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MisDrifter/eval_8B_base_on_train_armo	MisDrifter	2025-06-25T02:22:34Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-25T02:22:33Z	null	--- dataset_info: features: - name: prompt_id dtype: string - name: prompt dtype: string - name: response_0 dtype: string - name: response_0_reward dtype: float64 splits: - name: train num_bytes: 50189037 num_examples: 20000 download_size: 28411479 dataset_size: 50189037 configs: - config_name: default data_files: - split: train path: data/train-* ---
momo1942/x_dataset_33945	momo1942	2025-06-24T19:41:31Z	728	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" ]	2025-01-26T23:48:53Z	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: momo1942/x_dataset_33945 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5Eexfw8PQvNYvtG66oKZWsZLGcbjF2K6cGEtKarqSPn7cajP ### 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{momo19422025datauniversex_dataset_33945, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={momo1942}, year={2025}, url={https://huggingface.co/datasets/momo1942/x_dataset_33945}, } ``` ### 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: 49625357 - Date Range: 2025-01-21T00:00:00Z to 2025-02-12T00:00:00Z - Last Updated: 2025-02-18T17:21:47Z ### Data Distribution - Tweets with hashtags: 37.72% - Tweets without hashtags: 62.28% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| NULL \| 30908871 \| 62.28% \| \| 2 \| #riyadh \| 297876 \| 0.60% \| \| 3 \| #zelena \| 217496 \| 0.44% \| \| 4 \| #tiktok \| 180979 \| 0.36% \| \| 5 \| #bbb25 \| 109126 \| 0.22% \| \| 6 \| #ad \| 104845 \| 0.21% \| \| 7 \| #jhope_at_galadespiècesjaunes \| 95373 \| 0.19% \| \| 8 \| #superbowl \| 86641 \| 0.17% \| \| 9 \| #bbmzansi \| 60387 \| 0.12% \| \| 10 \| #pr \| 56901 \| 0.11% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-01-26T23:49:42Z \| 3195659 \| 3195659 \| \| 2025-01-30T11:52:49Z \| 9861630 \| 13057289 \| \| 2025-02-02T23:56:06Z \| 11110169 \| 24167458 \| \| 2025-02-06T11:58:49Z \| 7448788 \| 31616246 \| \| 2025-02-10T00:01:43Z \| 7309603 \| 38925849 \| \| 2025-02-17T02:22:24Z \| 9376101 \| 48301950 \| \| 2025-02-18T02:20:39Z \| 689645 \| 48991595 \| \| 2025-02-18T17:21:47Z \| 633762 \| 49625357 \|
Voxel51/ARCADE_FO	Voxel51	2025-06-24T19:06:42Z	0	0	[ "task_categories:object-detection", "language:en", "size_categories:1K<n<10K", "format:imagefolder", "modality:image", "library:datasets", "library:mlcroissant", "library:fiftyone", "region:us", "fiftyone", "image", "object-detection" ]	[ "object-detection" ]	2025-06-24T18:02:52Z	null	--- annotations_creators: [] language: en size_categories: - 1K<n<10K task_categories: - object-detection task_ids: [] pretty_name: arcade_combined_export tags: - fiftyone - image - object-detection dataset_summary: ' This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 3000 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("pjramg/arcade_fiftyone") # Launch the App session = fo.launch_app(dataset) ``` ' --- # Dataset Card for arcade_combined_export <!-- Provide a quick summary of the dataset. --> This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 3000 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("pjramg/arcade_fiftyone") # Launch the App session = fo.launch_app(dataset) ``` # ARCADE Combined Dataset (FiftyOne Format) The ARCADE Combined Dataset is a curated collection of coronary angiography images and annotations designed to evaluate coronary artery stenosis. This version has been processed and exported using [FiftyOne](https://voxel51.com/fiftyone), and includes cleaned segmentation data, metadata fields for clinical context, and embedded visual labels. ## Dataset Structure - `segmentations`: COCO-style detection masks per coronary artery segment. - `phase`: The acquisition phase of the angiography video. - `task`: A specific labeling task (segmentation or regression) is used. - `subset_name`: Subdivision info (train, val, test). - `coco_id`: Corresponding COCO ID for alignment with original sources. - `filepath`: Path to the image file. - `metadata`: Image metadata including dimensions and pixel spacing. ## Format This dataset is stored in FiftyOneDataset format, which consists of: - `data.json`: Metadata and label references - `data/`: Folder containing all image samples - Optional: auxiliary files (e.g., `README.md`, config, JSON index) To load it in Python: ```python import fiftyone as fo dataset = fo.Dataset.from_dir( dataset_dir="arcade_combined_fiftyone", dataset_type=fo.types.FiftyOneDataset, ) ``` ## Source The original ARCADE dataset was introduced in the paper: Labrecque Langlais et al. (2023) — Evaluation of Stenoses Using AI Video Models Applied to Coronary Angiographies. https://doi.org/10.21203/rs.3.rs-3610879/v1 This combined version aggregates and restructures subsets across tasks and phases, harmonized with FiftyOne tooling for streamlined model training and evaluation. ## License This dataset is shared for research and academic use only. Please consult the original dataset license for clinical or commercial applications. ## Citation ```bibtex @article{avram2023evaluation, title={Evaluation of Stenoses Using AI Video Models Applied to Coronary Angiographies}, author={Labrecque Langlais, E. and Corbin, D. and Tastet, O. and Hayek, A. and Doolub, G. and Mrad, S. and Tardif, J.-C. and Tanguay, J.-F. and Marquis-Gravel, G. and Tison, G. and Kadoury, S. and Le, W. and Gallo, R. and Lesage, F. and Avram, R.}, year={2023} } ``` ## Dataset Card Contact [Paula Ramos](https://huggingface.co/datasets/pjramg)
Zhitao-He/MATPBench	Zhitao-He	2025-06-24T16:36:55Z	127	3	[ "license:apache-2.0", "size_categories:1K<n<10K", "format:imagefolder", "modality:image", "library:datasets", "library:mlcroissant", "region:us" ]	[]	2025-05-16T04:21:03Z	null	--- license: apache-2.0 ---
graliuce/MedMCQA.24.01	graliuce	2025-06-24T16:31:07Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-24T16:25:42Z	null	--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: suffix dtype: string splits: - name: train num_bytes: 4582059 num_examples: 4780 - name: test num_bytes: 96564 num_examples: 100 download_size: 803311 dataset_size: 4678623 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
hshwk1983/x_dataset_2983	hshwk1983	2025-06-24T16:09:19Z	1,122	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" ]	2025-01-27T07:01:04Z	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: hshwk1983/x_dataset_2983 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5F2RCkLaXEwdz4PALA5iwSBQQ4rWEAioaniBHouRyhUSYjne ### 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{hshwk19832025datauniversex_dataset_2983, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={hshwk1983}, year={2025}, url={https://huggingface.co/datasets/hshwk1983/x_dataset_2983}, } ``` ### 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: 45361062 - Date Range: 2025-01-21T00:00:00Z to 2025-02-10T00:00:00Z - Last Updated: 2025-02-18T19:56:18Z ### Data Distribution - Tweets with hashtags: 49.23% - Tweets without hashtags: 50.77% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| NULL \| 23030513 \| 50.77% \| \| 2 \| #riyadh \| 389599 \| 0.86% \| \| 3 \| #zelena \| 270010 \| 0.60% \| \| 4 \| #tiktok \| 216661 \| 0.48% \| \| 5 \| #ad \| 127153 \| 0.28% \| \| 6 \| #bbb25 \| 124236 \| 0.27% \| \| 7 \| #jhope_at_galadespiècesjaunes \| 107356 \| 0.24% \| \| 8 \| #bbmzansi \| 73192 \| 0.16% \| \| 9 \| #granhermano \| 70611 \| 0.16% \| \| 10 \| #trump \| 67914 \| 0.15% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-01-27T07:02:01Z \| 2802800 \| 2802800 \| \| 2025-01-30T19:05:56Z \| 9696380 \| 12499180 \| \| 2025-02-03T07:09:45Z \| 10920384 \| 23419564 \| \| 2025-02-06T19:12:26Z \| 6138868 \| 29558432 \| \| 2025-02-10T07:16:07Z \| 8261798 \| 37820230 \| \| 2025-02-13T19:19:34Z \| 6252880 \| 44073110 \| \| 2025-02-18T04:54:59Z \| 640422 \| 44713532 \| \| 2025-02-18T19:56:18Z \| 647530 \| 45361062 \|
littleGuagua/x_dataset_24747	littleGuagua	2025-06-24T16:00:50Z	1,142	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" ]	2025-01-26T08:49:30Z	null	--- license: mit multilinguality: - multilingual source_datasets: - original task_categories: - text-classification - token-classification - question-answering - summarization - text-generation task_ids: - sentiment-analysis - topic-classification - named-entity-recognition - language-modeling - text-scoring - multi-class-classification - multi-label-classification - extractive-qa - news-articles-summarization --- # Bittensor Subnet 13 X (Twitter) Dataset <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/bittensor.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/macrocosmos-black.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> ## Dataset Description - Repository: littleGuagua/x_dataset_24747 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5EM4mwdfwdBzEbEqJ9KsFnj2sKpAjywcb5Ddz3CEoKV2ksj1 ### Dataset Summary This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed data from X (formerly Twitter). The data is continuously updated by network miners, providing a real-time stream of tweets for various analytical and machine learning tasks. For more information about the dataset, please visit the [official repository](https://github.com/macrocosm-os/data-universe). ### Supported Tasks The versatility of this dataset allows researchers and data scientists to explore various aspects of social media dynamics and develop innovative applications. Users are encouraged to leverage this data creatively for their specific research or business needs. For example: - Sentiment Analysis - Trend Detection - Content Analysis - User Behavior Modeling ### Languages Primary language: Datasets are mostly English, but can be multilingual due to decentralized ways of creation. ## Dataset Structure ### Data Instances Each instance represents a single tweet with the following fields: ### Data Fields - `text` (string): The main content of the tweet. - `label` (string): Sentiment or topic category of the tweet. - `tweet_hashtags` (list): A list of hashtags used in the tweet. May be empty if no hashtags are present. - `datetime` (string): The date when the tweet was posted. - `username_encoded` (string): An encoded version of the username to maintain user privacy. - `url_encoded` (string): An encoded version of any URLs included in the tweet. May be empty if no URLs are present. ### Data Splits This dataset is continuously updated and does not have fixed splits. Users should create their own splits based on their requirements and the data's timestamp. ## Dataset Creation ### Source Data Data is collected from public tweets on X (Twitter), adhering to the platform's terms of service and API usage guidelines. ### Personal and Sensitive Information All usernames and URLs are encoded to protect user privacy. The dataset does not intentionally include personal or sensitive information. ## Considerations for Using the Data ### Social Impact and Biases Users should be aware of potential biases inherent in X (Twitter) data, including demographic and content biases. This dataset reflects the content and opinions expressed on X and should not be considered a representative sample of the general population. ### Limitations - Data quality may vary due to the decentralized nature of collection and preprocessing. - The dataset may contain noise, spam, or irrelevant content typical of social media platforms. - Temporal biases may exist due to real-time collection methods. - The dataset is limited to public tweets and does not include private accounts or direct messages. - Not all tweets contain hashtags or URLs. ## Additional Information ### Licensing Information The dataset is released under the MIT license. The use of this dataset is also subject to X Terms of Use. ### Citation Information If you use this dataset in your research, please cite it as follows: ``` @misc{littleGuagua2025datauniversex_dataset_24747, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={littleGuagua}, year={2025}, url={https://huggingface.co/datasets/littleGuagua/x_dataset_24747}, } ``` ### 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: 157467919 - Date Range: 2025-01-21T00:00:00Z to 2025-02-13T00:00:00Z - Last Updated: 2025-02-18T16:32:12Z ### Data Distribution - Tweets with hashtags: 42.71% - Tweets without hashtags: 57.29% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| NULL \| 90209693 \| 57.29% \| \| 2 \| #riyadh \| 1088786 \| 0.69% \| \| 3 \| #zelena \| 820088 \| 0.52% \| \| 4 \| #tiktok \| 653763 \| 0.42% \| \| 5 \| #bbb25 \| 394331 \| 0.25% \| \| 6 \| #ad \| 378659 \| 0.24% \| \| 7 \| #jhope_at_galadespiècesjaunes \| 234371 \| 0.15% \| \| 8 \| #bbmzansi \| 213586 \| 0.14% \| \| 9 \| #pr \| 203109 \| 0.13% \| \| 10 \| #yahooニュース \| 190885 \| 0.12% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-01-26T08:50:16Z \| 2482006 \| 2482006 \| \| 2025-01-29T21:00:47Z \| 29908448 \| 32390454 \| \| 2025-02-02T09:11:30Z \| 28938392 \| 61328846 \| \| 2025-02-05T21:23:51Z \| 29767835 \| 91096681 \| \| 2025-02-09T09:36:47Z \| 29027751 \| 120124432 \| \| 2025-02-12T21:54:03Z \| 28620241 \| 148744673 \| \| 2025-02-16T09:45:11Z \| 7404661 \| 156149334 \| \| 2025-02-18T00:09:45Z \| 696224 \| 156845558 \| \| 2025-02-18T16:32:12Z \| 622361 \| 157467919 \|
hshwk1983/x_dataset_27221	hshwk1983	2025-06-24T15:59:18Z	1,245	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" ]	2025-01-27T01:57:54Z	null	--- license: mit multilinguality: - multilingual source_datasets: - original task_categories: - text-classification - token-classification - question-answering - summarization - text-generation task_ids: - sentiment-analysis - topic-classification - named-entity-recognition - language-modeling - text-scoring - multi-class-classification - multi-label-classification - extractive-qa - news-articles-summarization --- # Bittensor Subnet 13 X (Twitter) Dataset <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/bittensor.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/macrocosmos-black.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> ## Dataset Description - Repository: hshwk1983/x_dataset_27221 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5HozLaXwAyioW1oEwf6zAysEyyGXcCifVwCeYiz6SKvSrm52 ### 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{hshwk19832025datauniversex_dataset_27221, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={hshwk1983}, year={2025}, url={https://huggingface.co/datasets/hshwk1983/x_dataset_27221}, } ``` ### 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: 37381424 - Date Range: 2025-01-21T00:00:00Z to 2025-02-11T00:00:00Z - Last Updated: 2025-02-18T19:01:56Z ### Data Distribution - Tweets with hashtags: 29.18% - Tweets without hashtags: 70.82% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| NULL \| 26474119 \| 70.82% \| \| 2 \| #riyadh \| 165345 \| 0.44% \| \| 3 \| #zelena \| 147137 \| 0.39% \| \| 4 \| #tiktok \| 108254 \| 0.29% \| \| 5 \| #jhope_at_galadespiècesjaunes \| 96559 \| 0.26% \| \| 6 \| #ad \| 65237 \| 0.17% \| \| 7 \| #bbb25 \| 63704 \| 0.17% \| \| 8 \| #royalrumble \| 45208 \| 0.12% \| \| 9 \| #precure \| 44979 \| 0.12% \| \| 10 \| #bbmzansi \| 41847 \| 0.11% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-01-27T01:58:44Z \| 3242408 \| 3242408 \| \| 2025-01-30T14:08:14Z \| 6911604 \| 10154012 \| \| 2025-02-03T02:11:35Z \| 9565243 \| 19719255 \| \| 2025-02-06T14:13:40Z \| 5208295 \| 24927550 \| \| 2025-02-10T02:18:00Z \| 8468886 \| 33396436 \| \| 2025-02-13T14:19:50Z \| 2518336 \| 35914772 \| \| 2025-02-18T04:01:08Z \| 807421 \| 36722193 \| \| 2025-02-18T19:01:56Z \| 659231 \| 37381424 \|
arushisinha98/worldbank_dataset	arushisinha98	2025-06-24T15:22:31Z	41	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-20T16:44:40Z	null	--- dataset_info: features: - name: BG.GSR.NFSV.GD.ZS dtype: float64 - name: BM.GSR.CMCP.ZS dtype: float64 - name: BM.GSR.FCTY.CD dtype: float64 - name: BM.GSR.GNFS.CD dtype: float64 - name: BM.GSR.INSF.ZS dtype: float64 - name: BM.GSR.MRCH.CD dtype: float64 - name: BM.GSR.NFSV.CD dtype: float64 - name: BM.GSR.ROYL.CD dtype: float64 - name: BM.GSR.TOTL.CD dtype: float64 - name: BM.GSR.TRAN.ZS dtype: float64 - name: BM.GSR.TRVL.ZS dtype: float64 - name: BM.KLT.DINV.CD.WD dtype: float64 - name: BM.KLT.DINV.WD.GD.ZS dtype: float64 - name: BM.TRF.PRVT.CD dtype: float64 - name: BM.TRF.PWKR.CD.DT dtype: float64 - name: BN.CAB.XOKA.CD dtype: float64 - name: BN.CAB.XOKA.GD.ZS dtype: float64 - name: BN.FIN.TOTL.CD dtype: float64 - 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zephyr-1111/x_dataset_0708150	zephyr-1111	2025-06-24T14:27:35Z	1,323	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" ]	2025-01-25T07:15:24Z	null	--- license: mit multilinguality: - multilingual source_datasets: - original task_categories: - text-classification - token-classification - question-answering - summarization - text-generation task_ids: - sentiment-analysis - topic-classification - named-entity-recognition - language-modeling - text-scoring - multi-class-classification - multi-label-classification - extractive-qa - news-articles-summarization --- # Bittensor Subnet 13 X (Twitter) Dataset <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/bittensor.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/macrocosmos-black.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> ## Dataset Description - Repository: zephyr-1111/x_dataset_0708150 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5DaUuXQ38fukz4fZk7GZsKqAJC8Zum8K3HMhKirvjRGPxwTq ### 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_0708150, 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_0708150}, } ``` ### 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: 1771220 - Date Range: 2025-01-02T00:00:00Z to 2025-06-14T00:00:00Z - Last Updated: 2025-06-24T14:27:34Z ### Data Distribution - Tweets with hashtags: 13.88% - Tweets without hashtags: 86.12% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| NULL \| 1110845 \| 81.87% \| \| 2 \| #sixtonesann \| 26152 \| 1.93% \| \| 3 \| #thenextprinceep7 \| 22220 \| 1.64% \| \| 4 \| #ムサシノ輪舞曲 \| 11327 \| 0.83% \| \| 5 \| #サクサクヒムヒム \| 10727 \| 0.79% \| \| 6 \| #दहेज_दानव_से_मुक्ति \| 10291 \| 0.76% \| \| 7 \| #호시의_후반전도_함께할게 \| 9115 \| 0.67% \| \| 8 \| #riyadh \| 8184 \| 0.60% \| \| 9 \| #thameposeriesep9 \| 7605 \| 0.56% \| \| 10 \| #tiktok \| 6521 \| 0.48% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-01-25T07:15:23Z \| 414446 \| 414446 \| \| 2025-01-25T07:15:50Z \| 414446 \| 828892 \| \| 2025-02-18T03:37:16Z \| 463345 \| 1292237 \| \| 2025-06-24T14:27:34Z \| 478983 \| 1771220 \|
zephyr-1111/x_dataset_070513	zephyr-1111	2025-06-24T14:24:27Z	913	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" ]	2025-01-25T07:16:21Z	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_070513 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5Fc1jF83RPejiQ6nZyKdYY4dknDY6A8xPdje3GhF1CZzMNuv ### 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_070513, 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_070513}, } ``` ### 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: 2686761 - Date Range: 2025-01-02T00:00:00Z to 2025-06-14T00:00:00Z - Last Updated: 2025-06-24T14:24:26Z ### Data Distribution - Tweets with hashtags: 10.92% - Tweets without hashtags: 89.08% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| NULL \| 1110845 \| 79.10% \| \| 2 \| #sixtonesann \| 26152 \| 1.86% \| \| 3 \| #thenextprinceep7 \| 22220 \| 1.58% \| \| 4 \| #ムサシノ輪舞曲 \| 11327 \| 0.81% \| \| 5 \| #サクサクヒムヒム \| 10727 \| 0.76% \| \| 6 \| #दहेज_दानव_से_मुक्ति \| 10291 \| 0.73% \| \| 7 \| #riyadh \| 9169 \| 0.65% \| \| 8 \| #호시의_후반전도_함께할게 \| 9115 \| 0.65% \| \| 9 \| #tiktok \| 9014 \| 0.64% \| \| 10 \| #箱根駅伝 \| 8147 \| 0.58% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-01-25T07:15:23Z \| 414446 \| 414446 \| \| 2025-01-25T07:15:50Z \| 414446 \| 828892 \| \| 2025-01-25T07:16:19Z \| 453526 \| 1282418 \| \| 2025-01-25T07:16:50Z \| 453526 \| 1735944 \| \| 2025-02-18T03:38:23Z \| 471834 \| 2207778 \| \| 2025-06-24T14:24:26Z \| 478983 \| 2686761 \|
jinaai/student-enrollment_beir	jinaai	2025-06-24T13:55:29Z	1	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:eu" ]	[]	2025-06-19T11:33:44Z	null	--- dataset_info: - config_name: corpus features: - name: corpus-id dtype: int64 - name: image dtype: image splits: - name: test num_bytes: 468914392.0 num_examples: 489 download_size: 468924871 dataset_size: 468914392.0 - config_name: qrels features: - name: query-id dtype: int64 - name: corpus-id dtype: int64 - name: score dtype: int64 splits: - name: test num_bytes: 24000 num_examples: 1000 download_size: 9957 dataset_size: 24000 - config_name: queries features: - name: query-id dtype: int64 - name: query dtype: string splits: - name: test num_bytes: 119703 num_examples: 1000 download_size: 28021 dataset_size: 119703 configs: - config_name: corpus data_files: - split: test path: default/corpus/test-* - config_name: qrels data_files: - split: test path: default/qrels/test-* - config_name: queries data_files: - split: test path: default/queries/test-* --- This is a copy of https://huggingface.co/datasets/jinaai/student-enrollment reformatted into the BEIR format. For any further information like license, please refer to the original dataset. # Disclaimer This dataset may contain publicly available images or text data. All data is provided for research and educational purposes only. If you are the rights holder of any content and have concerns regarding intellectual property or copyright, please contact us at "support-data (at) jina.ai" for removal. We do not collect or process personal, sensitive, or private information intentionally. If you believe this dataset includes such content (e.g., portraits, location-linked images, medical or financial data, or NSFW content), please notify us, and we will take appropriate action. # Copyright All rights are reserved to the original authors of the documents.
jinaai/jdocqa	jinaai	2025-06-24T13:52:34Z	14	0	[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2403.19454", "region:eu" ]	[]	2025-06-10T12:45:03Z	null	--- dataset_info: features: - name: query dtype: string - name: image dtype: image - name: image_filename dtype: string - name: text_description dtype: string splits: - name: test num_bytes: 237420405.0 num_examples: 758 download_size: 237236360 dataset_size: 237420405.0 configs: - config_name: default data_files: - split: test path: data/test-* --- # Japanese Document Retrieval Document Question answering from [JDocQAJP dataset](https://huggingface.co/datasets/jlli/JDocQA-nonbinary), test split. The `text_description` column contains OCR text extracted from the images using EasyOCR. Paper: https://arxiv.org/abs/2403.19454 Questions: 758 Language: Japanese Example: ```python { 'query': '八王子神社は「はちおっつぁん」と呼ばれ住民に親しまれていますが、事故が起きたような言い伝えはありますか。\n解答は自由に記述してください。', 'image_filename': 'page_0.jpg', 'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=3814x5342 at 0x7B9DA7BD0B20>, 'answer': '牛や馬の商売をしている人が仏像を買い、拝んでいたところ「みんなが幸せになれるようにしなさい」と夢に金物が現れ、八つのかまを重ねて仏像を入れ、その上にモミの木を植え八王子神社と名付けたところ作物が良く実りましたが、馬鹿にしたよその村人が馬から落ちて亡くなったといわれています。' } ``` # Disclaimer This dataset may contain publicly available images or text data. All data is provided for research and educational purposes only. If you are the rights holder of any content and have concerns regarding intellectual property or copyright, please contact us at "support-data (at) jina.ai" for removal. We do not collect or process personal, sensitive, or private information intentionally. If you believe this dataset includes such content (e.g., portraits, location-linked images, medical or financial data, or NSFW content), please notify us, and we will take appropriate action. # Copyright All rights are reserved to the original authors of the documents.
jinaai/europeana-nl-legal	jinaai	2025-06-24T13:52:19Z	65	0	[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:eu" ]	[]	2024-12-18T15:58:26Z	null	--- dataset_info: features: - name: query dtype: string - name: image dtype: image - name: image_filename dtype: string - name: links dtype: string - name: attributions dtype: string - name: text_description dtype: string splits: - name: test num_bytes: 132202700.0 num_examples: 380 download_size: 90299186 dataset_size: 132202700.0 configs: - config_name: default data_files: - split: test path: data/test-* --- # Europeana Legal Dutch Documents Dataset This dataset was created from records of the [Europeana online collection](https://europeana.eu) by selecting scans of Dutch historical legal documents. These documents consist of scans of typed paper documents. Queries are generated with Qwen2b, and manually verified by a human annotator. Attributions to the sources are added in the `attributions` column of each dataset item, and links to the original document in the `links` column. The `text_description` column contains OCR text extracted from the images using EasyOCR. Example: ``` { 'query': 'In welk jaar werd de Koninklijke Boodschap van Colombia gesloten?', 'image_filename': 'images/9200401__BibliographicResource_1000056988753.jpg', 'links': 'https://www.europeana.eu/en/item/9200401/BibliographicResource_1000056988753', 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=L size=4118x5683 at 0x11FCD9950>, 'attributions': Tractaat van vriendschap, handel en scheepvaart, gesloten met de Republiek Colombia - https://www.europeana.eu/item/9200401/BibliographicResource_1000056988753. Koninklijke Bibliotheek - http://www.statengeneraaldigitaal.nl/document?id=sgd:mpeg21:18291830:0000325. Public Domain Mark - http://creativecommons.org/publicdomain/mark/1.0/', } ``` # Disclaimer This dataset may contain publicly available images or text data. All data is provided for research and educational purposes only. If you are the rights holder of any content and have concerns regarding intellectual property or copyright, please contact us at "support-data (at) jina.ai" for removal. We do not collect or process personal, sensitive, or private information intentionally. If you believe this dataset includes such content (e.g., portraits, location-linked images, medical or financial data, or NSFW content), please notify us, and we will take appropriate action. # Copyright All rights are reserved to the original authors of the documents.
jinaai/donut_vqa	jinaai	2025-06-24T13:51:48Z	21	0	[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:eu" ]	[]	2025-06-10T12:40:33Z	null	--- dataset_info: features: - name: query dtype: string - name: image dtype: image - name: image_filename dtype: string - name: text_description dtype: string splits: - name: test num_bytes: 100347289.0 num_examples: 800 download_size: 91711782 dataset_size: 100347289.0 configs: - config_name: default data_files: - split: test path: data/test-* --- # DonutVQA Dataset This dataset is derived from the [donut-vqa dataset](https://huggingface.co/datasets/warshakhan/donut_vqa_ISynHMP), reformatting the test split with modified field names, so that it can be used in the ViDoRe benchmark. The `text_description` column contains OCR text extracted from the images using EasyOCR. # Disclaimer This dataset may contain publicly available images or text data. All data is provided for research and educational purposes only. If you are the rights holder of any content and have concerns regarding intellectual property or copyright, please contact us at "support-data (at) jina.ai" for removal. We do not collect or process personal, sensitive, or private information intentionally. If you believe this dataset includes such content (e.g., portraits, location-linked images, medical or financial data, or NSFW content), please notify us, and we will take appropriate action. # Copyright All rights are reserved to the original authors of the documents.
czl/yongchun_public_gym	czl	2025-06-24T13:30:18Z	362	0	[ "task_categories:time-series-forecasting", "language:en", "language:zh", "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" ]	[ "time-series-forecasting" ]	2025-06-04T12:54:14Z	null	--- license: apache-2.0 task_categories: - time-series-forecasting language: - en - zh pretty_name: 永春活力館健身房人數 Dataset size_categories: - 1K<n<10K --- # 永春活力館健身房人數 Dataset The Timestamp is in seconds Source: https://xysc.teamxports.com/
sergiov2000/eval_act_so100_leaderarm_a4	sergiov2000	2025-06-24T13:14:54Z	0	0	[ "task_categories:robotics", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "tutorial" ]	[ "robotics" ]	2025-06-24T13:14:47Z	null	--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - tutorial configs: - config_name: default data_files: data//.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - Homepage: [More Information Needed] - Paper: [More Information Needed] - License: apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 1, "total_frames": 848, "total_tasks": 1, "total_videos": 2, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:1" }, "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.above": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.side": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation BibTeX: ```bibtex [More Information Needed] ```
nunoFcul/yourbench_advanced_example	nunoFcul	2025-06-24T11:43:09Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-24T11:00:34Z	null	--- dataset_info: - config_name: chunked features: - name: document_id dtype: string - name: document_text dtype: string - name: document_filename dtype: string - name: document_metadata struct: - name: file_size dtype: int64 - name: raw_chunk_summaries sequence: string - name: chunk_summaries sequence: string - name: raw_document_summary dtype: string - name: document_summary dtype: string - name: summarization_model dtype: string - name: chunks list: - name: chunk_id dtype: string - name: chunk_text dtype: string - name: multihop_chunks list: - name: chunk_ids sequence: string - name: chunks_text sequence: string splits: - name: train num_bytes: 1137063 num_examples: 40 download_size: 87758 dataset_size: 1137063 - config_name: ingested features: - name: document_id dtype: string - name: document_text dtype: string - name: document_filename dtype: string - name: document_metadata struct: - name: file_size dtype: int64 splits: - name: train num_bytes: 72116 num_examples: 8 download_size: 13742 dataset_size: 72116 - config_name: lighteval features: - name: question dtype: string - name: additional_instructions dtype: string - name: ground_truth_answer dtype: string - name: gold sequence: string - name: choices sequence: 'null' - name: question_category dtype: string - name: kind dtype: string - name: estimated_difficulty dtype: int64 - name: citations sequence: string - name: document_id dtype: string - name: chunk_ids sequence: string - name: question_generating_model dtype: string - name: chunks sequence: string - name: document dtype: string - name: document_summary dtype: string - name: answer_citation_score dtype: float64 - name: chunk_citation_score dtype: float64 - name: citation_score dtype: float64 splits: - name: train num_bytes: 3107530 num_examples: 256 download_size: 77196 dataset_size: 3107530 - config_name: multi_hop_questions features: - name: document_id dtype: string - name: additional_instructions dtype: string - name: question dtype: string - name: self_answer dtype: string - name: estimated_difficulty dtype: int64 - name: self_assessed_question_type dtype: string - name: generating_model dtype: string - name: thought_process dtype: string - name: raw_response dtype: string - name: citations sequence: string - name: source_chunk_ids sequence: string splits: - name: train num_bytes: 214485 num_examples: 35 download_size: 48172 dataset_size: 214485 - config_name: single_shot_questions features: - name: document_id dtype: string - name: additional_instructions dtype: string - name: question dtype: string - name: self_answer dtype: string - name: estimated_difficulty dtype: int64 - name: self_assessed_question_type dtype: string - name: generating_model dtype: string - name: thought_process dtype: string - name: raw_response dtype: string - name: citations sequence: string - name: chunk_id dtype: string splits: - name: train num_bytes: 321396 num_examples: 69 download_size: 62477 dataset_size: 321396 - config_name: summarized features: - name: document_id dtype: string - name: document_text dtype: string - name: document_filename dtype: string - name: document_metadata struct: - name: file_size dtype: int64 - name: raw_chunk_summaries sequence: string - name: chunk_summaries sequence: string - name: raw_document_summary dtype: string - name: document_summary dtype: string - name: summarization_model dtype: string splits: - name: train num_bytes: 249342 num_examples: 20 download_size: 56704 dataset_size: 249342 configs: - config_name: chunked data_files: - split: train path: chunked/train-* - config_name: ingested data_files: - split: train path: ingested/train-* - config_name: lighteval data_files: - split: train path: lighteval/train-* - config_name: multi_hop_questions data_files: - split: train path: multi_hop_questions/train-* - config_name: single_shot_questions data_files: - split: train path: single_shot_questions/train-* - config_name: summarized data_files: - split: train path: summarized/train-* ---
william-1111/x_dataset_0104179	william-1111	2025-06-24T11:16:45Z	985	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" ]	2025-01-27T06:43:47Z	null	--- license: mit multilinguality: - multilingual source_datasets: - original task_categories: - text-classification - token-classification - question-answering - summarization - text-generation task_ids: - sentiment-analysis - topic-classification - named-entity-recognition - language-modeling - text-scoring - multi-class-classification - multi-label-classification - extractive-qa - news-articles-summarization --- # Bittensor Subnet 13 X (Twitter) Dataset <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/bittensor.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/macrocosmos-black.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> ## Dataset Description - Repository: william-1111/x_dataset_0104179 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5CnoeLSSFZq9jmZfuKT7WpHoEEQJKvX3Nf4ZWFqiqjLZpfeS ### Miner Data Compliance Agreement In uploading this dataset, I am agreeing to the [Macrocosmos Miner Data Compliance Policy](https://github.com/macrocosm-os/data-universe/blob/add-miner-policy/docs/miner_policy.md). ### Dataset Summary This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed data from X (formerly Twitter). The data is continuously updated by network miners, providing a real-time stream of tweets for various analytical and machine learning tasks. For more information about the dataset, please visit the [official repository](https://github.com/macrocosm-os/data-universe). ### Supported Tasks The versatility of this dataset allows researchers and data scientists to explore various aspects of social media dynamics and develop innovative applications. Users are encouraged to leverage this data creatively for their specific research or business needs. For example: - Sentiment Analysis - Trend Detection - Content Analysis - User Behavior Modeling ### Languages Primary language: Datasets are mostly English, but can be multilingual due to decentralized ways of creation. ## Dataset Structure ### Data Instances Each instance represents a single tweet with the following fields: ### Data Fields - `text` (string): The main content of the tweet. - `label` (string): Sentiment or topic category of the tweet. - `tweet_hashtags` (list): A list of hashtags used in the tweet. May be empty if no hashtags are present. - `datetime` (string): The date when the tweet was posted. - `username_encoded` (string): An encoded version of the username to maintain user privacy. - `url_encoded` (string): An encoded version of any URLs included in the tweet. May be empty if no URLs are present. ### Data Splits This dataset is continuously updated and does not have fixed splits. Users should create their own splits based on their requirements and the data's timestamp. ## Dataset Creation ### Source Data Data is collected from public tweets on X (Twitter), adhering to the platform's terms of service and API usage guidelines. ### Personal and Sensitive Information All usernames and URLs are encoded to protect user privacy. The dataset does not intentionally include personal or sensitive information. ## Considerations for Using the Data ### Social Impact and Biases Users should be aware of potential biases inherent in X (Twitter) data, including demographic and content biases. This dataset reflects the content and opinions expressed on X and should not be considered a representative sample of the general population. ### Limitations - Data quality may vary due to the decentralized nature of collection and preprocessing. - The dataset may contain noise, spam, or irrelevant content typical of social media platforms. - Temporal biases may exist due to real-time collection methods. - The dataset is limited to public tweets and does not include private accounts or direct messages. - Not all tweets contain hashtags or URLs. ## Additional Information ### Licensing Information The dataset is released under the MIT license. The use of this dataset is also subject to X Terms of Use. ### Citation Information If you use this dataset in your research, please cite it as follows: ``` @misc{william-11112025datauniversex_dataset_0104179, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={william-1111}, year={2025}, url={https://huggingface.co/datasets/william-1111/x_dataset_0104179}, } ``` ### 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: 1455999 - Date Range: 2025-01-02T00:00:00Z to 2025-06-14T00:00:00Z - Last Updated: 2025-06-24T11:16:45Z ### Data Distribution - Tweets with hashtags: 23.58% - Tweets without hashtags: 76.42% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| NULL \| 1112737 \| 76.42% \| \| 2 \| #riyadh \| 23097 \| 1.59% \| \| 3 \| #マテムり \| 15966 \| 1.10% \| \| 4 \| #pbbcollab6thduoeviction \| 11023 \| 0.76% \| \| 5 \| #tiktok \| 8638 \| 0.59% \| \| 6 \| #箱根駅伝 \| 8147 \| 0.56% \| \| 7 \| #thameposeriesep9 \| 7605 \| 0.52% \| \| 8 \| #wtcfinal2025 \| 6398 \| 0.44% \| \| 9 \| #first_showcase \| 6311 \| 0.43% \| \| 10 \| #ad \| 5465 \| 0.38% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-01-27T06:44:27Z \| 471976 \| 471976 \| \| 2025-02-18T03:40:05Z \| 506494 \| 978470 \| \| 2025-06-24T11:16:45Z \| 477529 \| 1455999 \|
TitouanCh/drug-seq-u2os-novartis	TitouanCh	2025-06-24T10:47:18Z	114	0	[ "license:mit", "size_categories:10K<n<100K", "format:parquet", "modality:text", "modality:timeseries", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "biology" ]	[]	2025-06-20T13:15:21Z	null	--- license: mit tags: - biology configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: counts sequence: int32 - name: counts_norm sequence: float32 - name: counts_log sequence: float32 - name: counts_log_norm sequence: float32 - name: gene_names sequence: string - name: control_counts sequence: float32 - name: control_counts_norm sequence: float32 - name: control_counts_log sequence: float32 - name: control_counts_log_norm sequence: float32 - name: delta_counts sequence: float32 - name: delta_counts_norm sequence: float32 - name: delta_counts_log sequence: float32 - name: delta_counts_log_norm sequence: float32 - name: cell_line dtype: string - name: perturbation dtype: string - name: compound_concentration dtype: float64 - name: compound_unit dtype: string - name: compound_smiles dtype: string - name: mechanism dtype: string - name: moa dtype: string - name: biological_effect dtype: string - name: experimental_id dtype: string - name: timepoint dtype: string - name: text dtype: string - name: text_embeddings sequence: float32 - name: chembert_embeddings sequence: float32 splits: - name: train num_bytes: 176083286910 num_examples: 49392 download_size: 65016664023 dataset_size: 176083286910 --- I AM NOT AFFILIATED WITH NOVARTIS IN ANY WAY; THIS IS SIMPLY AN UPLOAD OF THEIR DATASET, "[NOVARTIS/DRUG-SEQ U2OS MOABOX DATASET](https://zenodo.org/records/14291446)." # Novartis DRUG-seq U2OS MoABox Dataset This dataset profiles transcriptomic responses of the U-2 OS human osteosarcoma cell line to a broad collection of small molecule perturbations. It contains 49,392 observations spanning 3,742 unique compounds tested at 4 distinct dosages + `0.0`, each annotated with their respective mechanisms of action (MoA). Each observation records gene expression data for 59,594 genes. The dataset was generated using the DRUG-seq platform, enabling high-throughput, unbiased transcriptomic readouts suited for drug discovery applications. ## Additional Information - Perturbation dosages range across 4 unique concentration values + `0.0`. - Observations include multiple experimental replicates and plate layouts. - Normalized counts were scaled so that the total expression per cell sums to `1e4`. - Control counts represent the average expression of each gene across all control cells. - Delta values are computed as the difference between each sample's expression and the corresponding control mean. - SMILES strings and mechanism of action (MoA) annotations curated by Novartis, retrieved from the [ChEMBL](https://www.ebi.ac.uk/chembl/) database and enhanced with additional sources. ## Citations > Hadjikyriacou, A., Yang, C., Henault, M., et al. > Novartis DRUG-seq U2OS MoABox Dataset > [Novartis DRUG-seq GitHub Repository](https://github.com/Novartis/DRUG-seq) > Hadjikyriacou, A., Yang, C., Henault, M., Ge, R., Mansur, L., Lindeman, A., Russ, C., Renner, S., Hild, M., Jenkins, J., Gubser-Keller, C., Li, J., Ho, D. J., Neri, M., Sigoillot, F. D., & Ihry, R. (2025). > Novartis/DRUG-seq U2OS MoABox Dataset (1.0.0) [Data set]. Zenodo. > https://doi.org/10.5281/zenodo.14291446 > Li, J., Ho, D. J., Henault, M., Yang, C., Neri, M., Ge, R., Renner, S., Mansur, L., Lindeman, A., Tumkaya, T., Russ, C., Hild, M., Gubser Keller, C., Jenkins, J. L., Worringer, K. A., Sigoillot, F. D., & Ihry, R. J. (2021). > DRUG-seq Provides Unbiased Biological Activity Readouts for Drug Discovery. bioRxiv. > https://doi.org/10.1101/2021.06.07.447456 > [Full text PDF](https://www.biorxiv.org/content/early/2021/06/08/2021.06.07.447456.full.pdf)
lowyun-izeno/guanaco-llama2-1k-eng	lowyun-izeno	2025-06-24T10:22:59Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-24T09:33:48Z	null	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1751774.1023466215 num_examples: 1000 download_size: 951148 dataset_size: 1751774.1023466215 configs: - config_name: default data_files: - split: train path: data/train-* ---
aketen0654/sendeneme	aketen0654	2025-06-24T10:22:37Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-24T10:21:08Z	null	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: instruction,"input","Response" dtype: string splits: - name: train num_bytes: 135991.45890410958 num_examples: 262 - name: test num_bytes: 15571.54109589041 num_examples: 30 download_size: 75411 dataset_size: 151563.0 --- # Dataset Card for "sendeneme" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Boadiwaa/interaction-log2	Boadiwaa	2025-06-24T10:10:29Z	29	0	[ "size_categories:n<1K", "format:csv", "modality:audio", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-06-04T13:06:11Z	null	--- configs: - config_name: default data_files: - split: train path: data.csv --- # Dataset Card for Dataset Name <!-- Provide a quick summary of the 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): [More Information Needed] - 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]
louisbrulenaudet/code-domaine-etat	louisbrulenaudet	2025-06-24T09:32:02Z	453	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 du domaine de l'Etat" ]	[ "text-generation", "table-question-answering", "summarization", "text-retrieval", "question-answering", "text-classification" ]	2024-03-25T20:35:57Z	null	--- license: apache-2.0 language: - fr multilinguality: - monolingual tags: - finetuning - legal - french law - droit français - Code du domaine de l'Etat source_datasets: - original pretty_name: Code du domaine de l'Etat task_categories: - text-generation - table-question-answering - summarization - text-retrieval - question-answering - text-classification size_categories: - 1K<n<10K --- # Code du domaine de l'Etat, non-instruct (2025-06-23) 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 [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com).
louisbrulenaudet/code-commande-publique	louisbrulenaudet	2025-06-24T09:31:54Z	467	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", "doi:10.57967/hf/1458", "region:us", "finetuning", "legal", "french law", "droit français", "Code de la commande publique" ]	[ "text-generation", "table-question-answering", "summarization", "text-retrieval", "question-answering", "text-classification" ]	2023-12-12T18:59:37Z	null	--- license: apache-2.0 language: - fr multilinguality: - monolingual tags: - finetuning - legal - french law - droit français - Code de la commande publique source_datasets: - original pretty_name: Code de la commande publique task_categories: - text-generation - table-question-answering - summarization - text-retrieval - question-answering - text-classification size_categories: - 1K<n<10K --- # Code de la commande publique, non-instruct (2025-06-23) 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 [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com).
louisbrulenaudet/code-civil	louisbrulenaudet	2025-06-24T09:31:54Z	467	1	[ "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", "doi:10.57967/hf/1442", "region:us", "finetuning", "legal", "french law", "droit français", "Code civil" ]	[ "text-generation", "table-question-answering", "summarization", "text-retrieval", "question-answering", "text-classification" ]	2023-12-12T01:26:22Z	null	--- license: apache-2.0 language: - fr multilinguality: - monolingual tags: - finetuning - legal - french law - droit français - Code civil source_datasets: - original pretty_name: Code civil task_categories: - text-generation - table-question-answering - summarization - text-retrieval - question-answering - text-classification size_categories: - 1K<n<10K --- # Code civil, non-instruct (2025-06-23) 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 [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com).
louisbrulenaudet/code-aviation-civile	louisbrulenaudet	2025-06-24T09:31:53Z	468	1	[ "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 l'aviation civile" ]	[ "text-generation", "table-question-answering", "summarization", "text-retrieval", "question-answering", "text-classification" ]	2024-03-25T19:06:39Z	null	--- license: apache-2.0 language: - fr multilinguality: - monolingual tags: - finetuning - legal - french law - droit français - Code de l'aviation civile source_datasets: - original pretty_name: Code de l'aviation civile task_categories: - text-generation - table-question-answering - summarization - text-retrieval - question-answering - text-classification size_categories: - 1K<n<10K --- # Code de l'aviation civile, non-instruct (2025-06-23) 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 [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com).
littleGuagua/x_dataset_39615	littleGuagua	2025-06-24T07:52:27Z	830	0	[ "task_categories:text-classification", "task_categories:token-classification", "task_categories:question-answering", "task_categories:summarization", "task_categories:text-generation", "task_ids:sentiment-analysis", "task_ids:topic-classification", "task_ids:named-entity-recognition", "task_ids:language-modeling", "task_ids:text-scoring", "task_ids:multi-class-classification", "task_ids:multi-label-classification", "task_ids:extractive-qa", "task_ids:news-articles-summarization", "multilinguality:multilingual", "source_datasets:original", "license:mit", "size_categories:100M<n<1B", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[ "text-classification", "token-classification", "question-answering", "summarization", "text-generation" ]	2025-01-26T13:52:27Z	null	--- license: mit multilinguality: - multilingual source_datasets: - original task_categories: - text-classification - token-classification - question-answering - summarization - text-generation task_ids: - sentiment-analysis - topic-classification - named-entity-recognition - language-modeling - text-scoring - multi-class-classification - multi-label-classification - extractive-qa - news-articles-summarization --- # Bittensor Subnet 13 X (Twitter) Dataset <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/bittensor.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/macrocosmos-black.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> ## Dataset Description - Repository: littleGuagua/x_dataset_39615 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5DcWuS46Y4kwHXeqaALnhFuxthsAyeQa2co4mH41c2SnvpxK ### Dataset Summary This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed data from X (formerly Twitter). The data is continuously updated by network miners, providing a real-time stream of tweets for various analytical and machine learning tasks. For more information about the dataset, please visit the [official repository](https://github.com/macrocosm-os/data-universe). ### Supported Tasks The versatility of this dataset allows researchers and data scientists to explore various aspects of social media dynamics and develop innovative applications. Users are encouraged to leverage this data creatively for their specific research or business needs. For example: - Sentiment Analysis - Trend Detection - Content Analysis - User Behavior Modeling ### Languages Primary language: Datasets are mostly English, but can be multilingual due to decentralized ways of creation. ## Dataset Structure ### Data Instances Each instance represents a single tweet with the following fields: ### Data Fields - `text` (string): The main content of the tweet. - `label` (string): Sentiment or topic category of the tweet. - `tweet_hashtags` (list): A list of hashtags used in the tweet. May be empty if no hashtags are present. - `datetime` (string): The date when the tweet was posted. - `username_encoded` (string): An encoded version of the username to maintain user privacy. - `url_encoded` (string): An encoded version of any URLs included in the tweet. May be empty if no URLs are present. ### Data Splits This dataset is continuously updated and does not have fixed splits. Users should create their own splits based on their requirements and the data's timestamp. ## Dataset Creation ### Source Data Data is collected from public tweets on X (Twitter), adhering to the platform's terms of service and API usage guidelines. ### Personal and Sensitive Information All usernames and URLs are encoded to protect user privacy. The dataset does not intentionally include personal or sensitive information. ## Considerations for Using the Data ### Social Impact and Biases Users should be aware of potential biases inherent in X (Twitter) data, including demographic and content biases. This dataset reflects the content and opinions expressed on X and should not be considered a representative sample of the general population. ### Limitations - Data quality may vary due to the decentralized nature of collection and preprocessing. - The dataset may contain noise, spam, or irrelevant content typical of social media platforms. - Temporal biases may exist due to real-time collection methods. - The dataset is limited to public tweets and does not include private accounts or direct messages. - Not all tweets contain hashtags or URLs. ## Additional Information ### Licensing Information The dataset is released under the MIT license. The use of this dataset is also subject to X Terms of Use. ### Citation Information If you use this dataset in your research, please cite it as follows: ``` @misc{littleGuagua2025datauniversex_dataset_39615, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={littleGuagua}, year={2025}, url={https://huggingface.co/datasets/littleGuagua/x_dataset_39615}, } ``` ### 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: 51398762 - Date Range: 2025-01-21T00:00:00Z to 2025-02-13T00:00:00Z - Last Updated: 2025-02-18T16:38:41Z ### Data Distribution - Tweets with hashtags: 38.97% - Tweets without hashtags: 61.03% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| NULL \| 31368286 \| 61.03% \| \| 2 \| #riyadh \| 282374 \| 0.55% \| \| 3 \| #zelena \| 215162 \| 0.42% \| \| 4 \| #tiktok \| 189061 \| 0.37% \| \| 5 \| #bbb25 \| 139383 \| 0.27% \| \| 6 \| #ad \| 107606 \| 0.21% \| \| 7 \| #theheartkillersep10 \| 72118 \| 0.14% \| \| 8 \| #bbmzansi \| 67297 \| 0.13% \| \| 9 \| #pr \| 61195 \| 0.12% \| \| 10 \| #theheartkillersep9 \| 57936 \| 0.11% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-01-26T13:53:02Z \| 1831074 \| 1831074 \| \| 2025-01-30T01:56:02Z \| 8072120 \| 9903194 \| \| 2025-02-02T13:58:25Z \| 7485404 \| 17388598 \| \| 2025-02-06T02:02:01Z \| 8826537 \| 26215135 \| \| 2025-02-09T14:05:24Z \| 8362749 \| 34577884 \| \| 2025-02-13T02:10:30Z \| 6902157 \| 41480041 \| \| 2025-02-16T13:55:11Z \| 8605299 \| 50085340 \| \| 2025-02-18T01:07:04Z \| 691061 \| 50776401 \| \| 2025-02-18T16:38:41Z \| 622361 \| 51398762 \|
UGRIP-LM-Polygraph/medmcqa-direct	UGRIP-LM-Polygraph	2025-06-24T07:45:55Z	53	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-19T14:29:06Z	null	--- dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 79149073 num_examples: 182822 - name: test num_bytes: 2522307 num_examples: 6150 - name: validation num_bytes: 1884252 num_examples: 4183 download_size: 27097865 dataset_size: 83555632 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---
laxmacl/synthetic-math-docs-rigorous-20250624_125646	laxmacl	2025-06-24T07:29:59Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-24T07:29:54Z	null	--- dataset_info: features: - name: image dtype: image - name: imagewidth dtype: int64 - name: pdf_name dtype: string - name: page_number dtype: int64 - name: markdown dtype: string - name: html dtype: string - name: layout dtype: string - name: lines dtype: string - name: images dtype: string - name: equations dtype: string - name: tables dtype: string - name: page_size dtype: string - name: content_list dtype: string - name: base_layout_detection dtype: string - name: pdf_info dtype: string - name: system_prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 2777444.0 num_examples: 7 download_size: 1497044 dataset_size: 2777444.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
sid003/coppercapON	sid003	2025-06-24T07:28:09Z	0	0	[ "task_categories:robotics", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot" ]	[ "robotics" ]	2025-06-23T12:16:46Z	null	--- license: apache-2.0 task_categories: - robotics tags: - LeRobot configs: - config_name: default data_files: data//.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - Homepage: [More Information Needed] - Paper: [More Information Needed] - License: apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so101_follower", "total_episodes": 2, "total_frames": 2319, "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": [ "shoulder_pan.pos", "shoulder_lift.pos", "elbow_flex.pos", "wrist_flex.pos", "wrist_roll.pos", "gripper.pos" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "shoulder_pan.pos", "shoulder_lift.pos", "elbow_flex.pos", "wrist_flex.pos", "wrist_roll.pos", "gripper.pos" ] }, "observation.images.wrist.left": { "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.side": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "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] ```
laxmacl/synthetic-math-docs-rigorous-20250624_125218	laxmacl	2025-06-24T07:25:08Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-24T07:24:53Z	null	--- dataset_info: features: - name: image dtype: image - name: imagewidth dtype: int64 - name: pdf_name dtype: string - name: page_number dtype: int64 - name: markdown dtype: string - name: html dtype: string - name: layout dtype: string - name: lines dtype: string - name: images dtype: string - name: equations dtype: string - name: tables dtype: string - name: page_size dtype: string - name: content_list dtype: string - name: base_layout_detection dtype: string - name: pdf_info dtype: string - name: system_prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 2350054.0 num_examples: 7 download_size: 1318202 dataset_size: 2350054.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
sistemas-upta/redes_instruction_dataset_sentence_t_rw2	sistemas-upta	2025-06-24T05:33:47Z	0	0	[ "region:us" ]	[]	2025-06-24T05:31:58Z	null	--- dataset_info: features: - name: Instruction dtype: string - name: Response dtype: string - name: Instruction_Len dtype: int64 - name: Response_Len dtype: int64 splits: - name: train num_bytes: 21384 num_examples: 102 download_size: 13561 dataset_size: 21384 configs: - config_name: default data_files: - split: train path: data/train-* ---
yoonholee/wikispeedia-paths	yoonholee	2025-06-24T05:24:16Z	0	0	[ "region:us" ]	[]	2025-06-24T05:17:32Z	null	--- dataset_info: features: - name: duration_sec dtype: int64 - name: path sequence: string - name: finished dtype: bool - name: rating dtype: string splits: - name: train num_bytes: 5688990 num_examples: 51318 download_size: 1526382 dataset_size: 5688990 configs: - config_name: default data_files: - split: train path: data/train-* ---
ConquestAce/wildcards	ConquestAce	2025-06-24T04:36:48Z	587	1	[ "license:unlicense", "size_categories:10K<n<100K", "format:text", "modality:text", "library:datasets", "library:mlcroissant", "region:us" ]	[]	2025-03-14T06:38:12Z	null	--- license: unlicense --- # 🎴 Wildcard Prompt Dataset for Natural Language & Danbooru A curated collection of wildcard prompt files designed for prompt templating systems like [PromptHero Wildcards](https://github.com/PromptHero/wildcards), [A1111 Dynamic Prompts](https://github.com/adieyal/sd-dynamic-prompts), and similar tools used in text-to-image generation (e.g. Stable Diffusion). Organized for both natural-language-friendly users and Danbooru-style taggers. --- ## 📁 Structure The repository is structured into two main directories (example): ``` wildcards/ ├── natural-language/ │ ├── artists.txt │ ├── characters.txt │ ├── clothing.txt │ ├── styles.txt │ └── poses.txt ├── danbooru/ \| ├── artists.txt \| ├──characters.txt \| ├──clothing.txt \| ├──styles.txt └── └──poses.txt ```` Each `.txt` file contains a list of interchangeable keywords or phrases, one per line. These are intended to be referenced in prompts using double curly braces syntax: ## 📌 Notes * Intended for artistic/educational research, especially in generative AI and prompt engineering. * Contributions welcome via PRs or forks. Check out: https://conquestace.com/wildcarder/ to see the wildcards in action.
youssefkhalil320/pairs_three_scores_v15	youssefkhalil320	2025-06-24T04:30:59Z	9	0	[ "size_categories:10M<n<100M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-23T00:33:20Z	null	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 546924841 num_examples: 9500043 - name: eval num_bytes: 28785839 num_examples: 500003 download_size: 588338366 dataset_size: 575710680 configs: - config_name: default data_files: - split: train path: data/train-* - split: eval path: data/eval-* ---
sleeping-ai/Sleeping-DISCO-9M	sleeping-ai	2025-06-24T04:29:32Z	707	7	[ "size_categories:1M<n<10M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2506.14293", "region:us" ]	[]	2025-02-23T08:34:40Z	null	--- configs: - config_name: a data_files: "a.jsonl" - config_name: b data_files: "b.jsonl" - config_name: c data_files: "c.jsonl" - config_name: d data_files: "d.jsonl" - config_name: e data_files: "e.jsonl" - config_name: f data_files: "f.jsonl" - config_name: g data_files: "g.jsonl" - config_name: h data_files: "h.jsonl" - config_name: i data_files: "i.jsonl" - config_name: j data_files: "j.jsonl" - config_name: k data_files: "k.jsonl" - config_name: l data_files: "l.jsonl" - config_name: m data_files: "m.jsonl" - config_name: n data_files: "n.jsonl" - config_name: o data_files: "o.jsonl" - config_name: p data_files: "p.jsonl" - config_name: q data_files: "q.jsonl" - config_name: r data_files: "r.jsonl" - config_name: s data_files: "s.jsonl" - config_name: t data_files: "t.jsonl" - config_name: u data_files: "u.jsonl" - config_name: v data_files: "v.jsonl" - config_name: w data_files: "w.jsonl" - config_name: x data_files: "x.jsonl" - config_name: y data_files: "y.jsonl" - config_name: z data_files: "z.jsonl" --- # Sleeping-DISCO-9M Sleeping-DISCO-9M is a large-scale foundation dataset for generative music modeling, featuring 9 million songs along with associated metadata, lyric embeddings, and song IDs. These IDs backlink to the original Genius pages, where the data was sourced. ## 🔹 Dataset Structure Sleeping-DISCO is split into two components: ### 1. Sleeping-DISCO-Public - Metadata for 8.89M songs - Lyric embeddings - YouTube video links for each song - YouTube video metadata ### 2. Sleeping-DISCO-Private (restricted) - Full lyrics - Genius annotations > ⚠️ Lyrics and annotations are not included in the public release. Access is available only to verified academic or research institutions for a limited period, upon request. To request access, please email: [sleeping4cat@gmail.com](mailto:sleeping4cat@gmail.com) ## 📄 Paper Read the first-version research paper on arXiv: 👉 [https://arxiv.org/abs/2506.14293](https://arxiv.org/abs/2506.14293) A full arXiv + conference version will be released in 2026. ## ⚖️ License This dataset is released under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license. More details: [https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en](https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en) - ✅ Attribution required - 🚫 Non-commercial use only - 🚫 No derivatives or redistribution allowed unless by the original authors. > For academic access to the private subset, contact: sleeping4cat@gmail.com
hazyresearch/MMLU-Pro_with_Llama_3.1_8B_Instruct_v1	hazyresearch	2025-06-24T04:27:17Z	22	0	[ "license:mit", "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2506.18203", "region:us" ]	[]	2025-05-29T01:54:10Z	null	--- dataset_info: features: - name: question_id dtype: int64 - name: question dtype: string - name: options sequence: string - name: answer dtype: string - name: answer_index dtype: int64 - name: category dtype: string - name: src dtype: string - name: instruction dtype: string - name: subject dtype: string - name: samples sequence: string - name: extracted_answers sequence: string - name: answer_correct sequence: bool - name: GRMGemma_scores sequence: float64 - name: GRM_scores sequence: float64 - name: Skyworks_scores sequence: float64 - name: URM_scores sequence: float64 - name: GPM_scores sequence: float64 - name: GRMLlama32_scores sequence: float64 - name: OffsetBias_scores sequence: float64 - name: ArmorRM_scores sequence: float64 - name: QwenPRM_min_scores sequence: float64 - name: QwenPRM_max_scores sequence: float64 - name: QwenPRM_avg_scores sequence: float64 - name: EurusPRMStage1_min_scores sequence: float64 - name: EurusPRMStage1_max_scores sequence: float64 - name: EurusPRMStage1_avg_scores sequence: float64 - name: EurusPRMStage2_min_scores sequence: float64 - name: EurusPRMStage2_max_scores sequence: float64 - name: EurusPRMStage2_avg_scores sequence: float64 - name: QRM_scores sequence: float64 - name: InternLM2Reward7B_scores sequence: float64 - name: weaver_scores sequence: float64 splits: - name: data num_bytes: 321458078 num_examples: 500 download_size: 58438362 dataset_size: 321458078 configs: - config_name: default data_files: - split: data path: data/data-* license: mit --- # MMLU-Pro with Llama-3.1-8B-Instruct This dataset contains 500 multiple-choice questions from the MMLU-Pro benchmark with 100 candidate responses generated by Llama-3.1-8B-Instruct for each problem. Each response has been evaluated for correctness using a mixture of GPT-4o-mini and procedural Python code to robustly parse different answer formats, and scored by multiple reward models (scalar values) and LM judges (boolean verdicts). ## Dataset Structure - Split: Single split named `"data"` - Num rows: 500 MMLU-Pro questions - Generations per query: 100 ### Key Fields \| Field \| Type \| Description \| \|-------\|------\|-------------\| \| `instruction` \| `str` \| Prompt given to Llama 3.1 8B Instruct \| \| `samples` \| `List[str]` \| Model-generated answers (100 per problem) \| \| `extracted_answers` \| `List[str]` \| Final answers extracted from completions (A, B, C, D, etc.) \| \| `answer_correct` \| `List[bool]` \| Whether each extracted answer matches the correct choice \| \| `_verdicts` \| `Dict[str, List[float]]` \| Binary signals from verifier models (e.g., LM judges) \| \| `_scores` \| `Dict[str, List[float]]` \| Scalar scores from reward models \| ## Example Entry ```json { "instruction": "The following is a multiple choice question. Answer with the letter of the correct choice.\n\nQuestion: Which of the following best describes quantum entanglement?\nA. Particles moving at light speed\nB. Correlated quantum states\nC. Energy conservation\nD. Wave-particle duality\n\nAnswer:", "samples": ["Quantum entanglement refers to...", "The answer is B", "I think the answer is B", ...], "extracted_answers": ["B", "B", "A", ...], "answer_correct": [true, true, false, ...], "Llama-3.3-70B-Instruct_verdicts": [1.0, 1.0, 0.0, ...], "GRMGemma_scores": [0.85, 0.79, 0.42, ...], ... } ``` ## Quick Start ```python from datasets import load_dataset # Load the dataset dataset = load_dataset("hazyresearch/MMLU-Pro_with_Llama_3.1_8B_Instruct_v1")["data"] # Get the first problem problem = dataset[0] print(f"Problem: {problem['instruction']}") # Select the best response using pre-computed Weaver scores best_idx = max(range(len(problem['weaver_scores'])), key=lambda i: problem['weaver_scores'][i]) best_response = problem['samples'][best_idx] print(f"\nBest response (Weaver): {best_response}") # Check if it's actually correct print(f"Is correct: {problem['answer_correct'][best_idx]}") ``` ## Source Original MMLU-Pro problems from [TIGER-Lab/MMLU-Pro](https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro). ## Usage with Weaver This dataset can be used with the [Weaver framework](https://github.com/HazyResearch/scaling-verification) for training and evaluating verifier aggregation methods. See the repository for detailed instructions on reproducing paper results. ## Citation ```bibtex @misc{saadfalcon2025shrinkinggenerationverificationgapweak, title={Shrinking the Generation-Verification Gap with Weak Verifiers}, author={Jon Saad-Falcon and E. Kelly Buchanan and Mayee F. Chen and Tzu-Heng Huang and Brendan McLaughlin and Tanvir Bhathal and Shang Zhu and Ben Athiwaratkun and Frederic Sala and Scott Linderman and Azalia Mirhoseini and Christopher Ré}, year={2025}, eprint={2506.18203}, archivePrefix={arXiv}, primaryClass={cs.CR}, url={https://arxiv.org/abs/2506.18203}, } ```
FlagEval/EmbodiedVerse-Bench	FlagEval	2025-06-24T02:59:04Z	224	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-18T07:53:50Z	null	--- dataset_info: features: - name: question_id dtype: int64 - name: raw_question_id dtype: string - name: level-1 dtype: string - name: level-2 dtype: string - name: question dtype: string - name: question_type dtype: string - name: img_path sequence: image - name: video_path sequence: video - name: mask_path sequence: image - name: answer dtype: string - name: options sequence: string - name: source dtype: string splits: - name: open num_bytes: 4895265213.102 num_examples: 2042 download_size: 1562859079 dataset_size: 4895265213.102 configs: - config_name: default data_files: - split: open path: data/open-* --- EmbodiedVerse-Bench is a meta-dataset composed of 10 datasets for comprehensively evaluating models in embodied intelligence scenarios, including: 1. Where2Place : The dataset is a collection of 100 real-world images from diverse cluttered environments, each annotated with a sentence describing a desired free space and a corresponding mask, designed to evaluate free space referencing using spatial relations. 2. Blink : Including some visual problems that can be easily solved by humans, EmbodiedVerse samples categories related to spatial understanding (Counting, Relative_Depth, Spatial_Relation, Multi-view_Reasoning, Visual_Correspondence). 3. CVBench : A vision-centric benchmarks, containing 2638 manually-inspected examples. 4. RoboSpatial-Home : A new spatial reasoning benchmark designed to evaluate vision-language models (VLMs) in real-world indoor environments for robotics. 5. EmbspatialBench : A benchmark for evaluating embodied spatial understanding of LVLM. The benchmark is automatically derived from embodied scenes and covers 6 spatial relationships from an egocentric perspective. 6. All-Angles Bench : A Benchmark for Multi-View Understanding, including over 2,100 human-annotated multi-view QA pairs across 90 real-world scenes. 7. VSI-Bench : A video-based benchmark dataset constructs questions from egocentric-view videos of real indoor scenes, aiming to evaluate the visual-spatial intelligence of multimodal large models. EmbodiedVerse uses a tiny subset containing 400 questions. 8. SAT : A challenging real-image dynamic spatial benchmark. 9. EgoPlan-Bench2 : A benchmark which encompasses everyday tasks spanning4 major domains and 24 detailed scenarios, closely aligned with human daily life. 10. ERQA : This evaluation benchmark covers a variety of topics related to spatial reasoning and world knowledge focused on real-world scenarios, particularly in the context of robotics. Please note: The images for the EgoPlan-Bench2 and All-Angles-Bench datasets are extracted from Ego4D videos. Due to licensing requirements, they are not provided directly here. You must obtain a license and download them yourself from the official Ego4D website: https://ego4d-data.org/#download
CatBarks/merged_prompt_injection_dataset	CatBarks	2025-06-24T02:44:31Z	0	0	[ "size_categories:1M<n<10M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-24T02:43:47Z	null	--- dataset_info: features: - name: Prompt dtype: string - name: Label dtype: int64 - name: Source dtype: string - name: Index dtype: int64 splits: - name: train num_bytes: 1094382261 num_examples: 1467947 download_size: 328216517 dataset_size: 1094382261 configs: - config_name: default data_files: - split: train path: data/train-* ---
whynlp/gsm8k-aug	whynlp	2025-06-24T01:53:18Z	4	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2405.14838", "arxiv:2506.18582", "region:us" ]	[]	2025-06-22T02:20:52Z	null	--- dataset_info: features: - name: question dtype: string - name: steps sequence: string - name: answer dtype: string splits: - name: train num_bytes: 92353643 num_examples: 385620 - name: validation num_bytes: 150156 num_examples: 500 - name: test num_bytes: 406195 num_examples: 1319 download_size: 50318247 dataset_size: 92909994 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- # GSM8K-AUG This dataset is an augmented version of the [GSM8K](https://huggingface.co/datasets/openai/gsm8k) dataset. It extends the original GSM8K training set to 385k samples by prompting GPT-4. The dataset was originally proposed in paper "[From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step](https://arxiv.org/pdf/2405.14838)". ## Usage Load the dataset using the `datasets` library: ```python from datasets import load_dataset dataset = load_dataset("whyNLP/gsm8k-aug") print(dataset["train"][0]) # {'question': 'Out of 600 employees in a company, 30% got promoted while 10% received bonus. How many employees did not get either a promotion or a bonus?', 'steps': ['<<60030/100=180>>', '<<60010/100=60>>', '<<180+60=240>>', '<<600-240=360>>'], 'answer': '360'} ``` ## The Augmentation Collection There are two versions of the augmented dataset: 1. GSM8K-AUG: The augmented dataset with the steps as mathematical expressions only. 2. [GSM8K-AUG-NL](https://huggingface.co/datasets/whynlp/gsm8k-aug-nl): The augmented dataset with the steps as natural language sentences. ## Disclaimer This dataset is literally the same as the one released by [CODI](https://huggingface.co/datasets/zen-E/GSM8k-Aug), but we use different format to facilitate the usage of the dataset in [our paper](https://arxiv.org/abs/2506.18582). When we started our project, there was no available source for this dataset in Hugging Face Hub.
stalaei/realmath_2025-2025-06	stalaei	2025-06-24T01:47:30Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-24T01:47:25Z	null	--- dataset_info: features: - name: paper_link dtype: string - name: theorem dtype: string - name: question dtype: string - name: answer dtype: string - name: context dtype: string - name: submission_date dtype: string splits: - name: train num_bytes: 229366830 num_examples: 485 download_size: 147283564 dataset_size: 229366830 configs: - config_name: default data_files: - split: train path: data/train-* ---
theprint/MixedConversations-s5	theprint	2025-06-24T01:32:11Z	0	0	[ "license:apache-2.0", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-24T01:30:41Z	null	--- license: apache-2.0 ---
forecastingresearch/forecastbench-datasets	forecastingresearch	2025-06-24T01:20:38Z	523	1	[ "language:en", "license:cc-by-sa-4.0", "arxiv:2409.19839", "region:us" ]	[]	2025-03-03T14:37:26Z	null	--- license: cc-by-sa-4.0 language: - en pretty_name: ForecastBench --- [![ICLR 2025](https://img.shields.io/badge/ICLR-2025-D5FFC1?labelColor=2A363F)](https://iclr.cc/virtual/2025/poster/28507) [![arXiv:2409.19839](https://img.shields.io/badge/arXiv-2409.19839-272727?logo=arxiv&labelColor=B31B1B)](https://arxiv.org/abs/2409.19839) ## ForecastBench Datasets This repository contains the datasets produced by ForecastBench, a forecasting benchmark for LLMs. More info at [https://www.forecastbench.org](https://www.forecastbench.org/). Code available at [https://github.com/forecastingresearch/forecastbench](https://github.com/forecastingresearch/forecastbench). ## License The datasets in this repository are distributed under the [CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/legalcode). ## Citation ```bibtex @inproceedings{karger2025forecastbench, title={ForecastBench: A Dynamic Benchmark of AI Forecasting Capabilities}, author={Ezra Karger and Houtan Bastani and Chen Yueh-Han and Zachary Jacobs and Danny Halawi and Fred Zhang and Philip E. Tetlock}, year={2025}, booktitle={International Conference on Learning Representations (ICLR)}, url={https://iclr.cc/virtual/2025/poster/28507} } ```
temp-enpaiva/temp-280525-oasst2_es	temp-enpaiva	2025-06-24T01:02:41Z	228	0	[ "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-29T02:03:40Z	null	--- license: apache-2.0 configs: - config_name: castellano data_files: - split: train path: castellano/train-* - config_name: jopara data_files: - split: train path: jopara/train-* dataset_info: - config_name: castellano features: - name: conversation_id dtype: int64 - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 11485286 num_examples: 3151 download_size: 4465385 dataset_size: 11485286 - config_name: jopara features: - name: conversation_id dtype: int64 - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 9092288 num_examples: 2430 download_size: 3610215 dataset_size: 9092288 ---
AntResearchNLP/ViLaSR-data	AntResearchNLP	2025-06-24T00:54:08Z	87	0	[ "language:en", "arxiv:2506.09965", "region:us" ]	[]	2025-06-14T09:05:02Z	null	--- datasets: - AntResearchNLP/ViLaSR-data language: - en --- This repository provides the ViLaSR-data introduced in the paper: [Reinforcing Spatial Reasoning in Vision-Language Models with Interwoven Thinking and Visual Drawing](https://arxiv.org/abs/2506.09965). ## Dataset Overview The dataset consists of three main components: - VILASR-ColdStart-33k: Initial data generated from cold-start prompts (`cold_start_path.zip`) - VILASR-RRS-8k: Data refined using Reflective Rejection Sampling (`reflective_rejection_sampling_part.zip`) - VILASR-RL-40k: Reinforcement Learning enhanced data (`rl_part.zip`) For more details on data processing and usage, please refer to the accompanying code repository: https://github.com/AntResearchNLP/ViLaSR To extract the zip files, run: ```bash python unzip.py ``` ``` @misc{wu2025reinforcingspatialreasoningvisionlanguage, title={Reinforcing Spatial Reasoning in Vision-Language Models with Interwoven Thinking and Visual Drawing}, author={Junfei Wu and Jian Guan and Kaituo Feng and Qiang Liu and Shu Wu and Liang Wang and Wei Wu and Tieniu Tan}, year={2025}, eprint={2506.09965}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2506.09965}, } ``` # License - The SR_91k* portion of this dataset is derived from the [RUBBISHLIKE/SpaceR-151k](https://huggingface.co/datasets/RUBBISHLIKE/SpaceR-151k) under the [CC BY-NC 4.0 License](https://creativecommons.org/licenses/by-nc/4.0/).
asingh0/StudyChatA6	asingh0	2025-06-24T00:34:08Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-23T17:40:54Z	null	--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: response dtype: string - name: timestamp dtype: timestamp[us] - name: chatId dtype: string - name: userId dtype: string - name: interactionCount dtype: int64 - name: llm_label struct: - name: justification dtype: string - name: label dtype: string - name: topic dtype: string - name: prompt dtype: string - name: label dtype: string - name: justification dtype: string splits: - name: train num_bytes: 26766962 num_examples: 889 download_size: 6670399 dataset_size: 26766962 configs: - config_name: default data_files: - split: train path: data/train-* ---
jacobmorrison/wildchat_perturbed_3000_replaced_no_keyword	jacobmorrison	2025-06-24T00:15:21Z	0	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-24T00:14:18Z	null	--- dataset_info: features: - name: conversation_hash dtype: string - name: model dtype: string - name: timestamp dtype: timestamp[us, tz=UTC] - name: conversation list: - name: content dtype: string - name: country dtype: string - name: hashed_ip dtype: string - name: header struct: - name: accept-language dtype: string - name: user-agent dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: state dtype: string - name: timestamp dtype: timestamp[us, tz=UTC] - name: toxic dtype: bool - name: turn_identifier dtype: int64 - name: turn dtype: int64 - name: language dtype: string - name: openai_moderation list: - name: categories struct: - name: harassment dtype: bool - name: harassment/threatening dtype: bool - name: harassment_threatening dtype: bool - name: hate dtype: bool - name: hate/threatening dtype: bool - name: hate_threatening dtype: bool - name: self-harm dtype: bool - name: self-harm/instructions dtype: bool - name: self-harm/intent dtype: bool - name: self_harm dtype: bool - name: self_harm_instructions dtype: bool - name: self_harm_intent dtype: bool - name: sexual dtype: bool - name: sexual/minors dtype: bool - name: sexual_minors dtype: bool - name: violence dtype: bool - name: violence/graphic dtype: bool - name: violence_graphic dtype: bool - name: category_scores struct: - name: harassment dtype: float64 - name: harassment/threatening dtype: float64 - name: harassment_threatening dtype: float64 - name: hate dtype: float64 - name: hate/threatening dtype: float64 - name: hate_threatening dtype: float64 - name: self-harm dtype: float64 - name: self-harm/instructions dtype: float64 - name: self-harm/intent dtype: float64 - name: self_harm dtype: float64 - name: self_harm_instructions dtype: float64 - name: self_harm_intent dtype: float64 - name: sexual dtype: float64 - name: sexual/minors dtype: float64 - name: sexual_minors dtype: float64 - name: violence dtype: float64 - name: violence/graphic dtype: float64 - name: violence_graphic dtype: float64 - name: flagged dtype: bool - name: detoxify_moderation list: - name: identity_attack dtype: float64 - name: insult dtype: float64 - name: obscene dtype: float64 - name: severe_toxicity dtype: float64 - name: sexual_explicit dtype: float64 - name: threat dtype: float64 - name: toxicity dtype: float64 - name: toxic dtype: bool - name: redacted dtype: bool - name: state dtype: string - name: country dtype: string - name: hashed_ip dtype: string - name: header struct: - name: accept-language dtype: string - name: user-agent dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: unique_id dtype: int64 splits: - name: train num_bytes: 2381492020 num_examples: 100000 download_size: 1260014903 dataset_size: 2381492020 configs: - config_name: default data_files: - split: train path: data/train-* ---
ToxicityPrompts/PolyGuardMix	ToxicityPrompts	2025-06-23T21:39:12Z	229	1	[ "task_categories:text2text-generation", "language:ar", "language:zh", "language:cs", "language:nl", "language:en", "language:fr", "language:de", "language:hi", "language:th", "language:it", "language:ja", "language:ko", "language:pl", "language:pt", "language:ru", "language:es", "language:sv", "license:cc-by-4.0", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2504.04377", "region:us", "safety", "multilingual" ]	[ "text2text-generation" ]	2025-02-18T06:58:07Z	null	--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: prompt_harm_label dtype: string - name: response_refusal_label dtype: string - name: response_harm_label dtype: string - name: prompt_safety_categories dtype: string - name: response_safety_categories dtype: string - name: metadata struct: - name: language dtype: string - name: source dtype: string splits: - name: train num_bytes: 3783037965 num_examples: 1910372 download_size: 2306303141 dataset_size: 3783037965 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - text2text-generation language: - ar - zh - cs - nl - en - fr - de - hi - th - it - ja - ko - pl - pt - ru - es - sv tags: - safety - multilingual size_categories: - 1M<n<10M license: cc-by-4.0 --- # PolyGuard: A Multilingual Safety Moderation Tool for 17 Languages Abstract: Truly multilingual safety moderation efforts for Large Language Models (LLMs) have been hindered by a narrow focus on a small set of languages (e.g., English, Chinese) as well as a limited scope of safety definition, resulting in significant gaps in moderation capabilities. To bridge these gaps, we release PolyGuard, a new state-of-the-art multilingual safety model for safeguarding LLM generations, and the corresponding training and evaluation datasets. PolyGuard is trained on PolyGuardMix, the largest multilingual safety training corpus to date containing 1.91M samples across 17 languages (e.g., Chinese, Czech, English, Hindi). We also introduce PolyGuardPrompts, a high quality multilingual benchmark with 29K samples for the evaluation of safety guardrails. Created by combining naturally occurring multilingual human-LLM interactions and human-verified machine translations of an English-only safety dataset (WildGuardMix; Han et al., 2024), our datasets contain prompt-output pairs with labels of prompt harmfulness, response harmfulness, and response refusal. Through extensive evaluations across multiple safety and toxicity benchmarks, we demonstrate that PolyGuard outperforms existing state-of-the-art open-weight and commercial safety classifiers by 5.5%. Our contributions advance efforts toward safer multilingual LLMs for all global users. ### Languages The data supports 17 languages and are reported in the table below. \| language code \| language name \| \|:----------------\|:---------------------\| \| ar \| Arabic \| \| cs \| Czech \| \| de \| German \| \| en \| English \| \| es \| Spanish \| \| hi \| Hindi \| \| it \| Italian \| \| ja \| Japanese \| \| ko \| Korean \| \| nl \| Dutch \| \| pl \| Polish \| \| pt \| Portuguese \| \| ru \| Russian \| \| sv \| Swedish \| \| zh \| Chinese \| \| th \| Thai \| ### Data Fields - `prompt`: user prompt input by user - `response`: model's response to the user prompt - `prompt_harm_label`: if the prompt is harmful - `response_refusal_label`: if the model refuses the user's request - `response_harm_label`: if the response is harmful - `prompt_safety_categories`: list of violated safety categories by harmful prompt - `response_safety_categories`: list of violated safety categories by harmful response - `metadata`: language and source of data sample ### Citation ``` @misc{kumar2025polyguardmultilingualsafetymoderation, title={PolyGuard: A Multilingual Safety Moderation Tool for 17 Languages}, author={Priyanshu Kumar and Devansh Jain and Akhila Yerukola and Liwei Jiang and Himanshu Beniwal and Thomas Hartvigsen and Maarten Sap}, year={2025}, eprint={2504.04377}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2504.04377}, } ```
lucadang/qwen2.7-72B-Sudoku	lucadang	2025-06-23T21:29:12Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-23T09:35:06Z	null	--- dataset_info: features: - name: prompt list: - name: content dtype: string - name: role dtype: string - name: completion list: - name: content dtype: string - name: role dtype: string - name: answer dtype: string - name: reward dtype: float64 - name: task dtype: string - name: format_reward_func dtype: float64 - name: check_answer_reward_func dtype: float64 splits: - name: train num_bytes: 1495096 num_examples: 2000 download_size: 210166 dataset_size: 1495096 configs: - config_name: default data_files: - split: train path: data/train-* ---
svjack/Anime_Landscape_Wan_FusionX	svjack	2025-06-23T20:26:14Z	0	0	[ "size_categories:n<1K", "modality:text", "modality:video", "library:datasets", "library:mlcroissant", "region:us" ]	[]	2025-06-23T20:25:57Z	null	--- configs: - config_name: default data_files: - split: train path: - "*.mp4" - "metadata.csv" ---

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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.