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mteb/KurdishSentimentClassification
mteb
2025-05-06T12:38:08Z
0
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:kur", "license:cc-by-4.0", "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" ]
2025-05-06T12:38:04Z
0
--- annotations_creators: - derived language: - kur license: cc-by-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: 698098 num_examples: 6000 - name: test num_bytes: 221218 num_examples: 1987 download_size: 444460 dataset_size: 919316 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* tags: - mteb - text --- <!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md --> <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;">KurdishSentimentClassification</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> Kurdish Sentiment Dataset | | | |---------------|---------------------------------------------| | Task category | t2c | | Domains | Web, Written | | Reference | https://link.springer.com/article/10.1007/s10579-023-09716-6 | ## 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(["KurdishSentimentClassification"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ``` <!-- Datasets want link to arxiv in readme to autolink dataset with paper --> 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{article, author = {Badawi, Soran and Kazemi, Arefeh and Rezaie, Vali}, doi = {10.1007/s10579-023-09716-6}, journal = {Language Resources and Evaluation}, month = {01}, pages = {1-20}, title = {KurdiSent: a corpus for kurdish sentiment analysis}, 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("KurdishSentimentClassification") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 1987, "number_of_characters": 111504, "number_texts_intersect_with_train": 5, "min_text_length": 9, "average_text_length": 56.11675893306492, "max_text_length": 282, "unique_text": 1987, "unique_labels": 2, "labels": { "1": { "count": 1065 }, "0": { "count": 922 } } }, "train": { "num_samples": 6000, "number_of_characters": 356322, "number_texts_intersect_with_train": null, "min_text_length": 7, "average_text_length": 59.387, "max_text_length": 7639, "unique_text": 5753, "unique_labels": 2, "labels": { "1": { "count": 3000 }, "0": { "count": 3000 } } } } ``` </details> --- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*
alckasoc/triviaqa_expel_train_100
alckasoc
2024-10-15T22:47:35Z
13
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-10-15T22:47:32Z
0
--- dataset_info: features: - name: question dtype: string - name: question_id dtype: string - name: question_source dtype: string - name: entity_pages struct: - name: doc_source sequence: string - name: filename sequence: string - name: title sequence: string - name: wiki_context sequence: string - name: search_results struct: - name: description sequence: string - name: filename sequence: string - name: rank sequence: int64 - name: search_context sequence: string - name: title sequence: string - name: url sequence: string - name: answer struct: - name: aliases sequence: string - name: matched_wiki_entity_name dtype: string - name: normalized_aliases sequence: string - name: normalized_matched_wiki_entity_name dtype: string - name: normalized_value dtype: string - name: type dtype: string - name: value dtype: string splits: - name: train num_bytes: 7232973 num_examples: 100 download_size: 4100112 dataset_size: 7232973 configs: - config_name: default data_files: - split: train path: data/train-* ---
DataScienceUIBK/ComplexTempQA
DataScienceUIBK
2024-09-22T21:26:34Z
42
3
[ "task_categories:question-answering", "language:en", "license:cc0-1.0", "size_categories:100M<n<1B", "region:us" ]
[ "question-answering" ]
2024-06-05T09:26:20Z
1
--- license: cc0-1.0 task_categories: - question-answering language: - en size_categories: - 100M<n<1B --- # ComplexTempQA Dataset ComplexTempQA is a large-scale dataset designed for complex temporal question answering (TQA). It consists of over 100 million question-answer pairs, making it one of the most extensive datasets available for TQA. The dataset is generated using data from Wikipedia and Wikidata and spans questions over a period of 36 years (1987-2023). **Note:** We have a smaller version consisting of questions from the time period 1987 until 2007. ## Dataset Description ComplexTempQA categorizes questions into three main types: - Attribute Questions - Comparison Questions - Counting Questions These categories are further divided based on their relation to events, entities, or time periods. ### Question Types and Counts | | Question Type | Subtype | Count | |--|-----------------------|---------------------|---------------| |1a| Attribute | Event | 83,798 | |1b| Attribute | Entity | 84,079 | |1c| Attribute | Time | 9,454 | |2a| Comparison | Event | 25,353,340 | |2b| Comparison | Entity | 74,678,117 | |2c| Comparison | Time | 54,022,952 | |3a| Counting | Event | 18,325 | |3b| Counting | Entity | 10,798 | |3c| Counting | Time | 12,732 | | | Multi-Hop | | 76,933 | | | Unnamed Event | | 8,707,123 | | | **Total** | | **100,228,457**| ### Metadata - **id**: A unique identifier for each question. - **question**: The text of the question being asked. - **answer**: The answer(s) to the question. - **type**: The type of question based on the dataset’s taxonomy. - **rating**: A numerical rating indicating the difficulty of the question (`0` for easy, `1` for hard). - **timeframe**: The start and end dates relevant to the question. - **question_entity**: List of Wikidata IDs related to the entities in the question. - **answer_entity**: List of Wikidata IDs related to the entities in the answer. - **question_country**: List of Wikidata IDs of the countries associated with the questioned entities or events. - **answer_country**: List of Wikidata IDs of the countries associated with the answered entities or events. - **is_unnamed**: A flag indicating if the question contains an implicitly described event (`1` for yes, `0` for no). ## Dataset Characteristics ### Size ComplexTempQA comprises over 100 million question-answer pairs, focusing on events, entities, and time periods from 1987 to 2023. ### Complexity Questions require advanced reasoning skills, including multi-hop question answering, temporal aggregation, and across-time comparisons. ### Taxonomy The dataset follows a unique taxonomy categorizing questions into attributes, comparisons, and counting types, ensuring comprehensive coverage of temporal queries. ### Evaluation The dataset has been evaluated for readability, ease of answering before and after web searches, and overall clarity. Human raters have assessed a sample of questions to ensure high quality. ## Usage ### Evaluation and Training ComplexTempQA can be used for: - Evaluating the temporal reasoning capabilities of large language models (LLMs) - Fine-tuning language models for better temporal understanding - Developing and testing retrieval-augmented generation (RAG) systems ### Research Applications The dataset supports research in: - Temporal question answering - Information retrieval - Language understanding ### Adaptation and Continual Learning ComplexTempQA's temporal metadata facilitates the development of online adaptation and continual training approaches for LLMs, aiding in the exploration of time-based learning and evaluation. ## Access The dataset and code are freely available at [https://github.com/DataScienceUIBK/ComplexTempQA](https://github.com/DataScienceUIBK/ComplexTempQA).
gigant/tib-bench-mm-part2
gigant
2025-02-02T22:06:19Z
7
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-02-02T22:03:12Z
0
--- dataset_info: features: - name: doi dtype: string - name: title dtype: string - name: url dtype: string - name: video_url dtype: string - name: license dtype: string - name: subject dtype: string - name: genre dtype: string - name: release_year dtype: string - name: author dtype: string - name: contributors dtype: string - name: abstract dtype: string - name: transcript dtype: string - name: transcript_segments struct: - name: avg_logprob sequence: float64 - name: compression_ratio sequence: float64 - name: end sequence: float64 - name: id sequence: int64 - name: no_speech_prob sequence: float64 - name: seek sequence: int64 - name: start sequence: float64 - name: temperature sequence: float64 - name: text sequence: string - name: tokens sequence: sequence: int64 - name: keyframes struct: - name: frames sequence: sequence: int64 - name: slide sequence: string - name: timestamp sequence: sequence: float64 - name: language dtype: string - name: slides list: - name: bytes dtype: binary - name: path dtype: 'null' splits: - name: train num_bytes: 1896483981 num_examples: 465 download_size: 1850736411 dataset_size: 1896483981 configs: - config_name: default data_files: - split: train path: data/train-* ---
RyanYr/reflect_ministral8Bit_math-test_t2_binlabel
RyanYr
2024-11-20T18:53:19Z
15
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-11-20T05:24:50Z
0
--- dataset_info: features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: response@0 sequence: string - name: response@1 sequence: string - name: response@2 sequence: string - name: response@0_ans sequence: string - name: response@0_correctness sequence: bool - name: response@2_ans sequence: string - name: response@2_correctness sequence: bool splits: - name: train num_bytes: 2474233 num_examples: 500 download_size: 1001745 dataset_size: 2474233 configs: - config_name: default data_files: - split: train path: data/train-* ---
bobertonthebuilder/zxyxxxl_batch_39
bobertonthebuilder
2025-03-20T05:54:01Z
13
0
[ "format:parquet", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-03-20T05:54:00Z
0
--- dataset_info: features: [] splits: - name: train num_bytes: 0 num_examples: 0 download_size: 324 dataset_size: 0 configs: - config_name: default data_files: - split: train path: data/train-* ---
shaznin/task3_impact_classification
shaznin
2025-01-25T05:44:50Z
55
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-01-25T05:10:22Z
0
--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 31720602 num_examples: 8040 - name: test num_bytes: 7570953 num_examples: 2010 download_size: 16093442 dataset_size: 39291555 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
reasoning-proj/c_dfiltered_DeepSeek-R1-Distill-Qwen-32B_madversarial_continue_unrelated_t10
reasoning-proj
2025-05-08T21:02:12Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-08T18:47:25Z
0
--- dataset_info: features: - name: question dtype: string - name: answer_content dtype: string - name: reference_answer dtype: string - name: id dtype: string - name: metadata struct: - name: question_license dtype: string - name: question_source dtype: string - name: model_name dtype: string - name: verifier_score dtype: int64 - name: mutated_answer_content dtype: string - name: continuation_model dtype: string - name: continuation_1 dtype: string - name: complete_answer_1 dtype: string - name: continuation_2 dtype: string - name: complete_answer_2 dtype: string - name: continuation_3 dtype: string - name: complete_answer_3 dtype: string - name: continuation_4 dtype: string - name: complete_answer_4 dtype: string - name: continuation_5 dtype: string - name: complete_answer_5 dtype: string - name: continuation_6 dtype: string - name: complete_answer_6 dtype: string - name: continuation_7 dtype: string - name: complete_answer_7 dtype: string - name: continuation_8 dtype: string - name: complete_answer_8 dtype: string splits: - name: train num_bytes: 48939914 num_examples: 304 download_size: 20832742 dataset_size: 48939914 configs: - config_name: default data_files: - split: train path: data/train-* ---
JakeOh/iself-preferences-gsm8k-llama1b
JakeOh
2024-12-18T05:46:37Z
31
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-12-17T09:42:14Z
0
--- dataset_info: features: - name: doc_hash dtype: string - name: prompt list: - name: content dtype: string - name: role dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 90945021 num_examples: 39558 - name: test num_bytes: 20043033 num_examples: 8732 download_size: 47888009 dataset_size: 110988054 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
ChavyvAkvar/synthetic-trades-BTC-batch-48
ChavyvAkvar
2025-06-04T11:14:48Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-06-04T11:13:50Z
0
--- dataset_info: features: - name: scenario_id dtype: string - name: final_pnl_ratio dtype: float64 - name: max_drawdown dtype: float64 - name: total_trades dtype: int64 - name: synthetic_ohlc_open sequence: float64 - name: synthetic_ohlc_high sequence: float64 - name: synthetic_ohlc_low sequence: float64 - name: synthetic_ohlc_close sequence: float64 - name: garch_params_used_for_sim_str dtype: string - name: strategy_params_str dtype: string - name: strategy_exit_rules_str dtype: string splits: - name: train num_bytes: 923450692 num_examples: 1000 download_size: 924478875 dataset_size: 923450692 configs: - config_name: default data_files: - split: train path: data/train-* ---
airabbitX/my-distiset-9cb75714
airabbitX
2025-02-27T16:37:17Z
12
0
[ "task_categories:text-classification", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "library:distilabel", "region:us", "synthetic", "distilabel", "rlaif", "datacraft" ]
[ "text-classification" ]
2025-02-27T16:37:13Z
0
--- size_categories: n<1K task_categories: - text-classification dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': politics '1': business '2': sports '3': technology '4': health '5': entertainment splits: - name: train num_bytes: 304 num_examples: 1 download_size: 2664 dataset_size: 304 configs: - config_name: default data_files: - split: train path: data/train-* tags: - synthetic - distilabel - rlaif - datacraft --- <p align="left"> <a href="https://github.com/argilla-io/distilabel"> <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/> </a> </p> # Dataset Card for my-distiset-9cb75714 This dataset has been created with [distilabel](https://distilabel.argilla.io/). ## Dataset Summary This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI: ```console distilabel pipeline run --config "https://huggingface.co/datasets/airabbitX/my-distiset-9cb75714/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/airabbitX/my-distiset-9cb75714/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "label": 1, "text": "A recent survey suggests that nearly all of the world\u0027s largest economies are experiencing economic downturns, with many nations struggling to recover from the impact of the COVID-19 pandemic. As a result, many people are starting to question the effectiveness of the current economic system." } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("airabbitX/my-distiset-9cb75714", "default") ``` Or simply as it follows, since there's only one configuration and is named `default`: ```python from datasets import load_dataset ds = load_dataset("airabbitX/my-distiset-9cb75714") ``` </details>
Vikir2411CS19/TrialDataset
Vikir2411CS19
2025-06-18T13:35:12Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-06-18T11:35:14Z
0
--- dataset_info: features: - name: image dtype: image - name: Question dtype: string - name: Answer dtype: string splits: - name: train num_bytes: 89226752.0 num_examples: 798 download_size: 13738737 dataset_size: 89226752.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
taewan21/klue-mrc-gpt4o-questions-answers-with-1-to-4-negative-samples
taewan21
2025-05-12T07:26:20Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-12T07:26:19Z
0
--- dataset_info: features: - name: title dtype: string - name: news_category dtype: string - name: source dtype: string - name: context dtype: string - name: question dtype: string - name: question_type dtype: int64 - name: is_impossible dtype: bool - name: answer_text dtype: string - name: answer_start dtype: int64 - name: negative_samples sequence: string - name: search_result sequence: string - name: answer dtype: string - name: extracted_ref_numbers sequence: int64 splits: - name: train num_bytes: 5307197 num_examples: 286 download_size: 3068347 dataset_size: 5307197 configs: - config_name: default data_files: - split: train path: data/train-* ---
KodCode/KodCode-V1-SFT-4o
KodCode
2025-03-16T21:59:33Z
191
5
[ "task_categories:question-answering", "language:en", "license:cc-by-nc-4.0", "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2503.02951", "region:us", "code" ]
[ "question-answering" ]
2025-03-13T07:17:15Z
0
--- dataset_info: features: - name: style dtype: string - name: subset dtype: string - name: question_id dtype: string - name: question dtype: string - name: solution dtype: string - name: test_code dtype: string - name: test_info list: - name: docstring dtype: string - name: function_declaration dtype: string - name: function_name dtype: string - name: parameter_list dtype: string - name: gpt_pass_sequence sequence: int64 - name: gpt_pass_trial_num dtype: int64 - name: gpt_difficulty dtype: string - name: gpt_pass_percentage dtype: float64 - name: 4o_pass_sequence sequence: int64 - name: 4o_pass_trial_num dtype: int64 - name: 4o_correctness dtype: string - name: 4o_solution dtype: string - name: metadata struct: - name: original_instruction dtype: string - name: prompt_id dtype: string - name: row_id dtype: int64 - name: seed_ids dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 2063448156.1198518 num_examples: 262659 - name: incorrect num_bytes: 1153990877.8945835 num_examples: 146893 download_size: 1294120098 dataset_size: 3217439034.0144353 configs: - config_name: default data_files: - split: train path: data/train-* - split: incorrect path: data/incorrect-* license: cc-by-nc-4.0 task_categories: - question-answering language: - en tags: - code size_categories: - 100K<n<1M --- # 🐱 KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding KodCode is the largest fully-synthetic open-source dataset providing verifiable solutions and tests for coding tasks. It contains 12 distinct subsets spanning various domains (from algorithmic to package-specific knowledge) and difficulty levels (from basic coding exercises to interview and competitive programming challenges). KodCode is designed for both supervised fine-tuning (SFT) and RL tuning. - 🕸️ [Project Website](https://kodcode-ai.github.io/) - To discover the reasoning for the name of KodCode 🤨 - 📄 [Technical Report](https://arxiv.org/abs/2503.02951) - Discover the methodology and technical details behind KodCode - 💾 [Github Repo](https://github.com/KodCode-AI/kodcode) - Access the complete pipeline used to produce KodCode V1 - 🤗 HF Datasets: - [KodCode-V1 (For RL)](https://huggingface.co/datasets/KodCode/KodCode-V1); - [KodCode-V1-SFT-R1 (for SFT)](https://huggingface.co/datasets/KodCode/KodCode-V1-SFT-R1); - [KodCode-V1-SFT-4o (for SFT)](https://huggingface.co/datasets/KodCode/KodCode-V1-SFT-4o) [You are here!] ![KodCode](https://kodcode-ai.github.io/static/images/kodcode-pipeline.jpg) ## 📊 Dataset Details This dataset is designed for supervised fine-tuning (SFT). Starting from questions from [KodCode-V1](https://huggingface.co/datasets/KodCode/KodCode-V1), we generate responses using `gpt-4o-2024-0513` for each question. To ensure the quality of the generated responses, we generate 3 times for each question and perform test-based reject sampling, yielding this dataset with verified responses. All responses are verified with the paired unit tests. We note that while `solution` in [KodCode-V1](https://huggingface.co/datasets/KodCode/KodCode-V1) can be used for SFT, it contains only code without explanations, making it potentially unsuitable for SFT. Therefore, we regenerated complete responses using `gpt-4o-2024-0513` for this SFT dataset. ### Subsets - Prefill (Simple Coding Questions, 43K) - Leetcode (Coding Assessment Questions, 27K) - Codeforces (Coding Assessment Questions, 33K) - Apps (Coding Assessment Questions, 21K) - Taco (Coding Assessment Questions, 81K) - Code Contests (Coding Assessment Questions, 36K) - Algorithm (DSA Knowledge, 31K) - Data Structure (DSA Knowledge, 34K) - Docs (Technical Documentations, 43K) - Filter (Others, 77K) - Package (Others，7K) - Evol (Others, 13K) ### Data Formats - `style`: Instruct / Complete. Instruct provides question in natural language, while Complete provides function signatures and test examples. - `subset`: As mentioned above. - `conversation_id`: Unique question identifier in KodCode. - `question`: Synthesized coding question. - `solution`: Verified implementation generated by `gpt-4o-0513`. - `test_code`: Unit tests generated by `gpt-4o-0513`. Paired with `solution`. Formatted in `Pytest`. - `test_info`: Contains function name, parameter list, declaration, and docstring. If you are doing RL, you are suggested to include this information in the prompt. - `gpt_pass_sequence`: We generate solution-test pairs up to 10 times. A value of 1 indicates the solution passed self-verification via unit tests on that trial, while 0 indicates failure. - `gpt_pass_trial_num`: Number of trials that passed self-verification. - `gpt_pass_percentage`: Percentage of passing trials relative to total trials. - `gpt_difficulty`: Question difficulty level derived from `gpt_pass_percentage`. - `4o_pass_sequence`: We generate 4o responses 3 times. A value of 1 indicates the solution passed unit tests, while 0 indicates failure. - `4o_pass_trial_num`: Number of trials that passed unit tests. - `4o_correctness`: "True" if at least one among the 3 trials is correct. - `4o_solution`: Only the code portion from 4o's full response. - `metadata`: Contains seed information for internal debugging purposes. - `conversation`: Paired question and verified R1 response. ## 🧐 Other Information **License**: Please follow [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/deed.en). **Contact**: Please contact [Zhangchen](mailto:[email protected]) by email. ## 📚 Citation If you find the data or code useful, please cite: ``` @article{xu2025kodcode, title={KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding}, author={Zhangchen Xu and Yang Liu and Yueqin Yin and Mingyuan Zhou and Radha Poovendran}, year={2025}, eprint={2503.02951}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2503.02951}, } ```
obiwan96/obiwan96owm_raw_v3__180000_200000
obiwan96
2025-02-26T20:28:13Z
15
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-02-26T16:07:54Z
0
--- dataset_info: features: - name: url dtype: string - name: text dtype: string - name: date dtype: string - name: metadata dtype: string - name: backtracking_raw dtype: string - name: verification_raw dtype: string - name: subgoal_setting_raw dtype: string - name: backward_chaining_raw dtype: string splits: - name: train num_bytes: 210238326 num_examples: 20000 download_size: 95451900 dataset_size: 210238326 configs: - config_name: default data_files: - split: train path: data/train-* ---
jkazdan/gemma-2-2b-it-refusal-5000-refusal-0-AMD
jkazdan
2025-01-03T07:33:32Z
15
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-01-03T07:33:31Z
0
--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 440426 num_examples: 300 download_size: 249406 dataset_size: 440426 configs: - config_name: default data_files: - split: train path: data/train-* ---
EasonFan/MMOral-Bench
EasonFan
2025-05-05T08:52:06Z
7
5
[ "task_categories:question-answering", "task_categories:zero-shot-classification", "language:en", "license:cc", "size_categories:100M<n<1B", "region:us", "medical" ]
[ "question-answering", "zero-shot-classification" ]
2025-05-03T03:23:08Z
2
--- license: cc task_categories: - question-answering - zero-shot-classification language: - en tags: - medical pretty_name: MM-Oral size_categories: - 100M<n<1B --- # MM-Oral - MM-Oral-VQA-Closed-Ended.tsv: TSV file for Close-ended VQA. - MM-Oral-VQA-Open-Ended.tsv: TSV file for Open-ended VQA (Should be judged by gpt-4o or other VLMs).
ahmedheakl/arabic_isidocvqa
ahmedheakl
2024-10-29T09:15:25Z
30
0
[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-10-12T04:31:00Z
0
--- dataset_info: features: - name: image dtype: image - name: question dtype: string - name: options sequence: string - name: answer dtype: string splits: - name: train num_bytes: 34336058.0 num_examples: 711 download_size: 12600587 dataset_size: 34336058.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
imanolcb/fruit_classification_dataset
imanolcb
2025-05-01T22:07:15Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:image", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T22:07:10Z
0
--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': fresa '1': limon '2': manzana '3': pera '4': platano '5': uva splits: - name: train num_bytes: 1783692.0 num_examples: 52 - name: validation num_bytes: 595513.0 num_examples: 18 download_size: 2381681 dataset_size: 2379205.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
harman/robreward
harman
2025-05-04T09:49:07Z
0
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-04T08:25:44Z
0
--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: chosen_model dtype: string - name: rejected dtype: string - name: rejected_model dtype: string - name: subset dtype: string - name: id dtype: int64 - name: transformation_name dtype: string - name: transformed_prompt dtype: string - name: transformed_chosen dtype: string - name: transformed_rejected dtype: string - name: reword_id dtype: int64 splits: - name: jb3 num_bytes: 2662774 num_examples: 354 - name: char_swap_sub_ins_del num_bytes: 5876560 num_examples: 1554 - name: comment_bad_good num_bytes: 290828 num_examples: 164 - name: punct_spaces num_bytes: 7690623 num_examples: 2001 - name: rot_13 num_bytes: 10033627 num_examples: 2985 - name: stresstest num_bytes: 8193827 num_examples: 2001 - name: add_quotes num_bytes: 9794827 num_examples: 2985 - name: comment_bad_bad num_bytes: 289556 num_examples: 164 - name: back_translation num_bytes: 5456141 num_examples: 1554 - name: back_transcription num_bytes: 5863524 num_examples: 1554 - name: twitter_url num_bytes: 7754952 num_examples: 2001 - name: twitter_handle num_bytes: 7673986 num_examples: 2001 - name: paraphrase num_bytes: 5701355 num_examples: 1554 - name: append_other_code num_bytes: 336249 num_examples: 164 - name: ignore_above num_bytes: 11566482 num_examples: 2985 - name: jb4 num_bytes: 1644670 num_examples: 354 - name: jb2 num_bytes: 2995180 num_examples: 354 - name: jb1 num_bytes: 1986634 num_examples: 354 - name: homoglyph_sub num_bytes: 8898245 num_examples: 1554 - name: rot_2 num_bytes: 10012732 num_examples: 2985 - name: swap_format num_bytes: 1540439 num_examples: 402 - name: back_transcription_old num_bytes: 5885697 num_examples: 1554 - name: ignore_below num_bytes: 11470962 num_examples: 2985 - name: code_minification num_bytes: 249838 num_examples: 164 download_size: 65152138 dataset_size: 133869708 configs: - config_name: default data_files: - split: jb3 path: data/jb3-* - split: char_swap_sub_ins_del path: data/char_swap_sub_ins_del-* - split: comment_bad_good path: data/comment_bad_good-* - split: punct_spaces path: data/punct_spaces-* - split: rot_13 path: data/rot_13-* - split: stresstest path: data/stresstest-* - split: add_quotes path: data/add_quotes-* - split: comment_bad_bad path: data/comment_bad_bad-* - split: back_translation path: data/back_translation-* - split: back_transcription path: data/back_transcription-* - split: twitter_url path: data/twitter_url-* - split: twitter_handle path: data/twitter_handle-* - split: paraphrase path: data/paraphrase-* - split: append_other_code path: data/append_other_code-* - split: ignore_above path: data/ignore_above-* - split: jb4 path: data/jb4-* - split: jb2 path: data/jb2-* - split: jb1 path: data/jb1-* - split: homoglyph_sub path: data/homoglyph_sub-* - split: rot_2 path: data/rot_2-* - split: swap_format path: data/swap_format-* - split: back_transcription_old path: data/back_transcription_old-* - split: ignore_below path: data/ignore_below-* - split: code_minification path: data/code_minification-* ---
mlfoundations-dev/nemo_nano_1000k
mlfoundations-dev
2025-04-28T06:27:49Z
27
0
[ "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-04-28T05:57:31Z
0
--- dataset_info: features: - name: input dtype: string - name: output dtype: string - name: category dtype: string - name: license dtype: string - name: reasoning dtype: string - name: generator dtype: string - name: used_in_training dtype: string - name: version dtype: string - name: system_prompt dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 32586501765.731556 num_examples: 1000000 download_size: 14441393429 dataset_size: 32586501765.731556 configs: - config_name: default data_files: - split: train path: data/train-* ---
45acp/agronomy
45acp
2025-04-24T16:25:19Z
67
0
[ "task_categories:text2text-generation", "language:en", "license:mit", "size_categories:1K<n<10K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "agriculture", "question-answering", "agronomy", "embrapa", "instituto-biologico", "bloomz" ]
[ "text2text-generation" ]
2025-04-24T16:13:31Z
0
--- dataset: true license: mit tags: - agriculture - question-answering - agronomy - embrapa - instituto-biologico - bloomz language: - en pretty_name: Agronomy_FL task_categories: - text2text-generation --- # Agronomy_FL Dataset ... # Agronomy_FL Dataset The **Agronomy_FL** dataset is a carefully curated corpus of question-answer (QA) pairs derived from a fusion of multiple high-quality public agricultural data sources. It is designed to support the development and fine-tuning of language models focused on agronomic knowledge, sustainable farming, biological control, and best practices in agriculture. ## 📚 Dataset Composition This dataset combines information from the following primary sources: - **EMBRAPA (Brazilian Agricultural Research Corporation)**: Public technical manuals and scientific publications. - **Instituto Biológico (São Paulo)**: Documents and training materials related to agricultural and biological research. - **Public Datasets**: Existing Hugging Face datasets in the agronomy and environmental sciences domains. All content used is publicly available and was filtered, cleaned, and standardized to create meaningful QA pairs for natural language processing tasks. ## 🔍 Dataset Structure The dataset consists of individual JSONL entries. Each entry includes: - `question`: A natural-language question about an agricultural topic. - `answer`: A factual and concise response to the question. - `loss`: A loss score assigned by a pre-trained language models to quantify the semantic coherence and relevance of the example. ### Example Entry ```json { "question": "How can I improve soil fertility?", "answer": "Soil fertility can be improved through practices such as crop rotation, composting, use of green manure, and regular soil testing.", "loss": 1.37 } ``` ### Fields | Field | Type | Description | |----------|--------|-----------------------------------------------------------------------------| | question | string | A concise question related to agronomy, plant health, or sustainable farming | | answer | string | A direct answer based on reliable agronomic sources | | loss | float | A filtered score based on language model perplexity or cross-entropy loss | ## ⚙️ Data Processing All QA pairs were evaluated using the language model. The loss was computed per-example, and only entries with a loss ≤ 2.5 were retained, ensuring high semantic clarity and relevance. Embeddings were then extracted using `all-MiniLM-L12-v2`, and representative examples were selected via KMeans clustering to reduce redundancy and improve dataset diversity. ## 📌 Use Cases This dataset is suitable for: - Fine-tuning instruction-following models (e.g., LLaMA, BLOOMZ, Falcon, Mistral) - Evaluating QA performance in low-resource domains - Creating conversational agents in the agricultural sector - Building expert systems for rural extension and farming support ## 🔓 License & Attribution All source documents are publicly available and were compiled in accordance with their respective open access policies. This dataset is distributed for academic and research use only. Please attribute the original sources (e.g., EMBRAPA, Instituto Biológico) when using the dataset in downstream projects. ## 🙌 Acknowledgments We thank the institutions whose public data made this work possible: - EMBRAPA - Instituto Biológico de São Paulo - Open dataset contributors on Hugging Face ## 📫 Contact If you have questions, suggestions, or collaboration proposals, feel free to contact: **Fernando Henrique Vinha** 📧 [email protected]
IMI-HD/pathology-corpus-sample
IMI-HD
2025-05-01T08:17:18Z
23
0
[ "language:de", "license:cc-by-sa-4.0", "size_categories:n<1K", "modality:text", "library:datasets", "library:mlcroissant", "region:us" ]
[]
2025-04-30T12:45:42Z
0
--- license: cc-by-sa-4.0 language: - de --- This data set contains annotated samples of pathology nodes as described in our manuscript. They are part of the corpus that was used to train the models. We recommend using [MedTator](https://github.com/OHNLP/MedTator) for viewing the files along with the dtd file published here.
Faltu28e/IdeaAscendBot
Faltu28e
2025-03-24T02:34:17Z
16
0
[ "license:apache-2.0", "region:us" ]
[]
2025-03-24T02:11:17Z
0
--- license: apache-2.0 ---
kgmyh/naver_economy_news_stock_instruct_dataset
kgmyh
2025-06-21T02:18:14Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-06-21T02:18:10Z
0
--- dataset_info: features: - name: date dtype: string - name: category dtype: string - name: press dtype: string - name: title dtype: string - name: document dtype: string - name: link dtype: string - name: summary dtype: string - name: label dtype: string splits: - name: train num_bytes: 4883337.9 num_examples: 1350 - name: test num_bytes: 542593.1 num_examples: 150 download_size: 2998233 dataset_size: 5425931.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
katarinayuan/scCello_ood_tissue_data2
katarinayuan
2025-01-21T01:32:25Z
28
0
[ "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-01-21T01:26:13Z
0
--- dataset_info: features: - name: gene_expression_nums sequence: float64 - name: gene_token_ids sequence: int64 - name: cell_dataset_id dtype: string - name: cell_disease dtype: string - name: cell_assay_ids dtype: int64 - name: cell_donor_local_ids dtype: int64 - name: cell_ct_ontology dtype: string - name: cell_type dtype: string - name: cell_tissue dtype: string - name: cell_tissue_ontology dtype: string - name: cell_dev dtype: string - name: cell_counts dtype: float64 - name: length dtype: int64 splits: - name: train num_bytes: 9185779121 num_examples: 341681 download_size: 1628275893 dataset_size: 9185779121 --- # Dataset Card for "scCello_ood_tissue_data2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-sk-unrevised_NoQuant_16_64_0.05_64_BestF1
ferrazzipietro
2024-11-25T14:02:27Z
14
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-11-25T11:09:48Z
0
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248146 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
Asap7772/d1shs0ap-medium-hintgen-qwen3-4b-lr1e6-shard5
Asap7772
2025-05-10T07:44:31Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-10T07:44:25Z
0
--- dataset_info: features: - name: problem dtype: string - name: answer dtype: string - name: solution dtype: string - name: reward dtype: float64 - name: length dtype: float64 - name: correct_length dtype: float64 - name: incorrect_length dtype: float64 - name: all_hints sequence: string splits: - name: train num_bytes: 70490926 num_examples: 1607 download_size: 30815710 dataset_size: 70490926 configs: - config_name: default data_files: - split: train path: data/train-* ---
Evangelinejy/math_level3to5_data_processed_with_qwen_prompt_dedup
Evangelinejy
2025-03-02T21:17:02Z
18
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-03-02T21:17:01Z
0
--- dataset_info: features: - name: input dtype: string - name: answer dtype: string - name: gt_answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: question dtype: string - name: ground_truth_answer dtype: string - name: target dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 5788435 num_examples: 8522 download_size: 2596223 dataset_size: 5788435 configs: - config_name: default data_files: - split: train path: data/train-* ---
tttx/5pc-short-collated-train
tttx
2025-02-21T11:12:30Z
16
0
[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-02-21T10:54:00Z
0
--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: difficulty dtype: int64 - name: problem_uid dtype: string - name: step dtype: int64 splits: - name: train num_bytes: 13027399 num_examples: 600 - name: test num_bytes: 22643 num_examples: 1 download_size: 3426757 dataset_size: 13050042 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
malhar39/MalharDeshmukh
malhar39
2025-03-06T15:52:03Z
8
0
[ "license:creativeml-openrail-m", "region:us" ]
[]
2025-03-06T15:49:28Z
0
--- license: creativeml-openrail-m ---
dgambettaphd/D_llm2_gen5_X_doc1000_synt64_rnd42_lr5e-05_acm_SYNLAST
dgambettaphd
2025-05-10T21:27:45Z
0
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-10T21:27:42Z
0
--- 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: 11832567 num_examples: 21000 download_size: 6998916 dataset_size: 11832567 configs: - config_name: default data_files: - split: train path: data/train-* ---
BranoSandy/eval_act_so100_test_2
BranoSandy
2025-05-05T14:24:09Z
0
0
[ "task_categories:robotics", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "so100", "tutorial" ]
[ "robotics" ]
2025-05-05T14:23:51Z
0
--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so100 - tutorial configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 2, "total_frames": 1634, "total_tasks": 1, "total_videos": 4, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:2" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.laptop": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.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.phone": { "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] ```
hu-po/eval_pickup_cube
hu-po
2025-04-01T01:13:11Z
26
0
[ "task_categories:robotics", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "pickup" ]
[ "robotics" ]
2025-04-01T01:13:02Z
0
--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - pickup 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": "trossen_ai_solo", "total_episodes": 3, "total_frames": 619, "total_tasks": 1, "total_videos": 6, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:3" }, "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": [ 7 ], "names": [ "main_joint_0", "main_joint_1", "main_joint_2", "main_joint_3", "main_joint_4", "main_joint_5", "main_joint_6" ] }, "observation.state": { "dtype": "float32", "shape": [ 7 ], "names": [ "main_joint_0", "main_joint_1", "main_joint_2", "main_joint_3", "main_joint_4", "main_joint_5", "main_joint_6" ] }, "observation.images.cam_high": { "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": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.cam_wrist": { "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": "av1", "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] ```
neelabh17/new_news_exploded_prompt_n_50_d_perc_80_num_gen_10_Qwen2.5-0.5B-Instruct_no_mcq
neelabh17
2025-05-17T16:07:24Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-17T16:07:21Z
0
--- dataset_info: features: - name: id dtype: string - name: name dtype: string - name: topic dtype: string - name: news dtype: string - name: category dtype: string - name: question dtype: string - name: option sequence: string - name: prompt dtype: string - name: response_0 dtype: string - name: answer_0 dtype: string - name: correct_0 dtype: int64 - name: response_1 dtype: string - name: answer_1 dtype: string - name: correct_1 dtype: int64 - name: response_2 dtype: string - name: answer_2 dtype: string - name: correct_2 dtype: int64 - name: response_3 dtype: string - name: answer_3 dtype: string - name: correct_3 dtype: int64 - name: response_4 dtype: string - name: answer_4 dtype: string - name: correct_4 dtype: int64 - name: response_5 dtype: string - name: answer_5 dtype: string - name: correct_5 dtype: int64 - name: response_6 dtype: string - name: answer_6 dtype: string - name: correct_6 dtype: int64 - name: response_7 dtype: string - name: answer_7 dtype: string - name: correct_7 dtype: int64 - name: response_8 dtype: string - name: answer_8 dtype: string - name: correct_8 dtype: int64 - name: response_9 dtype: string - name: answer_9 dtype: string - name: correct_9 dtype: int64 splits: - name: train num_bytes: 9958018 num_examples: 375 download_size: 2653042 dataset_size: 9958018 configs: - config_name: default data_files: - split: train path: data/train-* ---
SurAyush/News_Summary_Dataset
SurAyush
2025-03-31T13:12:16Z
36
0
[ "task_categories:summarization", "language:en", "license:mit", "size_categories:1K<n<10K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[ "summarization" ]
2025-03-31T12:56:57Z
0
--- license: mit task_categories: - summarization language: - en --- # Dataset Card for Dataset Name <!-- Provide a quick summary of the dataset. --> This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Dataset Origin:** [BBC News Summary] - **Data Source by:** [https://www.kaggle.com/datasets/pariza/bbc-news-summary/data] - **Language(s) (NLP):** [English] - **License:** [More Information Needed] <!-- Provide the basic links for the dataset. --> ## Uses [Used to summarize a language model like T5, to produce concise and clean summaries to news articles] <!-- Address questions around how the dataset is intended to be used. --> ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [It has two columsn: articles and summaries]
Antimage01/k12-critic
Antimage01
2025-04-26T12:54:32Z
26
0
[ "license:apache-2.0", "region:us" ]
[]
2025-04-26T12:54:24Z
0
--- license: apache-2.0 ---
Shubham45678/male_part3_taged_meta_to_text_from_edge
Shubham45678
2025-05-07T15:08:25Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-07T15:08:21Z
0
--- dataset_info: features: - name: audio_filepath dtype: string - name: text dtype: string - name: speaker_id dtype: string - name: duration dtype: float32 - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: string - name: phonemes dtype: string - name: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string splits: - name: train num_bytes: 368377 num_examples: 534 download_size: 128635 dataset_size: 368377 configs: - config_name: default data_files: - split: train path: data/train-* ---
R2E-Gym/R2E-Gym-Lite
R2E-Gym
2025-02-05T06:02:58Z
408
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-01-20T04:40:15Z
0
--- dataset_info: features: - name: repo_name dtype: string - name: docker_image dtype: string - name: commit_hash dtype: string - name: parsed_commit_content dtype: string - name: execution_result_content dtype: string - name: modified_files sequence: string - name: modified_entity_summaries list: - name: ast_type_str dtype: string - name: end_lineno dtype: int64 - name: file_name dtype: string - name: name dtype: string - name: start_lineno dtype: int64 - name: type dtype: string - name: relevant_files sequence: string - name: num_non_test_files dtype: int64 - name: num_non_test_func_methods dtype: int64 - name: num_non_test_lines dtype: int64 - name: prompt dtype: string - name: problem_statement dtype: string - name: expected_output_json dtype: string splits: - name: train num_bytes: 3665788272 num_examples: 4578 - name: dev_10pr_v1 num_bytes: 76023943 num_examples: 100 - name: dev_100pr_v1 num_bytes: 622926827 num_examples: 1000 - name: dev_200pr_v1 num_bytes: 1132552772 num_examples: 1876 - name: dev_100pr_v2 num_bytes: 622926827 num_examples: 1000 - name: dev_100pr_v3 num_bytes: 525281939 num_examples: 876 - name: dev_100pr_v4 num_bytes: 351584049 num_examples: 575 - name: dev_100pr_v5 num_bytes: 597512961 num_examples: 782 - name: dev_100pr_v6 num_bytes: 687531360 num_examples: 701 - name: dev_100pr_v7 num_bytes: 410029165 num_examples: 300 download_size: 2190758905 dataset_size: 8692158115 configs: - config_name: default data_files: - split: train path: data/train-* - split: dev_10pr_v1 path: data/dev_10pr_v1-* - split: dev_100pr_v1 path: data/dev_100pr_v1-* - split: dev_200pr_v1 path: data/dev_200pr_v1-* - split: dev_100pr_v2 path: data/dev_100pr_v2-* - split: dev_100pr_v3 path: data/dev_100pr_v3-* - split: dev_100pr_v4 path: data/dev_100pr_v4-* - split: dev_100pr_v5 path: data/dev_100pr_v5-* - split: dev_100pr_v6 path: data/dev_100pr_v6-* - split: dev_100pr_v7 path: data/dev_100pr_v7-* ---
nicolauduran45/scidocs-keywords-exkeyliword
nicolauduran45
2025-01-07T13:13:17Z
24
0
[ "task_categories:text-generation", "task_categories:text2text-generation", "language:en", "license:apache-2.0", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "keyword-generation", "Science", "Research", "Academia", "Innovation", "Technology" ]
[ "text-generation", "text2text-generation" ]
2025-01-07T09:47:45Z
0
--- license: apache-2.0 task_categories: - text-generation - text2text-generation language: - en tags: - keyword-generation - Science - Research - Academia - Innovation - Technology pretty_name: scientific papers with their author keywords configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: title dtype: string - name: abstract dtype: string - name: keywords dtype: string - name: source_name dtype: string splits: - name: train num_bytes: 2771926367 num_examples: 2640662 download_size: 1603171250 dataset_size: 2771926367 --- # SciDocs Keywords exKEYliWORD ## Dataset Description `SciDocs2Keywords` is a dataset consisting of scientific papers (title and abstract) and their associated author-provided keywords. It is designed for use in task of keyword extraction or abstraction. Each entry in the dataset includes: - Title: The title of the scientific paper. - Abstract: A brief summary of the paper. - Author Keywords: Keywords provided by the authors to highlight the main topics or concepts of the paper. - Source: Paper provider source API. ## Associated Model soon... ## How to Use To use this dataset for model training or evaluation, you can load it using the Hugging Face `datasets` library as follows: ```python from datasets import load_dataset dataset = load_dataset("nicolauduran45/scidocs-keywords-exkeyliword") print(dataset[0]) ```
tejfsingh/pick-place-eraser-lr
tejfsingh
2025-06-07T06:04:26Z
0
0
[ "task_categories:robotics", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot" ]
[ "robotics" ]
2025-06-07T04:43:12Z
0
--- license: apache-2.0 task_categories: - robotics tags: - LeRobot configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so100_follower", "total_episodes": 1, "total_frames": 752, "total_tasks": 1, "total_videos": 2, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:1" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "shoulder_pan.pos", "shoulder_lift.pos", "elbow_flex.pos", "wrist_flex.pos", "wrist_roll.pos", "gripper.pos" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "shoulder_pan.pos", "shoulder_lift.pos", "elbow_flex.pos", "wrist_flex.pos", "wrist_roll.pos", "gripper.pos" ] }, "observation.images.cam_high": { "dtype": "video", "shape": [ 1080, 1920, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 1080, "video.width": 1920, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.cam_low": { "dtype": "video", "shape": [ 1080, 1920, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 1080, "video.width": 1920, "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] ```
jdchang/distill-r1-qwen-1.5b-hmmt-feb-2024
jdchang
2025-04-28T00:51:35Z
19
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-04-28T00:51:22Z
0
--- dataset_info: features: - name: message_id dtype: string - name: problem dtype: string - name: answer dtype: string - name: processed_answer dtype: string - name: responses dtype: string - name: reward dtype: bool splits: - name: train num_bytes: 590013885 num_examples: 15360 download_size: 214067150 dataset_size: 590013885 configs: - config_name: default data_files: - split: train path: data/train-* ---
lecslab/porc-gpt2-v1-all
lecslab
2024-12-19T02:18:37Z
21
0
[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-12-19T02:15:18Z
0
--- dataset_info: features: - name: story dtype: string - name: generated_text_1 dtype: string - name: generated_text_2 dtype: string - name: mic_chosen dtype: int64 - name: mar_chosen dtype: int64 - name: ali_chosen dtype: int64 splits: - name: train num_bytes: 84559 num_examples: 150 download_size: 56058 dataset_size: 84559 configs: - config_name: default data_files: - split: train path: data/train-* ---
test-gen/num10_code_humaneval_qwen2.5-7b_t1.0_n8_tests_humaneval_o3_t0_n1
test-gen
2025-05-21T21:28:52Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-21T21:28:50Z
0
--- dataset_info: features: - name: task_id dtype: string - name: prompt dtype: string - name: canonical_solution dtype: string - name: test dtype: string - name: entry_point dtype: string - name: generated_code sequence: string - name: gt_rewards sequence: float64 - name: rewards sequence: float64 - name: verification_info struct: - name: language dtype: string - name: test_cases sequence: string splits: - name: test num_bytes: 1565577 num_examples: 164 download_size: 580772 dataset_size: 1565577 configs: - config_name: default data_files: - split: test path: data/test-* ---
gaurav312/indian_city_pollution
gaurav312
2025-01-16T05:55:21Z
18
0
[ "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-01-13T10:33:01Z
0
--- dataset_info: features: - name: Date dtype: string - name: PM2.5 (µg/m³) dtype: float64 - name: PM10 (µg/m³) dtype: float64 - name: NO (µg/m³) dtype: float64 - name: NO2 (µg/m³) dtype: float64 - name: NOx (ppb) dtype: float64 - name: NH3 (µg/m³) dtype: float64 - name: SO2 (µg/m³) dtype: float64 - name: CO (mg/m³) dtype: float64 - name: Ozone (µg/m³) dtype: float64 - name: Month dtype: float64 - name: Weekday dtype: float64 - name: AQI_calculated dtype: float64 - name: AQI_bucket dtype: int64 splits: - name: train num_bytes: 23865854 num_examples: 202253 download_size: 15368351 dataset_size: 23865854 configs: - config_name: default data_files: - split: train path: data/train-* ---
yoonholee/completions_Qwen3-4B_GSM
yoonholee
2025-05-13T23:02:31Z
5
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-13T02:56:31Z
0
--- dataset_info: features: - name: problem dtype: string - name: completions sequence: string - name: answer dtype: string - name: corrects sequence: bool - name: acc dtype: float64 splits: - name: train num_bytes: 11306993 num_examples: 200 download_size: 3601965 dataset_size: 11306993 configs: - config_name: default data_files: - split: train path: data/train-* ---
ieasybooks-org/waqfeya-library-compressed
ieasybooks-org
2025-04-25T15:09:42Z
653
4
[ "task_categories:image-to-text", "language:ar", "license:mit", "size_categories:10K<n<100K", "format:csv", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[ "image-to-text" ]
2025-04-23T05:19:54Z
4
--- license: mit task_categories: - image-to-text language: - ar pretty_name: Waqfeya Library - Compressed size_categories: - 10K<n<100K configs: - config_name: index data_files: - split: index path: index.tsv --- # Waqfeya Library - Compressed ## 📖 Overview [Waqfeya](https://waqfeya.net) is one of the primary online resources for Islamic books, similar to [Shamela](https://shamela.ws). It hosts more than 10,000 PDF books across over 80 categories. In this dataset, we processed the original PDF files using Google Document AI APIs and extracted their contents into two additional formats: TXT and DOCX. ## 📊 Dataset Contents This dataset is identical to [ieasybooks-org/waqfeya-library](https://huggingface.co/datasets/ieasybooks-org/waqfeya-library), with one key difference: the contents have been compressed for easier downloading. Specifically, the `pdf`, `txt`, and `docx` folders have been packaged into `pdf.zip`, `txt.zip`, and `docx.zip`, respectively. For detailed information about the dataset contents and usage instructions, please refer to the original dataset page: [ieasybooks-org/waqfeya-library](https://huggingface.co/datasets/ieasybooks-org/waqfeya-library).
abubasith86/titles-dpo
abubasith86
2025-03-13T13:18:02Z
15
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-03-13T13:17:59Z
0
--- dataset_info: features: - name: prompt list: - name: content dtype: string - name: role dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 36757 num_examples: 102 - name: valid num_bytes: 4258 num_examples: 12 download_size: 19891 dataset_size: 41015 configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* ---
ssktora/trec_ct_2021-train1000-bm25-pyserini-5-all-v2
ssktora
2025-04-29T07:26:21Z
17
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-04-29T07:26:17Z
0
--- dataset_info: features: - name: query_id dtype: string - name: query dtype: string - name: positive_passages list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string - name: negative_passages list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 19100904 num_examples: 50 download_size: 8737410 dataset_size: 19100904 configs: - config_name: default data_files: - split: train path: data/train-* ---
yukimasano/pass
yukimasano
2024-01-18T11:12:34Z
58
1
[ "task_categories:other", "annotations_creators:no-annotation", "language_creators:machine-generated", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:extended|yffc100M", "language:en", "license:cc-by-4.0", "size_categories:1M<n<10M", "arxiv:2109.13228", "region:us", "image-self-supervised pretraining" ]
[ "other" ]
2022-03-02T23:29:22Z
0
--- annotations_creators: - no-annotation language_creators: - machine-generated - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - extended|yffc100M task_categories: - other task_ids: [] paperswithcode_id: pass pretty_name: Pictures without humAns for Self-Supervision tags: - image-self-supervised pretraining dataset_info: features: - name: image dtype: image - name: creator_username dtype: string - name: hash dtype: string - name: gps_latitude dtype: float32 - name: gps_longitude dtype: float32 - name: date_taken dtype: timestamp[us] splits: - name: train num_bytes: 178563446100 num_examples: 1439588 download_size: 179640190811 dataset_size: 178563446100 --- # Dataset Card for PASS ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [PASS homepage](https://www.robots.ox.ac.uk/~vgg/research/pass/) - **Repository:** [PASS repository](https://github.com/yukimasano/PASS) - **Paper:** [PASS: An ImageNet replacement for self-supervised pretraining without humans](https://arxiv.org/abs/2109.13228) - **Leaderboard:** [Pretrained models with scores](https://github.com/yukimasano/PASS#pretrained-models) - **Point of Contact:** [Yuki M. Asano](mailto:yukiATMARKrobots.ox.ac.uk) ### Dataset Summary PASS is a large-scale image dataset, containing 1.4 million images, that does not include any humans and which can be used for high-quality pretraining while significantly reducing privacy concerns. ### Supported Tasks and Leaderboards From the paper: > **Has the dataset been used for any tasks already?** In the paper we show and benchmark the intended use of this dataset as a pretraining dataset. For this the dataset is used an unlabelled image collection on which visual features are learned and then transferred to downstream tasks. We show that with this dataset it is possible to learn competitive visual features, without any humans in the pretraining dataset and with complete license information. > **Is there a repository that links to any or all papers or systems that use the dataset?** We will be listing these at the repository. > **What (other) tasks could the dataset be used for?** We believe this dataset might allow researchers and practitioners to further evaluate the differences that pretraining datasets can have on the learned features. Furthermore, since the meta-data is available for the images, it is possible to investigate the effect of image resolution on self-supervised learning methods, a domain largely underresearched thus far, as the current de-facto standard, ImageNet, only comes in one size. > **Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses?** Given that this dataset is a subset of a dataset that randomly samples images from flickr, the image distribution is biased towards European and American creators. As in the main papers discussion, this can lead to non-generalizeable features, or even biased features as the images taken in other countries might be more likely to further reflect and propagate stereotypes [84], though in our case these do not refer to sterotypes about humans. > **Are there tasks for which the dataset should not be used?** This dataset is meant for research purposes only. The dataset should also not be used for, e.g. connecting images and usernames, as this might risk de-anonymising the dataset in the long term. The usernames are solely provided for attribution. ### Languages English. ## Dataset Structure ### Data Instances A data point comprises an image and its meta-data: ``` { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x375 at 0x7FFAD48E35F8>, 'creator_username': 'NTShieldsy', 'hash': 'e1662344ffa8c231d198c367c692cc', 'gps_latitude': 21.206675, 'gps_longitude': 39.166558, 'date_taken': datetime.datetime(2012, 8, 9, 18, 0, 20) } ``` ### Data Fields - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`. - `creator_username`: The photographer. - `hash`: The hash, as computed from YFCC-100M. - `gps_latitude`: Latitude of image if existent, otherwise None. - `gps_longitude`: Longitude of image if existent, otherwise None. - `date_taken`: Datetime of image if existent, otherwise None. ### Data Splits All the data is contained in the training set. The training set has 1,439,588 instances as this implementation corresponds to the most recent release (v3) from the [version history](https://github.com/yukimasano/PASS/blob/main/version_history.txt). From the paper: > **Are there recommended data splits (e.g., training, development/validation, testing)?** As outlined in the intended usecases, this dataset is meant for pretraining representations. As such, the models derived from training on this dataset need to be evaluated on different datasets, so called down-stream tasks. Thus the recommended split is to use all samples for training. ## Dataset Creation ### Curation Rationale From the paper: > **For what purpose was the dataset created?** Neural networks pretrained on large image collections have been shown to transfer well to other visual tasks where there is little labelled data, i.e. transferring a model works better than starting with a randomly initialized network every time for a new task, as many visual features can be repurposed. This dataset has as its goal to provide a safer large-scale dataset for such pretraining of visual features. In particular, this dataset does not contain any humans or human parts and does not contain any labels. The first point is important, as the current standard for pretraining, ImageNet and its face-blurred version only provide pseudo-anonymity and furthermore do not provide correct licences to the creators. The second point is relevant as pretraining is moving towards the self-supervised paradigm, where labels are not required. Yet most methods are developed on the highly curated ImageNet dataset, yielding potentially non-generalizeable research. ### Source Data #### Initial Data Collection and Normalization From the paper: * **Collection process**: > **How was the data associated with each instance acquired?** The data was collected from the publicly available dataset YFCC-100M which is hosted on the AWS public datasets platform. We have used the meta-data, namely the copyright information to filter only images with the CC-BY licence and have downloaded these using the aws command line interface, allowing for quick and stable downloading. In addition, all files were subsequently scanned for viruses using Sophos SAVScan virus detection utility, v.5.74.0. > **What mechanisms or procedures were used to collect the data (e.g., hardware apparatus or sensor, manual human curation, software program, software API)?** Our dataset is a subset of the YFCC-100M dataset. The YFCC-100M dataset itself was created by effectively randomly selecting publicly available images from flickr, resulting in approximately 98M images. > **Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set?** The dataset is a sample of a larger set—all possible digital photographs. As outlined in Section 3 we start from an existing dataset, YFCC-100M, and stratify the images (removing images with people and personal information, removing images with harmful content, removing images with unsuitable licenses, each user contributes at most 80 images to the dataset). This leaves 1.6M images, out of which we take a random sample of 1.28M images to replicate the size of the ImageNet dataset. While this dataset can thus be extended, this is the set that we have verified to not contain humans, human parts and disturbing content. > **Over what timeframe was the data collected?** The images underlying the dataset were downloaded between March and June 2021 from the AWS public datasets’ S3 bucket, following the download code provided in the repo. However the images contained were originally and taken anywhere from 2000 to 2015, with the majority being shot between 2010-2014. * **Preprocessing/cleaning/labeling**: > **Was any preprocessing/cleaning/labeling of the data done (e.g., discretization or bucketing,tokenization, part-of-speech tagging, SIFT feature extraction, removal of instances, processing of missing values)?** After the download of approx. 17M images, the corrupted, or single-color images were removed from the dataset prior to the generation of the dataset(s) used in the paper. The images were not further preprocessed or edited. > **Was the “raw” data saved in addition to the preprocessed/cleaned/labeled data (e.g., to support unanticipated future uses)?** Yes. The creators of the dataset maintain a copy of the 17M original images with the CC-BY licence of YFCC100M that sits at the start of our dataset creation pipeline. Is the software used to preprocess/clean/label the instances available? We have only used basic Python primitives for this. For the annotations we have used VIA [27, 28]. #### Who are the source language producers? From the paper: > **Who was involved in the data collection process (e.g., students, crowdworkers, contractors) and how were they compensated (e.g., how much were crowdworkers paid)?** As described, the data was collected automatically by simply downloading images from a publicly hosted S3 bucket. The human verification was done using a professional data annotation company that pays 150% of the local minimum wage. ### Annotations #### Annotation process This dataset doesn't contain annotations. #### Who are the annotators? This dataset doesn't contain annotations. ### Personal and Sensitive Information From the paper: > **Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor-patient confidentiality, data that includes the content of individuals’ non-public communications)?** No. > **Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety?** No. Besides checking for human presence in the images, the annotators were also given the choice of flagging images for disturbing content, which once flagged was removed. > **Does the dataset relate to people? If not, you may skip the remaining questions in this section.** No. > **Does the dataset identify any subpopulations (e.g., by age, gender)?** NA > **Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset?** NA > **Does the dataset contain data that might be considered sensitive in any way (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history)?** NA > **Were any ethical review processes conducted (e.g., by an institutional review board)?** No ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases From the paper: > **Is your dataset free of biases?** No. There are many kinds of biases that can either be quantified, e.g. geo-location (most images originate from the US and Europe) or camera-model (most images are taken with professional DSLR cameras not easily affordable), there are likely many more biases that this dataset does contain. The only thing that this dataset does not contain are humans and parts of humans, as far as our validation procedure is accurate. ### Other Known Limitations From the paper: > **Can you guarantee compliance to GDPR?** No, we cannot comment on legal issues. ## Additional Information ### Dataset Curators YM. Asano, C. Rupprecht, A. Zisserman and A. Vedaldi. From the paper: > **Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)?** The dataset has been constructed by the research group “Visual Geometry Group” at the University of Oxford at the Engineering Science Department. ### Licensing Information The PASS dataset is available to download for commercial/research purposes under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). A complete version of the license can be found [here](https://www.robots.ox.ac.uk/~vgg/research/pass/license_pass.txt). The whole dataset only contains CC-BY licensed images with full attribution information. ### Citation Information ```bibtex @Article{asano21pass, author = "Yuki M. Asano and Christian Rupprecht and Andrew Zisserman and Andrea Vedaldi", title = "PASS: An ImageNet replacement for self-supervised pretraining without humans", journal = "NeurIPS Track on Datasets and Benchmarks", year = "2021" } ``` ### Contributions Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.
RobertoSonic/DatasetDmaeDAV2
RobertoSonic
2024-11-19T03:43:34Z
16
0
[ "size_categories:n<1K", "format:parquet", "modality:image", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-11-19T03:37:54Z
0
--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': avanzada '1': avanzada humeda '2': leve '3': moderada '4': no dmae splits: - name: train num_bytes: 6262282.0 num_examples: 729 - name: test num_bytes: 24356742.0 num_examples: 52 - name: validation num_bytes: 15121812.0 num_examples: 52 download_size: 45462365 dataset_size: 45740836.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---
slinusc/PubMedAbstractsSubsetEmbedded
slinusc
2025-06-12T11:40:02Z
0
0
[ "language:en", "license:cc-by-4.0", "size_categories:1M<n<10M", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "arxiv:2505.07917", "region:us", "pubmed", "embeddings", "medcpt", "biomedical", "retrieval", "rag", "medical" ]
[]
2025-06-12T05:38:24Z
0
--- license: cc-by-4.0 language: - en tags: - pubmed - embeddings - medcpt - biomedical - retrieval - rag - medical pretty_name: PubMedAbstractsSubsetEmbedded --- # PubMed Abstracts Subset with MedCPT Embeddings (float32) This dataset contains a probabilistic sample of ~2.4 million PubMed abstracts, enriched with precomputed dense embeddings (title + abstract), from the **`ncbi/MedCPT-Article-Encoder`** model. It is derived from public metadata made available via the [National Library of Medicine (NLM)](https://pubmed.ncbi.nlm.nih.gov/) and was used in the paper [*Efficient and Reproducible Biomedical QA using Retrieval-Augmented Generation*](https://arxiv.org/abs/2505.07917). Each entry includes: - `title`: Title of the publication - `abstract`: Abstract content - `PMID`: PubMed identifier - `embedding`: 768-dimensional float32 vector from MedCPT --- ## 🔍 How to Access ### ▶️ Option 1: Load via Hugging Face `datasets` ```python from datasets import load_dataset dataset = load_dataset("slinusc/PubMedAbstractsSubsetEmbedded", streaming=True) for doc in dataset: print(doc["PMID"], doc["embedding"][:5]) # print first 5 dims break ``` > Each vector is stored as a list of 768 `float32` values (compact, no line breaks). --- ### 💾 Option 2: Git Clone with Git LFS ```bash git lfs install git clone https://huggingface.co/datasets/slinusc/PubMedAbstractsSubsetEmbedded cd PubMedAbstractsSubsetEmbedded ``` --- ## 📦 Format Each file is a `.jsonl` (JSON Lines) file, where each line is a valid JSON object: ```json { "title": "...", "abstract": "...", "PMID": 36464820, "embedding": [-0.1952481, ... , 0.2887376] } ``` > The embeddings are 768-dimensional dense vectors, serialized as 32-bit floats. --- ## 📚 Source and Licensing This dataset is derived from public domain PubMed metadata (titles and abstracts), redistributed in accordance with [NLM data usage policies](https://www.nlm.nih.gov/databases/download/data_distrib_main.html). MedCPT embeddings were generated using the [ncbi/MedCPT-Article-Encoder](https://huggingface.co/ncbi/MedCPT-Article-Encoder) model. --- ## 📣 Citation If you use this dataset or the included MedCPT embeddings, please cite: > **Stuhlmann et al. (2025)** > *Efficient and Reproducible Biomedical Question Answering using Retrieval Augmented Generation* > [arXiv:2505.07917](https://arxiv.org/abs/2505.07917) > [https://github.com/slinusc/medical_RAG_system](https://github.com/slinusc/medical_RAG_system) --- ## 🏷️ Version - `v1.0` – Initial release (2.39M samples, 24 JSONL files, float32 embeddings, ~23 GB total) --- ## 📬 Contact Maintained by [@slinusc](https://huggingface.co/slinusc). For questions or collaborations, open a discussion on the HF Hub.
Rudra-ai/ai-responses-dataset-math-modified
Rudra-ai
2024-10-29T17:19:07Z
19
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-10-29T14:57:18Z
0
--- dataset_info: features: - name: text dtype: string - name: query dtype: string - name: response dtype: string splits: - name: train num_bytes: 2811861 num_examples: 1000 download_size: 1000892 dataset_size: 2811861 configs: - config_name: default data_files: - split: train path: data/train-* ---
SayantanJoker/processed_seamless_align_hindi_new_chunk_38
SayantanJoker
2025-05-06T10:10:30Z
0
0
[ "region:us" ]
[]
2025-05-06T10:09:00Z
0
--- dataset_info: features: - name: audio dtype: audio - name: transcription dtype: string - name: file_name dtype: string splits: - name: train num_bytes: 2677291664.0 num_examples: 10000 download_size: 2547584664 dataset_size: 2677291664.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
dopaul/house
dopaul
2025-04-13T08:22:00Z
22
0
[ "task_categories:robotics", "region:us", "phosphobot", "so100", "phospho-dk1" ]
[ "robotics" ]
2025-04-13T08:21:53Z
0
--- tags: - phosphobot - so100 - phospho-dk1 task_categories: - robotics --- # house **This dataset was generated using a [phospho starter pack](https://robots.phospho.ai).** This dataset contains a series of episodes recorded with a robot and multiple cameras. It can be directly used to train a policy using imitation learning. It's compatible with LeRobot and RLDS.
mlfoundations-dev/b1_code_top_16
mlfoundations-dev
2025-04-18T23:59:01Z
75
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-04-16T16:41:13Z
0
--- dataset_info: features: - name: instruction_seed dtype: string - name: reasoning dtype: string - name: deepseek_solution dtype: string - name: __original_row_idx dtype: int64 - name: source dtype: string - name: final_reasoning_trace dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: version dtype: string - name: style dtype: string - name: subset dtype: string - name: question_id dtype: string - name: solution dtype: string - name: test dtype: string - name: test_info list: - name: docstring dtype: string - name: function_declaration dtype: string - name: function_name dtype: string - name: parameter_list dtype: string - name: gpt_pass_sequence sequence: int64 - name: gpt_pass_trial_num dtype: int64 - name: gpt_difficulty dtype: string - name: gpt_pass_percentage dtype: float64 - name: trials struct: - name: trial_gpt4o_0 struct: - name: file_source dtype: string - name: solution_code dtype: string - name: test_code dtype: string - name: test_coverage dtype: float64 - name: test_result dtype: string - name: trial_gpt4o_1 struct: - name: file_source dtype: string - name: solution_code dtype: string - name: test_code dtype: string - name: test_coverage dtype: float64 - name: test_result dtype: string - name: trial_gpt4o_2 struct: - name: file_source dtype: string - name: solution_code dtype: string - name: test_code dtype: string - name: test_coverage dtype: float64 - name: test_result dtype: string - name: trial_gpt4o_3 struct: - name: file_source dtype: string - name: solution_code dtype: string - name: test_code dtype: string - name: test_coverage dtype: float64 - name: test_result dtype: string - name: trial_gpt4o_4 struct: - name: file_source dtype: string - name: solution_code dtype: string - name: test_code dtype: string - name: test_coverage dtype: float64 - name: test_result dtype: string - name: trial_gpt4o_5 struct: - name: file_source dtype: string - name: solution_code dtype: string - name: test_code dtype: string - name: test_coverage dtype: float64 - name: test_result dtype: string - name: trial_gpt4o_6 struct: - name: file_source dtype: string - name: solution_code dtype: string - name: test_code dtype: string - name: test_coverage dtype: float64 - name: test_result dtype: string - name: trial_gpt4o_7 struct: - name: file_source dtype: string - name: solution_code dtype: string - name: test_code dtype: string - name: test_coverage dtype: float64 - name: test_result dtype: string - name: trial_gpt4o_8 struct: - name: file_source dtype: string - name: solution_code dtype: string - name: test_code dtype: string - name: test_coverage dtype: float64 - name: test_result dtype: string - name: trial_gpt4o_9 struct: - name: file_source dtype: string - name: solution_code dtype: string - name: test_code dtype: string - name: test_coverage dtype: float64 - name: test_result dtype: string - name: chosen_trial dtype: string - name: metadata struct: - name: original_instruction dtype: string - name: prompt_id dtype: string - name: row_id dtype: int64 - name: seed_ids dtype: string - name: benchmark_similarity dtype: float64 - name: benchmark_instruction dtype: string - name: benchmark_task_id dtype: string - name: filter_reason dtype: string - name: response_seed dtype: string - name: problem_id dtype: int64 - name: solutions dtype: string - name: input_output dtype: string - name: difficulty dtype: string - name: url dtype: string - name: starter_code dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1828381253.5081968 num_examples: 31600 download_size: 910403323 dataset_size: 1828381253.5081968 configs: - config_name: default data_files: - split: train path: data/train-* ---
tuenguyen/bespoke_stratos_17k-test
tuenguyen
2025-02-13T07:09:31Z
16
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-02-13T07:09:05Z
0
--- dataset_info: features: - name: system dtype: string - name: Prompt dtype: string - name: Solution_source dtype: string - name: Solution dtype: string - name: Thought dtype: string - name: Answer dtype: string - name: Verifiable dtype: int64 - name: Source dtype: string - name: System dtype: string splits: - name: train num_bytes: 606705373 num_examples: 16710 download_size: 245431908 dataset_size: 606705373 configs: - config_name: default data_files: - split: train path: data/train-* ---
inpaint-context/opa-uptrain
inpaint-context
2024-10-25T15:00:35Z
27
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-10-25T14:56:21Z
0
--- dataset_info: features: - name: image dtype: string - name: mask dtype: image splits: - name: train num_bytes: 229509833.234 num_examples: 26767 - name: validation num_bytes: 48218269.811 num_examples: 5273 download_size: 133803398 dataset_size: 277728103.045 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
ioi-leaderboard/ioi-eval-sglang_meta-llama_Llama-3.1-8B-Instruct-new-prompt
ioi-leaderboard
2025-03-03T23:33:09Z
16
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-03-03T23:33:01Z
0
--- dataset_info: features: - name: problem_id dtype: large_string - name: subtask dtype: large_string - name: prompt dtype: large_string - name: generation dtype: large_string - name: code dtype: large_string - name: language dtype: large_string - name: solution_number dtype: int64 - name: uuid dtype: large_string - name: model_kwargs struct: - name: seed dtype: int64 - name: metadata struct: - name: usage struct: - name: completion_tokens dtype: int64 - name: prompt_tokens dtype: int64 - name: total_tokens dtype: int64 - name: cost dtype: float64 - name: timestamp dtype: large_string splits: - name: train num_bytes: 28186121 num_examples: 2050 download_size: 3191375 dataset_size: 28186121 configs: - config_name: default data_files: - split: train path: data/train-* ---
orcn/dataVLM-Shapes-MS-Swift
orcn
2025-03-09T18:51:42Z
79
1
[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-03-09T15:52:20Z
0
--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: images sequence: image splits: - name: train num_bytes: 59175606.0 num_examples: 753 download_size: 38339270 dataset_size: 59175606.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
EleutherAI/mmlu_auxiliary_train_formatted_cloze_20250619-1339
EleutherAI
2025-06-19T18:51:05Z
0
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-06-19T18:51:04Z
0
--- dataset_info: features: - name: word_filter dtype: bool - name: word_filter_metadata struct: - name: keywords dtype: string - name: combined_filter dtype: bool splits: - name: train num_bytes: 493940 num_examples: 99842 download_size: 81658 dataset_size: 493940 configs: - config_name: default data_files: - split: train path: data/train-* ---
HHS-Official/cdc-pramstat-data-for-2010
HHS-Official
2025-05-07T19:24:32Z
0
0
[ "language:en", "license:odbl", "region:us", "hhs", "cdc", "abuse", "breastfeeding", "contraception", "medicaid", "morbidity", "obesity", "stress", "wic" ]
[]
2025-05-07T19:24:21Z
0
--- language: - en pretty_name: CDC PRAMStat Data for 2010 tags: - hhs - cdc - abuse - breastfeeding - contraception - medicaid - morbidity - obesity - stress - wic license: odbl --- # CDC PRAMStat Data for 2010 ## Description 2010. Centers for Disease Control and Prevention (CDC). PRAMS, the Pregnancy Risk Assessment Monitoring System, is a surveillance system collecting state-specific, population-based data on maternal attitudes and experiences before, during, and shortly after pregnancy. It is a collaborative project of the Centers for Disease Control and Prevention (CDC) and state health departments. PRAMS provides data for state health officials to use to improve the health of mothers and infants. PRAMS topics include abuse, alcohol use, contraception, breastfeeding, mental health, morbidity, obesity, preconception health, pregnancy history, prenatal-care, sleep behavior, smoke exposure, stress, tobacco use, WIC, Medicaid, infant health, and unintended pregnancy. Data will be updated annually as it becomes available. ## Dataset Details - **Publisher**: Centers for Disease Control and Prevention - **Last Modified**: 2023-09-05 - **Contact**: DRH Public Inquiries ([email protected]) ## Source Original data can be found at: https://www.cdc.gov/prams/index.htm ## Usage You can load this dataset using: ```python from datasets import load_dataset dataset = load_dataset('HHS-Official/cdc-pramstat-data-for-2010') ``` ## License This dataset is licensed under http://opendefinition.org/licenses/odc-odbl/
french-datasets/rcds-MultiLegalNeg
french-datasets
2025-03-31T08:13:13Z
25
0
[ "multilinguality:multilingual", "language:fra", "language:ita", "language:deu", "language:eng", "region:us" ]
[]
2025-03-30T17:13:18Z
0
--- language: - fra - ita - deu - eng multilinguality: - multilingual viewer: false --- Ce répertoire est vide, il a été créé pour améliorer le référencement du jeu de données https://huggingface.co/datasets/rcds/MultiLegalNeg.
supergoose/flan_source_task372_synthetic_palindrome_numbers_258
supergoose
2025-02-25T19:34:39Z
14
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-02-25T19:34:37Z
0
--- dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: _template_idx dtype: int64 - name: _task_source dtype: string - name: _task_name dtype: string - name: _template_type dtype: string splits: - name: train num_bytes: 13870369 num_examples: 19453 download_size: 3448730 dataset_size: 13870369 configs: - config_name: default data_files: - split: train path: data/train-* ---
VGraf/wildchat_data_unused_qualityFiltered_from8000_to11000
VGraf
2025-05-19T11:45:20Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-19T11:45:17Z
0
--- dataset_info: features: - name: index dtype: int64 - name: chunk_lengths sequence: int64 - name: length dtype: int64 - name: writing_style_response_chunks sequence: float64 - name: writing_style_response_average dtype: float64 - name: required_expertise_response_chunks sequence: float64 - name: required_expertise_response_average dtype: float64 - name: facts_and_trivia_response_chunks sequence: float64 - name: facts_and_trivia_response_average dtype: float64 - name: educational_value_response_chunks sequence: float64 - name: educational_value_response_average dtype: float64 - name: writing_style_chunks sequence: float64 - name: writing_style_average dtype: float64 - name: required_expertise_chunks sequence: float64 - name: required_expertise_average dtype: float64 - name: facts_and_trivia_chunks sequence: float64 - name: facts_and_trivia_average dtype: float64 - name: educational_value_chunks sequence: float64 - name: educational_value_average dtype: float64 - name: date dtype: timestamp[s] - name: id dtype: int64 - name: text dtype: string - name: prompt dtype: string - name: response dtype: string - name: label dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 28415018 num_examples: 1539 download_size: 10852902 dataset_size: 28415018 configs: - config_name: default data_files: - split: train path: data/train-* ---
dlhhhhhya/test0502
dlhhhhhya
2025-05-02T06:53:06Z
0
0
[ "license:apache-2.0", "region:us" ]
[]
2025-05-02T06:51:20Z
0
--- license: apache-2.0 ---
kgmyh/korean_stock_ticker_qa_data
kgmyh
2025-05-18T06:55:20Z
29
0
[ "task_categories:text-classification", "language:ko", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "finance", "korean", "stock_ticker", "ticker" ]
[ "text-classification" ]
2025-05-16T23:33:19Z
0
--- dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 1082966 num_examples: 13815 - name: test num_bytes: 3797 num_examples: 50 download_size: 300370 dataset_size: 1086763 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* license: apache-2.0 task_categories: - text-classification language: - ko tags: - finance - korean - stock_ticker - ticker --- # 개요 - 한국 증시에 상장된 회사이름으로 종목코드 물어보는 QA 데이터셋 - 데이터 출처 - https://kind.krx.co.kr/corpgeneral/corpList.do?method=loadInitPage - 2025년 05월 15일 데이터
twei11/node1_round_41
twei11
2025-04-16T16:28:48Z
14
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-04-16T16:28:47Z
0
--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 6926808 num_examples: 1800 download_size: 3365293 dataset_size: 6926808 configs: - config_name: default data_files: - split: train path: data/train-* ---
math-extraction-comp/cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo
math-extraction-comp
2025-01-26T13:20:56Z
17
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-01-11T10:30:16Z
0
--- dataset_info: features: - name: question dtype: string - name: gold dtype: string - name: target dtype: string - name: prediction dtype: string - name: subset dtype: string - name: lighteval-b200fe81_extracted_answer dtype: string - name: lighteval-b200fe81_score dtype: float64 - name: harness_score dtype: float64 - name: lighteval-c24870ea_score dtype: float64 - name: harness_extracted_answer dtype: string - name: lighteval-0f21c935_extracted_answer dtype: string - name: qwen_score dtype: float64 - name: lighteval-c24870ea_extracted_answer dtype: string - name: lighteval-0f21c935_score dtype: float64 - name: qwen_extracted_answer dtype: string splits: - name: train num_bytes: 4056217 num_examples: 1324 download_size: 1184203 dataset_size: 4056217 configs: - config_name: default data_files: - split: train path: data/train-* ---
kathleenge/ps-split
kathleenge
2025-06-18T15:47:09Z
0
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-06-18T15:47:04Z
0
--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output struct: - name: department dtype: string - name: case_type dtype: string - name: case_detail dtype: string splits: - name: train num_bytes: 104459951.77970108 num_examples: 10491 - name: test num_bytes: 26117477.220298916 num_examples: 2623 download_size: 3697178 dataset_size: 130577429.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
hrhraj/eval_calibrated_bbox_multiposition
hrhraj
2025-05-14T01:10:24Z
0
0
[ "task_categories:robotics", "license:apache-2.0", "region:us", "LeRobot", "so100", "calibrated", "bbox" ]
[ "robotics" ]
2025-05-14T01:01:12Z
0
--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so100 - calibrated - bbox configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 1, "total_frames": 1189, "total_tasks": 1, "total_videos": 1, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:1" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.phone": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```
ziyu3141/rf_newtrain_9_6
ziyu3141
2025-02-07T13:40:45Z
53
0
[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-02-07T13:40:41Z
0
--- dataset_info: features: - name: Filename dtype: string - name: Aesthetics score dtype: float64 - name: Artifact score dtype: float64 - name: Misalignment score dtype: float64 - name: Overall score dtype: float64 - name: Artifact heatmap sequence: sequence: sequence: int64 - name: Misalignment heatmap sequence: sequence: sequence: int64 - name: Misalignment token label dtype: string - name: is_uneven dtype: bool - name: preferred_image dtype: binary - name: unpreferred_image dtype: binary - name: revised_image dtype: binary - name: unrevised_id dtype: string - name: is_preferred dtype: bool splits: - name: train num_bytes: 676043198 num_examples: 100 download_size: 44589244 dataset_size: 676043198 configs: - config_name: default data_files: - split: train path: data/train-* ---
Orion-zhen/meissa-lima
Orion-zhen
2024-10-24T03:26:43Z
20
0
[ "task_categories:text-generation", "language:zh", "language:en", "license:gpl-3.0", "size_categories:1K<n<10K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2305.11206", "region:us", "lima" ]
[ "text-generation" ]
2024-10-24T02:52:07Z
0
--- license: gpl-3.0 task_categories: - text-generation language: - zh - en tags: - lima pretty_name: Meissa-LIMA size_categories: - 1K<n<10K --- # Meissa-LIMA 受[LIMA](https://arxiv.org/abs/2305.11206)启发, 我制作了这个数据集. 数据集由以下几个部分构成: 原始数据集, 中文翻译版, 破限数据集, 角色扮演数据集, Gutenberg数据集, 弱智吧问答. - 原始数据集: 原数据集中包含了13条拒绝/道德对齐的数据, 我将其找出并手动进行了修改 - 中文翻译版: 使用运行在 Great Server 上的 [Orion-zhen/Meissa-Qwen2.5-7B-Instruct-Q5_K_M-GGUF](https://huggingface.co/Orion-zhen/Meissa-Qwen2.5-7B-Instruct-Q5_K_M-GGUF) 完成翻译, 并由我进行校对 - 破限数据集: 从 [Orion-zhen/meissa-unalignments](https://huggingface.co/datasets/Orion-zhen/meissa-unalignments) 中选取了若干条目 - 角色扮演数据集: 从 [MinervaAI/Aesir-Preview](https://huggingface.co/datasets/MinervaAI/Aesir-Preview) 中选取了若干条目 - Gutenberg数据集: 从 [Orion-zhen/kto-gutenberg](https://huggingface.co/datasets/Orion-zhen/kto-gutenberg) 中选取了若干条目 - 弱智吧问答: 从 [LooksJuicy/ruozhiba](https://huggingface.co/datasets/LooksJuicy/ruozhiba) 中选取了若干问题并由我手动编写回答
teilomillet/wikipeqa
teilomillet
2025-06-18T10:54:38Z
12
1
[ "language:en", "license:mit", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "eval", "rag" ]
[]
2025-06-16T18:36:48Z
1
--- license: mit configs: - config_name: default data_files: - split: sample path: data/sample-* - split: eval path: data/eval-* dataset_info: features: - name: question dtype: large_string - name: answer dtype: large_string - name: source dtype: large_string - name: canary dtype: large_string splits: - name: sample num_bytes: 456906 num_examples: 200 - name: eval num_bytes: 9649060 num_examples: 3003 download_size: 9583773 dataset_size: 10105966 language: - en tags: - eval - rag --- # Dataset: wikiqa This dataset was generated using the [Kushim framework](https://github.com/teilomillet/kushim). This repository may contain multiple files, including: - A public, unencrypted sample of the Q&A data. - A main, encrypted version of the full Q&A dataset. - A JSON file containing the source article information.
davanstrien/ragged
davanstrien
2025-05-27T09:03:28Z
0
0
[ "region:us" ]
[]
2025-05-27T09:02:15Z
0
--- dataset_info: features: - name: system_id dtype: string - name: trajectories sequence: sequence: sequence: float64 - name: n_dims dtype: int32 - name: n_trajectories dtype: int32 splits: - name: train num_bytes: 2520822264 num_examples: 10000 download_size: 2347812878 dataset_size: 2520822264 configs: - config_name: default data_files: - split: train path: data/train-* ---
carlfeynman/Bharat_NanoQuoraRetrieval_sa
carlfeynman
2025-01-23T07:18:00Z
43
0
[ "task_categories:text-retrieval", "task_ids:document-retrieval", "multilinguality:monolingual", "source_datasets:NanoQuoraRetrieval", "language:sa", "license:cc-by-4.0", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "text-retrieval" ]
[ "text-retrieval" ]
2025-01-23T07:17:53Z
0
--- language: - sa license: cc-by-4.0 multilinguality: - monolingual source_datasets: - NanoQuoraRetrieval task_categories: - text-retrieval task_ids: - document-retrieval tags: - text-retrieval dataset_info: - config_name: corpus features: - name: _id dtype: string - name: text dtype: string splits: - name: train - config_name: qrels features: - name: query-id dtype: string - name: corpus-id dtype: string splits: - name: train - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: train configs: - config_name: corpus data_files: - split: train path: corpus/train-* - config_name: qrels data_files: - split: train path: qrels/train-* - config_name: queries data_files: - split: train path: queries/train-* --- # Bharat-NanoBEIR: Indian Language Information Retrieval Dataset ## Overview This dataset is part of the Bharat-NanoBEIR collection, which provides information retrieval datasets for Indian languages. It is derived from the NanoBEIR project, which offers smaller versions of BEIR datasets containing 50 queries and up to 10K documents each. ## Dataset Description This particular dataset is the Sanskrit version of the NanoQuoraRetrieval dataset, specifically adapted for information retrieval tasks. The translation and adaptation maintain the core structure of the original NanoBEIR while making it accessible for Sanskrit language processing. ## Usage This dataset is designed for: - Information Retrieval (IR) system development in Sanskrit - Evaluation of multilingual search capabilities - Cross-lingual information retrieval research - Benchmarking Sanskrit language models for search tasks ## Dataset Structure The dataset consists of three main components: 1. **Corpus**: Collection of documents in Sanskrit 2. **Queries**: Search queries in Sanskrit 3. **QRels**: Relevance judgments connecting queries to relevant documents ## Citation If you use this dataset, please cite: ``` @misc{bharat-nanobeir, title={Bharat-NanoBEIR: Indian Language Information Retrieval Datasets}, year={2024}, url={https://huggingface.co/datasets/carlfeynman/Bharat_NanoQuoraRetrieval_sa} } ``` ## Additional Information - **Language**: Sanskrit (sa) - **License**: CC-BY-4.0 - **Original Dataset**: NanoBEIR - **Domain**: Information Retrieval ## License This dataset is licensed under CC-BY-4.0. Please see the LICENSE file for details.
spr-serena/mri_scans_labelled_horizontal_only
spr-serena
2024-11-24T16:48:37Z
16
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-11-24T16:48:32Z
0
--- dataset_info: features: - name: image dtype: image - name: label dtype: string splits: - name: train num_bytes: 57518157.86 num_examples: 1251 download_size: 56951972 dataset_size: 57518157.86 configs: - config_name: default data_files: - split: train path: data/train-* ---
Aniruddh79012/x_dataset_122
Aniruddh79012
2025-06-22T10:06:51Z
5
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:n<1K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[ "text-classification", "token-classification", "question-answering", "summarization", "text-generation" ]
2025-06-21T21:41:22Z
0
--- 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:** Aniruddh79012/x_dataset_122 - **Subnet:** Bittensor Subnet 13 - **Miner Hotkey:** 5H5t56jr5unKiDiE9qEkXbxq3FELYeHpuVRtpMWu8PRShFzM ### 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{Aniruddh790122025datauniversex_dataset_122, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={Aniruddh79012}, year={2025}, url={https://huggingface.co/datasets/Aniruddh79012/x_dataset_122}, } ``` ### 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:** 741 - **Date Range:** 2025-06-06T00:00:00Z to 2025-06-22T00:00:00Z - **Last Updated:** 2025-06-22T10:06:51Z ### Data Distribution - Tweets with hashtags: 79.08% - Tweets without hashtags: 20.92% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. | Rank | Topic | Total Count | Percentage | |------|-------|-------------|-------------| | 1 | #bitcoin | 296 | 49.09% | | 2 | #tao | 42 | 6.97% | | 3 | #gamedev | 21 | 3.48% | | 4 | NULL | 17 | 2.82% | | 5 | #cars | 13 | 2.16% | | 6 | #bitcoinmining | 7 | 1.16% | | 7 | #btc | 6 | 1.00% | | 8 | #car | 5 | 0.83% | | 9 | #blender3d | 5 | 0.83% | | 10 | #roblox | 5 | 0.83% | ## Update History | Date | New Instances | Total Instances | |------|---------------|-----------------| | 2025-06-21T21:41:21Z | 138 | 138 | | 2025-06-21T21:41:23Z | 300 | 438 | | 2025-06-22T10:06:51Z | 303 | 741 |
alea-institute/kl3m-filter-data-dotgov-www.eucom.mil
alea-institute
2025-02-04T08:40:28Z
15
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-02-04T08:40:26Z
0
--- dataset_info: features: - name: identifier dtype: string - name: dataset dtype: string - name: mime_type dtype: string - name: score dtype: float64 - name: tokens sequence: int64 splits: - name: train num_bytes: 4699654 num_examples: 243 download_size: 1081606 dataset_size: 4699654 configs: - config_name: default data_files: - split: train path: data/train-* ---
khantivong/DA100
khantivong
2025-03-10T14:07:12Z
14
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-03-10T14:05:26Z
0
--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 21526 num_examples: 99 download_size: 10184 dataset_size: 21526 configs: - config_name: default data_files: - split: train path: data/train-* ---
alea-institute/kl3m-filter-data-dotgov-www.msha.gov
alea-institute
2025-02-04T17:35:45Z
16
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-02-04T17:35:39Z
0
--- dataset_info: features: - name: identifier dtype: string - name: dataset dtype: string - name: mime_type dtype: string - name: score dtype: float64 - name: tokens sequence: int64 splits: - name: train num_bytes: 12003528 num_examples: 475 download_size: 2871127 dataset_size: 12003528 configs: - config_name: default data_files: - split: train path: data/train-* ---
Chojins/chess_game_000_white_red
Chojins
2025-03-29T06:43:57Z
91
0
[ "task_categories:robotics", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "so100", "chess" ]
[ "robotics" ]
2025-03-27T06:47:35Z
0
--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so100 - chess 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": 10, "total_frames": 4094, "total_tasks": 1, "total_videos": 20, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:10" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.laptop": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.phone": { "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": "av1", "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] ```
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_16_32_0.05_64_BestF1_pl
ferrazzipietro
2024-12-03T08:36:39Z
15
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-12-03T08:36:36Z
0
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 157591 num_examples: 101 - name: test num_bytes: 1105280 num_examples: 654 download_size: 273369 dataset_size: 1262871 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
illuin-conteb/insurance
illuin-conteb
2025-05-30T14:26:47Z
42
1
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-02-02T00:24:00Z
0
--- dataset_info: - config_name: documents features: - name: chunk_id dtype: string - name: chunk dtype: string splits: - name: train num_bytes: 19898 num_examples: 60 download_size: 6011 dataset_size: 19898 - config_name: queries features: - name: chunk_id dtype: string - name: query dtype: string splits: - name: train num_bytes: 9514 num_examples: 120 download_size: 2687 dataset_size: 9514 configs: - config_name: documents data_files: - split: train path: documents/train-* - config_name: queries data_files: - split: train path: queries/train-* --- # ConTEB - Insurance This dataset is part of *ConTEB* (Context-aware Text Embedding Benchmark), designed for evaluating contextual embedding model capabilities. It focuses on the theme of **Insurance**, particularly stemming from a document of the EIOPA entity. ## Dataset Summary *Insurance* is composed of a long document with insurance-related statistics for each country of the European Union. To build the corpus, we extract the text of the document, and chunk it (using [LangChain](https://github.com/langchain-ai/langchain)'s RecursiveCharacterSplitter with a threshold of 1000 characters). Countries are often not referred to in-text, but only once in the section title. Therefore, certain chunks require knowledge of their position within the document to be properly disambiguated from others. Questions are manually crafted to require structural understanding for accurate chunk matching. Since questions are crafted after the chunking process, the annotation results directly from the manual question generation process. This dataset provides a focused benchmark for contextualized embeddings. It includes a curated set of original documents, chunks stemming from them, and queries. * **Number of Documents:** 1 * **Number of Chunks:** 60 * **Number of Queries:** 120 * **Average Number of Tokens per Doc:** 80.7 ## Dataset Structure (Hugging Face Datasets) The dataset is structured into the following columns: * **`documents`**: Contains chunk information: * `"chunk_id"`: The ID of the chunk, of the form `doc-id_chunk-id`, where `doc-id` is the ID of the original document and `chunk-id` is the position of the chunk within that document. * `"chunk"`: The text of the chunk * **`queries`**: Contains query information: * `"query"`: The text of the query. * `"chunk_id"`: The ID of the chunk that the query is related to, of the form `doc-id_chunk-id`, where `doc-id` is the ID of the original document and `chunk-id` is the position of the chunk within that document. ## Usage We will upload a Quickstart evaluation snippet soon. ## Citation We will add the corresponding citation soon. ## Acknowledgments This work is partially supported by [ILLUIN Technology](https://www.illuin.tech/), and by a grant from ANRT France. ## Copyright All rights are reserved to the original authors of the documents.
Igorrr0/Polish-wikipedia-selected-topics
Igorrr0
2025-05-23T16:46:44Z
88
0
[ "language:pl", "license:apache-2.0", "size_categories:1K<n<10K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "wikipedia", "polish", "pl", "polska" ]
[]
2025-05-22T09:37:20Z
0
--- license: apache-2.0 language: - pl tags: - wikipedia - polish - pl - polska pretty_name: polish wikipedia selected topics size_categories: - 1K<n<10K --- categories: "Nauki przyrodnicze", "Nauki humanistyczne", "Nauki biologiczne", "Biologia", "Metodologia nauk przyrodniczych", "Polska", "Nauka w Polsce", "Języki Polski", "Nauki humanistyczne", "Językoznawstwo", "Kultuta", "Kultura języka", "Myrmekologia", "Mrówkowate", "Ekologia", "Prawo w Polsce", "Językoznawstwo", "Dialektologia", "Odmiany i style językowe", "Futurologia" every example has max 2000 words in it.
magnifi/parser_user_v35a
magnifi
2025-02-28T14:28:02Z
18
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-02-26T04:05:25Z
0
--- dataset_info: features: - name: Query_id dtype: int64 - name: Query dtype: string - name: Elastic_search dtype: string - name: virtual_portfolios dtype: string - name: Parser_output dtype: string splits: - name: train num_bytes: 542942 num_examples: 2233 - name: validation num_bytes: 29703 num_examples: 149 download_size: 179873 dataset_size: 572645 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
michsethowusu/bambara-dinka_sentence-pairs
michsethowusu
2025-04-03T11:55:40Z
7
0
[ "size_categories:10K<n<100K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-04-03T11:55:37Z
0
--- dataset_info: features: - name: score dtype: float32 - name: Bambara dtype: string - name: Dinka dtype: string splits: - name: train num_bytes: 2910185 num_examples: 13958 download_size: 2910185 dataset_size: 2910185 configs: - config_name: default data_files: - split: train path: Bambara-Dinka_Sentence-Pairs.csv --- # Bambara-Dinka_Sentence-Pairs Dataset This dataset contains sentence pairs for African languages along with similarity scores. It can be used for machine translation, sentence alignment, or other natural language processing tasks. This dataset is based on the NLLBv1 dataset, published on OPUS under an open-source initiative led by META. You can find more information here: [OPUS - NLLB-v1](https://opus.nlpl.eu/legacy/NLLB-v1.php) ## Metadata - **File Name**: Bambara-Dinka_Sentence-Pairs - **Number of Rows**: 13958 - **Number of Columns**: 3 - **Columns**: score, Bambara, Dinka ## Dataset Description The dataset contains sentence pairs in African languages with an associated similarity score. Each row consists of three columns: 1. `score`: The similarity score between the two sentences (range from 0 to 1). 2. `Bambara`: The first sentence in the pair (language 1). 3. `Dinka`: The second sentence in the pair (language 2). This dataset is intended for use in training and evaluating machine learning models for tasks like translation, sentence similarity, and cross-lingual transfer learning. ## References Below are papers related to how the data was collected and used in various multilingual and cross-lingual applications: [1] Holger Schwenk and Matthijs Douze, Learning Joint Multilingual Sentence Representations with Neural Machine Translation, ACL workshop on Representation Learning for NLP, 2017 [2] Holger Schwenk and Xian Li, A Corpus for Multilingual Document Classification in Eight Languages, LREC, pages 3548-3551, 2018. [3] Holger Schwenk, Filtering and Mining Parallel Data in a Joint Multilingual Space ACL, July 2018 [4] Alexis Conneau, Guillaume Lample, Ruty Rinott, Adina Williams, Samuel R. Bowman, Holger Schwenk and Veselin Stoyanov, XNLI: Cross-lingual Sentence Understanding through Inference, EMNLP, 2018. [5] Mikel Artetxe and Holger Schwenk, Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings arXiv, Nov 3 2018. [6] Mikel Artetxe and Holger Schwenk, Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond arXiv, Dec 26 2018. [7] Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman, WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia arXiv, July 11 2019. [8] Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave and Armand Joulin CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB [9] Paul-Ambroise Duquenne, Hongyu Gong, Holger Schwenk, Multimodal and Multilingual Embeddings for Large-Scale Speech Mining, NeurIPS 2021, pages 15748-15761. [10] Kevin Heffernan, Onur Celebi, and Holger Schwenk, Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages
ankner/mmlu-pro-rl
ankner
2025-03-26T08:13:38Z
17
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-03-26T07:13:50Z
0
--- dataset_info: features: - name: input dtype: string - name: response dtype: string - name: test_cases dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 14453971.50652994 num_examples: 8337 - name: test num_bytes: 1733713.7467350296 num_examples: 1000 download_size: 7857646 dataset_size: 16187685.25326497 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
hcltech-robotics/gauge_data_industrial_env
hcltech-robotics
2025-03-01T00:54:04Z
16
0
[ "license:apache-2.0", "region:us" ]
[]
2025-02-25T01:42:11Z
0
--- license: apache-2.0 ---
konwoo/er-irl-2
konwoo
2025-04-19T07:33:49Z
22
0
[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-04-19T06:52:43Z
0
--- dataset_info: features: - name: text dtype: string splits: - name: validation num_bytes: 11217655 num_examples: 1000 - name: train num_bytes: 1116486492 num_examples: 100000 download_size: 640694393 dataset_size: 1127704147 configs: - config_name: default data_files: - split: validation path: data/validation-* - split: train path: data/train-* ---
yuhuanstudio/PTT-pretrain-zhtw
yuhuanstudio
2025-04-01T13:15:22Z
22
2
[ "language:zh", "license:apache-2.0", "size_categories:100K<n<1M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-01-26T04:05:49Z
0
--- license: apache-2.0 language: - zh size_categories: - 100K<n<1M --- # Dataset Card for "yuhuanstudio/PTT-pretrain-zhtw" ## 資料集摘要 本資料集擷取自台灣最大的 BBS 討論區——批踢踢實業坊（PTT），匯集多個看板的歷史與近期討論，提供豐富的繁體中文語料，適用於大型語言模型（LLM）預訓練與自然語言處理（NLP）研究。 數據來源：PTT 批踢踢實業坊（https://www.ptt.cc） 涵蓋看板：包含 Gossiping、Tech_Job、Stock、NBA 等所有討論區 時間範圍：擷取自 PTT 公開存檔前200頁，涵蓋多年歷史數據 (因各版頁數問題，熱門版面資料可能時間都較為古老) 語言：繁體中文 資料格式：JSON，適合 LLM 訓練與 NLP 應用 資料規模：包含數十萬條貼文與回應 ## 資料集結構 ```json { "text": "作者: Sonaten (=.=)\n看板: PC_Shopping\n標題: [閒聊] Gigabyte EP35-DS3 的DES...\n時間: Fri Jun 27 15:20:54 2008\n內文: 剛剛無聊用SuperPI跑一下我的E2220 完全沒超頻" } ``` ## 如何使用 ```python from datasets import load_dataset dataset = load_dataset("yuhuanstudio/PTT-pretrain-zhtw", split="pretrain") ``` ## 版權聲明 本資料集僅供學術研究與個人學習用途，嚴禁用於商業用途或任何違反 PTT 使用規範的行為。 數據來源：本資料集數據來自 PTT 批踢踢實業坊（https://www.ptt.cc) ，所有文章內容及回應的版權均屬於原作者所有。 使用限制：使用本資料集時，您應遵守台灣著作權法及相關法律法規，並尊重數據原創作者的智慧財產權。 責任聲明：本資料集僅供資訊整理與技術研究之用，作者不對數據內容的準確性、完整性或合法性承擔任何責任。 使用本資料集即表示您已閱讀並同意本聲明的條款。 ## 注意事項 PTT 內容屬於用戶生成內容，可能包含不當言論，請謹慎使用。 ## 許可資訊 [apache-2.0] 📌 如果你在其他專案中引用了這個資料集，歡迎順手標註一下作者或專案連結，讓我也能看到它的應用！😊
jason1234309/dnd_race_images_labels
jason1234309
2024-12-16T17:31:58Z
17
0
[ "size_categories:n<1K", "format:parquet", "modality:image", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-12-14T22:56:32Z
0
--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': aarakocra '1': dragonborn '2': drow '3': dwarf '4': goblin '5': lizardfolk '6': orc '7': tabaxi '8': tiefling '9': warforged splits: - name: train num_bytes: 81686693.0 num_examples: 300 - name: test num_bytes: 31714923.0 num_examples: 118 download_size: 113411158 dataset_size: 113401616.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
aadarsh99/kyvo-datasets-and-codebooks
aadarsh99
2025-06-09T21:03:43Z
0
0
[ "license:apache-2.0", "size_categories:10K<n<100K", "format:imagefolder", "modality:image", "library:datasets", "library:mlcroissant", "region:us" ]
[]
2025-02-19T22:56:18Z
0
--- license: apache-2.0 --- # Kyvo Dataset and Codebooks Details This document provides details about the dataset and codebooks provided in the `kyvo-datasets-and-codebooks` repository. We will provide the details about each of the folders in the repository and the contents of each folder. ## Data Generation Pipeline The pipeline that we follow to generate the pre-tokenized data is as follows: * **3D Scenes**: 3D Scene JSON --> Serialized 3D Scene --> Tokenized 3D Scene * **Images**: Image --> VQGAN Codebook Indices --> Tokenized Image * **Text**: Text --> Tokenized Text ## Pre-tokenized Data The `pretokenized-data` folder contains all the pre-tokenized data for the datasets used in the Kyvo project. The pre-tokenized data is stored in the following structure: ```python pretokenized-data/ |-- clevr/ | |-- 3d-scenes/ # contains all pre-tokenized 3D scenes for CLEVR for all tasks | |-- images/ # contains all pre-tokenized images for CLEVR for all tasks | |-- text/ # contains all pre-tokenized text for CLEVR for all tasks |-- objaworld/ | |-- 3d-scenes/ # contains all pre-tokenized 3D scenes for ObjaWorld for all tasks | |-- images/ # contains all pre-tokenized images for ObjaWorld for all tasks |-- objectron/ | |-- 3d-scenes/ # contains all pre-tokenized 3D scenes for Objectron for all tasks | |-- images/ # contains all pre-tokenized images for Objectron for all tasks ``` For a given task, an input can be any combination of 3d-scenes, images, and text. The output can be any combination of images, text, and 3d-scenes. In the following table we outline the tasks for each dataset and the corresponding input and output data that are needed for each task. | **Task** | **Input Image** | **Input 3D Scene** | **Input Text** | **Output Image** | **Output 3D Scene** | **Output Text** | |:----------------------:|:------------------:|:----------------------:|:-----------------:|:------------------:|:-----------------------:|:-----------------:| | **CLEVR** | | | | | | | | Rendering | 𐄂 | ✓ | 𐄂 | ✓ | 𐄂 | 𐄂 | | Recognition | ✓ | 𐄂 | 𐄂 | 𐄂 | ✓ | 𐄂 | | Instruction-Following | ✓ | ✓ | ✓ | ✓ | ✓ | 𐄂 | | Question-Answering | ✓ | ✓ | ✓ | 𐄂 | 𐄂 | ✓ | | | | | | | | | | **ObjaWorld** | | | | | | | | Rendering | 𐄂 | ✓ | 𐄂 | ✓ | 𐄂 | 𐄂 | | Recognition | ✓ | 𐄂 | 𐄂 | 𐄂 | ✓ | 𐄂 | | | | | | | | | | **Objectron** | | | | | | | | Recognition | ✓ | 𐄂 | 𐄂 | 𐄂 | ✓ | 𐄂 | For the exact files that correspond to the input and output data for each task, please refer to the corresponding configuration files in the `configs/llama3_2/train` folder. ## VQGAN Models and Codebooks The `vqgan-models-and-codebooks` folder contains all the VQGAN model checkpoints and codebooks for the datasets used in the Kyvo project. The VQGAN model checkpoints and codebooks are stored in the following structure: ```python vqgan-models-and-codebooks/ |-- clevr/ | |-- 2024-10-10T09-21-36_custom_vqgan_CLEVR-LARGE/ # contains the VQGAN model checkpoint for CLEVR | |-- custom_vqgan_embedding_1024CLEVRLARGE_256dim.npy # contains the VQGAN codebook for CLEVR |-- objaworld/ | |-- 2025-01-17T09-02-22_custom_vqgan_SYNTHETIC_LIVINGROOM_PARK_LARGE_EP100/ # contains the VQGAN model checkpoint for ObjaWorld | |-- custom_vqgan_embedding_256SYNTHETIC_LIVINGROOM_PARK_LARGE_EP100_256dim.npy # contains the VQGAN codebook for ObjaWorld |-- objectron/ | |-- 2024-11-03T05-41-42_custom_vqgan_OMNI3D_OBJECTRON_ep200/ # contains the VQGAN model checkpoint for Objectron | |-- custom_vqgan_embedding_256Omni3D-OBJECTRON_256dim.npy # contains the VQGAN codebook for Objectron ``` ## Images and Scenes for Evaluation The `images-and-scenes-for-evaluation` folder contains all the groundtruth images and scenes for the datasets used in the Kyvo project. The images and scenes are used to compute the evaluation metrics for the different tasks. The images and scenes are stored in the following structure: ```python images-and-scenes-for-evaluation/ |-- clevr/ # contains all images and scenes for evaluation for CLEVR |-- objaworld/ # contains all images and scenes for evaluation for ObjaWorld |-- objectron/ # contains all scenes for evaluation for Objectron ```
kaiwenw/open_r1_mar2
kaiwenw
2025-03-02T18:37:30Z
16
0
[ "size_categories:10K<n<100K", "modality:text", "region:us" ]
[]
2025-03-02T18:23:02Z
0
--- 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: 2695034906 num_examples: 49488 download_size: 1164300716 dataset_size: 2695034906 configs: - config_name: default data_files: - split: train path: data/train-* ---
wawiesel/scale-origen-v1
wawiesel
2025-02-08T21:59:30Z
19
0
[ "license:cc-by-nd-4.0", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-02-08T18:55:48Z
0
--- license: cc-by-nd-4.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 1184 num_examples: 1 - name: validation num_bytes: 2498 num_examples: 9 - name: test num_bytes: 873 num_examples: 4 download_size: 15773 dataset_size: 4555 ---
phonemefake/PhonemeFakeV2
phonemefake
2025-04-12T13:15:58Z
26
0
[ "language:en", "license:cc-by-4.0", "size_categories:n<1K", "format:audiofolder", "modality:audio", "modality:text", "library:datasets", "library:mlcroissant", "region:us", "audio", "speech", "synthetic" ]
[]
2025-02-13T00:31:26Z
0
--- datasets: - phonemefake/PhonemeFakeV2 # Change this to your actual dataset ID language: - en # Change if your dataset is in another language license: "cc-by-4.0" # Adjust license if needed tags: - audio - speech - synthetic --- # PhonemeFake - A Phonetic Deepfake Dataset <!-- Provide a quick summary of the dataset. --> We introduce PhonemeFake, a DF attack that manipulates critical speech segments using language reasoning, significantly reducing human perception and SoTA model accuracies. ## Dataset Details We provide example spoof audio in the viewers tab along with the transcription of the bonafide sample, the manipulated transcription and the audio timings for a small set of the data. The dataset is split into three subsets. The spoof samples generated using the ASVspoof21 Deep Fake, In the Wild and LJ-Speech bonafide samples. Each can be downloaded and extracted. We provide all the metadata in `pf_metadata.csv`. The dataset consists of 42,895 spoof samples corresponding to a total of 57.91 hours of spoof audio.
ziyu3141/rf_newtrain_8_54
ziyu3141
2025-02-08T04:03:31Z
14
0
[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-02-08T04:03:22Z
0
--- dataset_info: features: - name: Filename dtype: string - name: Aesthetics score dtype: float64 - name: Artifact score dtype: float64 - name: Misalignment score dtype: float64 - name: Overall score dtype: float64 - name: Artifact heatmap sequence: sequence: sequence: int64 - name: Misalignment heatmap sequence: sequence: sequence: int64 - name: Misalignment token label dtype: string - name: is_uneven dtype: bool - name: preferred_image dtype: binary - name: unpreferred_image dtype: binary - name: revised_image dtype: binary - name: unrevised_id dtype: string - name: is_preferred dtype: bool splits: - name: train num_bytes: 677051534 num_examples: 100 download_size: 45501086 dataset_size: 677051534 configs: - config_name: default data_files: - split: train path: data/train-* ---
mims-harvard/ProCyon-Instruct
mims-harvard
2025-03-30T19:11:39Z
720
4
[ "license:apache-2.0", "doi:10.57967/hf/3840", "region:us" ]
[]
2024-10-21T18:31:25Z
0
--- license: apache-2.0 viewer: false --- This repository contains the `ProCyon-Instruct` used to train the [`ProCyon`](https://huggingface.co/collections/mims-harvard/procyon-6716632e60e4b3785bf8bd04) family of models. Please see [installation instructions](https://github.com/mims-harvard/ProCyon?tab=readme-ov-file#installation) on our GitHub repo for details on how to configure the dataset for use with pre-trained ProCyon models. For additional technical details, please refer to our [overview page](https://zitniklab.hms.harvard.edu/ProCyon/) or the [paper](https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1). The repository contains three top-level directories: - `integrated_data/v1` - The primary component of the dataset: the amino acid sequences and associated phenotypes used for constructing instruction tuning examples. - `generated_data` - Contains additonal artifacts beyond amino acids and phenotypes. Generated by the ProCyon team and used for model training and evaluation. - `model_weights` - Contains pre-trained model weights used for initializing ProCyon models. Note that the model weights themselves are not contained in this repository but rather are expected to be downloaded here from their respective repositories. Within `integrated_data`, there are four main types of directories: - `{amino_acid_seq_type}` - directories containing information for amino acid sequences themselves, where `amino_acid_seq_type` is one of `["domain", "peptide", "protein"]`. Each directory contains the following files: - `{amino_acid_seq_type}_sequences.fa` - FASTA file containing the raw amino acid sequence for each entity - `{amino_acid_seq_type}_info_filtered.pkl` - Pickled Pandas DataFrame containing the mapping from the amino acid sequence's database ID (e.g. UniProt ID for proteins) to a numeric index used within ProCyon-Instruct. Two columns: - `index` - numeric ID within ProCyon-Instruct - `{amino_acid_seq_type}_id` - ID within original database - `{phenotype_type}` - directories containing information for each phenotype entity. Each directory contains the following files: - `{phenotype_type}_info_filtered.pkl` - Pickled Pandas DataFrame containing mapping from phenotype's database ID to numeric ID within ProCyon-Instruct, and various textual descriptions within each database. Has the following columns: - `index` - numeric ID within ProCyon-Instruct - `{phenotype_type}_id` - ID within original database - additional columns coming from the original databases giving various textual descriptions of the phenotype. Used to create the instruction tuning examples - `{phenotype_type}_info_filtered_composed.pkl` - Pickled Pandas DataFrame containing the same data as `{phenotype_type}_info_filtered.pkl` but with additional columns giving compositions of individual text columns from the original DataFrame. - `{amino_acid_seq_type}_{phenotype_type}` - directories containing information on the associations between amino acid sequences and phenotypes. Each directory contains a subdirectory named based on the method used for generating dataset splits within that database. Please see the methods section of our manuscript for more details. Within these subdirectories there are two files: - `{amino_acid_seq_type}_{phenotype_type}_relations.unified.csv` - CSV file containing relations expressed in original database IDs. Contains six columns: - `text_id` - ID from original phenotype database - `seq_id` - ID from original sequence database - `text_type` - largely redundant with `phenotype_type`, may be helpful when concatenating many assocations files - `seq_type` - largely redundant with `amino_acid_seq_type`, may be helpful when concatenating many assocations files - `relation` - largely redundant with f`{amino_acid_seq_type}_{phenotype_type}`, may be helpful when concatenating many assocations files. For some datasets such as DrugBank and GO, this column takes on different values within the same file and expresses distinct relations, e.g. GO molecular function vs GO biological process. - `split` - Assigned data split for this association. `CL_train` are training associations, `CL_val_*` are validation associations, and `eval_*` are test associations. Both `CL_val` and `eval` have sufficies indicating whether these relations are zero-shot with respect to the phenotype, where `_zero_shot` indicates a zero-shot relation, `_[num]_shot` indicates a few-shot relation, and `_pt_ft` indicates relations where the phenotype is seen frequently in training. - `{amino_acid_seq_type}_{phenotype_type}_relations_indexed.unified.csv` - Identical to the above CSV file, but with relations expressed using ProCyon internal numeric IDs. - `{amino_acid_seq_type}_{amino_acid_seq_type}` - directories containing information on the associations between two amino acid sequences, e.g. protein-protein interactions. Format is largely the same as above except with `seq_id_1` and `seq_id_2` columns instead of `seq_id` and `text_id`
yueliu1999/GuardReasoner-VLTrain-Image
yueliu1999
2025-05-29T04:18:07Z
197
0
[ "license:apache-2.0", "modality:image", "region:us" ]
[]
2025-05-29T04:09:46Z
0
--- license: apache-2.0 ---
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Dataset Card for Hugging Face Hub Dataset Cards

This datasets consists of dataset cards for models hosted on the Hugging Face Hub. The dataset cards are created by the community and provide information about datasets hosted on the Hugging Face Hub. This dataset is updated on a daily basis and includes publicly available datasets on the Hugging Face Hub.

This dataset is made available to help support users wanting to work with a large number of Dataset Cards from the Hub. We hope that this dataset will help support research in the area of Dataset Cards and their use but the format of this dataset may not be useful for all use cases. If there are other features that you would like to see included in this dataset, please open a new discussion.

Dataset Details

Uses

There are a number of potential uses for this dataset including:

  • text mining to find common themes in dataset cards
  • analysis of the dataset card format/content
  • topic modelling of dataset cards
  • training language models on the dataset cards

Out-of-Scope Use

[More Information Needed]

Dataset Structure

This dataset has a single split.

Dataset Creation

Curation Rationale

The dataset was created to assist people in working with dataset cards. In particular it was created to support research in the area of dataset cards and their use. It is possible to use the Hugging Face Hub API or client library to download dataset cards and this option may be preferable if you have a very specific use case or require a different format.

Source Data

The source data is README.md files for datasets hosted on the Hugging Face Hub. We do not include any other supplementary files that may be included in the dataset directory.

Data Collection and Processing

The data is downloaded using a CRON job on a daily basis.

Who are the source data producers?

The source data producers are the creators of the dataset cards on the Hugging Face Hub. This includes a broad variety of people from the community ranging from large companies to individual researchers. We do not gather any information about who created the dataset card in this repository although this information can be gathered from the Hugging Face Hub API.

Annotations [optional]

There are no additional annotations in this dataset beyond the dataset card content.

Annotation process

N/A

Who are the annotators?

N/A

Personal and Sensitive Information

We make no effort to anonymize the data. Whilst we don't expect the majority of dataset cards to contain personal or sensitive information, it is possible that some dataset cards may contain this information. Dataset cards may also link to websites or email addresses.

Bias, Risks, and Limitations

Dataset cards are created by the community and we do not have any control over the content of the dataset cards. We do not review the content of the dataset cards and we do not make any claims about the accuracy of the information in the dataset cards. Some dataset cards will themselves discuss bias and sometimes this is done by providing examples of bias in either the training data or the responses provided by the dataset. As a result this dataset may contain examples of bias.

Whilst we do not directly download any images linked to in the dataset cards, some dataset cards may include images. Some of these images may not be suitable for all audiences.

Recommendations

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

Citation

No formal citation is required for this dataset but if you use this dataset in your work, please include a link to this dataset page.

Dataset Card Authors

@davanstrien

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@davanstrien

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