385433 num_examples: 442 download_size: 78075 dataset_size: 385433 configs: - config_name: default data_files: - split: test path: data/test-* ---
hamishivi/simple_qa_rlvr_no_prompt	hamishivi	2025-05-12T21:46:33Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T21:46:29Z	null	--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: ground_truth dtype: string - name: dataset dtype: string splits: - name: train num_bytes: 555145 num_examples: 3893 - name: test num_bytes: 62109 num_examples: 433 download_size: 355065 dataset_size: 617254 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
LadyMia/x_dataset_17682	LadyMia	2025-05-12T21:18:24Z	1,241	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" ]	2025-01-29T03:07:52Z	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: LadyMia/x_dataset_17682 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5FgYXBnD63LNLkArKfbK1i4K2gbLbs6zULHA2DXFmhLdtFHe ### 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{LadyMia2025datauniversex_dataset_17682, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={LadyMia}, year={2025}, url={https://huggingface.co/datasets/LadyMia/x_dataset_17682}, } ``` ### 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: 37658378 - Date Range: 2025-01-22T00:00:00Z to 2025-02-13T00:00:00Z - Last Updated: 2025-02-18T21:42:55Z ### Data Distribution - Tweets with hashtags: 45.42% - Tweets without hashtags: 54.58% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| NULL \| 20555610 \| 54.58% \| \| 2 \| #riyadh \| 235778 \| 0.63% \| \| 3 \| #zelena \| 224298 \| 0.60% \| \| 4 \| #tiktok \| 161795 \| 0.43% \| \| 5 \| #ad \| 91773 \| 0.24% \| \| 6 \| #jhope_at_galadespiècesjaunes \| 85795 \| 0.23% \| \| 7 \| #bbb25 \| 79808 \| 0.21% \| \| 8 \| #transferlerlebirliktezafere \| 58256 \| 0.15% \| \| 9 \| #theheartkillersep10 \| 55037 \| 0.15% \| \| 10 \| #bbmzansi \| 51161 \| 0.14% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-01-29T03:08:57Z \| 2977993 \| 2977993 \| \| 2025-02-01T15:11:35Z \| 7083709 \| 10061702 \| \| 2025-02-05T03:15:34Z \| 8967127 \| 19028829 \| \| 2025-02-08T15:19:06Z \| 9885163 \| 28913992 \| \| 2025-02-12T03:23:51Z \| 7367286 \| 36281278 \| \| 2025-02-18T06:41:11Z \| 659231 \| 36940509 \| \| 2025-02-18T21:42:55Z \| 717869 \| 37658378 \|
CohenQu/HintGen-STaR.01.00	CohenQu	2025-05-12T21:15:25Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T21:15:24Z	null	--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: suffix dtype: string splits: - name: train num_bytes: 13129492 num_examples: 2367 - name: test num_bytes: 1456620 num_examples: 264 download_size: 6587593 dataset_size: 14586112 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
Asap7772/dapo5k-offlinedata-hintgen-qwen3-4b-lr1e6-warmstartfinal	Asap7772	2025-05-12T21:11:36Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T21:10:43Z	null	--- dataset_info: features: - name: completion dtype: string - name: query dtype: string splits: - name: train num_bytes: 16788904.2 num_examples: 4500 - name: test num_bytes: 1865433.8 num_examples: 500 download_size: 8033471 dataset_size: 18654338.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
Instinct-AI/xerxes-singleshot-dataset	Instinct-AI	2025-05-12T21:05:10Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T21:05:08Z	null	--- dataset_info: features: - name: conversation list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 1424803 num_examples: 1578 download_size: 666578 dataset_size: 1424803 configs: - config_name: default data_files: - split: train path: data/train-* ---
hurrutia/coser_resumenes	hurrutia	2025-05-12T20:54:02Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T14:19:10Z	null	--- dataset_info: features: - name: prompt dtype: string - name: input dtype: string - name: respuesta dtype: string - name: pregunta dtype: string splits: - name: train num_bytes: 3842543 num_examples: 230 download_size: 2077797 dataset_size: 3842543 configs: - config_name: default data_files: - split: train path: data/train-* ---
Asap7772/d1shs0ap-star-data-balanceFalse-nohintTrue-subsample0.1	Asap7772	2025-05-12T20:37:17Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T20:37:11Z	null	--- dataset_info: features: - name: is_no_hint dtype: bool - name: query dtype: string - name: completion dtype: string splits: - name: train num_bytes: 9955332.85380117 num_examples: 4450 - name: test num_bytes: 1107390.957894737 num_examples: 495 download_size: 6488646 dataset_size: 11062723.811695907 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
yoonholee/completions_SFT_Qwen3-0.6B_GSM	yoonholee	2025-05-12T20:36:01Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T20:35:59Z	null	--- dataset_info: features: - name: problem dtype: string - name: hint dtype: string - name: completions sequence: string - name: corrects sequence: bool - name: acc dtype: float64 - name: answer dtype: string splits: - name: train num_bytes: 8557589 num_examples: 1600 download_size: 3189484 dataset_size: 8557589 configs: - config_name: default data_files: - split: train path: data/train-* ---
harpreetsahota/INQUIRE_Rerank	harpreetsahota	2025-05-12T19:59:55Z	0	0	[ "task_categories:image-classification", "language:en", "size_categories:10K<n<100K", "format:imagefolder", "modality:image", "library:datasets", "library:mlcroissant", "library:fiftyone", "region:us", "fiftyone", "image", "image-classification" ]	[ "image-classification" ]	2025-05-12T19:25:19Z	null	--- annotations_creators: [] language: en size_categories: - 10K<n<100K task_categories: - image-classification task_ids: [] pretty_name: INQUIRE_Rerank tags: - fiftyone - image - image-classification dataset_summary: ' This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 16000 samples. ## Installation If you haven''t already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo from fiftyone.utils.huggingface import load_from_hub # Load the dataset # Note: other available arguments include ''max_samples'', etc dataset = load_from_hub("harpreetsahota/INQUIRE_Rerank") # Launch the App session = fo.launch_app(dataset) ``` ' --- # Dataset Card for INQUIRE_Rerank <!-- Provide a quick summary of the dataset. --> This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 16000 samples. ## Installation If you haven't already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo from fiftyone.utils.huggingface import load_from_hub # Load the dataset # Note: other available arguments include 'max_samples', etc dataset = load_from_hub("harpreetsahota/INQUIRE_Rerank") # Launch the App session = fo.launch_app(dataset) ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
bursomi/so101_test	bursomi	2025-05-12T18:41:40Z	104	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" ]	2025-05-11T07:51:00Z	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": 2, "total_frames": 1787, "total_tasks": 1, "total_videos": 4, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:2" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.laptop": { "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] ```
funasoft/EURUSD-2025-02-01_2025-05-12	funasoft	2025-05-12T17:21:33Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:timeseries", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T17:21:31Z	null	--- dataset_info: features: - name: input_ids sequence: float32 - name: label dtype: float64 splits: - name: train num_bytes: 30044 num_examples: 29 download_size: 6898 dataset_size: 30044 configs: - config_name: default data_files: - split: train path: data/train-* ---
Jensen-holm/statcast-era-pitches	Jensen-holm	2025-05-12T17:12:58Z	609	2	[ "license:mit", "size_categories:1M<n<10M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "baseball", "sports-analytics" ]	[]	2024-04-24T13:32:40Z	null	--- license: mit tags: - baseball - sports-analytics pretty_name: Don't scrape statcast data anymore size_categories: - 1M<n<10M --- # statcast-pitches [![Latest Update](https://github.com/Jensen-holm/statcast-era-pitches/actions/workflows/update_statcast_data.yml/badge.svg)](https://github.com/Jensen-holm/statcast-era-pitches/actions/workflows/update_statcast_data.yml) [pybaseball](https://github.com/jldbc/pybaseball) is a great tool for downloading baseball data. Even though the library is optimized and scrapes this data in parallel, it can be time consuming. The point of this repository is to utilize GitHub Actions to scrape new baseball data weekly during the MLB season, and update a parquet file hosted as a huggingface dataset. Reading this data as a huggingface dataset is much faster than scraping the new data each time you re run your code, or just want updated statcast pitch data in general. The `update.py` script updates each week during the MLB season, updating the [statcast-era-pitches HuggingFace Dataset](https://huggingface.co/datasets/Jensen-holm/statcast-era-pitches) so that you don't have to re scrape this data yourself. You can explore the entire dataset in your browser [at this link](https://huggingface.co/datasets/Jensen-holm/statcast-era-pitches/viewer/default/train) # Installation ```bash pip install statcast-pitches ``` # Usage ### With statcast_pitches package Example 1 w/ polars (suggested) ```python import statcast_pitches import polars as pl # load all pitches from 2015-present pitches_lf = statcast_pitches.load() # filter to get 2024 bat speed data bat_speed_24_df = (pitches_lf .filter(pl.col("game_date").dt.year() == 2024) .select("bat_speed", "swing_length") .collect()) print(bat_speed_24_df.head(3)) ``` output: \| \| bat_speed \| swing_length \| \|-\|------------\|--------------\| \| 0 \| 73.61710 \| 6.92448 \| \| 1 \| 58.63812 \| 7.56904 \| \| 2 \| 71.71226 \| 6.46088 \| Notes - Because `statcast_pitches.load()` uses a LazyFrame, we can load it much faster and even perform operations on it before 'collecting' it into memory. If it were loaded as a DataFrame, this code would execute in ~30-60 seconds, instead it runs between 2-8 seconds. Example 2 Duckdb ```python import statcast_pitches # get bat tracking data from 2024 params = ("2024",) query_2024_bat_speed = f""" SELECT bat_speed, swing_length FROM pitches WHERE YEAR(game_date) =? AND bat_speed IS NOT NULL; """ bat_speed_24_df = statcast_pitches.load( query=query_2024_bat_speed, params=params, ).collect() print(bat_speed_24_df.head(3)) ``` output: \| \| bat_speed \| swing_length \| \|-\|------------\|--------------\| \| 0 \| 73.61710 \| 6.92448 \| \| 1 \| 58.63812 \| 7.56904 \| \| 2 \| 71.71226 \| 6.46088 \| Notes: - If no query is specified, all data from 2015-present will be loaded into a DataFrame. - The table in your query MUST be called 'pitches', or it will fail. - Since `load()` returns a LazyFrame, notice that I had to call `pl.DataFrame.collect()` before calling `head()` - This is slower than the other polars approach, however sometimes using SQL is fun ### With HuggingFace API (not recommended) *Pandas* ```python import pandas as pd df = pd.read_parquet("hf://datasets/Jensen-holm/statcast-era-pitches/data/statcast_era_pitches.parquet") ``` *Polars* ```python import polars as pl df = pl.read_parquet('hf://datasets/Jensen-holm/statcast-era-pitches/data/statcast_era_pitches.parquet') ``` *Duckdb* ```sql SELECT * FROM 'hf://datasets/Jensen-holm/statcast-era-pitches/data/statcast_era_pitches.parquet'; ``` *HuggingFace Dataset* ```python from datasets import load_dataset ds = load_dataset("Jensen-holm/statcast-era-pitches") ``` *Tidyverse* ```r library(tidyverse) statcast_pitches <- read_parquet( "https://huggingface.co/datasets/Jensen-holm/statcast-era-pitches/resolve/main/data/statcast_era_pitches.parquet" ) ``` see the [dataset](https://huggingface.co/datasets/Jensen-holm/statcast-era-pitches) on HugingFace itself for more details. ## Eager Benchmarking ![dataset_load_times](dataset_load_times.png) \| Eager Load Time (s) \| API \| \|---------------\|-----\| \| 1421.103 \| pybaseball \| \| 26.899 \| polars \| \| 33.093 \| pandas \| \| 68.692 \| duckdb \| # ⚠️ Data-Quality Warning ⚠️ MLB states that real time `pitch_type` classification is automated and subject to change as data gets reviewed. This is currently not taken into account as the huggingface dataset gets updated. `pitch_type` is the only column that is affected by this. # Contributing Feel free to submit issues and PR's if you have a contribution you would like to make.
zephyr-1111/x_dataset_0710195	zephyr-1111	2025-05-12T17:04:49Z	704	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" ]	2025-01-27T06:45:55Z	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: zephyr-1111/x_dataset_0710195 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5DLo559kDSNaYzERTHQBRNT4ZocqDUVMWE24cqJnAXQjUerx ### 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{zephyr-11112025datauniversex_dataset_0710195, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={zephyr-1111}, year={2025}, url={https://huggingface.co/datasets/zephyr-1111/x_dataset_0710195}, } ``` ### 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: 1892123 - Date Range: 2025-01-02T00:00:00Z to 2025-05-02T00:00:00Z - Last Updated: 2025-05-12T17:04:49Z ### Data Distribution - Tweets with hashtags: 8.99% - Tweets without hashtags: 91.01% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| NULL \| 1249951 \| 88.02% \| \| 2 \| #箱根駅伝 \| 8147 \| 0.57% \| \| 3 \| #thameposeriesep9 \| 7605 \| 0.54% \| \| 4 \| #tiktok \| 6394 \| 0.45% \| \| 5 \| #zelena \| 4878 \| 0.34% \| \| 6 \| #smackdown \| 4844 \| 0.34% \| \| 7 \| #कबीर_परमेश्वर_निर्वाण_दिवस \| 4843 \| 0.34% \| \| 8 \| #riyadh \| 4529 \| 0.32% \| \| 9 \| #الاتحاد_الاسيوي_يفتح_تحقيققق \| 3830 \| 0.27% \| \| 10 \| #ad \| 3533 \| 0.25% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-01-27T06:45:54Z \| 471976 \| 471976 \| \| 2025-01-27T06:46:25Z \| 471976 \| 943952 \| \| 2025-02-18T03:42:42Z \| 506494 \| 1450446 \| \| 2025-05-12T17:04:49Z \| 441677 \| 1892123 \|
LadyMia/x_dataset_14253	LadyMia	2025-05-12T17:02:15Z	1,474	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" ]	2025-01-27T10:50:36Z	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: LadyMia/x_dataset_14253 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5GNLhLLyvBTMn5vMG9wjYwsEkkbPUz8qc8EP4kD6yxFfVZvf ### 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{LadyMia2025datauniversex_dataset_14253, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={LadyMia}, year={2025}, url={https://huggingface.co/datasets/LadyMia/x_dataset_14253}, } ``` ### 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: 49224416 - Date Range: 2025-01-21T00:00:00Z to 2025-02-11T00:00:00Z - Last Updated: 2025-02-18T21:15:30Z ### Data Distribution - Tweets with hashtags: 40.31% - Tweets without hashtags: 59.69% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| NULL \| 29379649 \| 59.69% \| \| 2 \| #riyadh \| 321016 \| 0.65% \| \| 3 \| #zelena \| 231873 \| 0.47% \| \| 4 \| #tiktok \| 192361 \| 0.39% \| \| 5 \| #ad \| 112070 \| 0.23% \| \| 6 \| #bbb25 \| 93287 \| 0.19% \| \| 7 \| #bbmzansi \| 66665 \| 0.14% \| \| 8 \| #jhope_at_galadespiècesjaunes \| 65469 \| 0.13% \| \| 9 \| #pr \| 59072 \| 0.12% \| \| 10 \| #แจกจริง \| 58666 \| 0.12% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-01-27T10:51:32Z \| 3271055 \| 3271055 \| \| 2025-01-30T22:54:15Z \| 8517152 \| 11788207 \| \| 2025-02-03T10:57:17Z \| 8908420 \| 20696627 \| \| 2025-02-06T23:00:41Z \| 9258617 \| 29955244 \| \| 2025-02-10T11:03:54Z \| 6611835 \| 36567079 \| \| 2025-02-13T23:09:48Z \| 11367296 \| 47934375 \| \| 2025-02-18T06:14:16Z \| 630810 \| 48565185 \| \| 2025-02-18T21:15:30Z \| 659231 \| 49224416 \|
CohenQu/cheatsheet_sol_cond_hint_qwen3-4b-lr1e6_merged	CohenQu	2025-05-12T16:54:29Z	19	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-11T14:34:38Z	null	--- dataset_info: features: - name: problem dtype: string - name: answer dtype: string - name: hint dtype: string - name: prompt dtype: string - name: responses sequence: string - name: final_answers sequence: string - name: rewards sequence: int64 - name: mean_reward dtype: float64 splits: - name: train num_bytes: 5572888855 num_examples: 22000 download_size: 1922912866 dataset_size: 5572888855 configs: - config_name: default data_files: - split: train path: data/train-* ---
french-datasets/baconnier_RAG_poor_query_generate_pair_dataset	french-datasets	2025-05-12T16:41:03Z	0	0	[ "language:fra", "region:us" ]	[]	2025-05-12T16:39:39Z	null	--- language: - fra viewer: false --- Ce répertoire est vide, il a été créé pour améliorer le référencement du jeu de données [baconnier/RAG_poor_query_generate_pair_dataset](https://huggingface.co/datasets/baconnier/RAG_poor_query_generate_pair_dataset).
john-1111/x_dataset_061079	john-1111	2025-05-12T16:32:02Z	521	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" ]	2025-01-25T07:14:14Z	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_061079 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5HNgYmgPxanejF99DA9ewrE9XxiDDz1piS1kgyxYJVTqR3KL ### 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_061079, 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_061079}, } ``` ### 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: 1799007 - Date Range: 2025-01-02T00:00:00Z to 2025-05-02T00:00:00Z - Last Updated: 2025-05-12T16:32:02Z ### Data Distribution - Tweets with hashtags: 8.05% - Tweets without hashtags: 91.95% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| NULL \| 1239753 \| 89.54% \| \| 2 \| #箱根駅伝 \| 8147 \| 0.59% \| \| 3 \| #riyadh \| 8087 \| 0.58% \| \| 4 \| #thameposeriesep9 \| 7605 \| 0.55% \| \| 5 \| #tiktok \| 7181 \| 0.52% \| \| 6 \| #zelena \| 4878 \| 0.35% \| \| 7 \| #smackdown \| 4844 \| 0.35% \| \| 8 \| #कबीर_परमेश्वर_निर्वाण_दिवस \| 4843 \| 0.35% \| \| 9 \| #ad \| 4562 \| 0.33% \| \| 10 \| #lineマンガガチャ \| 3587 \| 0.26% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-01-25T07:14:13Z \| 414446 \| 414446 \| \| 2025-01-25T07:14:44Z \| 453526 \| 867972 \| \| 2025-01-27T06:45:09Z \| 6411 \| 874383 \| \| 2025-02-18T03:37:18Z \| 472745 \| 1347128 \| \| 2025-05-12T16:32:02Z \| 451879 \| 1799007 \|
robert-1111/x_dataset_0409154	robert-1111	2025-05-12T16:09:43Z	455	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" ]	2025-01-25T07:10:29Z	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: robert-1111/x_dataset_0409154 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5H3o9Y7Unjx1XWc2QU4WZTEz9yy2jwTWCJvsxw7wzy17wZgM ### 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{robert-11112025datauniversex_dataset_0409154, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={robert-1111}, year={2025}, url={https://huggingface.co/datasets/robert-1111/x_dataset_0409154}, } ``` ### 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: 1740567 - Date Range: 2025-01-02T00:00:00Z to 2025-05-02T00:00:00Z - Last Updated: 2025-05-12T16:09:42Z ### Data Distribution - Tweets with hashtags: 4.96% - Tweets without hashtags: 95.04% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| NULL \| 1239753 \| 93.49% \| \| 2 \| #thameposeriesep9 \| 7605 \| 0.57% \| \| 3 \| #riyadh \| 7102 \| 0.54% \| \| 4 \| #smackdown \| 4844 \| 0.37% \| \| 5 \| #कबीर_परमेश्वर_निर्वाण_दिवस \| 4843 \| 0.37% \| \| 6 \| #tiktok \| 4688 \| 0.35% \| \| 7 \| #lineマンガガチャ \| 3587 \| 0.27% \| \| 8 \| #delhielectionresults \| 3476 \| 0.26% \| \| 9 \| #bbb25 \| 2411 \| 0.18% \| \| 10 \| #kimpauridetoposterreveal \| 2217 \| 0.17% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-01-25T07:10:27Z \| 414446 \| 414446 \| \| 2025-01-25T07:10:56Z \| 414446 \| 828892 \| \| 2025-02-18T03:37:17Z \| 463345 \| 1292237 \| \| 2025-05-12T16:09:42Z \| 448330 \| 1740567 \|
icedwind/x_dataset_27136	icedwind	2025-05-12T15:38:41Z	1,374	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" ]	2025-01-29T03:47:04Z	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_27136 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5F7Yv3NUJVv8TDjhnjJ5dzRjuWX5HeRMUKLZ5H8AVdDqWm58 ### 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_27136, 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_27136}, } ``` ### 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: 42408319 - Date Range: 2025-01-23T00:00:00Z to 2025-02-13T00:00:00Z - Last Updated: 2025-02-18T21:40:29Z ### Data Distribution - Tweets with hashtags: 47.74% - Tweets without hashtags: 52.26% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| NULL \| 22162724 \| 52.26% \| \| 2 \| #riyadh \| 349431 \| 0.82% \| \| 3 \| #zelena \| 255291 \| 0.60% \| \| 4 \| #tiktok \| 195180 \| 0.46% \| \| 5 \| #bbb25 \| 120794 \| 0.28% \| \| 6 \| #ad \| 114569 \| 0.27% \| \| 7 \| #jhope_at_galadespiècesjaunes \| 108631 \| 0.26% \| \| 8 \| #royalrumble \| 94317 \| 0.22% \| \| 9 \| #transferlerlebirliktezafere \| 88686 \| 0.21% \| \| 10 \| #bbmzansi \| 62869 \| 0.15% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-01-29T03:48:09Z \| 3201249 \| 3201249 \| \| 2025-02-01T15:51:17Z \| 9440598 \| 12641847 \| \| 2025-02-05T03:54:15Z \| 8653858 \| 21295705 \| \| 2025-02-08T15:58:09Z \| 11544891 \| 32840596 \| \| 2025-02-12T04:05:35Z \| 8047653 \| 40888249 \| \| 2025-02-18T06:39:09Z \| 700362 \| 41588611 \| \| 2025-02-18T21:40:29Z \| 819708 \| 42408319 \|
Aman-J/OPEN-LBP-RF	Aman-J	2025-05-12T15:19:59Z	4	0	[ "task_categories:text-classification", "language:en", "license:mit", "size_categories:1K<n<10K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "Clinical", "LowerBackPain", "Multi-label" ]	[ "text-classification" ]	2025-05-04T20:41:07Z	null	--- license: mit task_categories: - text-classification language: - en tags: - Clinical - LowerBackPain - Multi-label size_categories: - 1K<n<10K --- # OPEN-LBP-RF We present Open-LBP-RF, the first publicly available clinical note dataset annotated with lower back pain imaging risk factors. Choosing Wisely Canada identifies key risk factors that warrant imaging: - History of cancer - Unexplained weight loss - Recent infection - Fever - Loss of bowel or bladder control - Abnormal reflexes, or loss of muscle power or feeling in the legs. ## Dataset Details ### Dataset Description The Open-LBP-RF is a collection of publicly available clinical notes annotated with lower back pain risk-factors using our novel R2D2-G prompting framework. - Curated by: Aman Jaiswal - Funded by [optional]: We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Canadian Institutes of Health Research (CIHR) - Language(s) (NLP): English - License: [![CC BY 4.0][cc-by-shield]][cc-by] This work is licensed under a [Creative Commons Attribution 4.0 International License][cc-by]. [![CC BY 4.0][cc-by-image]][cc-by] [cc-by]: http://creativecommons.org/licenses/by/4.0/ [cc-by-image]: https://i.creativecommons.org/l/by/4.0/88x31.png [cc-by-shield]: https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg ### Source Data We curated data from three sources: 1. Asclepius: A collection synthetic clinical notes described [here](https://aclanthology.org/2024.findings-acl.305/) and [here](https://huggingface.co/datasets/starmpcc/Asclepius-Synthetic-Clinical-Notes). 2. Mimic-IV-Note: A collection of deidentified free-text clinical notes for patients included in the MIMIC-IV clinical database. See [Here](https://physionet.org/content/mimic-iv-note/2.2/). 3. PMC-Patients: A collection of patient summaries extracted from case reports in PubMed Central (PMC), described [here](https://pmc-patients.github.io/) and [here](https://pmc-patients.github.io/). Note: We do not include the clinical notes from MIMIC-IV-Note. The ‘note’ columns refer to note IDs. Only credentialed users who have signed the Data Use Agreement (DUA) can access these files. Please complete the requirements described [here](https://physionet.org/content/mimic-iv-note/2.2/) to request access. ### Annotations The dataset contains binary labels for six lower-back pain risk-factors as mentioned by [Choosing Wisely Canada](https://choosingwiselycanada.org/pamphlet/imaging-tests-for-lower-back-pain/). #### Annotation process We use a novel R2D2-G framework to label lower back pain risk-factors. (To be described) ## Citation <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> [To be added] ## Dataset Card Contact For more information contact: Aman Jaiswal ([email protected])
TarikKarol/mag-map-tune	TarikKarol	2025-05-12T15:16:23Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T14:59:00Z	null	--- dataset_info: features: - name: image_map dtype: image - name: image_photo dtype: image - name: split_name dtype: int32 - name: city dtype: string - name: cell_id dtype: int32 splits: - name: train num_bytes: 2647993954.672 num_examples: 5632 download_size: 3550515944 dataset_size: 2647993954.672 configs: - config_name: default data_files: - split: train path: data/train-* ---
k-l-lambda/NotaGenX-opus	k-l-lambda	2025-05-12T14:41:51Z	12,086	0	[ "license:mit", "region:us" ]	[]	2025-04-09T09:30:23Z	null	--- license: mit --- This dataset is generated by [NotaGenX](https://huggingface.co/ElectricAlexis/NotaGen/blob/main/weights_notagenx_p_size_16_p_length_1024_p_layers_20_h_size_1280.pth) model. Thanks to [ElectricAlexis](https://huggingface.co/ElectricAlexis)! ## [abc/](abc/) [This folder](https://huggingface.co/datasets/k-l-lambda/NotaGenX-opus/tree/main/abc) contains pure ABC Notation files. It is intended to include up to 1 million score pieces. (The generation needs some time) Sorry for sub directories splitting, but HuggingFace limits a single directory direct sub items number up to 10000. You can rearrange them by `mv abc///* ./abc/`. ## [media-samples/](media-samples/) [This folder](https://huggingface.co/datasets/k-l-lambda/NotaGenX-opus/tree/main/media-samples) contains full media format files showcasing a small number of samples. Components list as following: \| Period \| Composer \| Genre \| Count \| \|-------------\|--------------------------\|-------------\|-------\| \| Romantic \| Liszt, Franz \| Keyboard \| 100 \| \| Romantic \| Chopin, Frederic \| Keyboard \| 100 \| \| Romantic \| Debussy, Claude \| Keyboard \| 100 \| \| Romantic \| Tchaikovsky, Pyotr \| Keyboard \| 100 \| \| Classical \| Beethoven, Ludwig van \| Orchestral \| 20 \| \| Classical \| Beethoven, Ludwig van \| Keyboard \| 20 \| \| Classical \| Mozart, Wolfgang Amadeus \| Orchestral \| 20 \| \| Classical \| Mozart, Wolfgang Amadeus \| Keyboard \| 10 \| \| Classical \| Mozart, Wolfgang Amadeus \| Chamber \| 10 \| \| Classical \| Haydn, Joseph \| Keyboard \| 20 \| \| Baroque \| Bach, Johann Sebastian \| Keyboard \| 20 \| \| Classical \| Mendelssohn, Felix \| Keyboard \| 10 \| \| Classical \| Schubert, Franz \| Chamber \| 10 \| \| Classical \| Schubert, Franz \| Art Song \| 10 \| \| Romantic * \| Bach, Johann Sebastian \| Keyboard \| 5 \| \| Romantic * \| Mozart, Wolfgang Amadeus \| Keyboard \| 5 \| `*`: Bach and Mozart are not considered as romantic composers, this is an intended cross-style test.
dwb2023/gdelt-mentions-2025-v4	dwb2023	2025-05-12T14:39:53Z	10	0	[ "license:cc-by-4.0", "size_categories:1M<n<10M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-12T05:35:39Z	null	--- license: cc-by-4.0 --- # Dataset Card for dwb2023/gdelt-mentions-2025-v4 This dataset contains the mentions records from the GDELT (Global Database of Events, Language, and Tone) Project for May 1 - 11, tracking how global events are mentioned across media sources over time. ## Dataset Details ### Dataset Description The GDELT Mentions table is a component of the GDELT Event Database that tracks each mention of an event across all monitored news sources. Unlike the Event table which records unique events, the Mentions table records every time an event is referenced in media, allowing researchers to track the network trajectory and media lifecycle of stories as they flow through the global information ecosystem. - Curated by: The GDELT Project - Funded by: Google Ideas, supported by Google Cloud Platform - Language(s) (NLP): Multi-language source data, processed into standardized English format - License: All GDELT data is available for free download and use with proper attribution - Updates: Every 15 minutes, 24/7 ### Dataset Sources - Repository: http://gdeltproject.org/ - Documentation: http://data.gdeltproject.org/documentation/GDELT-Event_Codebook-V2.0.pdf ## Uses ### Direct Use - Tracking media coverage patterns for specific events - Analyzing information diffusion across global media - Measuring event importance through mention frequency - Studying reporting biases across different media sources - Assessing the confidence of event reporting - Analyzing narrative framing through tonal differences - Tracking historical event references and anniversary coverage ### Out-of-Scope Use - Exact source text extraction (only character offsets are provided) - Definitive audience reach measurement (mentions don't equate to readership) - Direct access to all mentioned source documents (URLs are provided but access may be limited) - Language analysis of original non-English content (translation information is provided but original text is not included) ## Dataset Structure The dataset consists of tab-delimited files with 16 fields per mention record: 1. Event Reference Information - GlobalEventID: Links to the event being mentioned - EventTimeDate: Timestamp when the event was first recorded (YYYYMMDDHHMMSS) - MentionTimeDate: Timestamp of the mention (YYYYMMDDHHMMSS) 2. Source Information - MentionType: Numeric identifier for source collection (1=Web, 2=Citation, etc.) - MentionSourceName: Human-friendly identifier (domain name, "BBC Monitoring", etc.) - MentionIdentifier: Unique external identifier (URL, DOI, citation) 3. Mention Context Details - SentenceID: Sentence number within the article where the event was mentioned - Actor1CharOffset: Character position where Actor1 was found in the text - Actor2CharOffset: Character position where Actor2 was found in the text - ActionCharOffset: Character position where the core Action was found - InRawText: Whether event was found in original text (1) or required processing (0) - Confidence: Percent confidence in the extraction (10-100%) - MentionDocLen: Length of source document in characters - MentionDocTone: Average tone of the document (-100 to +100) - MentionDocTranslationInfo: Info about translation (semicolon delimited) - Extras: Reserved for future use ## Dataset Creation ### Curation Rationale The GDELT Mentions table was created to track the lifecycle of news stories and provide a deeper understanding of how events propagate through the global media ecosystem. It enables analysis of the importance of events based on coverage patterns and allows researchers to trace narrative evolution across different sources and time periods. ### Curation Method - Prefect based python extract script: https://gist.github.com/donbr/704789a6131bb4a92c9810185c63a16a ### Source Data #### Data Collection and Processing - Every mention of an event is tracked across all monitored sources - Each mention is recorded regardless of when the original event occurred - Translation information is preserved for non-English sources - Confidence scores indicate the level of natural language processing required - Character offsets are provided to locate mentions within articles #### Who are the source data producers? Primary sources include: - International news media - Web news - Broadcast transcripts - Print media - Academic repositories (with DOIs) - Various online platforms ### Personal and Sensitive Information Similar to the Events table, this dataset focuses on public events and may contain: - URLs to news articles mentioning public figures and events - Information about how events were framed by different media outlets - Translation metadata for non-English sources - Document tone measurements ## Bias, Risks, and Limitations 1. Media Coverage Biases - Over-representation of widely covered events - Variance in coverage across different regions and languages - Digital divide affecting representation of less-connected regions 2. Technical Limitations - Varying confidence levels in event extraction - Translation quality differences across languages - Character offsets may not perfectly align with rendered web content - Not all MentionIdentifiers (URLs) remain accessible over time 3. Coverage Considerations - Higher representation of English and major world languages - Potential duplication when similar articles appear across multiple outlets - Varying confidence scores based on linguistic complexity ### Recommendations 1. Users should: - Consider confidence scores when analyzing mentions - Account for translation effects when studying non-English sources - Use MentionDocLen to distinguish between focused coverage and passing references - Recognize that URL accessibility may diminish over time - Consider SentenceID to assess prominence of event mention within articles 2. Best Practices: - Filter by Confidence level appropriate to research needs - Use InRawText field to identify direct versus synthesized mentions - Analyze MentionDocTone in context with the overall event - Account for temporal patterns in media coverage - Cross-reference with Events table for comprehensive analysis ## Citation BibTeX: ```bibtex @inproceedings{leetaru2013gdelt, title={GDELT: Global Data on Events, Language, and Tone, 1979-2012}, author={Leetaru, Kalev and Schrodt, Philip}, booktitle={International Studies Association Annual Conference}, year={2013}, address={San Francisco, CA} } ``` APA: Leetaru, K., & Schrodt, P. (2013). GDELT: Global Data on Events, Language, and Tone, 1979-2012. Paper presented at the International Studies Association Annual Conference, San Francisco, CA. ## Dataset Card Contact dwb2023

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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.