john-1111/x_dataset_0603159

john-1111

2025-05-05T01:25:33Z

278

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" ]

2025-01-25T07:17:19Z

null

--- license: mit multilinguality: - multilingual source_datasets: - original task_categories: - text-classification - token-classification - question-answering - summarization - text-generation task_ids: - sentiment-analysis - topic-classification - named-entity-recognition - language-modeling - text-scoring - multi-class-classification - multi-label-classification - extractive-qa - news-articles-summarization --- # Bittensor Subnet 13 X (Twitter) Dataset <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/bittensor.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/macrocosmos-black.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> ## Dataset Description - **Repository:** john-1111/x_dataset_0603159 - **Subnet:** Bittensor Subnet 13 - **Miner Hotkey:** 5G1Mrxdg6y9yfDmFfYJUnfdedoRQuF52WcURHjbJvVFWr1Jj ### Miner Data Compliance Agreement In uploading this dataset, I am agreeing to the [Macrocosmos Miner Data Compliance Policy](https://github.com/macrocosm-os/data-universe/blob/add-miner-policy/docs/miner_policy.md). ### Dataset Summary This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed data from X (formerly Twitter). The data is continuously updated by network miners, providing a real-time stream of tweets for various analytical and machine learning tasks. For more information about the dataset, please visit the [official repository](https://github.com/macrocosm-os/data-universe). ### Supported Tasks The versatility of this dataset allows researchers and data scientists to explore various aspects of social media dynamics and develop innovative applications. Users are encouraged to leverage this data creatively for their specific research or business needs. For example: - Sentiment Analysis - Trend Detection - Content Analysis - User Behavior Modeling ### Languages Primary language: Datasets are mostly English, but can be multilingual due to decentralized ways of creation. ## Dataset Structure ### Data Instances Each instance represents a single tweet with the following fields: ### Data Fields - `text` (string): The main content of the tweet. - `label` (string): Sentiment or topic category of the tweet. - `tweet_hashtags` (list): A list of hashtags used in the tweet. May be empty if no hashtags are present. - `datetime` (string): The date when the tweet was posted. - `username_encoded` (string): An encoded version of the username to maintain user privacy. - `url_encoded` (string): An encoded version of any URLs included in the tweet. May be empty if no URLs are present. ### Data Splits This dataset is continuously updated and does not have fixed splits. Users should create their own splits based on their requirements and the data's timestamp. ## Dataset Creation ### Source Data Data is collected from public tweets on X (Twitter), adhering to the platform's terms of service and API usage guidelines. ### Personal and Sensitive Information All usernames and URLs are encoded to protect user privacy. The dataset does not intentionally include personal or sensitive information. ## Considerations for Using the Data ### Social Impact and Biases Users should be aware of potential biases inherent in X (Twitter) data, including demographic and content biases. This dataset reflects the content and opinions expressed on X and should not be considered a representative sample of the general population. ### Limitations - Data quality may vary due to the decentralized nature of collection and preprocessing. - The dataset may contain noise, spam, or irrelevant content typical of social media platforms. - Temporal biases may exist due to real-time collection methods. - The dataset is limited to public tweets and does not include private accounts or direct messages. - Not all tweets contain hashtags or URLs. ## Additional Information ### Licensing Information The dataset is released under the MIT license. The use of this dataset is also subject to X Terms of Use. ### Citation Information If you use this dataset in your research, please cite it as follows: ``` @misc{john-11112025datauniversex_dataset_0603159, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={john-1111}, year={2025}, url={https://huggingface.co/datasets/john-1111/x_dataset_0603159}, } ``` ### 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:** 4558340 - **Date Range:** 2025-01-02T00:00:00Z to 2025-04-25T00:00:00Z - **Last Updated:** 2025-05-05T01:25:33Z ### Data Distribution - Tweets with hashtags: 3.54% - Tweets without hashtags: 96.46% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. | Rank | Topic | Total Count | Percentage | |------|-------|-------------|-------------| | 1 | NULL | 1261593 | 88.67% | | 2 | #granhermano | 10301 | 0.72% | | 3 | #riyadh | 9374 | 0.66% | | 4 | #箱根駅伝 | 8147 | 0.57% | | 5 | #thameposeriesep9 | 7605 | 0.53% | | 6 | #tiktok | 6765 | 0.48% | | 7 | #ad | 5367 | 0.38% | | 8 | #zelena | 4878 | 0.34% | | 9 | #smackdown | 4844 | 0.34% | | 10 | #कबीर_परमेश्वर_निर्वाण_दिवस | 4843 | 0.34% | ## Update History | Date | New Instances | Total Instances | |------|---------------|-----------------| | 2025-01-25T07:14:13Z | 414446 | 414446 | | 2025-01-25T07:14:44Z | 453526 | 867972 | | 2025-01-25T07:15:15Z | 453526 | 1321498 | | 2025-01-25T07:15:45Z | 453526 | 1775024 | | 2025-01-25T07:16:15Z | 453526 | 2228550 | | 2025-01-25T07:16:47Z | 453526 | 2682076 | | 2025-01-25T07:17:17Z | 453526 | 3135602 | | 2025-01-25T07:17:48Z | 453526 | 3589128 | | 2025-02-18T03:40:33Z | 471834 | 4060962 | | 2025-05-05T01:25:33Z | 497378 | 4558340 |

test-gen/mbpp_Qwen2.5-Coder-7B-Instruct_t0.0_n1_generated_tests

test-gen

2025-05-04T22:50:14Z

0

[ "region:us" ]

[]

2025-05-04T22:50:13Z

null

--- dataset_info: features: - name: task_id dtype: int32 - name: text dtype: string - name: code dtype: string - name: test_list sequence: string - name: test_setup_code dtype: string - name: challenge_test_list sequence: string - name: verification_info struct: - name: language dtype: string - name: test_cases sequence: string splits: - name: test num_bytes: 602744 num_examples: 500 download_size: 233989 dataset_size: 602744 configs: - config_name: default data_files: - split: test path: data/test-* ---

BasedLukas/so101_test_5

BasedLukas

2025-05-04T21:20:29Z

0

[ "task_categories:robotics", "license:apache-2.0", "region:us", "LeRobot", "so101", "tutorial" ]

[ "robotics" ]

2025-05-04T21:20:16Z

null

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so101 - tutorial configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so101", "total_episodes": 2, "total_frames": 1793, "total_tasks": 1, "total_videos": 2, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:2" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.webcam": { "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] ```

ronenEl/Mistral-PRM-Data-for-ST-History-10

ronenEl

2025-05-04T20:45:08Z

0

[ "region:us" ]

[]

2025-05-04T20:44:45Z

null

--- dataset_info: features: - name: prev_steps dtype: string - name: current_step dtype: string - name: label dtype: int64 - name: problem_id dtype: int64 - name: solution_id dtype: int64 - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 815588193 num_examples: 909558 - name: validation num_bytes: 95810881 num_examples: 110553 - name: test num_bytes: 106356437 num_examples: 118839 download_size: 225288440 dataset_size: 1017755511 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

HungVu2003/opt-350m_beta_0.5_alpha_0.8_num-company_3_dataset_0_for_gen_2

HungVu2003

2025-05-04T20:13:31Z

0

[ "region:us" ]

[]

2025-05-04T20:13:30Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 8023196 num_examples: 12498 download_size: 3346167 dataset_size: 8023196 configs: - config_name: default data_files: - split: train path: data/train-* ---

mteb/RuToxicOKMLCUPClassification

mteb

2025-05-04T16:31:06Z

91

0

[ "task_categories:text-classification", "task_ids:sentiment-analysis", "task_ids:sentiment-scoring", "task_ids:sentiment-classification", "task_ids:hate-speech-detection", "annotations_creators:derived", "multilinguality:monolingual", "language:rus", "license:unknown", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]

[ "text-classification" ]

2025-04-19T13:26:57Z

null

--- annotations_creators: - derived language: - rus license: unknown multilinguality: monolingual task_categories: - text-classification task_ids: - sentiment-analysis - sentiment-scoring - sentiment-classification - hate-speech-detection dataset_info: features: - name: text dtype: string - name: toxic dtype: int64 - name: labels sequence: int64 splits: - name: train num_bytes: 345040 num_examples: 2000 - name: test num_bytes: 323745 num_examples: 2000 download_size: 347393 dataset_size: 668785 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* tags: - mteb - text ---  <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">RuToxicOKMLCUPMultilabelClassification</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div> On the Odnoklassniki social network, users post a huge number of comments of various directions and nature every day. | | | |---------------|---------------------------------------------| | Task category | t2t | | Domains | None | | Reference | https://cups.online/ru/contests/okmlcup2020 | ## How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: ```python import mteb task = mteb.get_tasks(["RuToxicOKMLCUPMultilabelClassification"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ```  To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @article{enevoldsen2025mmtebmassivemultilingualtext, title={MMTEB: Massive Multilingual Text Embedding Benchmark}, author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff}, publisher = {arXiv}, journal={arXiv preprint arXiv:2502.13595}, year={2025}, url={https://arxiv.org/abs/2502.13595}, doi = {10.48550/arXiv.2502.13595}, } @article{muennighoff2022mteb, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} url = {https://arxiv.org/abs/2210.07316}, doi = {10.48550/ARXIV.2210.07316}, } ``` # Dataset Statistics <details> <summary> Dataset Statistics</summary> The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("RuToxicOKMLCUPMultilabelClassification") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 2000, "number_of_characters": 152400, "number_texts_intersect_with_train": 0, "min_text_length": 6, "average_text_length": 76.2, "max_text_length": 790, "unique_texts": 2000, "min_labels_per_text": 1, "average_label_per_text": 1.0885, "max_labels_per_text": 3, "unique_labels": 4, "labels": { "1": { "count": 1000 }, "0": { "count": 810 }, "3": { "count": 275 }, "2": { "count": 92 } } }, "train": { "num_samples": 2000, "number_of_characters": 163893, "number_texts_intersect_with_train": null, "min_text_length": 5, "average_text_length": 81.9465, "max_text_length": 965, "unique_texts": 2000, "min_labels_per_text": 1, "average_label_per_text": 1.093, "max_labels_per_text": 3, "unique_labels": 4, "labels": { "1": { "count": 1000 }, "0": { "count": 824 }, "3": { "count": 260 }, "2": { "count": 102 } } } } ``` </details> --- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*

mteb/WikipediaRerankingMultilingual

mteb

2025-05-04T16:12:04Z

617

0

[ "task_categories:text-ranking", "annotations_creators:LM-generated and reviewed", "multilinguality:multilingual", "language:ben", "language:bul", "language:ces", "language:dan", "language:deu", "language:eng", "language:fas", "language:fin", "language:hin", "language:ita", "language:nld", "language:nor", "language:por", "language:ron", "language:srp", "language:swe", "license:cc-by-sa-3.0", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]

[ "text-ranking" ]

2025-02-19T09:28:42Z

null

--- annotations_creators: - LM-generated and reviewed language: - ben - bul - ces - dan - deu - eng - fas - fin - hin - ita - nld - nor - por - ron - srp - swe license: cc-by-sa-3.0 multilinguality: multilingual task_categories: - text-ranking task_ids: [] dataset_info: - config_name: bg-corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 9604308 num_examples: 13500 download_size: 4593991 dataset_size: 9604308 - config_name: bg-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 778020 num_examples: 13500 download_size: 101652 dataset_size: 778020 - config_name: bg-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 197625 num_examples: 1500 download_size: 96857 dataset_size: 197625 - config_name: bg-top_ranked features: - name: query-id dtype: string - name: corpus-ids sequence: string splits: - name: test num_bytes: 468900 num_examples: 1500 download_size: 97689 dataset_size: 468900 - config_name: bn-corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 14497846 num_examples: 13500 download_size: 5486517 dataset_size: 14497846 - config_name: bn-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 778020 num_examples: 13500 download_size: 101652 dataset_size: 778020 - config_name: bn-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 222824 num_examples: 1500 download_size: 95032 dataset_size: 222824 - config_name: bn-top_ranked features: - name: query-id dtype: string - name: corpus-ids sequence: string splits: - name: test num_bytes: 468900 num_examples: 1500 download_size: 97689 dataset_size: 468900 - config_name: cs-corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 6098076 num_examples: 13500 download_size: 3914545 dataset_size: 6098076 - config_name: cs-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 778020 num_examples: 13500 download_size: 101652 dataset_size: 778020 - config_name: cs-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 124465 num_examples: 1500 download_size: 82189 dataset_size: 124465 - config_name: cs-top_ranked features: - name: query-id dtype: string - name: corpus-ids sequence: string splits: - name: test num_bytes: 468900 num_examples: 1500 download_size: 97689 dataset_size: 468900 - config_name: da-corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 5309400 num_examples: 13500 download_size: 3172960 dataset_size: 5309400 - config_name: da-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 778020 num_examples: 13500 download_size: 101652 dataset_size: 778020 - config_name: da-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 118643 num_examples: 1500 download_size: 73789 dataset_size: 118643 - config_name: da-top_ranked features: - name: query-id dtype: string - name: corpus-ids sequence: string splits: - name: test num_bytes: 468900 num_examples: 1500 download_size: 97689 dataset_size: 468900 - config_name: de-corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 6019751 num_examples: 13500 download_size: 3594010 dataset_size: 6019751 - config_name: de-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 778020 num_examples: 13500 download_size: 101652 dataset_size: 778020 - config_name: de-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 138167 num_examples: 1500 download_size: 88032 dataset_size: 138167 - config_name: de-top_ranked features: - name: query-id dtype: string - name: corpus-ids sequence: string splits: - name: test num_bytes: 468900 num_examples: 1500 download_size: 97689 dataset_size: 468900 - config_name: en-corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 6671388 num_examples: 13500 download_size: 3961948 dataset_size: 6671388 - config_name: en-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 778020 num_examples: 13500 download_size: 101652 dataset_size: 778020 - config_name: en-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 134536 num_examples: 1500 download_size: 83004 dataset_size: 134536 - config_name: en-top_ranked features: - name: query-id dtype: string - name: corpus-ids sequence: string splits: - name: test num_bytes: 468900 num_examples: 1500 download_size: 97689 dataset_size: 468900 - config_name: fa-corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 8973566 num_examples: 13500 download_size: 4213163 dataset_size: 8973566 - config_name: fa-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 778020 num_examples: 13500 download_size: 101652 dataset_size: 778020 - config_name: fa-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 167018 num_examples: 1500 download_size: 85233 dataset_size: 167018 - config_name: fa-top_ranked features: - name: query-id dtype: string - name: corpus-ids sequence: string splits: - name: test num_bytes: 468900 num_examples: 1500 download_size: 97689 dataset_size: 468900 - config_name: fi-corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 5866641 num_examples: 13500 download_size: 3485556 dataset_size: 5866641 - config_name: fi-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 778020 num_examples: 13500 download_size: 101652 dataset_size: 778020 - config_name: fi-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 117859 num_examples: 1500 download_size: 74406 dataset_size: 117859 - config_name: fi-top_ranked features: - name: query-id dtype: string - name: corpus-ids sequence: string splits: - name: test num_bytes: 468900 num_examples: 1500 download_size: 97689 dataset_size: 468900 - config_name: hi-corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 14696552 num_examples: 13500 download_size: 5583513 dataset_size: 14696552 - config_name: hi-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 778020 num_examples: 13500 download_size: 101652 dataset_size: 778020 - config_name: hi-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 229970 num_examples: 1500 download_size: 98256 dataset_size: 229970 - config_name: hi-top_ranked features: - name: query-id dtype: string - name: corpus-ids sequence: string splits: - name: test num_bytes: 468900 num_examples: 1500 download_size: 97689 dataset_size: 468900 - config_name: it-corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 5899305 num_examples: 13500 download_size: 3566485 dataset_size: 5899305 - config_name: it-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 778020 num_examples: 13500 download_size: 101652 dataset_size: 778020 - config_name: it-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 137965 num_examples: 1500 download_size: 84180 dataset_size: 137965 - config_name: it-top_ranked features: - name: query-id dtype: string - name: corpus-ids sequence: string splits: - name: test num_bytes: 468900 num_examples: 1500 download_size: 97689 dataset_size: 468900 - config_name: nl-corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 5628451 num_examples: 13500 download_size: 3254369 dataset_size: 5628451 - config_name: nl-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 778020 num_examples: 13500 download_size: 101652 dataset_size: 778020 - config_name: nl-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 130098 num_examples: 1500 download_size: 79310 dataset_size: 130098 - config_name: nl-top_ranked features: - name: query-id dtype: string - name: corpus-ids sequence: string splits: - name: test num_bytes: 468900 num_examples: 1500 download_size: 97689 dataset_size: 468900 - config_name: no-corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 5603404 num_examples: 13500 download_size: 3361788 dataset_size: 5603404 - config_name: no-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 778020 num_examples: 13500 download_size: 101652 dataset_size: 778020 - config_name: no-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 116210 num_examples: 1500 download_size: 72568 dataset_size: 116210 - config_name: no-top_ranked features: - name: query-id dtype: string - name: corpus-ids sequence: string splits: - name: test num_bytes: 468900 num_examples: 1500 download_size: 97689 dataset_size: 468900 - config_name: pt-corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 6078548 num_examples: 13500 download_size: 3644877 dataset_size: 6078548 - config_name: pt-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 778020 num_examples: 13500 download_size: 101652 dataset_size: 778020 - config_name: pt-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 132902 num_examples: 1500 download_size: 82274 dataset_size: 132902 - config_name: pt-top_ranked features: - name: query-id dtype: string - name: corpus-ids sequence: string splits: - name: test num_bytes: 468900 num_examples: 1500 download_size: 97689 dataset_size: 468900 - config_name: ro-corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 5487340 num_examples: 13500 download_size: 3314140 dataset_size: 5487340 - config_name: ro-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 778020 num_examples: 13500 download_size: 101652 dataset_size: 778020 - config_name: ro-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 128853 num_examples: 1500 download_size: 80958 dataset_size: 128853 - config_name: ro-top_ranked features: - name: query-id dtype: string - name: corpus-ids sequence: string splits: - name: test num_bytes: 468900 num_examples: 1500 download_size: 97689 dataset_size: 468900 - config_name: sr-corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 9362172 num_examples: 13500 download_size: 4727113 dataset_size: 9362172 - config_name: sr-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 778020 num_examples: 13500 download_size: 101652 dataset_size: 778020 - config_name: sr-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 173594 num_examples: 1500 download_size: 95366 dataset_size: 173594 - config_name: sr-top_ranked features: - name: query-id dtype: string - name: corpus-ids sequence: string splits: - name: test num_bytes: 468900 num_examples: 1500 download_size: 97689 dataset_size: 468900 - config_name: sv-corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 5727305 num_examples: 13500 download_size: 3383922 dataset_size: 5727305 - config_name: sv-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 778020 num_examples: 13500 download_size: 101652 dataset_size: 778020 - config_name: sv-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 121800 num_examples: 1500 download_size: 77079 dataset_size: 121800 - config_name: sv-top_ranked features: - name: query-id dtype: string - name: corpus-ids sequence: string splits: - name: test num_bytes: 468900 num_examples: 1500 download_size: 97689 dataset_size: 468900 configs: - config_name: bg-corpus data_files: - split: test path: bg-corpus/test-* - config_name: bg-qrels data_files: - split: test path: bg-qrels/test-* - config_name: bg-queries data_files: - split: test path: bg-queries/test-* - config_name: bg-top_ranked data_files: - split: test path: bg-top_ranked/test-* - config_name: bn-corpus data_files: - split: test path: bn-corpus/test-* - config_name: bn-qrels data_files: - split: test path: bn-qrels/test-* - config_name: bn-queries data_files: - split: test path: bn-queries/test-* - config_name: bn-top_ranked data_files: - split: test path: bn-top_ranked/test-* - config_name: cs-corpus data_files: - split: test path: cs-corpus/test-* - config_name: cs-qrels data_files: - split: test path: cs-qrels/test-* - config_name: cs-queries data_files: - split: test path: cs-queries/test-* - config_name: cs-top_ranked data_files: - split: test path: cs-top_ranked/test-* - config_name: da-corpus data_files: - split: test path: da-corpus/test-* - config_name: da-qrels data_files: - split: test path: da-qrels/test-* - config_name: da-queries data_files: - split: test path: da-queries/test-* - config_name: da-top_ranked data_files: - split: test path: da-top_ranked/test-* - config_name: de-corpus data_files: - split: test path: de-corpus/test-* - config_name: de-qrels data_files: - split: test path: de-qrels/test-* - config_name: de-queries data_files: - split: test path: de-queries/test-* - config_name: de-top_ranked data_files: - split: test path: de-top_ranked/test-* - config_name: en-corpus data_files: - split: test path: en-corpus/test-* - config_name: en-qrels data_files: - split: test path: en-qrels/test-* - config_name: en-queries data_files: - split: test path: en-queries/test-* - config_name: en-top_ranked data_files: - split: test path: en-top_ranked/test-* - config_name: fa-corpus data_files: - split: test path: fa-corpus/test-* - config_name: fa-qrels data_files: - split: test path: fa-qrels/test-* - config_name: fa-queries data_files: - split: test path: fa-queries/test-* - config_name: fa-top_ranked data_files: - split: test path: fa-top_ranked/test-* - config_name: fi-corpus data_files: - split: test path: fi-corpus/test-* - config_name: fi-qrels data_files: - split: test path: fi-qrels/test-* - config_name: fi-queries data_files: - split: test path: fi-queries/test-* - config_name: fi-top_ranked data_files: - split: test path: fi-top_ranked/test-* - config_name: hi-corpus data_files: - split: test path: hi-corpus/test-* - config_name: hi-qrels data_files: - split: test path: hi-qrels/test-* - config_name: hi-queries data_files: - split: test path: hi-queries/test-* - config_name: hi-top_ranked data_files: - split: test path: hi-top_ranked/test-* - config_name: it-corpus data_files: - split: test path: it-corpus/test-* - config_name: it-qrels data_files: - split: test path: it-qrels/test-* - config_name: it-queries data_files: - split: test path: it-queries/test-* - config_name: it-top_ranked data_files: - split: test path: it-top_ranked/test-* - config_name: nl-corpus data_files: - split: test path: nl-corpus/test-* - config_name: nl-qrels data_files: - split: test path: nl-qrels/test-* - config_name: nl-queries data_files: - split: test path: nl-queries/test-* - config_name: nl-top_ranked data_files: - split: test path: nl-top_ranked/test-* - config_name: no-corpus data_files: - split: test path: no-corpus/test-* - config_name: no-qrels data_files: - split: test path: no-qrels/test-* - config_name: no-queries data_files: - split: test path: no-queries/test-* - config_name: no-top_ranked data_files: - split: test path: no-top_ranked/test-* - config_name: pt-corpus data_files: - split: test path: pt-corpus/test-* - config_name: pt-qrels data_files: - split: test path: pt-qrels/test-* - config_name: pt-queries data_files: - split: test path: pt-queries/test-* - config_name: pt-top_ranked data_files: - split: test path: pt-top_ranked/test-* - config_name: ro-corpus data_files: - split: test path: ro-corpus/test-* - config_name: ro-qrels data_files: - split: test path: ro-qrels/test-* - config_name: ro-queries data_files: - split: test path: ro-queries/test-* - config_name: ro-top_ranked data_files: - split: test path: ro-top_ranked/test-* - config_name: sr-corpus data_files: - split: test path: sr-corpus/test-* - config_name: sr-qrels data_files: - split: test path: sr-qrels/test-* - config_name: sr-queries data_files: - split: test path: sr-queries/test-* - config_name: sr-top_ranked data_files: - split: test path: sr-top_ranked/test-* - config_name: sv-corpus data_files: - split: test path: sv-corpus/test-* - config_name: sv-qrels data_files: - split: test path: sv-qrels/test-* - config_name: sv-queries data_files: - split: test path: sv-queries/test-* - config_name: sv-top_ranked data_files: - split: test path: sv-top_ranked/test-* tags: - mteb - text ---  <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">WikipediaRerankingMultilingual</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div> The dataset is derived from Cohere's wikipedia-2023-11 dataset and contains synthetically generated queries. | | | |---------------|---------------------------------------------| | Task category | t2t | | Domains | Encyclopaedic, Written | | Reference | https://huggingface.co/datasets/ellamind/wikipedia-2023-11-reranking-multilingual | ## How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: ```python import mteb task = mteb.get_tasks(["WikipediaRerankingMultilingual"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ```  To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @online{wikidump, author = {Wikimedia Foundation}, title = {Wikimedia Downloads}, url = {https://dumps.wikimedia.org}, } @article{enevoldsen2025mmtebmassivemultilingualtext, title={MMTEB: Massive Multilingual Text Embedding Benchmark}, author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff}, publisher = {arXiv}, journal={arXiv preprint arXiv:2502.13595}, year={2025}, url={https://arxiv.org/abs/2502.13595}, doi = {10.48550/arXiv.2502.13595}, } @article{muennighoff2022mteb, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} url = {https://arxiv.org/abs/2210.07316}, doi = {10.48550/ARXIV.2210.07316}, } ``` # Dataset Statistics <details> <summary> Dataset Statistics</summary> The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("WikipediaRerankingMultilingual") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 240000, "number_of_characters": 83866932, "num_documents": 216000, "min_document_length": 100, "average_document_length": 381.70714351851854, "max_document_length": 9461, "unique_documents": 216000, "num_queries": 24000, "min_query_length": 7, "average_query_length": 59.091208333333334, "max_query_length": 180, "unique_queries": 24000, "none_queries": 0, "num_relevant_docs": 216000, "min_relevant_docs_per_query": 9, "average_relevant_docs_per_query": 1.0, "max_relevant_docs_per_query": 9, "unique_relevant_docs": 216000, "num_instructions": null, "min_instruction_length": null, "average_instruction_length": null, "max_instruction_length": null, "unique_instructions": null, "num_top_ranked": 24000, "min_top_ranked_per_query": 9, "average_top_ranked_per_query": 9.0, "max_top_ranked_per_query": 9 } } ``` </details> --- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*

nhagar/c4_urls_en.noclean

nhagar

2025-05-04T16:11:42Z

356

0

[ "task_categories:text-generation", "language:en", "license:odc-by", "size_categories:100M<n<1B", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[ "text-generation" ]

2025-03-09T18:13:45Z

null

--- dataset_info: features: - name: url dtype: string - name: domain dtype: string splits: - name: train num_bytes: 134877251 num_examples: 1396098 download_size: 100774629 dataset_size: 134877251 configs: - config_name: default data_files: - split: train path: batch*/train-* license: odc-by task_categories: - text-generation language: - en size_categories: - 10B<n<100B --- # Dataset Card for c4_urls_en.noclean This dataset provides the URLs and top-level domains associated with training records in [allenai/c4](https://huggingface.co/datasets/allenai/c4) (English no clean variant). It is part of a [collection of datasets](https://huggingface.co/collections/nhagar/llm-urls-neurips-681698adac0862be6c65c72b) curated to make exploring LLM training datasets more straightforward and accessible. ## Dataset Details ### Dataset Description This dataset was created by downloading the source data, extracting URLs and top-level domains, and retaining only those record identifiers. In doing so, it allows researchers and practitioners to explore the contents of these training datasets without having to manage terabytes of raw text. You can explore the pipeline used to construct this dataset on [GitHub](https://github.com/NHagar/cc-genealogy). - **Curated by:** [Nick Hagar](https://huggingface.co/nhagar) and [Jack Bandy](https://huggingface.co/jackbandy) - **License:** Same as source dataset ### Dataset Sources - **Repository:** [allenai/c4](https://huggingface.co/datasets/allenai/c4) ## Uses This dataset is intended to allow researchers and practitioners to analyze the contents of large LLM training datasets without having to wade through terabytes of unwieldy text data. ### Direct Use The main use case for these data is to explore the contents of LLM training datasets at scale. This might involve: - Identifying the most-used websites - Categorizing URLs to understand domain- or topic-level dataset composition - Comparing URLs across datasets - Digging into inclusion/exclusion patterns for a particular website ### Out-of-Scope Use This dataset is not intend to replicate or replace the source data, nor is it intended to enable large-scale scraping of the URLs listed. For source text, refer to the original dataset. ## Dataset Structure This dataset contains every record with a URL from the source dataset. It contains two columns: - `url`: The raw URL associated with each record - `domain`: The top-level domain for each URL, extracted with `tldextract` ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed]

mteb/NQ-PL

mteb

2025-05-04T16:11:20Z

16

0

[ "task_categories:text-retrieval", "multilinguality:translated", "source_datasets:mteb/nq", "language:pol", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2305.19840", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]

[ "text-retrieval" ]

2025-02-05T18:39:22Z

null

--- language: - pol multilinguality: translated source_datasets: - mteb/nq task_categories: - text-retrieval task_ids: [] dataset_info: - config_name: corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 1481700950 num_examples: 2681468 download_size: 897798855 dataset_size: 1481700950 - config_name: default features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 133323 num_examples: 4201 download_size: 51009 dataset_size: 133323 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 230227 num_examples: 3452 download_size: 157883 dataset_size: 230227 configs: - config_name: corpus data_files: - split: test path: corpus/test-* - config_name: default data_files: - split: test path: data/test-* - config_name: queries data_files: - split: test path: queries/test-* tags: - mteb - text ---  <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">NQ-PL</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div> Natural Questions: A Benchmark for Question Answering Research | | | |---------------|---------------------------------------------| | Task category | t2t | | Domains | None | | Reference | https://ai.google.com/research/NaturalQuestions/ | ## How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: ```python import mteb task = mteb.get_tasks(["NQ-PL"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ```  To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @misc{wojtasik2024beirpl, archiveprefix = {arXiv}, author = {Konrad Wojtasik and Vadim Shishkin and Kacper Wołowiec and Arkadiusz Janz and Maciej Piasecki}, eprint = {2305.19840}, primaryclass = {cs.IR}, title = {BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language}, year = {2024}, } @article{enevoldsen2025mmtebmassivemultilingualtext, title={MMTEB: Massive Multilingual Text Embedding Benchmark}, author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff}, publisher = {arXiv}, journal={arXiv preprint arXiv:2502.13595}, year={2025}, url={https://arxiv.org/abs/2502.13595}, doi = {10.48550/arXiv.2502.13595}, } @article{muennighoff2022mteb, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} url = {https://arxiv.org/abs/2210.07316}, doi = {10.48550/ARXIV.2210.07316}, } ``` # Dataset Statistics <details> <summary> Dataset Statistics</summary> The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("NQ-PL") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 2684920, "number_of_characters": 1349328700, "num_documents": 2681468, "min_document_length": 5, "average_document_length": 503.14302128535564, "max_document_length": 17008, "unique_documents": 2681468, "num_queries": 3452, "min_query_length": 18, "average_query_length": 48.31662804171495, "max_query_length": 111, "unique_queries": 3452, "none_queries": 0, "num_relevant_docs": 4201, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 1.2169756662804172, "max_relevant_docs_per_query": 4, "unique_relevant_docs": 4201, "num_instructions": null, "min_instruction_length": null, "average_instruction_length": null, "max_instruction_length": null, "unique_instructions": null, "num_top_ranked": null, "min_top_ranked_per_query": null, "average_top_ranked_per_query": null, "max_top_ranked_per_query": null } } ``` </details> --- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*

mteb/germanquad-retrieval

mteb

2025-05-04T16:09:32Z

67

0

[ "task_categories:text-retrieval", "task_ids:multiple-choice-qa", "annotations_creators:human-annotated", "multilinguality:monolingual", "language:deu", "license:cc-by-4.0", "size_categories:1K<n<10K", "format:arrow", "modality:text", "library:datasets", "library:mlcroissant", "arxiv:2104.12741", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]

[ "text-retrieval" ]

2024-01-07T20:17:07Z

null

--- annotations_creators: - human-annotated language: - deu license: cc-by-4.0 multilinguality: monolingual task_categories: - text-retrieval task_ids: - multiple-choice-qa configs: - config_name: corpus data_files: - split: corpus path: corpus/data-00000-of-00001.arrow - config_name: queries data_files: - split: queries path: queries/data-00000-of-00001.arrow tags: - mteb - text ---  <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">GermanQuAD-Retrieval</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div> Context Retrieval for German Question Answering | | | |---------------|---------------------------------------------| | Task category | t2t | | Domains | Written, Non-fiction, Web | | Reference | https://huggingface.co/datasets/deepset/germanquad | ## How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: ```python import mteb task = mteb.get_tasks(["GermanQuAD-Retrieval"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ```  To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @misc{möller2021germanquad, archiveprefix = {arXiv}, author = {Timo Möller and Julian Risch and Malte Pietsch}, eprint = {2104.12741}, primaryclass = {cs.CL}, title = {GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval}, year = {2021}, } @article{enevoldsen2025mmtebmassivemultilingualtext, title={MMTEB: Massive Multilingual Text Embedding Benchmark}, author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff}, publisher = {arXiv}, journal={arXiv preprint arXiv:2502.13595}, year={2025}, url={https://arxiv.org/abs/2502.13595}, doi = {10.48550/arXiv.2502.13595}, } @article{muennighoff2022mteb, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} url = {https://arxiv.org/abs/2210.07316}, doi = {10.48550/ARXIV.2210.07316}, } ``` # Dataset Statistics <details> <summary> Dataset Statistics</summary> The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("GermanQuAD-Retrieval") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 2678, "number_of_characters": 1045149, "num_documents": 474, "min_document_length": 507, "average_document_length": 1941.090717299578, "max_document_length": 11647, "unique_documents": 474, "num_queries": 2204, "min_query_length": 15, "average_query_length": 56.74773139745916, "max_query_length": 130, "unique_queries": 2204, "none_queries": 0, "num_relevant_docs": 2204, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 1.0, "max_relevant_docs_per_query": 1, "unique_relevant_docs": 474, "num_instructions": null, "min_instruction_length": null, "average_instruction_length": null, "max_instruction_length": null, "unique_instructions": null, "num_top_ranked": null, "min_top_ranked_per_query": null, "average_top_ranked_per_query": null, "max_top_ranked_per_query": null } } ``` </details> --- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*

mteb/XNLIV2

mteb

2025-05-04T16:09:29Z

351

0

[ "task_categories:text-classification", "task_ids:semantic-similarity-classification", "annotations_creators:expert-annotated", "multilinguality:translated", "language:asm", "language:ben", "language:bho", "language:ell", "language:guj", "language:kan", "language:mar", "language:ory", "language:pan", "language:rus", "language:san", "language:tam", "language:tur", "license:unknown", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2301.06527", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]

[ "text-classification" ]

2024-11-30T15:15:38Z

null

--- annotations_creators: - expert-annotated language: - asm - ben - bho - ell - guj - kan - mar - ory - pan - rus - san - tam - tur license: unknown multilinguality: translated task_categories: - text-classification task_ids: - semantic-similarity-classification tags: - mteb - text dataset_info: - config_name: assamese features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 565556 num_examples: 1365 download_size: 230705 dataset_size: 565556 - config_name: bengali features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 567227 num_examples: 1365 download_size: 223053 dataset_size: 567227 - config_name: bhojpuri features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 549145 num_examples: 1365 download_size: 220031 dataset_size: 549145 - config_name: greek features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 446843 num_examples: 1365 download_size: 224614 dataset_size: 446843 - config_name: gujrati features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 550823 num_examples: 1365 download_size: 224504 dataset_size: 550823 - config_name: kannada features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 622208 num_examples: 1365 download_size: 239158 dataset_size: 622208 - config_name: marathi features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 569028 num_examples: 1365 download_size: 225578 dataset_size: 569028 - config_name: odiya features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 571151 num_examples: 1365 download_size: 228006 dataset_size: 571151 - config_name: punjabi features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 565812 num_examples: 1365 download_size: 224326 dataset_size: 565812 - config_name: russian features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 418863 num_examples: 1365 download_size: 213532 dataset_size: 418863 - config_name: sanskrit features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 598335 num_examples: 1365 download_size: 235984 dataset_size: 598335 - config_name: tamil features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 676943 num_examples: 1365 download_size: 245022 dataset_size: 676943 - config_name: turkish features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 246707 num_examples: 1365 download_size: 156292 dataset_size: 246707 configs: - config_name: assamese data_files: - split: test path: assamese/test-* - config_name: bengali data_files: - split: test path: bengali/test-* - config_name: bhojpuri data_files: - split: test path: bhojpuri/test-* - config_name: greek data_files: - split: test path: greek/test-* - config_name: gujrati data_files: - split: test path: gujrati/test-* - config_name: kannada data_files: - split: test path: kannada/test-* - config_name: marathi data_files: - split: test path: marathi/test-* - config_name: odiya data_files: - split: test path: odiya/test-* - config_name: punjabi data_files: - split: test path: punjabi/test-* - config_name: russian data_files: - split: test path: russian/test-* - config_name: sanskrit data_files: - split: test path: sanskrit/test-* - config_name: tamil data_files: - split: test path: tamil/test-* - config_name: turkish data_files: - split: test path: turkish/test-* ---  <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">XNLIV2</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div> This is subset of 'XNLI 2.0: Improving XNLI dataset and performance on Cross Lingual Understanding' with languages that were not part of the original XNLI plus three (verified) languages that are not strongly covered in MTEB | | | |---------------|---------------------------------------------| | Task category | t2t | | Domains | Non-fiction, Fiction, Government, Written | | Reference | https://arxiv.org/pdf/2301.06527 | ## How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: ```python import mteb task = mteb.get_tasks(["XNLIV2"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ```  To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @inproceedings{upadhyay2023xnli, author = {Upadhyay, Ankit Kumar and Upadhya, Harsit Kumar}, booktitle = {2023 IEEE 8th International Conference for Convergence in Technology (I2CT)}, organization = {IEEE}, pages = {1--6}, title = {XNLI 2.0: Improving XNLI dataset and performance on Cross Lingual Understanding (XLU)}, year = {2023}, } @article{enevoldsen2025mmtebmassivemultilingualtext, title={MMTEB: Massive Multilingual Text Embedding Benchmark}, author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff}, publisher = {arXiv}, journal={arXiv preprint arXiv:2502.13595}, year={2025}, url={https://arxiv.org/abs/2502.13595}, doi = {10.48550/arXiv.2502.13595}, } @article{muennighoff2022mteb, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} url = {https://arxiv.org/abs/2210.07316}, doi = {10.48550/ARXIV.2210.07316}, } ``` # Dataset Statistics <details> <summary> Dataset Statistics</summary> The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("XNLIV2") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 17745, "number_of_characters": 2778287, "unique_pairs": 17745, "min_sentence1_length": 5, "avg_sentence1_length": 105.99329388560157, "max_sentence1_length": 339, "unique_sentence1": 14234, "min_sentence2_length": 8, "avg_sentence2_length": 50.57402085094393, "max_sentence2_length": 162, "unique_sentence2": 17745, "unique_labels": 2, "labels": { "0": { "count": 8879 }, "1": { "count": 8866 } } } } ``` </details> --- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*

mteb/DutchBookReviewSentimentClassification

mteb

2025-05-04T16:08:19Z

10

0

[ "task_categories:text-classification", "task_ids:sentiment-analysis", "task_ids:sentiment-scoring", "task_ids:sentiment-classification", "task_ids:hate-speech-detection", "annotations_creators:derived", "multilinguality:monolingual", "language:nld", "license:cc-by-nc-sa-4.0", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:1910.00896", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]

[ "text-classification" ]

2024-12-21T12:08:33Z

null

--- annotations_creators: - derived language: - nld license: cc-by-nc-sa-4.0 multilinguality: monolingual task_categories: - text-classification task_ids: - sentiment-analysis - sentiment-scoring - sentiment-classification - hate-speech-detection dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 29496321 num_examples: 20028 - name: test num_bytes: 3246239 num_examples: 2224 - name: unsupervised num_bytes: 152732991 num_examples: 96264 download_size: 116070515 dataset_size: 185475551 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: unsupervised path: data/unsupervised-* tags: - mteb - text ---  <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">DutchBookReviewSentimentClassification</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div> A Dutch book review for sentiment classification. | | | |---------------|---------------------------------------------| | Task category | t2c | | Domains | Reviews, Written | | Reference | https://github.com/benjaminvdb/DBRD | ## How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: ```python import mteb task = mteb.get_tasks(["DutchBookReviewSentimentClassification"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ```  To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @article{DBLP:journals/corr/abs-1910-00896, archiveprefix = {arXiv}, author = {Benjamin, van der Burgh and Suzan, Verberne}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/journals/corr/abs-1910-00896.bib}, eprint = {1910.00896}, journal = {CoRR}, timestamp = {Fri, 04 Oct 2019 12:28:06 +0200}, title = {The merits of Universal Language Model Fine-tuning for Small Datasets - a case with Dutch book reviews}, url = {http://arxiv.org/abs/1910.00896}, volume = {abs/1910.00896}, year = {2019}, } @article{enevoldsen2025mmtebmassivemultilingualtext, title={MMTEB: Massive Multilingual Text Embedding Benchmark}, author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff}, publisher = {arXiv}, journal={arXiv preprint arXiv:2502.13595}, year={2025}, url={https://arxiv.org/abs/2502.13595}, doi = {10.48550/arXiv.2502.13595}, } @article{muennighoff2022mteb, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} url = {https://arxiv.org/abs/2210.07316}, doi = {10.48550/ARXIV.2210.07316}, } ``` # Dataset Statistics <details> <summary> Dataset Statistics</summary> The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("DutchBookReviewSentimentClassification") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 2224, "number_of_characters": 3209177, "number_texts_intersect_with_train": 0, "min_text_length": 4, "average_text_length": 1442.9752697841727, "max_text_length": 11140, "unique_text": 2224, "unique_labels": 2, "labels": { "1": { "count": 1112 }, "0": { "count": 1112 } } }, "train": { "num_samples": 20028, "number_of_characters": 29162515, "number_texts_intersect_with_train": null, "min_text_length": 4, "average_text_length": 1456.0872278809666, "max_text_length": 22676, "unique_text": 20028, "unique_labels": 2, "labels": { "1": { "count": 10014 }, "0": { "count": 10014 } } } } ``` </details> --- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*

mteb/HotelReviewSentimentClassification

mteb

2025-05-04T16:07:41Z

11

0

[ "task_categories:text-classification", "task_ids:sentiment-analysis", "task_ids:sentiment-scoring", "task_ids:sentiment-classification", "task_ids:hate-speech-detection", "annotations_creators:derived", "multilinguality:monolingual", "language:ara", "license:unknown", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]

[ "text-classification" ]

2024-12-21T10:42:59Z

null

--- annotations_creators: - derived language: - ara license: unknown multilinguality: monolingual task_categories: - text-classification task_ids: - sentiment-analysis - sentiment-scoring - sentiment-classification - hate-speech-detection dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 536128 num_examples: 2048 download_size: 274359 dataset_size: 536128 configs: - config_name: default data_files: - split: train path: data/train-* tags: - mteb - text ---  <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">HotelReviewSentimentClassification</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div> HARD is a dataset of Arabic hotel reviews collected from the Booking.com website. | | | |---------------|---------------------------------------------| | Task category | t2c | | Domains | Reviews, Written | | Reference | https://link.springer.com/chapter/10.1007/978-3-319-67056-0_3 | ## How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: ```python import mteb task = mteb.get_tasks(["HotelReviewSentimentClassification"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ```  To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @article{elnagar2018hotel, author = {Elnagar, Ashraf and Khalifa, Yasmin S and Einea, Anas}, journal = {Intelligent natural language processing: Trends and applications}, pages = {35--52}, publisher = {Springer}, title = {Hotel Arabic-reviews dataset construction for sentiment analysis applications}, year = {2018}, } @article{enevoldsen2025mmtebmassivemultilingualtext, title={MMTEB: Massive Multilingual Text Embedding Benchmark}, author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff}, publisher = {arXiv}, journal={arXiv preprint arXiv:2502.13595}, year={2025}, url={https://arxiv.org/abs/2502.13595}, doi = {10.48550/arXiv.2502.13595}, } @article{muennighoff2022mteb, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} url = {https://arxiv.org/abs/2210.07316}, doi = {10.48550/ARXIV.2210.07316}, } ``` # Dataset Statistics <details> <summary> Dataset Statistics</summary> The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("HotelReviewSentimentClassification") desc_stats = task.metadata.descriptive_stats ``` ```json { "train": { "num_samples": 2048, "number_of_characters": 282368, "number_texts_intersect_with_train": null, "min_text_length": 11, "average_text_length": 137.875, "max_text_length": 2698, "unique_text": 2044, "unique_labels": 4, "labels": { "4": { "count": 512 }, "3": { "count": 512 }, "0": { "count": 279 }, "1": { "count": 745 } } } } ``` </details> --- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*

rainbowbridge/x_dataset_62648

rainbowbridge

2025-05-04T16:04:40Z

953

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" ]

2025-01-27T08:03:15Z

null

--- license: mit multilinguality: - multilingual source_datasets: - original task_categories: - text-classification - token-classification - question-answering - summarization - text-generation task_ids: - sentiment-analysis - topic-classification - named-entity-recognition - language-modeling - text-scoring - multi-class-classification - multi-label-classification - extractive-qa - news-articles-summarization --- # Bittensor Subnet 13 X (Twitter) Dataset <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/bittensor.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/macrocosmos-black.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> ## Dataset Description - **Repository:** rainbowbridge/x_dataset_62648 - **Subnet:** Bittensor Subnet 13 - **Miner Hotkey:** 5GmwRWW178RdVKEfua2F8uM7JGj6ctCN8fXwiq1aY9mYUJsB ### 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{rainbowbridge2025datauniversex_dataset_62648, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={rainbowbridge}, year={2025}, url={https://huggingface.co/datasets/rainbowbridge/x_dataset_62648}, } ``` ### 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:** 48799192 - **Date Range:** 2025-01-21T00:00:00Z to 2025-02-08T00:00:00Z - **Last Updated:** 2025-02-13T20:22:55Z ### Data Distribution - Tweets with hashtags: 46.38% - Tweets without hashtags: 53.62% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. | Rank | Topic | Total Count | Percentage | |------|-------|-------------|-------------| | 1 | NULL | 26164065 | 53.62% | | 2 | #riyadh | 360980 | 0.74% | | 3 | #zelena | 259485 | 0.53% | | 4 | #tiktok | 220229 | 0.45% | | 5 | #ad | 130307 | 0.27% | | 6 | #bbb25 | 128842 | 0.26% | | 7 | #jhope_at_galadespiècesjaunes | 111027 | 0.23% | | 8 | #bbmzansi | 71199 | 0.15% | | 9 | #pr | 69792 | 0.14% | | 10 | #yahooニュース | 67778 | 0.14% | ## Update History | Date | New Instances | Total Instances | |------|---------------|-----------------| | 2025-01-27T08:03:45Z | 1599427 | 1599427 | | 2025-01-30T20:06:41Z | 9852425 | 11451852 | | 2025-02-03T08:09:26Z | 7943066 | 19394918 | | 2025-02-06T20:15:04Z | 11440012 | 30834930 | | 2025-02-10T08:19:07Z | 9278875 | 40113805 | | 2025-02-13T20:22:55Z | 8685387 | 48799192 |

HungVu2003/opt-350m_beta_1.0_alpha_0.2_num-company_2_dataset_0_for_gen_16_v2

HungVu2003

2025-05-04T15:51:47Z

0

[ "region:us" ]

[]

2025-05-04T15:51:45Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 1998629 num_examples: 13750 download_size: 1093688 dataset_size: 1998629 configs: - config_name: default data_files: - split: train path: data/train-* ---

nhagar/fineweb-2_urls

nhagar

2025-05-04T15:50:27Z

67

0

[ "task_categories:text-generation", "license:odc-by", "size_categories:10B<n<100B", "region:us" ]

[ "text-generation" ]

2025-04-23T19:09:53Z

null

--- license: odc-by task_categories: - text-generation size_categories: - 10B<n<100B --- # Dataset Card for fineweb-2_urls This dataset provides the URLs and top-level domains associated with training records in [HuggingFaceFW/fineweb-2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2). It is part of a [collection of datasets](https://huggingface.co/collections/nhagar/llm-urls-neurips-681698adac0862be6c65c72b) curated to make exploring LLM training datasets more straightforward and accessible. ## Dataset Details ### Dataset Description This dataset was created by downloading the source data, extracting URLs and top-level domains, and retaining only those record identifiers. In doing so, it allows researchers and practitioners to explore the contents of these training datasets without having to manage terabytes of raw text. You can explore the pipeline used to construct this dataset on [GitHub](https://github.com/NHagar/cc-genealogy). - **Curated by:** [Nick Hagar](https://huggingface.co/nhagar) and [Jack Bandy](https://huggingface.co/jackbandy) - **License:** Same as source dataset ### Dataset Sources - **Repository:** [HuggingFaceFW/fineweb-2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2) ## Uses This dataset is intended to allow researchers and practitioners to analyze the contents of large LLM training datasets without having to wade through terabytes of unwieldy text data. ### Direct Use The main use case for these data is to explore the contents of LLM training datasets at scale. This might involve: - Identifying the most-used websites - Categorizing URLs to understand domain- or topic-level dataset composition - Comparing URLs across datasets - Digging into inclusion/exclusion patterns for a particular website ### Out-of-Scope Use This dataset is not intend to replicate or replace the source data, nor is it intended to enable large-scale scraping of the URLs listed. For source text, refer to the original dataset. ## Dataset Structure This dataset contains every record with a URL from the source dataset. It contains two columns: - `url`: The raw URL associated with each record - `domain`: The top-level domain for each URL, extracted with `tldextract` ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed]

zhengbang0707/Llama3.1-8B-IT_M-DPO_v2_30k

zhengbang0707

2025-05-04T13:48:49Z

6

0

[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-04T02:57:25Z

null

--- dataset_info: features: - name: trajectory list: - name: content dtype: string - name: role dtype: string - name: trajectory_reward sequence: float64 splits: - name: train num_bytes: 11791459 num_examples: 500 download_size: 3416106 dataset_size: 11791459 configs: - config_name: default data_files: - split: train path: data/train-* ---

rainbowbridge/x_dataset_55757

rainbowbridge

2025-05-04T13:11:20Z

884

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" ]

2025-01-29T00:12:22Z

null

--- license: mit multilinguality: - multilingual source_datasets: - original task_categories: - text-classification - token-classification - question-answering - summarization - text-generation task_ids: - sentiment-analysis - topic-classification - named-entity-recognition - language-modeling - text-scoring - multi-class-classification - multi-label-classification - extractive-qa - news-articles-summarization --- # Bittensor Subnet 13 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:** rainbowbridge/x_dataset_55757 - **Subnet:** Bittensor Subnet 13 - **Miner Hotkey:** 5DMFuv1TnSV1kvrVpcTZShpj1cSjUAdCLmvtEecDPP6mi9dp ### 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{rainbowbridge2025datauniversex_dataset_55757, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={rainbowbridge}, year={2025}, url={https://huggingface.co/datasets/rainbowbridge/x_dataset_55757}, } ``` ### 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:** 37470244 - **Date Range:** 2025-01-22T00:00:00Z to 2025-02-13T00:00:00Z - **Last Updated:** 2025-02-18T20:41:53Z ### Data Distribution - Tweets with hashtags: 39.63% - Tweets without hashtags: 60.37% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. | Rank | Topic | Total Count | Percentage | |------|-------|-------------|-------------| | 1 | NULL | 22620167 | 60.37% | | 2 | #riyadh | 268021 | 0.72% | | 3 | #zelena | 201584 | 0.54% | | 4 | #tiktok | 145532 | 0.39% | | 5 | #bbb25 | 90347 | 0.24% | | 6 | #ad | 86092 | 0.23% | | 7 | #jhope_at_galadespiècesjaunes | 85923 | 0.23% | | 8 | #transferlerlebirliktezafere | 79350 | 0.21% | | 9 | #theheartkillersep10 | 55726 | 0.15% | | 10 | #grammys | 51671 | 0.14% | ## Update History | Date | New Instances | Total Instances | |------|---------------|-----------------| | 2025-01-29T00:13:22Z | 2565348 | 2565348 | | 2025-02-01T12:16:08Z | 8200262 | 10765610 | | 2025-02-05T00:18:46Z | 7053334 | 17818944 | | 2025-02-08T12:22:11Z | 8374018 | 26192962 | | 2025-02-12T00:28:20Z | 9601849 | 35794811 | | 2025-02-18T05:40:52Z | 855725 | 36650536 | | 2025-02-18T20:41:53Z | 819708 | 37470244 |

Kallia/stock-news-summaries

Kallia

2025-05-04T10:27:28Z

0

[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-04T10:27:25Z

null

--- dataset_info: features: - name: article dtype: string - name: summary dtype: string splits: - name: train num_bytes: 7220091 num_examples: 2680 download_size: 4340695 dataset_size: 7220091 configs: - config_name: default data_files: - split: train path: data/train-* ---

AprilBoy/interview-assesment-questions

AprilBoy

2025-05-04T09:10:09Z

0

[ "license:apache-2.0", "size_categories:n<1K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-04T08:36:56Z

null

--- license: apache-2.0 ---

taetae030/fin-term-instruct

taetae030

2025-05-04T08:12:41Z

0

[ "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "korean", "finance", "chatbot", "instruction", "question-answering" ]

[]

2025-05-04T05:49:38Z

null

--- license: apache-2.0 tags: - korean - finance - chatbot - instruction - question-answering --- # 📘 fin-term-instruct: 한국어 금융 용어 질의응답 데이터셋 `fin-term-instruct`는 **한국어 금융 용어 설명에 특화된 instruct-style 질문-응답 데이터셋**입니다. Meta의 LLaMA 시리즈 등 대형 언어모델(LLM)을 한국어 금융 챗봇으로 튜닝하기 위해 구축되었습니다. --- ## 📦 원본 출처: AI 허브 이 데이터는 AI 허브의 **"금융·법률 문서 기계독해 데이터"**를 기반으로 구축하였습니다. - 📂 원본 주소: [AI 허브 – 금융·법률 문서 기계독해 데이터](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=71610) - **데이터 구축년도**: 2022년 - **총 구축량**: 400,000건 - **데이터 형식**: JSON (지문 - 질문 - 정답 구성) ### 🔍 사용 범위 - 전체 데이터 중 **`금융경제` 분야(약 17.3%)**만 선별하여 사용 - 기존 MRC 형태에서 **instruction-style QA 포맷**으로 재가공 - GPT 기반 요약·정제를 통해 간결한 응답 형식으로 통일 ### 🏛 출처 기관 - 한국은행 - 금융위원회 - 금융감독원 - 국회입법조사처 - 법제처 - 한국금융연구원 등 --- ## 📑 데이터 구조 Alpaca-style instruction 포맷 사용: - `instruction`: 질문 (자연어 문장) - `input`: 문맥 (본 데이터셋에서는 생략됨) - `output`: 질문에 대한 짧고 정확한 답변 ### 📋 예시 | instruction | input | output | |------------------------------------------------------------------------|-------|---------------------------| | 한국은행이 업무 추진 과정에서 생길 수 있는 리스크 예방을 위해 해마다 실시하는 게 뭐야 | | 리스크 통제 자가진단 | | 데이터 사이언스에 대한 프로그램을 보강하여 2021년에 연수를 진행한 기관은 어디야 | | 한국은행 | | 디지털 경제 시대의 데이터 관리와 이용을 위해 만든 제도는 뭐야 | | 데이터 거버넌스 규정 | ### 📂 JSON 샘플 ```json { "instruction": "디지털 혁신을 위한 경제전망 시스템을 만들 때 이용한 기술은 뭐야", "input": "", "output": "인공지능" }

yoihibino/so100_complete

yoihibino

2025-05-04T07:17:33Z

0

[ "task_categories:robotics", "license:apache-2.0", "region:us", "LeRobot", "so100", "tutorial" ]

[ "robotics" ]

2025-05-04T07:14:47Z

null

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so100 - tutorial configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 20, "total_frames": 4921, "total_tasks": 1, "total_videos": 0, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:20" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": null, "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.cam1": { "dtype": "image", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": null }, "observation.images.cam2": { "dtype": "image", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": null }, "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] ```

juni3227/so100_test06

juni3227

2025-05-04T06:33:49Z

0

[ "task_categories:robotics", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "so100", "tutorial" ]

[ "robotics" ]

2025-05-04T06:33:38Z

null

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so100 - tutorial configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 5, "total_frames": 2735, "total_tasks": 1, "total_videos": 10, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:5" }, "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.airial": { "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.right_follower": { "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] ```

dgambettaphd/D_llm2_gen4_WXS_doc1000_synt64_lr1e-04_acm_FRESH

dgambettaphd

2025-05-04T05:35:40Z

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-04T05:35:36Z

null

--- dataset_info: features: - name: id_doc dtype: int64 - name: text dtype: string - name: dataset dtype: string - name: gen dtype: int64 - name: synt dtype: int64 - name: MPP dtype: float64 splits: - name: train num_bytes: 11487388 num_examples: 20000 download_size: 6936359 dataset_size: 11487388 configs: - config_name: default data_files: - split: train path: data/train-* ---

flyingbugs/OpenR1-Math-220k-pruned-keep-0.75-end-start-0.0

flyingbugs

2025-05-04T05:15:27Z

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-04T05:14:21Z

null

--- dataset_info: features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: problem_type dtype: string - name: question_type dtype: string - name: source dtype: string - name: uuid dtype: string - name: is_reasoning_complete sequence: bool - name: generations sequence: string - name: correctness_math_verify sequence: bool - name: correctness_llama sequence: bool - name: finish_reasons sequence: string - name: correctness_count dtype: int64 - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 4693668410 num_examples: 93733 download_size: 2033374084 dataset_size: 4693668410 configs: - config_name: default data_files: - split: train path: data/train-* ---

HungVu2003/opt-350m_beta_0.0_alpha_0.2_num-company_2_dataset_1_for_gen_5_v2

HungVu2003

2025-05-04T04:33:17Z

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-04T04:33:16Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 2292678 num_examples: 13750 download_size: 1031494 dataset_size: 2292678 configs: - config_name: default data_files: - split: train path: data/train-* ---

HungVu2003/opt-350m_beta_0.5_alpha_0.6_num-company_3_dataset_1_for_gen_16

HungVu2003

2025-05-04T03:47:06Z

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-04T03:47:05Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 2447771 num_examples: 12500 download_size: 1301121 dataset_size: 2447771 configs: - config_name: default data_files: - split: train path: data/train-* ---

ma921/oasst1-filtered

ma921

2025-05-04T03:04:05Z

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-04T03:04:03Z

null

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 36308251.51736613 num_examples: 16419 - name: test num_bytes: 1922678.4789915967 num_examples: 872 download_size: 18327886 dataset_size: 38230929.99635773 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

marcuscedricridia/OpenCodeInstruct-1000-sample

marcuscedricridia

2025-05-04T02:55:33Z

0

[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-04T02:55:11Z

null

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 1871144.295 num_examples: 1000 download_size: 823543 dataset_size: 1871144.295 configs: - config_name: default data_files: - split: train path: data/train-* ---

zerostratos/music_test

zerostratos

2025-05-04T02:00:04Z

0

[ "license:apache-2.0", "size_categories:n<1K", "format:arrow", "modality:audio", "library:datasets", "library:mlcroissant", "region:us" ]

[]

2025-05-04T01:55:13Z

null

--- license: apache-2.0 ---

mlfoundations-dev/mix_avg_domain

mlfoundations-dev

2025-05-04T01:58:57Z

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-04T01:54:40Z

null

--- dataset_info: features: - name: instruction_seed dtype: string - name: _source dtype: string - name: gpt41_mini_response dtype: string - name: __original_row_idx dtype: int64 - name: length dtype: int64 - name: domain dtype: string - name: r1_response dtype: string - name: r1_reasoning_content dtype: string - name: extract_solution dtype: string - name: url dtype: string - name: filename dtype: string - name: success dtype: bool - name: page_count dtype: int64 - name: page_number dtype: int64 - name: question_choices_solutions dtype: string - name: extracted_question dtype: string - name: extracted_answer_choices sequence: string - name: matched_solution dtype: string - name: qa_validation_outputs dtype: bool - name: classifier_reasoning dtype: string - name: is_organic_chemistry dtype: bool - name: ms_id dtype: int64 - name: reasoning dtype: string - name: deepseek_solution dtype: string - name: final_reasoning_trace dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: id dtype: string - name: output dtype: string - name: source dtype: string - name: license dtype: string - name: dataset dtype: string - name: split dtype: string - name: difficulty dtype: int64 - name: solution dtype: string - name: index dtype: string - name: difficulty_reasoning dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: response_seed dtype: string splits: - name: train num_bytes: 12328252550.0 num_examples: 94797 download_size: 5254951315 dataset_size: 12328252550.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

ParkSY/data_nerf_moreconcept_4styles_randomsample_anything_depthmap_normalmap

ParkSY

2025-05-04T01:28:45Z

0

[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-04T01:28:41Z

null

--- dataset_info: features: - name: input_image dtype: string - name: edit_prompt dtype: string - name: edited_image dtype: string - name: label dtype: int64 - name: depthmap dtype: string - name: normal_map dtype: string splits: - name: train num_bytes: 220509 num_examples: 438 download_size: 21321 dataset_size: 220509 configs: - config_name: default data_files: - split: train path: data/train-* ---

HungVu2003/opt-350m_beta_0.0_alpha_0.2_num-company_2_dataset_1_for_gen_3_v2

HungVu2003

2025-05-04T00:23:27Z

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-04T00:23:16Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 2007819 num_examples: 13750 download_size: 1031137 dataset_size: 2007819 configs: - config_name: default data_files: - split: train path: data/train-* ---

gabrielbo/mmlu-pro-verifiers-specific-choice

gabrielbo

2025-05-03T23:36:23Z

0

[ "license:mit", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-03T07:41:53Z

null

--- license: mit configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: question_index dtype: int64 - name: question dtype: string - name: options dtype: string - name: category dtype: string - name: correct_answer dtype: string - name: target_option_letter dtype: string - name: samples sequence: string splits: - name: train num_bytes: 979529 num_examples: 106 download_size: 287899 dataset_size: 979529 ---

Asap7772/omnimath-hint-generator-deepscaler-qwen3-8b-filtered-base-1k

Asap7772

2025-05-03T22:49:10Z

0

[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-03T22:49:05Z

null

--- dataset_info: features: - name: domain sequence: string - name: difficulty dtype: float64 - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: source dtype: string - name: note1 dtype: string - name: note2 dtype: string - name: note3 dtype: string - name: note4 dtype: string - name: note5 dtype: string - name: all_hints dtype: string splits: - name: train num_bytes: 31855009 num_examples: 4428 download_size: 16669680 dataset_size: 31855009 configs: - config_name: default data_files: - split: train path: data/train-* ---

VGraf/single_goal_alpacaeval_repeat_5_turns

VGraf

2025-05-03T22:21:10Z

0

[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-03T22:21:08Z

null

--- dataset_info: features: - name: conv list: - name: user dtype: string - name: sys dtype: string - name: id dtype: string - name: do_inference dtype: bool - name: inst dtype: string - name: key dtype: int64 - name: prompt dtype: string - name: instruction_id_list sequence: 'null' - name: kwargs sequence: 'null' - name: id dtype: int64 splits: - name: train num_bytes: 3820518 num_examples: 805 download_size: 1291948 dataset_size: 3820518 configs: - config_name: default data_files: - split: train path: data/train-* ---

kothasuhas/llp-gold-37m-1.5m_clip0.99_T2048.0_I2048

kothasuhas

2025-05-03T22:11:22Z

0

[ "size_categories:1M<n<10M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-03T22:09:30Z

null

--- dataset_info: features: - name: text dtype: string - name: p_log_probs dtype: float32 - name: q_log_probs dtype: float32 - name: num_tokens dtype: float32 - name: log_weight dtype: float64 splits: - name: train num_bytes: 3605804917.0 num_examples: 1500000 download_size: 1563384486 dataset_size: 3605804917.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

cchoi1/kodcode-complete_1000_qwen7b_sol_iter0_att10_sol5_lr1e5_3ep_topp0.9

cchoi1

2025-05-03T21:25:02Z

0

[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-03T21:25:01Z

null

--- dataset_info: features: - name: mutation_id dtype: int64 - name: task_id dtype: string - name: mutator_prompt dtype: string - name: solver_prompt dtype: string - name: response dtype: string - name: mutation_explanation dtype: string - name: mutation_info dtype: string - name: mutator_score dtype: float64 - name: solution_scores dtype: string - name: solutions dtype: string - name: solutions_explanation dtype: string - name: solutions_info dtype: string splits: - name: train num_bytes: 34375838 num_examples: 2924 download_size: 6803636 dataset_size: 34375838 configs: - config_name: default data_files: - split: train path: data/train-* ---

TylerS00/docstring-chat-ds

TylerS00

2025-05-03T21:00:29Z

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-03T21:00:05Z

null

--- dataset_info: features: - name: messages list: - name: role dtype: string - name: content dtype: string splits: - name: train num_bytes: 28919181 num_examples: 15414 download_size: 7775875 dataset_size: 28919181 configs: - config_name: default data_files: - split: train path: data/train-* ---

YYT-t/rs-math_math-Mistral-7B-Instruct-v0.2-iter_sample_7500_temp_1.0_gen_30_mlr5e-5

YYT-t

2025-05-03T20:25:31Z

0

[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-03T20:25:30Z

null

--- dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: rational_answer dtype: string splits: - name: train num_bytes: 3918 num_examples: 3 download_size: 9562 dataset_size: 3918 configs: - config_name: default data_files: - split: train path: data/train-* ---

HungVu2003/opt-350m_beta_1.0_alpha_0.0_num-company_2_dataset_1_for_gen_9_v2

HungVu2003

2025-05-03T20:15:56Z

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-03T20:15:55Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 6615412 num_examples: 12500 download_size: 3365999 dataset_size: 6615412 configs: - config_name: default data_files: - split: train path: data/train-* ---

HungVu2003/opt-350m_beta_1.0_alpha_0.0_num-company_2_dataset_0_for_gen_6_v2

HungVu2003

2025-05-03T20:09:58Z

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-03T20:09:56Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 1151427 num_examples: 12500 download_size: 701524 dataset_size: 1151427 configs: - config_name: default data_files: - split: train path: data/train-* ---

mteb/banking77

mteb

2025-05-03T20:01:44Z

6,306

3

[ "task_categories:text-classification", "annotations_creators:human-annotated", "multilinguality:monolingual", "language:eng", "license:mit", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2003.04807", "arxiv:2502.13595", "arxiv:2210.07316", "region:us", "mteb", "text" ]

[ "text-classification" ]

2022-05-17T12:14:06Z

null

--- annotations_creators: - human-annotated language: - eng license: mit multilinguality: monolingual task_categories: - text-classification task_ids: [] tags: - mteb - text configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 715028 num_examples: 10003 - name: test num_bytes: 204010 num_examples: 3080 download_size: 379134 dataset_size: 919038 ---  <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">Banking77Classification</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div> Dataset composed of online banking queries annotated with their corresponding intents. | | | |---------------|---------------------------------------------| | Task category | t2c | | Domains | Written | | Reference | https://arxiv.org/abs/2003.04807 | ## How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: ```python import mteb task = mteb.get_tasks(["Banking77Classification"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ```  To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @inproceedings{casanueva-etal-2020-efficient, address = {Online}, author = {Casanueva, I{\~n}igo and Tem{\v{c}}inas, Tadas and Gerz, Daniela and Henderson, Matthew and Vuli{\'c}, Ivan}, booktitle = {Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI}, doi = {10.18653/v1/2020.nlp4convai-1.5}, editor = {Wen, Tsung-Hsien and Celikyilmaz, Asli and Yu, Zhou and Papangelis, Alexandros and Eric, Mihail and Kumar, Anuj and Casanueva, I{\~n}igo and Shah, Rushin}, month = jul, pages = {38--45}, publisher = {Association for Computational Linguistics}, title = {Efficient Intent Detection with Dual Sentence Encoders}, url = {https://aclanthology.org/2020.nlp4convai-1.5}, year = {2020}, } @article{enevoldsen2025mmtebmassivemultilingualtext, title={MMTEB: Massive Multilingual Text Embedding Benchmark}, author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff}, publisher = {arXiv}, journal={arXiv preprint arXiv:2502.13595}, year={2025}, url={https://arxiv.org/abs/2502.13595}, doi = {10.48550/arXiv.2502.13595}, } @article{muennighoff2022mteb, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} url = {https://arxiv.org/abs/2210.07316}, doi = {10.48550/ARXIV.2210.07316}, } ``` # Dataset Statistics <details> <summary> Dataset Statistics</summary> The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("Banking77Classification") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 3080, "number_of_characters": 167036, "number_texts_intersect_with_train": 0, "min_text_length": 13, "average_text_length": 54.23246753246753, "max_text_length": 368, "unique_text": 3080, "unique_labels": 77, "labels": { "11": { "count": 40 }, "13": { "count": 40 }, "32": { "count": 40 }, "17": { "count": 40 }, "34": { "count": 40 }, "46": { "count": 40 }, "36": { "count": 40 }, "12": { "count": 40 }, "4": { "count": 40 }, "14": { "count": 40 }, "33": { "count": 40 }, "41": { "count": 40 }, "1": { "count": 40 }, "49": { "count": 40 }, "23": { "count": 40 }, "56": { "count": 40 }, "47": { "count": 40 }, "8": { "count": 40 }, "60": { "count": 40 }, "75": { "count": 40 }, "15": { "count": 40 }, "66": { "count": 40 }, "54": { "count": 40 }, "40": { "count": 40 }, "10": { "count": 40 }, "61": { "count": 40 }, "6": { "count": 40 }, "16": { "count": 40 }, "30": { "count": 40 }, "74": { "count": 40 }, "68": { "count": 40 }, "38": { "count": 40 }, "73": { "count": 40 }, "62": { "count": 40 }, "29": { "count": 40 }, "22": { "count": 40 }, "3": { "count": 40 }, "28": { "count": 40 }, "44": { "count": 40 }, "26": { "count": 40 }, "45": { "count": 40 }, "42": { "count": 40 }, "52": { "count": 40 }, "27": { "count": 40 }, "51": { "count": 40 }, "25": { "count": 40 }, "48": { "count": 40 }, "55": { "count": 40 }, "18": { "count": 40 }, "63": { "count": 40 }, "70": { "count": 40 }, "67": { "count": 40 }, "53": { "count": 40 }, "21": { "count": 40 }, "7": { "count": 40 }, "64": { "count": 40 }, "50": { "count": 40 }, "35": { "count": 40 }, "65": { "count": 40 }, "71": { "count": 40 }, "39": { "count": 40 }, "58": { "count": 40 }, "43": { "count": 40 }, "72": { "count": 40 }, "76": { "count": 40 }, "37": { "count": 40 }, "59": { "count": 40 }, "5": { "count": 40 }, "20": { "count": 40 }, "31": { "count": 40 }, "57": { "count": 40 }, "0": { "count": 40 }, "19": { "count": 40 }, "9": { "count": 40 }, "2": { "count": 40 }, "69": { "count": 40 }, "24": { "count": 40 } } }, "train": { "num_samples": 10003, "number_of_characters": 594916, "number_texts_intersect_with_train": null, "min_text_length": 13, "average_text_length": 59.47375787263821, "max_text_length": 433, "unique_text": 10003, "unique_labels": 77, "labels": { "11": { "count": 153 }, "13": { "count": 139 }, "32": { "count": 112 }, "17": { "count": 167 }, "34": { "count": 166 }, "46": { "count": 143 }, "36": { "count": 126 }, "12": { "count": 112 }, "4": { "count": 127 }, "14": { "count": 112 }, "33": { "count": 118 }, "41": { "count": 82 }, "1": { "count": 110 }, "49": { "count": 115 }, "23": { "count": 35 }, "56": { "count": 111 }, "47": { "count": 149 }, "8": { "count": 157 }, "60": { "count": 97 }, "75": { "count": 180 }, "15": { "count": 187 }, "66": { "count": 171 }, "54": { "count": 129 }, "40": { "count": 98 }, "10": { "count": 59 }, "61": { "count": 146 }, "6": { "count": 181 }, "16": { "count": 168 }, "30": { "count": 121 }, "74": { "count": 121 }, "68": { "count": 102 }, "38": { "count": 106 }, "73": { "count": 135 }, "62": { "count": 103 }, "29": { "count": 121 }, "22": { "count": 86 }, "3": { "count": 87 }, "28": { "count": 182 }, "44": { "count": 105 }, "26": { "count": 173 }, "45": { "count": 159 }, "42": { "count": 121 }, "52": { "count": 169 }, "27": { "count": 133 }, "51": { "count": 162 }, "25": { "count": 153 }, "48": { "count": 148 }, "55": { "count": 108 }, "18": { "count": 61 }, "63": { "count": 175 }, "70": { "count": 113 }, "67": { "count": 128 }, "53": { "count": 161 }, "21": { "count": 122 }, "7": { "count": 156 }, "64": { "count": 172 }, "50": { "count": 95 }, "35": { "count": 137 }, "65": { "count": 113 }, "71": { "count": 126 }, "39": { "count": 129 }, "58": { "count": 114 }, "43": { "count": 120 }, "72": { "count": 41 }, "76": { "count": 163 }, "37": { "count": 97 }, "59": { "count": 145 }, "5": { "count": 171 }, "20": { "count": 160 }, "31": { "count": 121 }, "57": { "count": 114 }, "0": { "count": 159 }, "19": { "count": 177 }, "9": { "count": 129 }, "2": { "count": 126 }, "69": { "count": 104 }, "24": { "count": 129 } } } } ``` </details> --- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*

Samarth0710/neurips-2024-peer-reviews

Samarth0710

2025-05-03T18:22:43Z

0

[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-03T18:14:28Z

null

--- dataset_info: features: - name: paper_id dtype: string - name: title dtype: string - name: abstract dtype: string - name: pdf_url dtype: string - name: reviews list: - name: confidence dtype: int64 - name: rating dtype: int64 - name: review_id dtype: string - name: review_text dtype: string splits: - name: train num_bytes: 50663353 num_examples: 4236 download_size: 26840387 dataset_size: 50663353 configs: - config_name: default data_files: - split: train path: data/train-* ---

HungVu2003/opt-350m_beta_0.0_alpha_0.0_num-company_2_dataset_1_for_gen_16_v2

HungVu2003

2025-05-03T18:10:38Z

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-03T18:10:36Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 819598 num_examples: 12500 download_size: 566940 dataset_size: 819598 configs: - config_name: default data_files: - split: train path: data/train-* ---

HungVu2003/opt-350m_beta_0.0_alpha_0.0_num-company_2_dataset_1_for_gen_15_v2

HungVu2003

2025-05-03T18:05:18Z

0

[ "size_categories:10K<n<100K", "modality:text", "region:us" ]

[]

2025-05-03T18:05:17Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 806306 num_examples: 12500 download_size: 555290 dataset_size: 806306 configs: - config_name: default data_files: - split: train path: data/train-* ---

HungVu2003/opt-350m_beta_0.0_alpha_0.0_num-company_2_dataset_0_for_gen_2_v2

HungVu2003

2025-05-03T16:56:08Z

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-03T16:56:05Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 818212 num_examples: 12500 download_size: 565767 dataset_size: 818212 configs: - config_name: default data_files: - split: train path: data/train-* ---

AleNunezArroyo/spacecraft-dataset

AleNunezArroyo

2025-05-03T16:53:31Z

81

0

[ "task_categories:image-classification", "size_categories:1K<n<10K", "format:imagefolder", "modality:image", "library:datasets", "library:mlcroissant", "arxiv:2106.08186", "region:us", "space", "spacecraft" ]

[ "image-classification" ]

2025-04-25T14:03:12Z

null

--- task_categories: - image-classification tags: - space - spacecraft size_categories: - 1K<n<10K --- 🛰️ Dataset: A Spacecraft Dataset for Detection, Segmentation and Parts Recognition This dataset is an adaptation of the work “A Spacecraft Dataset for Detection, Segmentation and Parts Recognition,” using the same data but reformatted for use on HuggingFace. I am not the original author; the reference to the original work is provided below. The only modification is that the test set was created by sampling from the original validation set. In the following repository, you will find a basic YOLO training setup, along with data visualization and conversion to YOLO-compatible format. - [YOLO Training Repository on GitHub](https://github.com/AleNunezArroyo/spacecraft-detection) - 📘 [Paper: _A Spacecraft Dataset for Detection, Segmentation and Parts Recognition_](https://arxiv.org/pdf/2106.08186) - 💾 [Official repository on GitHub](https://github.com/Yurushia1998/SatelliteDataset) ```bash @misc{hoang2021spacecraftdatasetdetectionsegmentation, title={A Spacecraft Dataset for Detection, Segmentation and Parts Recognition}, author={Dung Anh Hoang and Bo Chen and Tat-Jun Chin}, year={2021}, eprint={2106.08186}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2106.08186}, } ```

HungVu2003/opt-350m_beta_0.5_alpha_0.6_num-company_3_dataset_0_for_gen_13

HungVu2003

2025-05-03T16:41:46Z

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-03T16:41:44Z

null

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 7304720 num_examples: 12500 download_size: 1973081 dataset_size: 7304720 configs: - config_name: default data_files: - split: train path: data/train-* ---

littleGuagua/x_dataset_31933

littleGuagua

2025-05-03T16:40:50Z

1,275

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" ]

2025-01-26T13:49:43Z

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_31933 - **Subnet:** Bittensor Subnet 13 - **Miner Hotkey:** 5Fv7zb16pPjz3PRat6fhJytGWW53dLFtQUQvnfaX7bpR5YEy ### 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_31933, 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_31933}, } ``` ### 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:** 45517419 - **Date Range:** 2025-01-21T00:00:00Z to 2025-02-12T00:00:00Z - **Last Updated:** 2025-02-18T21:16:27Z ### Data Distribution - Tweets with hashtags: 41.23% - Tweets without hashtags: 58.77% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. | Rank | Topic | Total Count | Percentage | |------|-------|-------------|-------------| | 1 | NULL | 26750172 | 58.77% | | 2 | #riyadh | 324609 | 0.71% | | 3 | #zelena | 201974 | 0.44% | | 4 | #tiktok | 178927 | 0.39% | | 5 | #ad | 105436 | 0.23% | | 6 | #bbb25 | 82587 | 0.18% | | 7 | #jhope_at_galadespiècesjaunes | 70006 | 0.15% | | 8 | #bbmzansi | 62802 | 0.14% | | 9 | #trump | 57439 | 0.13% | | 10 | #pr | 56224 | 0.12% | ## Update History | Date | New Instances | Total Instances | |------|---------------|-----------------| | 2025-01-26T13:50:33Z | 2863904 | 2863904 | | 2025-01-30T01:54:18Z | 10112334 | 12976238 | | 2025-02-02T13:57:30Z | 9473545 | 22449783 | | 2025-02-06T02:00:48Z | 7706558 | 30156341 | | 2025-02-09T14:03:35Z | 5776384 | 35932725 | | 2025-02-13T02:27:40Z | 8126791 | 44059516 | | 2025-02-18T06:15:19Z | 648961 | 44708477 | | 2025-02-18T21:16:27Z | 808942 | 45517419 |

littleGuagua/x_dataset_11627

littleGuagua

2025-05-03T16:37:25Z

1,557

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" ]

2025-01-26T13:13:48Z

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_11627 - **Subnet:** Bittensor Subnet 13 - **Miner Hotkey:** 5FUByNzgdM2eukk6SwetFsZ4EPTxRqaV4YNEhNcusS1SxRVX ### Dataset Summary This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed data from X (formerly Twitter). The data is continuously updated by network miners, providing a real-time stream of tweets for various analytical and machine learning tasks. For more information about the dataset, please visit the [official repository](https://github.com/macrocosm-os/data-universe). ### Supported Tasks The versatility of this dataset allows researchers and data scientists to explore various aspects of social media dynamics and develop innovative applications. Users are encouraged to leverage this data creatively for their specific research or business needs. For example: - Sentiment Analysis - Trend Detection - Content Analysis - User Behavior Modeling ### Languages Primary language: Datasets are mostly English, but can be multilingual due to decentralized ways of creation. ## Dataset Structure ### Data Instances Each instance represents a single tweet with the following fields: ### Data Fields - `text` (string): The main content of the tweet. - `label` (string): Sentiment or topic category of the tweet. - `tweet_hashtags` (list): A list of hashtags used in the tweet. May be empty if no hashtags are present. - `datetime` (string): The date when the tweet was posted. - `username_encoded` (string): An encoded version of the username to maintain user privacy. - `url_encoded` (string): An encoded version of any URLs included in the tweet. May be empty if no URLs are present. ### Data Splits This dataset is continuously updated and does not have fixed splits. Users should create their own splits based on their requirements and the data's timestamp. ## Dataset Creation ### Source Data Data is collected from public tweets on X (Twitter), adhering to the platform's terms of service and API usage guidelines. ### Personal and Sensitive Information All usernames and URLs are encoded to protect user privacy. The dataset does not intentionally include personal or sensitive information. ## Considerations for Using the Data ### Social Impact and Biases Users should be aware of potential biases inherent in X (Twitter) data, including demographic and content biases. This dataset reflects the content and opinions expressed on X and should not be considered a representative sample of the general population. ### Limitations - Data quality may vary due to the decentralized nature of collection and preprocessing. - The dataset may contain noise, spam, or irrelevant content typical of social media platforms. - Temporal biases may exist due to real-time collection methods. - The dataset is limited to public tweets and does not include private accounts or direct messages. - Not all tweets contain hashtags or URLs. ## Additional Information ### Licensing Information The dataset is released under the MIT license. The use of this dataset is also subject to X Terms of Use. ### Citation Information If you use this dataset in your research, please cite it as follows: ``` @misc{littleGuagua2025datauniversex_dataset_11627, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={littleGuagua}, year={2025}, url={https://huggingface.co/datasets/littleGuagua/x_dataset_11627}, } ``` ### Contributions To report issues or contribute to the dataset, please contact the miner or use the Bittensor Subnet 13 governance mechanisms. ## Dataset Statistics [This section is automatically updated] - **Total Instances:** 149000631 - **Date Range:** 2025-01-21T00:00:00Z to 2025-02-10T00:00:00Z - **Last Updated:** 2025-02-18T20:47:28Z ### Data Distribution - Tweets with hashtags: 42.62% - Tweets without hashtags: 57.38% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. | Rank | Topic | Total Count | Percentage | |------|-------|-------------|-------------| | 1 | NULL | 85489881 | 57.38% | | 2 | #riyadh | 1033096 | 0.69% | | 3 | #zelena | 790108 | 0.53% | | 4 | #tiktok | 618215 | 0.41% | | 5 | #bbb25 | 362232 | 0.24% | | 6 | #ad | 356819 | 0.24% | | 7 | #jhope_at_galadespiècesjaunes | 234343 | 0.16% | | 8 | #bbmzansi | 207541 | 0.14% | | 9 | #pr | 188395 | 0.13% | | 10 | #yahooニュース | 178958 | 0.12% | ## Update History | Date | New Instances | Total Instances | |------|---------------|-----------------| | 2025-01-26T13:14:32Z | 2274090 | 2274090 | | 2025-01-30T01:26:02Z | 29523249 | 31797339 | | 2025-02-02T13:36:10Z | 29333848 | 61131187 | | 2025-02-06T01:47:05Z | 28740147 | 89871334 | | 2025-02-09T14:00:59Z | 29293177 | 119164511 | | 2025-02-13T02:15:32Z | 28379764 | 147544275 | | 2025-02-18T05:45:25Z | 808939 | 148353214 | | 2025-02-18T20:47:28Z | 647417 | 149000631 |

FrancophonIA/Lexique_ZLEA

FrancophonIA

2025-05-03T16:34:27Z

0

[ "task_categories:translation", "language:fra", "language:eng", "region:us" ]

[ "translation" ]

2025-05-03T16:33:27Z

null

--- language: - fra - eng viewer: false task_categories: - translation --- > [!NOTE] > Dataset origin: https://publications.gc.ca/site/eng/9.800970/publication.html

amekerishvili/ATCO2_full_files

amekerishvili

2025-05-03T16:14:01Z

0

[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-03T12:01:28Z

null

--- dataset_info: features: - name: ID dtype: string - name: audio_file dtype: string - name: start_time dtype: float64 - name: end_time dtype: float64 - name: airport dtype: string - name: channel dtype: string - name: frequency dtype: string - name: time dtype: string - name: waypoints dtype: string - name: callsigns dtype: string - name: ground_truth_raw dtype: string - name: ground_truth dtype: string - name: non_Eng_ground_truth dtype: string - name: tags dtype: string - name: values_tags dtype: string - name: commands_tags dtype: string - name: callsigns_tags dtype: string - name: unnamed_tags dtype: string splits: - name: train num_bytes: 1558206 num_examples: 612 - name: validation num_bytes: 362174 num_examples: 136 - name: test num_bytes: 356108 num_examples: 129 download_size: 397317 dataset_size: 2276488 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

haibaraconan/tif

haibaraconan

2025-05-03T16:12:00Z

310

1

[ "size_categories:100B<n<1T", "modality:image", "region:us", "art" ]

[]

2024-07-22T04:11:31Z

null

--- tags: - art size_categories: - 100B<n<1T --- This directory includes a few sample datasets to get you started. * `california_housing_data*.csv` is California housing data from the 1990 US Census; more information is available at: https://developers.google.com/machine-learning/crash-course/california-housing-data-description * `mnist_*.csv` is a small sample of the [MNIST database](https://en.wikipedia.org/wiki/MNIST_database), which is described at: http://yann.lecun.com/exdb/mnist/ * `anscombe.json` contains a copy of [Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet); it was originally described in Anscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American Statistician. 27 (1): 17-21. JSTOR 2682899. and our copy was prepared by the [vega_datasets library](https://github.com/altair-viz/vega_datasets/blob/4f67bdaad10f45e3549984e17e1b3088c731503d/vega_datasets/_data/anscombe.json).

jaeyong2/Qwen3-06B-Ko-KTO

jaeyong2

2025-05-03T15:42:25Z

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-03T07:13:48Z

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: label dtype: bool splits: - name: train num_bytes: 78379361 num_examples: 13526 download_size: 24394518 dataset_size: 78379361 configs: - config_name: default data_files: - split: train path: data/train-* ---

mlfoundations-dev/no_pipeline_math_100k

mlfoundations-dev

2025-05-03T15:39:48Z

0

[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-03T15:39:07Z

null

--- dataset_info: features: - name: instruction_seed dtype: string - name: reasoning dtype: string - name: deepseek_solution dtype: string - name: source dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: shard_id dtype: string splits: - name: train num_bytes: 2409250240.506329 num_examples: 100000 download_size: 1062629811 dataset_size: 2409250240.506329 configs: - config_name: default data_files: - split: train path: data/train-* ---

mlfoundations-dev/no_pipeline_math_1k

mlfoundations-dev

2025-05-03T15:38:46Z

0

[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-03T15:38:44Z

null

--- dataset_info: features: - name: instruction_seed dtype: string - name: reasoning dtype: string - name: deepseek_solution dtype: string - name: source dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: shard_id dtype: string splits: - name: train num_bytes: 24092502.40506329 num_examples: 1000 download_size: 10837192 dataset_size: 24092502.40506329 configs: - config_name: default data_files: - split: train path: data/train-* ---

t2ance/polymnist-upd10

t2ance

2025-05-03T15:30:26Z

0

[ "region:us" ]

[]

2025-05-03T14:29:24Z

null

--- dataset_info: features: - name: m0 dtype: image - name: m1 dtype: image - name: m2 dtype: image - name: m3 dtype: image - name: m4 dtype: image - name: m5 dtype: image - name: m6 dtype: image - name: m7 dtype: image - name: m8 dtype: image - name: m9 dtype: image - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' '5': '5' '6': '6' '7': '7' '8': '8' '9': '9' - name: sample_id dtype: string splits: - name: train num_bytes: 632476369.0 num_examples: 50000 - name: validation num_bytes: 126523764.0 num_examples: 10000 - name: test num_bytes: 126347943.0 num_examples: 10000 download_size: 916377594 dataset_size: 885348076.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

gunnybd01/Momentum_smr

gunnybd01

2025-05-03T15:13:39Z

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-02T15:19:07Z

null

--- dataset_info: features: - name: Keys dtype: string - name: reports dtype: string - name: labels dtype: string splits: - name: train num_bytes: 179112899 num_examples: 60000 download_size: 29686617 dataset_size: 179112899 configs: - config_name: default data_files: - split: train path: data/train-* ---

FrancophonIA/Vocabulaire-de-la-biologie-2017

FrancophonIA

2025-05-03T15:11:38Z

6

0

[ "task_categories:translation", "language:fra", "language:eng", "region:us" ]

[ "translation" ]

2025-04-28T20:15:12Z

null

--- language: - fra - eng viewer: false task_categories: - translation --- > [!NOTE] > Dataset origin: https://www.culture.gouv.fr/fr/thematiques/langue-francaise-et-langues-de-france/agir-pour-les-langues/moderniser-et-enrichir-la-langue-francaise/nos-publications/Vocabulaire-de-la-biologie-2017 ## Description La Délégation générale à la langue française et aux langues de France publie pour la première fois un Vocabulaire de la biologie : 611 termes et définitions concernant des notions nouvelles dont beaucoup n’avaient pas de désignation en français.

FrancophonIA/Vocabulaire-du-Petrole-et-du-Gaz-2015

FrancophonIA

2025-05-03T15:08:24Z

6

0

[ "task_categories:translation", "language:fra", "language:eng", "region:us" ]

[ "translation" ]

2025-04-28T20:19:44Z

null

--- language: - fra - eng viewer: false task_categories: - translation --- > [!NOTE] > Dataset origin: https://www.culture.gouv.fr/fr/thematiques/langue-francaise-et-langues-de-france/agir-pour-les-langues/moderniser-et-enrichir-la-langue-francaise/nos-publications/Vocabulaire-du-Petrole-et-du-Gaz-2015 ## Description Ce vocabulaire est une édition revue et complétée de l’édition de 2007. Il reprend plus de 300 termes et définitions concernant des notions nouvelles dont la plupart n’avaient pas encore de désignation en français.

FrancophonIA/References-2016-l-enrichissement-de-la-langue-francaise

FrancophonIA

2025-05-03T15:07:10Z

6

0

[ "task_categories:translation", "language:fra", "language:eng", "region:us" ]

[ "translation" ]

2025-04-28T20:17:26Z

null

--- language: - fra - eng viewer: false task_categories: - translation --- > [!NOTE] > Dataset origin: https://www.culture.gouv.fr/fr/thematiques/langue-francaise-et-langues-de-france/agir-pour-les-langues/moderniser-et-enrichir-la-langue-francaise/nos-publications/References-2016-l-enrichissement-de-la-langue-francaise ## Description Pour permettre de nommer en français les réalités nouvelles et les innovations techniques, le dispositif d'enrichissement de la langue française mis en place par le décret du 3 juillet 1996 (et modifié par le décret du 25 mars 2015) élabore une terminologie de qualité, conforme aux règles de formation des mots, facilement compréhensible et faisant référence.

FrancophonIA/Vocabulaire-de-la-sante-2013

FrancophonIA

2025-05-03T14:59:54Z

3

0

[ "task_categories:translation", "language:fra", "language:eng", "region:us" ]

[ "translation" ]

2025-04-29T20:41:50Z

null

--- language: - fra - eng viewer: false task_categories: - translation --- > [!NOTE] > Dataset origin: https://www.culture.gouv.fr/fr/thematiques/langue-francaise-et-langues-de-france/agir-pour-les-langues/moderniser-et-enrichir-la-langue-francaise/nos-publications/Vocabulaire-de-la-sante-2013

FrancophonIA/Vocabulaire-de-l-audiovisuel-et-de-la-communication-2010

FrancophonIA

2025-05-03T14:43:53Z

3

0

[ "task_categories:translation", "language:fra", "language:eng", "region:us" ]

[ "translation" ]

2025-04-29T20:50:04Z

null

--- language: - fra - eng viewer: false task_categories: - translation --- > [!NOTE] > Dataset origin: https://www.culture.gouv.fr/fr/thematiques/langue-francaise-et-langues-de-france/agir-pour-les-langues/moderniser-et-enrichir-la-langue-francaise/nos-publications/Vocabulaire-de-l-audiovisuel-et-de-la-communication-2010