librarian-bots/dataset_cards_with_metadata

datasetId large_stringlengths 6 118	author large_stringlengths 2 42	last_modified large_stringdate 2021-04-29 15:34:29 2025-09-30 12:15:29	downloads int64 0 3.97M	likes int64 0 7.74k	tags large listlengths 1 7.92k	task_categories large listlengths 0 48	createdAt large_stringdate 2022-03-02 23:29:22 2025-09-30 12:08:01	card large_stringlengths 31 1M
danthepol/mnlp-m2-rag-eval	danthepol	2025-05-23T19:18:27Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-23T19:16:12Z	--- dataset_info: features: - name: question dtype: string - name: context dtype: string - name: dataset dtype: string splits: - name: test num_bytes: 4918807 num_examples: 1000 download_size: 2712384 dataset_size: 4918807 configs: - config_name: default data_files: - split: test path: data/test-* ---
owengretzinger/mlh-top-hackers-2020-2023	owengretzinger	2025-03-27T15:11:01Z	16	0	[ "license:mit", "size_categories:n<1K", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "region:us" ]	[]	2025-03-27T15:01:33Z	--- license: mit --- Between 2020-2023 MLH published a top 50 hackers list. I scraped this data from top hacker pages. The data is available in both JSON and CSV files. Source code: https://github.com/owengretzinger/mlh-top-hackers-scrape
GeeveGeorge/miko-story-checkpoint-35	GeeveGeorge	2024-10-01T14:37:16Z	16	0	[ "format:parquet", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-10-01T14:37:15Z	--- dataset_info: features: - name: user dtype: 'null' - name: assistant dtype: 'null' splits: - name: train num_bytes: 0 num_examples: 0 download_size: 726 dataset_size: 0 configs: - config_name: default data_files: - split: train path: data/train-* ---
withmartian/cs3_dataset_synonyms	withmartian	2025-01-09T05:32:16Z	47	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-01-09T05:32:05Z	--- dataset_info: features: - name: command_set dtype: int64 - name: table_name dtype: string - name: create_statement dtype: string - name: english_prompt dtype: string - name: sql_statement dtype: string - name: table_fields dtype: string - name: select dtype: string - name: order_by dtype: string splits: - name: train num_bytes: 112705709.625 num_examples: 76500 - name: validation num_bytes: 19889242.875 num_examples: 13500 - name: test num_bytes: 14732772.5 num_examples: 10000 download_size: 42707420 dataset_size: 147327725.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-GR-revised_NoQuant_16_32_0.01_64_BestF1	ferrazzipietro	2024-11-28T17:42:50Z	16	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-11-28T17:42:47Z	--- 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: 172478 num_examples: 94 - name: test num_bytes: 1556265 num_examples: 738 download_size: 311863 dataset_size: 1728743 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
ai-chem/Cytotox	ai-chem	2025-06-02T13:37:31Z	69	0	[ "license:mit", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "doi:10.57967/hf/5581", "region:us" ]	[]	2025-05-11T19:12:58Z	--- dataset_info: features: - name: sn dtype: int64 - name: material dtype: string - name: shape dtype: string - name: coat_functional_group dtype: string - name: synthesis_method dtype: string - name: surface_charge dtype: string - name: size_in_medium_nm dtype: float64 - name: zeta_in_medium_mv dtype: float64 - name: no_of_cells_cells_well dtype: float64 - name: human_animal dtype: string - name: cell_source dtype: string - name: cell_tissue dtype: string - name: cell_morphology dtype: string - name: cell_age dtype: string - name: time_hr dtype: int64 - name: concentration dtype: float64 - name: test dtype: string - name: test_indicator dtype: string - name: viability_% dtype: float64 - name: doi dtype: string - name: article_list dtype: int64 - name: core_nm dtype: float64 - name: hydrodynamic_nm dtype: float64 - name: potential_mv dtype: float64 - name: cell_type dtype: string - name: journal_name dtype: string - name: publisher dtype: string - name: year dtype: int64 - name: title dtype: string - name: journal_is_oa dtype: bool - name: is_oa dtype: string - name: oa_status dtype: string - name: pdf dtype: string - name: access dtype: int64 splits: - name: train num_bytes: 2557938 num_examples: 5476 download_size: 172810 dataset_size: 2557938 configs: - config_name: default data_files: - split: train path: data/train-* license: mit --- Information about the dataset is detailed in the documentation: https://ai-chem.github.io/ChemX/overview/datasets_description.html You can find the Croissant file in our GitHub repository: https://github.com/ai-chem/ChemX/tree/main/datasets/croissants
emradar/0.1_augmented_implicit_hate_speech_dataset	emradar	2025-05-25T20:26:01Z	0	0	[ "task_categories:text-classification", "language:en", "license:mit", "size_categories:10K<n<100K", "format:arrow", "modality:text", "library:datasets", "library:mlcroissant", "region:us", "hate-speech" ]	[ "text-classification" ]	2025-05-25T19:13:32Z	--- license: mit task_categories: - text-classification language: - en tags: - hate-speech --- # Description This is a public release of the dataset described in ElSherief et al. (2022) and augmented by Adar and Wiberg (2025). This dataset card is a work in progress and will be improved over time. ## Contributions Dataset augmented by [@emradar](https://github.com/emradar) and [@Wiberacci](https://github.com/Wiberacci). ## References ElSherief, M., Ziems, C., Muchlinski, D., Anupindi, V., Seybolt, J., De Choudhury, M., & Yang, D. (2021). Latent Hatred: A Benchmark for Understanding Implicit Hate Speech. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP). ## Copyright MIT License Copyright (c) 2023 SALT Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
dwb2023/gdelt-event-2025-v3	dwb2023	2025-03-21T18:18:30Z	18	0	[ "license:cc-by-4.0", "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-03-21T17:40:46Z	--- license: cc-by-4.0 --- # Dataset Card for dwb2023/gdelt-event-2025-v3 This dataset contains global event records from the GDELT (Global Database of Events, Language, and Tone) Project, capturing real-world events and their characteristics across the globe through news media coverage. ## Dataset Details ### Dataset Description The GDELT Event Database is a comprehensive repository of human societal-scale behavior and beliefs across all countries of the world, connecting every person, organization, location, count, theme, news source, and event across the planet into a single massive network. The database records what happens in every corner of the world, containing over a quarter-billion event records in over 300 categories. - Curated by: The GDELT Project - Funded by: Google Ideas, supported by Google Cloud Platform - Language(s) (NLP): Multi-language source data, processed into standardized English format - License: All GDELT event data is available for free download and use with proper attribution - Updates: Every 15 minutes, 24/7 ### Dataset Sources - Repository: http://gdeltproject.org/ - Paper: Leetaru, Kalev and Schrodt, Philip. (2013). "GDELT: Global Data on Events, Language, and Tone, 1979-2012." International Studies Association Annual Conference, April 2013. San Francisco, CA. - Documentation: - http://data.gdeltproject.org/documentation/GDELT-Event_Codebook-V2.0.pdf - https://www.gdeltproject.org/data/documentation/CAMEO.Manual.1.1b3.pdf ## Uses ### Direct Use - Monitoring global events and conflicts in real-time - Analyzing international relations and diplomatic activities - Tracking social movements and protests - Studying media coverage patterns - Research in political science, international relations, and social sciences - Crisis early warning systems - Geopolitical risk assessment ### Out-of-Scope Use - Real-time emergency response (due to potential reporting delays) - Individual-level surveillance or tracking - Definitive source of truth for events (should be cross-referenced with other sources) - Prediction of future events without careful consideration of limitations ## Dataset Structure The dataset consists of tab-delimited files with 61 fields per event record, including: 1. Event Identification - GlobalEventID: Unique identifier for each event - DATEADDED: Timestamp in YYYYMMDDHHMMSS format - Day, MonthYear, Year: Various date formats 2. Actor Information (for both Actor1 and Actor2) - ActorCode: CAMEO-coded actor identifier - ActorName: Name of the actor - ActorCountryCode: 3-character country code - Various actor attribute codes (ethnic, religious, type) 3. Event Details - EventCode: CAMEO action code - EventBaseCode: Root event category - QuadClass: Primary event classification (Verbal/Material Cooperation/Conflict) - GoldsteinScale: Event impact score (-10 to +10) - AvgTone: Average tone of coverage (-100 to +100) 4. Geographic Information - Multiple geographic fields for each actor and action - Includes country codes, feature IDs, and lat/long coordinates ## Dataset Creation ### Curation Rationale GDELT was created to capture and analyze global human society in real-time, providing a platform for understanding global behavior through media coverage. It processes news media from around the world to identify and code events using the CAMEO (Conflict and Mediation Event Observations) coding system. ### Curation Method - Prefect based python extract script: https://gist.github.com/donbr/704789a6131bb4a92c9810185c63a16a ### Source Data #### Data Collection and Processing - Continuous monitoring of news media worldwide - Automated processing using natural language processing - Event coding using CAMEO taxonomy - Geographic coding using full-text geocoding - 15-minute update cycle - Machine translation for non-English sources #### Who are the source data producers? Primary sources include: - International news media - Web news - Broadcast transcripts - Print media - Various online platforms ### Personal and Sensitive Information While the dataset primarily focuses on public events and public figures, it may contain: - Names of public figures and officials - Locations of public events - Public statements and actions - Media coverage details ## Bias, Risks, and Limitations 1. Media Bias - Over-representation of English-language media - Varying media coverage across regions - Event selection bias based on news worthiness 2. Technical Limitations - Machine coding errors - Translation inaccuracies - Geographic coding challenges - Duplicate event reporting 3. Coverage Gaps - Limited coverage in media-restricted regions - Potential missed events in less-covered areas - Varying detail levels across events ### Recommendations 1. Users should: - Cross-reference critical events with other sources - Consider media bias in coverage - Account for regional differences in coverage - Use appropriate statistical methods for aggregation - Be aware of potential duplicates and coding errors 2. Best Practices: - Aggregate data over appropriate time periods - Consider confidence scores in analysis - Use multiple GDELT fields for validation - Account for regional and temporal variations in coverage ## Citation BibTeX: ```bibtex @inproceedings{leetaru2013gdelt, title={GDELT: Global Data on Events, Language, and Tone, 1979-2012}, author={Leetaru, Kalev and Schrodt, Philip}, booktitle={International Studies Association Annual Conference}, year={2013}, address={San Francisco, CA} } ``` APA: Leetaru, K., & Schrodt, P. (2013). GDELT: Global Data on Events, Language, and Tone, 1979-2012. Paper presented at the International Studies Association Annual Conference, San Francisco, CA. ## Dataset Card Contact dwb2023
magnifi/Phi3_intent_v66_1_w_unknown_upper_lower	magnifi	2025-09-29T10:00:09Z	35	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-09-24T18:32:27Z	--- dataset_info: features: - name: Query dtype: string - name: true_intent dtype: string splits: - name: train num_bytes: 3143608.0 num_examples: 43362 - name: validation num_bytes: 25818.0 num_examples: 383 download_size: 1215798 dataset_size: 3169426.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
Marta102/MNLP_M3_dpo_dataset_updated	Marta102	2025-06-04T13:29:37Z	0	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-04T12:18:15Z	--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: dataset dtype: string splits: - name: train num_bytes: 348868872 num_examples: 112239 - name: validation num_bytes: 61680674 num_examples: 19809 download_size: 228049045 dataset_size: 410549546 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
ali-sh07/PhenoBench_images_semantics	ali-sh07	2025-05-17T02:31:49Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:image", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-16T23:43:35Z	--- dataset_info: features: - name: image dtype: image - name: label dtype: image splits: - name: train num_bytes: 3926148196.654 num_examples: 1407 - name: val num_bytes: 2019915541.0 num_examples: 772 download_size: 5665498678 dataset_size: 5946063737.653999 configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* ---
kevin017/bioS_inverse_QA_all_small_all_answer	kevin017	2025-04-10T10:05:25Z	16	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-10T10:05:19Z	--- dataset_info: features: - name: question dtype: string - name: all_answers sequence: string splits: - name: train num_bytes: 75370 num_examples: 350 - name: test num_bytes: 75370 num_examples: 350 download_size: 44360 dataset_size: 150740 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
mlfoundations-dev/embedding_apply_tiger_math	mlfoundations-dev	2025-02-26T23:08:40Z	39	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-02-26T23:08:32Z	--- dataset_info: features: - name: instruction_seed dtype: string - name: source dtype: string - name: embeddings sequence: float64 - name: mean_positive_score dtype: float64 - name: mean_negative_score dtype: float64 - name: difference_score dtype: float64 splits: - name: train num_bytes: 34283406.4 num_examples: 4000 download_size: 27034393 dataset_size: 34283406.4 configs: - config_name: default data_files: - split: train path: data/train-* ---
ZiyiXia/SWE-bench__style-3__fs-bm25	ZiyiXia	2025-05-06T08:24:59Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-06T08:24:53Z	--- dataset_info: features: - name: instance_id dtype: string - name: text dtype: string - name: repo dtype: string - name: base_commit dtype: string - name: problem_statement dtype: string - name: hints_text dtype: string - name: created_at dtype: string - name: patch dtype: string - name: test_patch dtype: string - name: version dtype: string - name: FAIL_TO_PASS dtype: string - name: PASS_TO_PASS dtype: string - name: environment_setup_commit dtype: string splits: - name: test num_bytes: 8430964 num_examples: 6 download_size: 3885049 dataset_size: 8430964 configs: - config_name: default data_files: - split: test path: data/test-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-GR-revised_NoQuant_32_64_0.05_64_BestF1	ferrazzipietro	2024-11-28T17:47:34Z	16	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-11-28T17:47:26Z	--- 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: 172478 num_examples: 94 - name: test num_bytes: 1556265 num_examples: 738 download_size: 312438 dataset_size: 1728743 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
TOBEAI/KOR_Merged_data_v3	TOBEAI	2024-10-28T02:11:47Z	20	0	[ "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-10-28T02:10:54Z	--- dataset_info: features: - name: system dtype: string - name: user dtype: string - name: assistant dtype: string splits: - name: train num_bytes: 1392182088 num_examples: 1740818 download_size: 773822365 dataset_size: 1392182088 configs: - config_name: default data_files: - split: train path: data/train-* ---
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_b56b5e5d-d682-4f17-b00a-b14b2f7285ec	argilla-internal-testing	2024-10-11T09:46:11Z	18	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-10-11T09:46:10Z	--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1454 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_7d9c8002-6b5d-448c-a671-5eaef0a95f27	argilla-internal-testing	2024-12-10T10:59:48Z	14	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-12-10T10:59:47Z	--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---
macavaney/msmarco-passage.splade-lg.cache	macavaney	2025-02-08T11:39:59Z	37	0	[ "task_categories:text-retrieval", "region:us", "pyterrier", "pyterrier-artifact", "pyterrier-artifact.indexer_cache", "pyterrier-artifact.indexer_cache.lz4pickle" ]	[ "text-retrieval" ]	2025-02-08T11:27:44Z	--- # pretty_name: "" # Example: "MS MARCO Terrier Index" tags: - pyterrier - pyterrier-artifact - pyterrier-artifact.indexer_cache - pyterrier-artifact.indexer_cache.lz4pickle task_categories: - text-retrieval viewer: false --- # msmarco-passage.splade-lg.cache ## Description TODO: What is the artifact? ## Usage ```python # Load the artifact import pyterrier as pt artifact = pt.Artifact.from_hf('macavaney/msmarco-passage.splade-lg.cache') # TODO: Show how you use the artifact ``` ## Benchmarks TODO: Provide benchmarks for the artifact. ## Reproduction ```python # TODO: Show how you constructed the artifact. ``` ## Metadata ``` { "type": "indexer_cache", "format": "lz4pickle", "record_count": 8841823 } ```
somosnlp-hackathon-2025/gastronomia-hispana-dpo	somosnlp-hackathon-2025	2025-06-02T00:53:04Z	99	0	[ "language:es", "license:mit", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "dpo", "food", "recipes" ]	[]	2025-05-29T17:34:22Z	--- license: mit language: - es tags: - dpo - food - recipes pretty_name: Recetas en Español DPO size_categories: - 1K<n<10K --- # Gastronomía Hispana DPO ## Descripción del Dataset Este dataset contiene pares de preferencias para el entrenamiento de modelos de lenguaje especializados en gastronomía hispana utilizando la técnica DPO (Direct Preference Optimization). Los datos incluyen conversaciones sobre cocina internacional con un enfoque particular en recetas, ingredientes, técnicas culinarias y tradiciones gastronómicas del mundo hispano. ## Estructura del Dataset El dataset contiene las siguientes columnas: - `chosen`: Lista de mensajes de la conversación preferida (formato ChatML) - `rejected`: Lista de mensajes de la conversación rechazada (formato ChatML) - `recipe_id`: Identificador único de la receta (1-471) - `recipe_name`: Nombre de la receta o plato - `category`: Categoría del contenido ### Categorías Disponibles - `ingredients`: Preguntas sobre ingredientes, sustituciones y propiedades - `cooking_techniques`: Técnicas de cocción, temperaturas y métodos - `basic_recipe`: Recetas básicas y preparaciones - `cultural_context`: Contexto cultural e histórico de los platos ## Estadísticas del Dataset - Total de ejemplos: ~470 pares de preferencias - Recetas únicas: 471 - Idioma: Español - Formato: Conversaciones multi-turno ## Uso del Dataset ### Instalación ```python from datasets import load_dataset # Cargar el dataset dataset = load_dataset("somosnlp-hackathon-2025/gastronomia-hispana-dpo") ``` ### Ejemplo de Uso ```python # Acceder a un ejemplo example = dataset['train'][0] print("Conversación elegida:") for message in example['chosen']: print(f"{message['role']}: {message['content'][:100]}...") print(f"\nReceta: {example['recipe_name']}") print(f"Categoría: {example['category']}") ``` ### Entrenamiento DPO Este dataset está diseñado para ser usado con bibliotecas como TRL (Transformer Reinforcement Learning) para entrenar modelos con DPO: ```python from trl import DPOTrainer from transformers import AutoTokenizer, AutoModelForCausalLM # Preparar datos para DPO def format_dataset(example): return { "prompt": example["chosen"][0]["content"], "chosen": example["chosen"][1]["content"], "rejected": example["rejected"][1]["content"], } formatted_dataset = dataset.map(format_dataset) # Entrenar con DPOTrainer trainer = DPOTrainer( model=model, tokenizer=tokenizer, train_dataset=formatted_dataset["train"], # ... otros parámetros ) ``` ## Características del Dataset ### Roles del Sistema El dataset incluye diferentes roles especializados: - Experto en ingredientes: Conocimiento sobre ingredientes internacionales, propiedades y sustituciones - Maestro culinario: Especialista en técnicas de cocción y métodos tradicionales - Chef instructor: Explicaciones paso a paso de recetas y técnicas - Historiador gastronómico: Contexto cultural y origen de platos tradicionales ### Calidad de las Respuestas - Respuestas preferidas: Detalladas, culturalmente informadas y técnicamente precisas - Respuestas rechazadas: Más básicas, menos contexto cultural o información limitada - Enfoque educativo: Las respuestas incluyen contexto cultural, técnicas tradicionales y consejos prácticos ## Aplicaciones Este dataset es útil para: - Entrenamiento de chatbots culinarios especializados en gastronomía hispana - Modelos de recomendación de recetas y técnicas de cocina - Sistemas de tutoría para enseñanza de cocina - Investigación en NLP aplicado al dominio gastronómico - Preservación del conocimiento culinario tradicional ## Limitaciones - El dataset se centra principalmente en gastronomía hispana e internacional - Las respuestas están en español - El conocimiento está limitado a la fecha de creación (enero 2025) - Algunas recetas pueden requerir ingredientes específicos de ciertas regiones ## Consideraciones Éticas - Las recetas y técnicas representan tradiciones culturales que deben ser respetadas - Se recomienda verificar la información culinaria antes de aplicarla en contextos comerciales - El dataset respeta la diversidad cultural de la gastronomía internacional ## Licencia [Incluir información de licencia apropiada] ## Citación Si utilizas este dataset en tu investigación, por favor cita: ```bibtex @dataset{gastronomia_hispana_dpo_2025, title={Gastronomía Hispana DPO: Dataset for Culinary Instruction Following}, author={SomosNLP Hackathon 2025}, year={2025}, url={https://huggingface.co/datasets/somosnlp-hackathon-2025/gastronomia-hispana-dpo} } ``` ## Contribuciones Este dataset fue creado como parte del Hackathon SomosNLP 2025. Agradecemos las contribuciones de la comunidad para expandir y mejorar el conocimiento gastronómico representado. ## Contacto Para preguntas sobre el dataset, por favor abre un issue en este repositorio o contacta al equipo de SomosNLP.
shanchen/combine1k_ds	shanchen	2025-04-26T19:35:40Z	35	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-26T19:35:37Z	--- dataset_info: features: - name: source_dataset dtype: string - name: text dtype: string splits: - name: train num_bytes: 23008137.0 num_examples: 1000 download_size: 8932967 dataset_size: 23008137.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
hpprc/honyaku	hpprc	2024-11-20T09:06:40Z	18	2	[ "task_categories:translation", "language:ja", "language:en", "license:cc-by-sa-4.0", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[ "translation" ]	2024-11-20T04:42:28Z	--- language: - ja - en license: cc-by-sa-4.0 size_categories: - n<1K task_categories: - translation pretty_name: honyaku dataset_info: - config_name: passage features: - name: id dtype: int64 - name: en dtype: string - name: ja dtype: string - name: url_en dtype: string - name: url_ja dtype: string splits: - name: train num_bytes: 34839 num_examples: 33 download_size: 29554 dataset_size: 34839 - config_name: sentence features: - name: id dtype: int64 - name: en dtype: string - name: ja dtype: string - name: url_en dtype: string - name: url_ja dtype: string splits: - name: train num_bytes: 44734 num_examples: 104 download_size: 26899 dataset_size: 44734 configs: - config_name: passage data_files: - split: train path: passage/train-* - config_name: sentence data_files: - split: train path: sentence/train-* --- 英語Wikipedia記事の冒頭複数文を抽出し、人手で日本語翻訳した文章レベル対訳データセットです。日英対訳コーパスはライセンスが厳しいものが多く、自由に利用できる高品質なパッセージレベルの対訳データセットが少なかったため作成しました。翻訳は大体を[hpprc](https://x.com/hpp_ricecake)が、数件を[yano氏](https://x.com/yano0_c)が行いました。 `passage`サブセットは文章レベルの翻訳(対応する文ごとに改行区切り)を、`sentence`サブセットは文ごとの対訳(こちらは代名詞の翻訳の齟齬など誤った翻訳になっている可能性がある)を収載したデータセットです。 ## 翻訳方針 DeepLやGoogle翻訳など、既存の翻訳ツールは翻訳結果を機械学習モデルの入力として使用することを禁じています。本データセットは機械学習用途にも利用できる寛容なライセンスの元で公開したかったため、安全のため、できるだけこれらのツールを使用せずに英日翻訳を行いました。その代わり、ライセンスの寛容なLLMによる翻訳結果を参考に翻訳を行いました。具体的には、日本語における性能が高くライセンスがApache 2.0で配布されている[CALM3-22B-Chat](https://huggingface.co/cyberagent/calm3-22b-chat)および[Qwen 2.5 32B](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct)を利用しました。翻訳対象としたテキストは、邦訳にある程度の合意が取れそうなものを厳選して利用しました。具体的には、まず日本語Wikipediaの記事から一般性の高いもの(人名や地名っぽさが過ぎるもの以外)をランダムに選出して、英語に対応する記事が存在するかを調べました。英語に対応する記事が存在する日本語記事は、内容が大きく異なることが多いため翻訳としては利用できませんが、邦訳の参考としては非常に有用なので、そのようなペアを選んで、対応する英語記事の冒頭の数文を抽出しました。翻訳については、できるだけ日本語テキストがそれ単体として成り立つように作成しました。例えば、英語での発音記号が記載されているテキストについては、元の英語表記を残さないと発音記号単体では意味不明となってしまうので、そのような部分を残しました。日本語においてテクニカルタームが定まっている場合は、できるだけそれを反映した翻訳となるように留意しました。そのほかにもテキスト中に定型の翻訳が存在しないか確認する作業を全事例に対して行いました。そのため、翻訳には1件あたり15分程度を要しました。 ## ライセンス翻訳元テキストが英語Wikipediaであること、そして人手で翻訳を行なっていることから、本データセットはCC-BY-SA 4.0ライセンスでの配布とします。 ## Note 翻訳結果に誤りや修正事項がある場合、遠慮なくお教えいただけますと幸いです。
math-extraction-comp/princeton-nlp__Mistral-7B-Base-SFT-SLiC-HF	math-extraction-comp	2025-01-11T16:17:17Z	54	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-01-11T16:17:14Z	--- dataset_info: features: - name: question dtype: string - name: gold dtype: string - name: target dtype: string - name: prediction dtype: string - name: subset dtype: string - name: harness_extracted_answer dtype: string - name: harness_score dtype: float64 - name: qwen_extracted_answer dtype: string - name: qwen_score dtype: float64 - name: lighteval-0f21c935_extracted_answer dtype: string - name: lighteval-0f21c935_score dtype: float64 splits: - name: train num_bytes: 3227346 num_examples: 1324 download_size: 1323081 dataset_size: 3227346 configs: - config_name: default data_files: - split: train path: data/train-* ---
qfq/MetaMathQANoGSM8k	qfq	2024-10-28T21:21:01Z	20	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-10-28T21:20:56Z	--- dataset_info: features: - name: type dtype: string - name: problem dtype: string - name: original_question dtype: string - name: solution dtype: string - name: answer dtype: string splits: - name: train num_bytes: 146011835.67088607 num_examples: 155000 download_size: 62945481 dataset_size: 146011835.67088607 configs: - config_name: default data_files: - split: train path: data/train-* ---
mlfoundations-dev/olympiads_figures	mlfoundations-dev	2025-01-23T20:30:40Z	14	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-01-23T14:09:35Z	--- dataset_info: features: - name: source dtype: string - name: problem dtype: string - name: solution dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 11830252.961538998 num_examples: 4075 download_size: 9350602 dataset_size: 11830252.961538998 configs: - config_name: default data_files: - split: train path: data/train-* ---
AfriMM/AFRICaption_filtered	AfriMM	2025-03-25T05:50:39Z	10	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-03-25T05:50:25Z	--- dataset_info: features: - name: image_id dtype: string - name: caption dtype: string splits: - name: afr num_bytes: 691661 num_examples: 8089 - name: amh num_bytes: 1002994 num_examples: 8042 - name: bem num_bytes: 322159 num_examples: 3523 - name: cjk num_bytes: 113865 num_examples: 1181 - name: dik num_bytes: 88243 num_examples: 975 - name: dyu num_bytes: 12547 num_examples: 156 - name: ewe num_bytes: 26016 num_examples: 289 - name: fuv num_bytes: 45757 num_examples: 496 - name: hau num_bytes: 747641 num_examples: 7943 - name: ibo num_bytes: 826094 num_examples: 8078 - name: kik num_bytes: 67534 num_examples: 654 - name: kab num_bytes: 10841 num_examples: 121 - name: kam num_bytes: 33640 num_examples: 348 - name: kon num_bytes: 91343 num_examples: 927 - name: kmb num_bytes: 15833 num_examples: 180 - name: lua num_bytes: 103114 num_examples: 1075 - name: lug num_bytes: 340830 num_examples: 3710 - name: lin num_bytes: 131232 num_examples: 1269 - name: kin num_bytes: 652547 num_examples: 7088 - name: yor num_bytes: 902706 num_examples: 7526 download_size: 3497744 dataset_size: 6226597 configs: - config_name: default data_files: - split: afr path: data/afr-* - split: amh path: data/amh-* - split: bem path: data/bem-* - split: cjk path: data/cjk-* - split: dik path: data/dik-* - split: dyu path: data/dyu-* - split: ewe path: data/ewe-* - split: fuv path: data/fuv-* - split: hau path: data/hau-* - split: ibo path: data/ibo-* - split: kik path: data/kik-* - split: kab path: data/kab-* - split: kam path: data/kam-* - split: kon path: data/kon-* - split: kmb path: data/kmb-* - split: lua path: data/lua-* - split: lug path: data/lug-* - split: lin path: data/lin-* - split: kin path: data/kin-* - split: yor path: data/yor-* ---
andreina-covi/vqasynth_sample_spatial	andreina-covi	2024-10-24T15:12:32Z	69	0	[ "size_categories:n<1K", "format:parquet", "modality:image", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "vqasynth", "remyx" ]	[]	2024-10-16T02:57:37Z	--- dataset_info: features: - name: image dtype: image - name: messages sequence: 'null' splits: - name: train num_bytes: 1363377.0 num_examples: 10 download_size: 1364998 dataset_size: 1363377.0 configs: - config_name: default data_files: - split: train path: data/train-* tags: - vqasynth - remyx ---
Shalini731/ICONS_25_data_short	Shalini731	2025-02-05T17:02:27Z	15	0	[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-02-05T17:02:24Z	--- dataset_info: features: - name: image dtype: image - name: prompt dtype: string splits: - name: train num_bytes: 352596.0 num_examples: 25 download_size: 354360 dataset_size: 352596.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
haorandai/png_bicycle_gaussian_noise_5samples_epsilon0.1	haorandai	2024-10-08T21:20:01Z	19	0	[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-10-08T21:20:00Z	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1100851.0 num_examples: 10 download_size: 1102557 dataset_size: 1100851.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
RabotniKuma/Fast-Math-R1-GRPO	RabotniKuma	2025-04-15T01:55:46Z	74	1	[ "license:apache-2.0", "size_categories:1K<n<10K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-15T01:30:44Z	--- license: apache-2.0 --- We extracted the answers from [the 2nd stage SFT data of Light-R1](https://huggingface.co/datasets/qihoo360/Light-R1-SFTData).
lighteval/RULER-8192-SmolLM3-11T-32k-v1-remote-code	lighteval	2025-06-20T16:57:26Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-20T16:57:14Z	--- dataset_info: features: - name: index dtype: int64 - name: input dtype: string - name: outputs sequence: string - name: length dtype: int64 splits: - name: fwe num_bytes: 10481423 num_examples: 500 - name: niah_single_1 num_bytes: 14822605 num_examples: 500 - name: qa_2 num_bytes: 15858056 num_examples: 500 - name: niah_multikey_1 num_bytes: 17675160 num_examples: 500 - name: niah_multivalue num_bytes: 17699318 num_examples: 500 - name: niah_multikey_3 num_bytes: 7378000 num_examples: 500 - name: niah_single_3 num_bytes: 17599964 num_examples: 500 - name: niah_single_2 num_bytes: 17576096 num_examples: 500 - name: qa_1 num_bytes: 15574631 num_examples: 500 - name: niah_multikey_2 num_bytes: 14262757 num_examples: 500 - name: niah_multiquery num_bytes: 17754657 num_examples: 500 - name: cwe num_bytes: 11307523 num_examples: 500 download_size: 65357941 dataset_size: 177990190 configs: - config_name: default data_files: - split: fwe path: data/fwe-* - split: niah_single_1 path: data/niah_single_1-* - split: qa_2 path: data/qa_2-* - split: niah_multikey_1 path: data/niah_multikey_1-* - split: niah_multivalue path: data/niah_multivalue-* - split: niah_multikey_3 path: data/niah_multikey_3-* - split: niah_single_3 path: data/niah_single_3-* - split: niah_single_2 path: data/niah_single_2-* - split: qa_1 path: data/qa_1-* - split: niah_multikey_2 path: data/niah_multikey_2-* - split: niah_multiquery path: data/niah_multiquery-* - split: cwe path: data/cwe-* ---
Asap7772/prm800k_4_shot_formatted	Asap7772	2024-10-23T00:06:08Z	20	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-10-23T00:06:03Z	--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 62286782 num_examples: 12000 download_size: 27228532 dataset_size: 62286782 configs: - config_name: default data_files: - split: train path: data/train-* ---
OmkaRane/Major_Legal3	OmkaRane	2025-03-12T10:15:21Z	15	0	[ "format:parquet", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-03-12T10:15:18Z	--- 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-* --- # Dataset Card for "Major_Legal3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kh4dien/mc-german	kh4dien	2025-01-24T01:02:37Z	54	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-01-24T01:02:36Z	--- dataset_info: features: - name: question dtype: string - name: correct dtype: string - name: incorrect dtype: string splits: - name: train num_bytes: 326045.99022736336 num_examples: 5145 download_size: 161034 dataset_size: 326045.99022736336 configs: - config_name: default data_files: - split: train path: data/train-* ---
dvgodoy/CUAD_v1_Contract_Understanding_PDF	dvgodoy	2025-01-29T18:32:20Z	97	0	[ "language:en", "license:cc-by-4.0", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2103.06268", "region:us", "PDF" ]	[]	2025-01-20T14:29:20Z	--- dataset_info: features: - name: file_name dtype: string - name: pdf_bytes_base64 dtype: binary - name: text dtype: string splits: - name: train num_bytes: 159348380 num_examples: 509 download_size: 138630085 dataset_size: 159348380 configs: - config_name: default data_files: - split: train path: data/train-* tags: - PDF size_categories: - n<1K license: cc-by-4.0 language: - en --- # Dataset Card for Contract Understanding Atticus Dataset (CUAD) PDF This dataset contains the PDFs and the full text of 509 commercial legal contracts from the original [CUAD](https://www.atticusprojectai.org/cuad) dataset. One of the original 510 contracts was removed due to being a scanned copy. The extracted text was cleaned using [`clean-text`](https://github.com/jfilter/clean-text). The PDFs were encoded in base64 and added as the `pdf_bytes_base64` feature. You can easily and quickly load it: ```python dataset = load_dataset("dvgodoy/CUAD_v1_Contract_Understanding_PDF") ``` ``` Dataset({ features: ['file_name', 'pdf_bytes_base64', 'text'], num_rows: 509 }) ``` ## Decoding PDFs To handle the PDFs, you will need to decode the `pdf_bytes_base64` feature and load it into a PDF object using your favorite PDF library (e.g. `pdfplumber`): ```python import io import pdfplumber # load the bytes into your favorite PDF library e.g., `pdfplumber` for encoded in mini_batch['pdf_bytes_base64']: bytes_content = io.BytesIO(base64.b64decode(encoded)) pdf_obj = pdfplumber.open(bytes_content) # process the PDFs # ... # CLOSE the objects after you've used them bytes_content.close() pdf_obj.close() ``` You can use any other library/package to load the PDF, just make sure it can open a PDF from bytes. ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Additional Information](#additional-information) - [Credits](#credits) - [Licensing Information](#licensing-information) ## Dataset Description - Homepage: [Contract Understanding Atticus Dataset (CUAD)](https://www.atticusprojectai.org/cuad) - Repository: https://github.com/TheAtticusProject/cuad - Paper: [CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review](https://arxiv.org/abs/2103.06268) - Leaderboard: - Point of Contact: ### Dataset Summary The original CUAD v1 dataset contained 510 commercial legals contracts. This version contains 509 contracts, as one of those contracts was removed due to being a scanned copy. ## Dataset Structure ### Data Instances A sample from the training set is provided below : ``` { 'file_name': '2ThemartComInc_19990826_10-12G_EX-10.10_6700288_EX-10.10_Co-Branding Agreement_ Agency Agreement.pdf', 'pdf_bytes_base64': b'SlZCRVJpMHhMalF...', 'text': 'CO-BRANDING AND ADVERTISING AGREEMENT\nTHIS CO-BRANDING AND ADVERTISING AGREEMENT...' } ``` ## Dataset Creation ### Curation Rationale CUAD v1 is a corpus of 13,000+ labels in 510 commercial legal contracts with rich expert annotations curated for AI training purposes. The dataset has been manually labeled under the supervision of experienced attorneys to identify 41 types of legal clauses in commercial contracts that are considered important in contract review in connection with a corporate transaction, including mergers & acquisitions, corporate finance, investments & IPOs. ### Source Data #### Initial Data Collection and Normalization The beta version of CUAD was released in October 2020 under the name AOK v.1 with 200 contracts. CUAD v1 has 510 contracts and includes both PDF and TXT versions of the full contracts, one master CSV file and 27 Excel files corresponding to one or more types of legal clauses to ensure ease of use by both developers and attorneys. ## Additional Information ### Credits ``` @article{hendrycks2021cuad, title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review}, author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball}, journal={NeurIPS}, year={2021} } ``` ### Licensing Information - [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)
svjack/Product_Posters_Singer_DESC	svjack	2025-06-15T14:51:03Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-15T14:50:58Z	--- dataset_info: features: - name: product_category dtype: string - name: poster_prompt dtype: string - name: final_prompt dtype: string splits: - name: train num_bytes: 54202 num_examples: 50 download_size: 30844 dataset_size: 54202 configs: - config_name: default data_files: - split: train path: data/train-* ---
Thermostatic/CommonVoice-17.0-Spanish-Filtered	Thermostatic	2025-06-20T01:03:10Z	0	0	[ "license:cc0-1.0", "size_categories:100K<n<1M", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-20T00:57:10Z	--- license: cc0-1.0 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: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string splits: - name: train num_bytes: 13030072526.0 num_examples: 336846 - name: validation num_bytes: 766524228.0 num_examples: 15857 - name: test num_bytes: 744404420.0 num_examples: 15857 download_size: 14292272484 dataset_size: 14541001174.0 ---
candywal/code_rule_violation	candywal	2025-04-19T21:29:44Z	21	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-19T21:29:43Z	--- dataset_info: features: - name: user dtype: string - name: assistant dtype: string - name: classification dtype: string - name: explanation dtype: string - name: rule dtype: string splits: - name: train num_bytes: 3498524 num_examples: 1328 download_size: 1351865 dataset_size: 3498524 configs: - config_name: default data_files: - split: train path: data/train-* ---
gokceKy/earthquake	gokceKy	2024-11-15T14:00:11Z	18	0	[ "size_categories:n<1K", "format:parquet", "modality:image", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-11-09T23:59:15Z	--- dataset_info: features: - name: image dtype: image - name: label dtype: image - name: pixel_values dtype: image - name: labels dtype: image splits: - name: train num_bytes: 9888970.0 num_examples: 75 - name: test num_bytes: 880548.0 num_examples: 8 download_size: 10247557 dataset_size: 10769518.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
ljnlonoljpiljm/docci	ljnlonoljpiljm	2024-12-17T04:42:15Z	61	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-12-15T19:18:43Z	--- dataset_info: features: - name: uuid dtype: string - name: url dtype: string - name: image dtype: image - name: caption dtype: string - name: detailed_caption dtype: string - name: tags sequence: string - name: dataset dtype: string - name: points sequence: - name: uuid dtype: string - name: x dtype: float32 - name: y dtype: float32 - name: label dtype: string - name: objects sequence: - name: uuid dtype: string - name: x_min dtype: float32 - name: y_min dtype: float32 - name: x_max dtype: float32 - name: y_max dtype: float32 - name: label dtype: string - name: image_width dtype: int32 - name: image_height dtype: int32 - name: aesthetic_score dtype: float32 - name: sensitivity_score dtype: float32 splits: - name: train num_bytes: 7550643050.0 num_examples: 14647 download_size: 7542100387 dataset_size: 7550643050.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
zkpbeats/reddit_ds_100415	zkpbeats	2025-05-22T10:06:16Z	1,867	0	[ "task_categories:text-classification", "task_categories:token-classification", "task_categories:question-answering", "task_categories:summarization", "task_categories:text-generation", "task_ids:sentiment-analysis", "task_ids:topic-classification", "task_ids:named-entity-recognition", "task_ids:language-modeling", "task_ids:text-scoring", "task_ids:multi-class-classification", "task_ids:multi-label-classification", "task_ids:extractive-qa", "task_ids:news-articles-summarization", "multilinguality:multilingual", "source_datasets:original", "license:mit", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[ "text-classification", "token-classification", "question-answering", "summarization", "text-generation" ]	2025-04-03T12:12:07Z	--- license: mit multilinguality: - multilingual source_datasets: - original task_categories: - text-classification - token-classification - question-answering - summarization - text-generation task_ids: - sentiment-analysis - topic-classification - named-entity-recognition - language-modeling - text-scoring - multi-class-classification - multi-label-classification - extractive-qa - news-articles-summarization --- # Bittensor Subnet 13 Reddit Dataset <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/bittensor.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/macrocosmos-black.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> ## Dataset Description - Repository: zkpbeats/reddit_ds_100415 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5H3AggXAqErtsYWdn5A2cnf2MhkVS45HzqyErD3VxoDGWuxC ### Dataset Summary This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed Reddit data. The data is continuously updated by network miners, providing a real-time stream of Reddit content for various analytical and machine learning tasks. For more information about the dataset, please visit the [official repository](https://github.com/macrocosm-os/data-universe). ### Supported Tasks The versatility of this dataset allows researchers and data scientists to explore various aspects of social media dynamics and develop innovative applications. Users are encouraged to leverage this data creatively for their specific research or business needs. For example: - Sentiment Analysis - Topic Modeling - Community Analysis - Content Categorization ### Languages Primary language: Datasets are mostly English, but can be multilingual due to decentralized ways of creation. ## Dataset Structure ### Data Instances Each instance represents a single Reddit post or comment with the following fields: ### Data Fields - `text` (string): The main content of the Reddit post or comment. - `label` (string): Sentiment or topic category of the content. - `dataType` (string): Indicates whether the entry is a post or a comment. - `communityName` (string): The name of the subreddit where the content was posted. - `datetime` (string): The date when the content was posted or commented. - `username_encoded` (string): An encoded version of the username to maintain user privacy. - `url_encoded` (string): An encoded version of any URLs included in the content. ### Data Splits This dataset is continuously updated and does not have fixed splits. Users should create their own splits based on their requirements and the data's timestamp. ## Dataset Creation ### Source Data Data is collected from public posts and comments on Reddit, adhering to the platform's terms of service and API usage guidelines. ### Personal and Sensitive Information All usernames and URLs are encoded to protect user privacy. The dataset does not intentionally include personal or sensitive information. ## Considerations for Using the Data ### Social Impact and Biases Users should be aware of potential biases inherent in Reddit data, including demographic and content biases. This dataset reflects the content and opinions expressed on Reddit and should not be considered a representative sample of the general population. ### Limitations - Data quality may vary due to the nature of media sources. - The dataset may contain noise, spam, or irrelevant content typical of social media platforms. - Temporal biases may exist due to real-time collection methods. - The dataset is limited to public subreddits and does not include private or restricted communities. ## Additional Information ### Licensing Information The dataset is released under the MIT license. The use of this dataset is also subject to Reddit Terms of Use. ### Citation Information If you use this dataset in your research, please cite it as follows: ``` @misc{zkpbeats2025datauniversereddit_ds_100415, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={zkpbeats}, year={2025}, url={https://huggingface.co/datasets/zkpbeats/reddit_ds_100415}, } ``` ### 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: 2046436 - Date Range: 2025-04-14T00:00:00Z to 2025-05-22T00:00:00Z - Last Updated: 2025-05-22T10:06:14Z ### Data Distribution - Posts: 2.20% - Comments: 97.45% ### Top 10 Subreddits For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| r/worldnews \| 119635 \| 5.87% \| \| 2 \| r/Millennials \| 105681 \| 5.18% \| \| 3 \| r/mildlyinteresting \| 80343 \| 3.94% \| \| 4 \| r/redscarepod \| 44502 \| 2.18% \| \| 5 \| r/mexico \| 43052 \| 2.11% \| \| 6 \| r/india \| 39499 \| 1.94% \| \| 7 \| r/Grimdank \| 38994 \| 1.91% \| \| 8 \| r/ShitAmericansSay \| 38710 \| 1.90% \| \| 9 \| r/wallstreetbets \| 38257 \| 1.88% \| \| 10 \| r/BravoRealHousewives \| 36118 \| 1.77% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-04-14T14:31:44Z \| 6673 \| 6673 \| \| 2025-04-16T08:55:17Z \| 513 \| 7186 \| \| 2025-04-18T17:12:54Z \| 6171 \| 13357 \| \| 2025-04-18T19:38:03Z \| 6914 \| 20271 \| \| 2025-04-18T22:02:52Z \| 7520 \| 27791 \| \| 2025-04-19T00:26:05Z \| 5532 \| 33323 \| \| 2025-04-19T02:49:42Z \| 6645 \| 39968 \| \| 2025-04-19T05:16:23Z \| 4202 \| 44170 \| \| 2025-04-19T07:42:07Z \| 4649 \| 48819 \| \| 2025-04-19T10:07:00Z \| 2362 \| 51181 \| \| 2025-04-19T12:29:29Z \| 2887 \| 54068 \| \| 2025-04-19T14:52:07Z \| 5581 \| 59649 \| \| 2025-04-19T17:14:54Z \| 4835 \| 64484 \| \| 2025-04-19T19:38:30Z \| 5905 \| 70389 \| \| 2025-04-19T22:01:12Z \| 6387 \| 76776 \| \| 2025-04-20T00:25:18Z \| 6282 \| 83058 \| \| 2025-04-20T02:49:26Z \| 3961 \| 87019 \| \| 2025-04-20T05:12:31Z \| 4729 \| 91748 \| \| 2025-04-20T07:35:25Z \| 4478 \| 96226 \| \| 2025-04-20T09:57:13Z \| 2655 \| 98881 \| \| 2025-04-20T12:19:06Z \| 4021 \| 102902 \| \| 2025-04-20T14:41:14Z \| 5906 \| 108808 \| \| 2025-04-20T17:04:41Z \| 8013 \| 116821 \| \| 2025-04-20T19:27:42Z \| 5631 \| 122452 \| \| 2025-04-20T21:51:21Z \| 6411 \| 128863 \| \| 2025-04-21T00:13:38Z \| 5824 \| 134687 \| \| 2025-04-21T02:36:17Z \| 4309 \| 138996 \| \| 2025-04-21T05:01:15Z \| 4852 \| 143848 \| \| 2025-04-21T07:23:24Z \| 2730 \| 146578 \| \| 2025-04-21T09:47:13Z \| 2943 \| 149521 \| \| 2025-04-21T12:10:27Z \| 4326 \| 153847 \| \| 2025-04-21T14:33:50Z \| 5710 \| 159557 \| \| 2025-04-21T16:57:09Z \| 7218 \| 166775 \| \| 2025-04-21T19:20:09Z \| 8127 \| 174902 \| \| 2025-04-21T21:43:20Z \| 6225 \| 181127 \| \| 2025-04-22T00:08:03Z \| 6598 \| 187725 \| \| 2025-04-22T02:30:40Z \| 5490 \| 193215 \| \| 2025-04-22T04:53:06Z \| 4215 \| 197430 \| \| 2025-04-22T07:19:07Z \| 3912 \| 201342 \| \| 2025-04-22T09:42:40Z \| 4005 \| 205347 \| \| 2025-04-22T12:05:12Z \| 4493 \| 209840 \| \| 2025-04-22T14:28:03Z \| 4894 \| 214734 \| \| 2025-04-22T16:50:41Z \| 8216 \| 222950 \| \| 2025-04-22T19:14:24Z \| 6801 \| 229751 \| \| 2025-04-22T21:37:05Z \| 5624 \| 235375 \| \| 2025-04-23T00:00:02Z \| 4731 \| 240106 \| \| 2025-04-23T02:22:21Z \| 6388 \| 246494 \| \| 2025-04-23T04:45:31Z \| 5112 \| 251606 \| \| 2025-04-23T07:08:24Z \| 3077 \| 254683 \| \| 2025-04-23T09:31:32Z \| 4866 \| 259549 \| \| 2025-04-23T10:47:48Z \| 1339 \| 260888 \| \| 2025-04-23T13:10:54Z \| 3924 \| 264812 \| \| 2025-04-23T15:33:55Z \| 7979 \| 272791 \| \| 2025-04-23T17:56:36Z \| 6625 \| 279416 \| \| 2025-04-23T20:19:21Z \| 6475 \| 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\| 2025-04-26T03:05:10Z \| 4562 \| 428321 \| \| 2025-04-26T05:27:09Z \| 6095 \| 434416 \| \| 2025-04-26T07:48:52Z \| 3612 \| 438028 \| \| 2025-04-26T10:10:51Z \| 3086 \| 441114 \| \| 2025-04-26T12:33:17Z \| 3439 \| 444553 \| \| 2025-04-26T15:00:29Z \| 7397 \| 451950 \| \| 2025-04-26T17:22:53Z \| 7048 \| 458998 \| \| 2025-04-26T19:47:35Z \| 6964 \| 465962 \| \| 2025-04-26T22:10:58Z \| 6460 \| 472422 \| \| 2025-04-27T00:33:42Z \| 7338 \| 479760 \| \| 2025-04-27T02:55:53Z \| 6334 \| 486094 \| \| 2025-04-27T05:18:25Z \| 6222 \| 492316 \| \| 2025-04-27T07:40:51Z \| 5159 \| 497475 \| \| 2025-04-27T10:03:05Z \| 2964 \| 500439 \| \| 2025-04-27T12:25:30Z \| 4022 \| 504461 \| \| 2025-04-27T14:48:21Z \| 7933 \| 512394 \| \| 2025-04-27T17:13:44Z \| 7546 \| 519940 \| \| 2025-04-27T19:36:15Z \| 7962 \| 527902 \| \| 2025-04-27T21:58:30Z \| 5000 \| 532902 \| \| 2025-04-28T00:21:59Z \| 5738 \| 538640 \| \| 2025-04-28T02:45:06Z \| 6255 \| 544895 \| \| 2025-04-28T05:07:08Z \| 4881 \| 549776 \| \| 2025-04-28T07:29:45Z \| 4842 \| 554618 \| \| 2025-04-28T09:52:39Z \| 4563 \| 559181 \| \| 2025-04-28T12:15:39Z \| 3516 \| 562697 \| \| 2025-04-28T14:38:12Z \| 7026 \| 569723 \| \| 2025-04-28T17:01:05Z \| 6173 \| 575896 \| \| 2025-04-28T19:23:58Z \| 6904 \| 582800 \| \| 2025-04-28T21:47:11Z \| 7422 \| 590222 \| \| 2025-04-29T00:09:34Z \| 5669 \| 595891 \| \| 2025-04-29T02:32:31Z \| 5035 \| 600926 \| \| 2025-04-29T04:55:25Z \| 6811 \| 607737 \| \| 2025-04-29T07:17:27Z \| 4580 \| 612317 \| \| 2025-04-29T09:40:18Z \| 3572 \| 615889 \| \| 2025-04-29T12:04:13Z \| 5189 \| 621078 \| \| 2025-04-29T14:26:42Z \| 6134 \| 627212 \| \| 2025-04-29T16:48:49Z \| 5725 \| 632937 \| \| 2025-04-29T19:12:12Z \| 8269 \| 641206 \| \| 2025-04-29T21:35:08Z \| 7741 \| 648947 \| \| 2025-04-29T23:57:26Z \| 7825 \| 656772 \| \| 2025-04-30T02:19:53Z \| 4065 \| 660837 \| \| 2025-04-30T04:42:16Z \| 5086 \| 665923 \| \| 2025-04-30T07:04:32Z \| 4704 \| 670627 \| \| 2025-04-30T09:28:04Z \| 3608 \| 674235 \| \| 2025-04-30T11:50:14Z \| 5846 \| 680081 \| \| 2025-04-30T14:12:22Z \| 7098 \| 687179 \| \| 2025-04-30T16:34:44Z \| 8904 \| 696083 \| \| 2025-04-30T18:59:01Z \| 5544 \| 701627 \| \| 2025-04-30T21:21:06Z \| 6703 \| 708330 \| \| 2025-04-30T23:43:26Z \| 7272 \| 715602 \| \| 2025-05-01T02:07:20Z \| 6315 \| 721917 \| \| 2025-05-01T04:30:14Z \| 5673 \| 727590 \| \| 2025-05-01T06:54:49Z \| 4787 \| 732377 \| \| 2025-05-01T09:17:54Z \| 4126 \| 736503 \| \| 2025-05-01T11:40:22Z \| 2985 \| 739488 \| \| 2025-05-01T14:02:56Z \| 6913 \| 746401 \| \| 2025-05-01T16:26:00Z \| 7966 \| 754367 \| \| 2025-05-01T18:48:30Z \| 6874 \| 761241 \| \| 2025-05-01T21:12:58Z \| 8503 \| 769744 \| \| 2025-05-01T23:35:27Z \| 6871 \| 776615 \| \| 2025-05-02T01:57:54Z \| 6292 \| 782907 \| \| 2025-05-02T04:20:13Z \| 7125 \| 790032 \| \| 2025-05-02T06:43:10Z \| 5111 \| 795143 \| \| 2025-05-02T09:05:25Z \| 4295 \| 799438 \| \| 2025-05-02T11:27:56Z \| 3304 \| 802742 \| \| 2025-05-02T13:52:15Z \| 7162 \| 809904 \| \| 2025-05-02T16:14:29Z \| 8007 \| 817911 \| \| 2025-05-02T18:36:41Z \| 7826 \| 825737 \| \| 2025-05-02T20:59:13Z \| 6862 \| 832599 \| \| 2025-05-02T23:21:43Z \| 6934 \| 839533 \| \| 2025-05-03T01:44:23Z \| 6926 \| 846459 \| \| 2025-05-03T04:06:58Z \| 6269 \| 852728 \| \| 2025-05-03T06:30:10Z \| 5893 \| 858621 \| \| 2025-05-03T08:52:13Z \| 4328 \| 862949 \| \| 2025-05-03T11:14:30Z \| 2742 \| 865691 \| \| 2025-05-03T13:37:06Z \| 5906 \| 871597 \| \| 2025-05-03T15:59:34Z \| 6432 \| 878029 \| \| 2025-05-03T18:22:12Z \| 6930 \| 884959 \| \| 2025-05-03T20:44:37Z \| 7420 \| 892379 \| \| 2025-05-03T23:06:52Z \| 7364 \| 899743 \| \| 2025-05-04T01:29:49Z \| 5757 \| 905500 \| \| 2025-05-04T03:51:49Z \| 5738 \| 911238 \| \| 2025-05-04T06:13:45Z \| 4217 \| 915455 \| \| 2025-05-04T08:35:53Z \| 3333 \| 918788 \| \| 2025-05-04T10:58:18Z \| 3020 \| 921808 \| \| 2025-05-04T13:20:37Z \| 3502 \| 925310 \| \| 2025-05-04T15:43:13Z \| 6520 \| 931830 \| \| 2025-05-04T18:05:44Z \| 7524 \| 939354 \| \| 2025-05-04T20:28:05Z \| 7386 \| 946740 \| \| 2025-05-04T22:50:26Z \| 6014 \| 952754 \| \| 2025-05-05T01:12:56Z \| 8336 \| 961090 \| \| 2025-05-05T03:35:18Z \| 5919 \| 967009 \| \| 2025-05-05T05:57:34Z \| 4551 \| 971560 \| \| 2025-05-05T08:19:41Z \| 4397 \| 975957 \| \| 2025-05-05T10:42:17Z \| 2881 \| 978838 \| \| 2025-05-05T13:04:28Z \| 3834 \| 982672 \| \| 2025-05-05T15:26:54Z \| 6271 \| 988943 \| \| 2025-05-05T17:49:31Z \| 6878 \| 995821 \| \| 2025-05-05T20:12:02Z \| 8960 \| 1004781 \| \| 2025-05-05T22:34:32Z \| 7150 \| 1011931 \| \| 2025-05-06T00:57:02Z \| 6813 \| 1018744 \| \| 2025-05-06T03:19:26Z \| 8478 \| 1027222 \| \| 2025-05-06T05:41:35Z \| 4649 \| 1031871 \| \| 2025-05-06T08:04:13Z \| 3824 \| 1035695 \| \| 2025-05-06T10:26:38Z \| 3650 \| 1039345 \| \| 2025-05-06T12:48:51Z \| 6481 \| 1045826 \| \| 2025-05-06T15:11:09Z \| 6622 \| 1052448 \| \| 2025-05-06T17:33:58Z \| 8696 \| 1061144 \| \| 2025-05-06T19:57:06Z \| 7521 \| 1068665 \| \| 2025-05-06T22:20:12Z \| 7746 \| 1076411 \| \| 2025-05-07T00:43:15Z \| 7306 \| 1083717 \| \| 2025-05-07T03:06:05Z \| 6757 \| 1090474 \| \| 2025-05-07T05:28:28Z \| 6072 \| 1096546 \| \| 2025-05-07T07:50:33Z \| 4436 \| 1100982 \| \| 2025-05-07T10:12:43Z \| 5652 \| 1106634 \| \| 2025-05-07T12:34:44Z \| 3694 \| 1110328 \| \| 2025-05-07T14:57:11Z \| 8226 \| 1118554 \| \| 2025-05-07T17:19:48Z \| 9114 \| 1127668 \| \| 2025-05-07T19:42:30Z \| 9541 \| 1137209 \| \| 2025-05-07T22:04:58Z \| 9539 \| 1146748 \| \| 2025-05-08T00:27:37Z \| 6664 \| 1153412 \| \| 2025-05-08T02:50:19Z \| 9841 \| 1163253 \| \| 2025-05-08T05:12:27Z \| 5400 \| 1168653 \| \| 2025-05-08T07:34:47Z \| 3427 \| 1172080 \| \| 2025-05-08T09:57:17Z \| 4060 \| 1176140 \| \| 2025-05-08T12:20:24Z \| 5275 \| 1181415 \| \| 2025-05-08T14:43:07Z \| 8854 \| 1190269 \| \| 2025-05-08T17:05:46Z \| 9541 \| 1199810 \| \| 2025-05-08T19:28:22Z \| 9742 \| 1209552 \| \| 2025-05-08T21:51:45Z \| 6099 \| 1215651 \| \| 2025-05-09T00:14:14Z \| 5912 \| 1221563 \| \| 2025-05-09T02:36:51Z \| 6879 \| 1228442 \| \| 2025-05-09T04:59:11Z \| 7747 \| 1236189 \| \| 2025-05-09T07:21:38Z \| 4184 \| 1240373 \| \| 2025-05-09T09:44:15Z \| 4371 \| 1244744 \| \| 2025-05-09T12:06:36Z \| 3647 \| 1248391 \| \| 2025-05-09T14:29:16Z \| 5890 \| 1254281 \| \| 2025-05-09T16:53:09Z \| 7717 \| 1261998 \| \| 2025-05-09T19:15:46Z \| 7368 \| 1269366 \| \| 2025-05-09T21:38:19Z \| 7879 \| 1277245 \| \| 2025-05-10T00:00:58Z \| 6570 \| 1283815 \| \| 2025-05-10T02:23:31Z \| 5730 \| 1289545 \| \| 2025-05-10T04:45:51Z \| 5362 \| 1294907 \| \| 2025-05-10T07:08:08Z \| 4617 \| 1299524 \| \| 2025-05-10T09:30:21Z \| 3101 \| 1302625 \| \| 2025-05-10T11:52:51Z \| 3653 \| 1306278 \| \| 2025-05-10T14:14:50Z \| 6043 \| 1312321 \| \| 2025-05-10T16:36:54Z \| 8235 \| 1320556 \| \| 2025-05-10T18:58:59Z \| 8370 \| 1328926 \| \| 2025-05-10T21:21:14Z \| 6141 \| 1335067 \| \| 2025-05-10T23:44:04Z \| 6265 \| 1341332 \| \| 2025-05-11T02:07:00Z \| 7315 \| 1348647 \| \| 2025-05-11T04:29:19Z \| 5421 \| 1354068 \| \| 2025-05-11T06:51:58Z \| 5463 \| 1359531 \| \| 2025-05-11T09:14:38Z \| 3980 \| 1363511 \| \| 2025-05-11T11:37:39Z \| 4431 \| 1367942 \| \| 2025-05-11T13:59:50Z \| 6181 \| 1374123 \| \| 2025-05-11T16:22:14Z \| 7736 \| 1381859 \| \| 2025-05-11T18:45:19Z \| 6791 \| 1388650 \| \| 2025-05-11T21:08:49Z \| 6579 \| 1395229 \| \| 2025-05-11T23:32:14Z \| 7701 \| 1402930 \| \| 2025-05-12T01:54:40Z \| 6786 \| 1409716 \| \| 2025-05-12T04:16:44Z \| 6802 \| 1416518 \| \| 2025-05-12T06:39:01Z \| 3979 \| 1420497 \| \| 2025-05-12T09:03:11Z \| 4558 \| 1425055 \| \| 2025-05-12T11:25:38Z \| 4377 \| 1429432 \| \| 2025-05-12T13:48:02Z \| 6324 \| 1435756 \| \| 2025-05-12T16:10:47Z \| 9365 \| 1445121 \| \| 2025-05-12T18:33:55Z \| 11055 \| 1456176 \| \| 2025-05-12T20:57:18Z \| 9381 \| 1465557 \| \| 2025-05-12T23:19:54Z \| 8918 \| 1474475 \| \| 2025-05-13T01:43:10Z \| 7599 \| 1482074 \| \| 2025-05-13T04:05:16Z \| 7517 \| 1489591 \| \| 2025-05-13T06:27:34Z \| 5505 \| 1495096 \| \| 2025-05-13T08:50:58Z \| 3874 \| 1498970 \| \| 2025-05-13T11:13:21Z \| 3143 \| 1502113 \| \| 2025-05-13T13:36:09Z \| 5539 \| 1507652 \| \| 2025-05-13T15:59:58Z \| 9643 \| 1517295 \| \| 2025-05-13T18:22:45Z \| 6962 \| 1524257 \| \| 2025-05-13T20:46:29Z \| 6749 \| 1531006 \| \| 2025-05-13T23:09:32Z \| 9450 \| 1540456 \| \| 2025-05-14T01:32:19Z \| 7048 \| 1547504 \| \| 2025-05-14T03:54:58Z \| 8498 \| 1556002 \| \| 2025-05-14T06:17:04Z \| 4854 \| 1560856 \| \| 2025-05-14T08:39:47Z \| 3784 \| 1564640 \| \| 2025-05-14T11:02:47Z \| 3460 \| 1568100 \| \| 2025-05-14T13:25:01Z \| 5587 \| 1573687 \| \| 2025-05-14T15:47:30Z \| 8370 \| 1582057 \| \| 2025-05-14T18:10:21Z \| 8253 \| 1590310 \| \| 2025-05-14T20:33:17Z \| 7316 \| 1597626 \| \| 2025-05-14T22:56:47Z \| 6516 \| 1604142 \| \| 2025-05-15T01:19:36Z \| 5547 \| 1609689 \| \| 2025-05-15T03:42:05Z \| 8617 \| 1618306 \| \| 2025-05-15T06:04:30Z \| 5333 \| 1623639 \| \| 2025-05-15T08:28:22Z \| 4609 \| 1628248 \| \| 2025-05-15T10:51:36Z \| 4085 \| 1632333 \| \| 2025-05-15T13:15:28Z \| 6781 \| 1639114 \| \| 2025-05-15T15:38:39Z \| 9008 \| 1648122 \| \| 2025-05-15T15:39:15Z \| 9008 \| 1657130 \| \| 2025-05-15T18:02:16Z \| 7464 \| 1664594 \| \| 2025-05-15T20:27:30Z \| 7837 \| 1672431 \| \| 2025-05-15T22:52:22Z \| 6106 \| 1678537 \| \| 2025-05-16T01:15:56Z \| 7440 \| 1685977 \| \| 2025-05-16T03:40:49Z \| 5423 \| 1691400 \| \| 2025-05-16T06:03:43Z \| 4286 \| 1695686 \| \| 2025-05-16T08:29:29Z \| 4233 \| 1699919 \| \| 2025-05-16T10:52:28Z \| 2931 \| 1702850 \| \| 2025-05-16T13:17:57Z \| 5866 \| 1708716 \| \| 2025-05-16T15:43:13Z \| 7311 \| 1716027 \| \| 2025-05-16T18:06:29Z \| 8174 \| 1724201 \| \| 2025-05-16T20:29:58Z \| 7819 \| 1732020 \| \| 2025-05-16T22:54:39Z \| 6752 \| 1738772 \| \| 2025-05-17T01:18:03Z \| 6382 \| 1745154 \| \| 2025-05-17T03:40:48Z \| 6051 \| 1751205 \| \| 2025-05-17T06:03:19Z \| 3672 \| 1754877 \| \| 2025-05-17T08:25:46Z \| 3530 \| 1758407 \| \| 2025-05-17T10:50:23Z \| 4177 \| 1762584 \| \| 2025-05-17T13:15:07Z \| 4038 \| 1766622 \| \| 2025-05-17T15:38:41Z \| 6297 \| 1772919 \| \| 2025-05-17T18:01:58Z \| 4948 \| 1777867 \| \| 2025-05-17T20:24:30Z \| 5059 \| 1782926 \| \| 2025-05-17T22:47:29Z \| 6712 \| 1789638 \| \| 2025-05-18T01:10:48Z \| 5350 \| 1794988 \| \| 2025-05-18T03:32:58Z \| 5936 \| 1800924 \| \| 2025-05-18T05:55:36Z \| 4483 \| 1805407 \| \| 2025-05-18T08:19:35Z \| 4392 \| 1809799 \| \| 2025-05-18T10:42:00Z \| 3959 \| 1813758 \| \| 2025-05-18T13:04:32Z \| 4301 \| 1818059 \| \| 2025-05-18T15:27:47Z \| 5957 \| 1824016 \| \| 2025-05-18T17:51:32Z \| 7226 \| 1831242 \| \| 2025-05-18T20:14:39Z \| 5501 \| 1836743 \| \| 2025-05-18T22:39:03Z \| 5990 \| 1842733 \| \| 2025-05-19T01:01:47Z \| 6209 \| 1848942 \| \| 2025-05-19T03:24:31Z \| 5727 \| 1854669 \| \| 2025-05-19T05:47:09Z \| 4786 \| 1859455 \| \| 2025-05-19T08:11:58Z \| 4459 \| 1863914 \| \| 2025-05-19T10:35:14Z \| 3981 \| 1867895 \| \| 2025-05-19T12:58:03Z \| 3385 \| 1871280 \| \| 2025-05-19T15:20:57Z \| 5702 \| 1876982 \| \| 2025-05-19T17:44:21Z \| 7794 \| 1884776 \| \| 2025-05-19T20:08:26Z \| 9671 \| 1894447 \| \| 2025-05-19T22:31:29Z \| 8001 \| 1902448 \| \| 2025-05-20T00:54:35Z \| 6063 \| 1908511 \| \| 2025-05-20T03:18:15Z \| 7571 \| 1916082 \| \| 2025-05-20T05:42:05Z \| 4629 \| 1920711 \| \| 2025-05-20T08:05:38Z \| 4244 \| 1924955 \| \| 2025-05-20T10:29:07Z \| 3897 \| 1928852 \| \| 2025-05-20T12:53:19Z \| 4809 \| 1933661 \| \| 2025-05-20T15:17:08Z \| 6091 \| 1939752 \| \| 2025-05-20T17:40:47Z \| 7407 \| 1947159 \| \| 2025-05-20T20:03:39Z \| 10047 \| 1957206 \| \| 2025-05-20T22:26:07Z \| 8196 \| 1965402 \| \| 2025-05-21T00:48:58Z \| 5330 \| 1970732 \| \| 2025-05-21T03:11:28Z \| 6717 \| 1977449 \| \| 2025-05-21T05:34:18Z \| 4849 \| 1982298 \| \| 2025-05-21T07:57:03Z \| 3652 \| 1985950 \| \| 2025-05-21T10:19:15Z \| 3610 \| 1989560 \| \| 2025-05-21T12:42:34Z \| 3372 \| 1992932 \| \| 2025-05-21T15:05:07Z \| 6436 \| 1999368 \| \| 2025-05-21T17:27:46Z \| 6563 \| 2005931 \| \| 2025-05-21T19:50:35Z \| 8433 \| 2014364 \| \| 2025-05-21T22:13:20Z \| 5952 \| 2020316 \| \| 2025-05-22T00:36:19Z \| 5190 \| 2025506 \| \| 2025-05-22T02:58:36Z \| 6844 \| 2032350 \| \| 2025-05-22T05:21:02Z \| 6449 \| 2038799 \| \| 2025-05-22T07:43:09Z \| 3942 \| 2042741 \| \| 2025-05-22T10:06:14Z \| 3695 \| 2046436 \|
samabena/dreambooth-hackathon-images	samabena	2025-04-17T18:58:05Z	17	0	[ "size_categories:n<1K", "format:parquet", "modality:image", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-17T17:32:33Z	--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 3440294.0 num_examples: 14 download_size: 3270864 dataset_size: 3440294.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
neginashz/ins-medqa-dataset_winst	neginashz	2024-12-11T13:35:40Z	13	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-12-11T13:35:40Z	--- dataset_info: features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 9544494 num_examples: 10178 download_size: 5250508 dataset_size: 9544494 configs: - config_name: default data_files: - split: train path: data/train-* ---
nickfuryavg/Audio_Sample_1	nickfuryavg	2024-11-01T07:52:53Z	18	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-11-01T07:52:49Z	--- dataset_info: features: - name: job_id dtype: int64 - name: english_sentence dtype: string - name: english_audio_bytes dtype: string - name: hindi_sentence dtype: string - name: hindi_audio_bytes dtype: string splits: - name: train num_bytes: 17706348 num_examples: 50 download_size: 12249570 dataset_size: 17706348 configs: - config_name: default data_files: - split: train path: data/train-* ---
cauaveiga/moss_test	cauaveiga	2024-10-27T11:36:30Z	21	0	[ "task_categories:robotics", "region:us", "LeRobot", "moss", "tutorial" ]	[ "robotics" ]	2024-10-27T11:26:20Z	--- task_categories: - robotics tags: - LeRobot - moss - tutorial --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
pclucas14/nqa-RAG-64_1_24	pclucas14	2024-12-04T23:08:44Z	15	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-12-04T23:08:42Z	--- dataset_info: features: - name: text sequence: sequence: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 5935246 num_examples: 66 download_size: 2094096 dataset_size: 5935246 configs: - config_name: default data_files: - split: train path: data/train-* ---
SnehaPriyaaMP/llama-pre-finetuned-Apr-21-2025-Updated	SnehaPriyaaMP	2025-04-21T13:34:12Z	19	0	[ "size_categories:n<1K", "modality:text", "region:us" ]	[]	2025-04-21T13:34:02Z	--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 114289 num_examples: 70 download_size: 26451 dataset_size: 114289 configs: - config_name: default data_files: - split: train path: data/train-* ---
SayantanJoker/Hindi_1000hr_Train_Subset_44100Hz_quality_metadata	SayantanJoker	2025-04-13T10:31:09Z	70	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-13T10:31:08Z	--- dataset_info: features: - name: text dtype: string - name: file_name dtype: string - 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: stoi dtype: float64 - name: si-sdr dtype: float64 - name: pesq dtype: float64 - name: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string - name: sdr_noise dtype: string - name: pesq_speech_quality dtype: string splits: - name: train num_bytes: 1307557 num_examples: 3843 download_size: 255513 dataset_size: 1307557 configs: - config_name: default data_files: - split: train path: data/train-* ---
mlfoundations-dev/GeneralThought-feb25_10k	mlfoundations-dev	2025-03-21T22:28:30Z	13	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-03-21T22:28:18Z	--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 120276586.92596735 num_examples: 10000 download_size: 52837405 dataset_size: 120276586.92596735 configs: - config_name: default data_files: - split: train path: data/train-* ---
kh4dien/talktuner-openai	kh4dien	2025-02-18T23:49:17Z	20	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-02-13T05:50:19Z	--- dataset_info: features: - name: label dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: age num_bytes: 6957752.147239264 num_examples: 2880 - name: education num_bytes: 13866627.816924142 num_examples: 4263 - name: gender num_bytes: 4336468.098876405 num_examples: 1762 - name: socioeconomic num_bytes: 5369022.74964904 num_examples: 2124 download_size: 14962444 dataset_size: 30529870.812688854 configs: - config_name: default data_files: - split: age path: data/age-* - split: education path: data/education-* - split: gender path: data/gender-* - split: socioeconomic path: data/socioeconomic-* ---
Asap7772/d1shs0ap-hard-hintgen-qwen3-4b-lr1e6-shard5	Asap7772	2025-05-10T07:47:20Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-10T07:47:14Z	--- 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: 71420403 num_examples: 1586 download_size: 31277734 dataset_size: 71420403 configs: - config_name: default data_files: - split: train path: data/train-* ---
IAlsace/affiche_gestes_barrieres	IAlsace	2025-03-29T22:12:24Z	29	0	[ "task_categories:translation", "multilinguality:multilingual", "language:gsw", "language:fra", "region:us", "welche", "polatt" ]	[ "translation" ]	2025-01-12T14:16:14Z	--- language: - gsw - fra multilinguality: - multilingual viewer: false task_categories: - translation tags: - welche - polatt --- > [!NOTE] > Dataset origin: https://www.olcalsace.org/fr/affiches-cartes-et-signaletique ## Description Les gestes à adopter pour se protéger et protéger les autres du coronavirus. Affiches conçues par le gouvernement dans le cadre de la lutte contre la Covid-19 et traduite par l'OLCA.
wangx0t/numina-deepseek-r1-qwen-7b	wangx0t	2025-01-30T13:12:36Z	35	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "library:distilabel", "region:us", "synthetic", "distilabel", "rlaif" ]	[]	2025-01-30T12:56:24Z	--- size_categories: n<1K dataset_info: features: - name: problem dtype: string - name: solution dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: generation dtype: string - name: distilabel_metadata struct: - name: raw_input_text_generation_0 list: - name: content dtype: string - name: role dtype: string - name: raw_output_text_generation_0 dtype: string - name: statistics_text_generation_0 struct: - name: input_tokens dtype: int64 - name: output_tokens dtype: int64 - name: model_name dtype: string splits: - name: train num_bytes: 751350 num_examples: 40 download_size: 277645 dataset_size: 751350 configs: - config_name: default data_files: - split: train path: data/train-* tags: - synthetic - distilabel - rlaif --- <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 numina-deepseek-r1-qwen-7b 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/wangx0t/numina-deepseek-r1-qwen-7b/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/wangx0t/numina-deepseek-r1-qwen-7b/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "distilabel_metadata": { "raw_input_text_generation_0": [ { "content": "You will be given a problem. Please reason step by step, and put your final answer within \boxed{}:\nWhat is the coefficient of $x^2y^6$ in the expansion of $\\left(\\frac{3}{5}x-\\frac{y}{2}\\right)^8$? Express your answer as a common fraction.", "role": "user" } ], "raw_output_text_generation_0": "\u003cthink\u003e\nOkay, so I need to find the coefficient of \\( x^2 y^6 \\) in the expansion of \\( \\left( \\frac{3}{5}x - \\frac{y}{2} \\right)^8 \\). Hmm, I remember that when expanding binomials raised to a power, the binomial theorem is useful here. Let me recall the binomial theorem.\n\nThe binomial theorem states that \\( (a + b)^n = \\sum_{k=0}^{n} \\binom{n}{k} a^{n - k} b^{k} \\). So in this case, \\( a \\) is \\( \\frac{3}{5}x \\) and \\( b \\) is \\( -\\frac{y}{2} \\). The exponent \\( n \\) is 8.\n\nI need to find the term where the exponents of \\( x \\) is 2 and the exponent of \\( y \\) is 6. Let me think: Since each term in the expansion is \\( \\binom{8}{k} \\left( \\frac{3}{5}x \\right)^{8 - k} \\left( -\\frac{y}{2} \\right)^k \\). \n\nSo, for the term to have \\( x^2 y^6 \\), the exponent on \\( x \\) should be 2, which means \\( 8 - k = 2 \\). Solving for \\( k \\), that gives \\( k = 6 \\). \n\nLet me verify that: if \\( k = 6 \\), then the exponent on \\( y \\) is \\( 6 \\), which is exactly what we need. So, that term will be the one we\u0027re interested in.\n\nNow, let me write out that term:\n\n\\( \\binom{8}{6} \\left( \\frac{3}{5}x \\right)^{8 - 6} \\left( -\\frac{y}{2} \\right)^6 \\)\n\nSimplifying the exponents first: \\( 8 - 6 = 2 \\), so:\n\n\\( \\binom{8}{6} \\left( \\frac{3}{5}x \\right)^2 \\left( -\\frac{y}{2} \\right)^6 \\)\n\nCalculating each part separately. First, \\( \\binom{8}{6} \\). I know that \\( \\binom{8}{6} \\) is equal to \\( \\binom{8}{2} \\) because of the symmetry in the binomial coefficients, right? \\( \\binom{8}{6} = \\binom{8}{2} \\). And \\( \\binom{8}{2} = \\frac{8 \\times 7}{2 \\times 1} = 28 \\). So that part is 28.\n\nNext, \\( \\left( \\frac{3}{5}x \\right)^2 \\). Squaring \\( \\frac{3}{5} \\) gives \\( \\frac{9}{25} \\), and squaring \\( x \\) is still \\( x^2 \\). So that part is \\( \\frac{9}{25}x^2 \\).\n\nThen, \\( \\left( -\\frac{y}{2} \\right)^6 \\). When I raise a negative to an even power, it becomes positive. So, \\( \\left( -\\frac{1}{2} \\right)^6 = \\left( \\frac{1}{2} \\right)^6 = \\frac{1}{64} \\). So that part is \\( \\frac{1}{64} y^6 \\).\n\nNow, putting it all together: 28 multiplied by \\( \\frac{9}{25}x^2 \\) multiplied by \\( \\frac{1}{64} y^6 \\).\n\nLet me compute the constants first: 28 times \\( \\frac{9}{25} \\) times \\( \\frac{1}{64} \\).\n\nFirst, multiply 28 and 9: 28 * 9 = 252.\n\nThen, 252 divided by 25 is \\( 10.08 \\), but I should keep it as a fraction. 252 divided by 25 is \\( \\frac{252}{25} \\).\n\nThen, \\( \\frac{252}{25} \\times \\frac{1}{64} \\). Multiply the numerators: 252 * 1 = 252, denominators: 25 * 64 = 1600. So that\u0027s \\( \\frac{252}{1600} \\).\n\nSimplify \\( \\frac{252}{1600} \\): Let me see if both numbers can be divided by a common factor. 252 and 1600. Let\u0027s try 4: 252 \u00f7 4 = 63, 1600 \u00f7 4 = 400. So, \\( \\frac{63}{400} \\). Hmm, 63 and 400. 63 is 79, 400 is 1625. They have no common factors besides 1, so that\u0027s the simplified fraction.\n\nTherefore, the coefficient is \\( \\frac{63}{400} \\).\n\nWait, let me double-check my calculations to make sure I didn\u0027t make a mistake. So, 28 * 9 is indeed 252, correct. 252 divided by 25 is 10.08? Wait, 10.08 times 25 is 252, yes. Then 252 divided by 64 is 3.9375, which is \\( \\frac{63}{16} \\). Wait, no, hold on, that doesn\u0027t make sense. Wait, I think I messed up the multiplication.\n\nWait, no, wait. Let\u0027s go back. The constants: 28 * (9/25) * (1/64). Let me compute 28 * 9 first. 289: 209=180, 89=72, so 180+72=252. Then, 252 (1/25) = 252/25. Then, 252/25 * (1/64) = 252/(2564) = 252/1600.\n\nThen, 252 divided by 1600: let me divide numerator and denominator by 4: 252 \u00f74=63, 1600 \u00f74=400. So, 63/400. That simplifies correctly.\n\nWait, but 63/400 is 0.1575, and 252/1600 is 0.1575. So that\u0027s correct.\n\nBut wait, let me check 252 divided by 1600:\n\n252 / 1600 = (252 \u00f7 4) / (1600 \u00f74) = 63/400. Yes, correct.\n\nSo, okay, so the coefficient is 63/400.\n\nWait, but let me double-check the binomial coefficient. So, \\( \\binom{8}{6} \\). Since \\( \\binom{8}{6} = \\binom{8}{2} \\) because \\( \\binom{n}{k} = \\binom{n}{n - k} \\). So, \\( \\binom{8}{2} \\) is (87)/2 = 28, correct. So that\u0027s correct.\n\nSo, 28 * (9/25) * (1/64) = 28(9)/(2564) = 252/1600 = 63/400.\n\nTherefore, the coefficient is 63/400.\n\nWait, let me think again about the signs. In the term, the \\( b^k \\) is \\( (-\\frac{y}{2})^6 \\). Since 6 is even, the negative becomes positive, so that\u0027s correct, it\u0027s positive. So, the coefficient is positive 63/400.\n\nTherefore, after going through all that, I think the coefficient is 63/400.\n\nFinal Answer\nThe coefficient of \\( x^2 y^6 \\) is \\boxed{\\dfrac{63}{400}}.\n\u003c/think\u003e\n\nTo find the coefficient of \\( x^2 y^6 \\) in the expansion of \\( \\left( \\frac{3}{5}x - \\frac{y}{2} \\right)^8 \\), we use the binomial theorem. The binomial theorem states that:\n\n\\[\n(a + b)^n = \\sum_{k=0}^{n} \\binom{n}{k} a^{n - k} b^{k}\n\\]\n\nHere, \\( a = \\frac{3}{5}x \\), \\( b = -\\frac{y}{2} \\), and \\( n = 8 \\). We need the term where the exponent of \\( x \\) is 2 and the exponent of \\( y \\) is 6. This corresponds to \\( k = 6 \\) because \\( 8 - k = 2 \\) implies \\( k = 6 \\).\n\nThe term is given by:\n\n\\[\n\\binom{8}{6} \\left( \\frac{3}{5}x \\right)^{2} \\left( -\\frac{y}{2} \\right)^{6}\n\\]\n\nCalculating each part:\n\n1. The binomial coefficient \\( \\binom{8}{6} = \\binom{8}{2} = \\frac{8 \\times 7}{2 \\times 1} = 28 \\).\n2. \\( \\left( \\frac{3}{5}x \\right)^2 = \\frac{9}{25}x^2 \\).\n3. \\( \\left( -\\frac{y}{2} \\right)^6 = \\left( \\frac{1}{2} \\right)^6 = \\frac{1}{64} y^6 \\).\n\nCombining these, we get:\n\n\\[\n28 \\times \\frac{9}{25} \\times \\frac{1}{64} = 28 \\times \\frac{9}{1600} = \\frac{252}{1600} = \\frac{63}{400}\n\\]\n\nThus, the coefficient of \\( x^2 y^6 \\) is \\boxed{\\dfrac{63}{400}}.", "statistics_text_generation_0": { "input_tokens": 77, "output_tokens": 2155 } }, "generation": "\u003cthink\u003e\nOkay, so I need to find the coefficient of \\( x^2 y^6 \\) in the expansion of \\( \\left( \\frac{3}{5}x - \\frac{y}{2} \\right)^8 \\). Hmm, I remember that when expanding binomials raised to a power, the binomial theorem is useful here. Let me recall the binomial theorem.\n\nThe binomial theorem states that \\( (a + b)^n = \\sum_{k=0}^{n} \\binom{n}{k} a^{n - k} b^{k} \\). So in this case, \\( a \\) is \\( \\frac{3}{5}x \\) and \\( b \\) is \\( -\\frac{y}{2} \\). The exponent \\( n \\) is 8.\n\nI need to find the term where the exponents of \\( x \\) is 2 and the exponent of \\( y \\) is 6. Let me think: Since each term in the expansion is \\( \\binom{8}{k} \\left( \\frac{3}{5}x \\right)^{8 - k} \\left( -\\frac{y}{2} \\right)^k \\). \n\nSo, for the term to have \\( x^2 y^6 \\), the exponent on \\( x \\) should be 2, which means \\( 8 - k = 2 \\). Solving for \\( k \\), that gives \\( k = 6 \\). \n\nLet me verify that: if \\( k = 6 \\), then the exponent on \\( y \\) is \\( 6 \\), which is exactly what we need. So, that term will be the one we\u0027re interested in.\n\nNow, let me write out that term:\n\n\\( \\binom{8}{6} \\left( \\frac{3}{5}x \\right)^{8 - 6} \\left( -\\frac{y}{2} \\right)^6 \\)\n\nSimplifying the exponents first: \\( 8 - 6 = 2 \\), so:\n\n\\( \\binom{8}{6} \\left( \\frac{3}{5}x \\right)^2 \\left( -\\frac{y}{2} \\right)^6 \\)\n\nCalculating each part separately. First, \\( \\binom{8}{6} \\). I know that \\( \\binom{8}{6} \\) is equal to \\( \\binom{8}{2} \\) because of the symmetry in the binomial coefficients, right? \\( \\binom{8}{6} = \\binom{8}{2} \\). And \\( \\binom{8}{2} = \\frac{8 \\times 7}{2 \\times 1} = 28 \\). So that part is 28.\n\nNext, \\( \\left( \\frac{3}{5}x \\right)^2 \\). Squaring \\( \\frac{3}{5} \\) gives \\( \\frac{9}{25} \\), and squaring \\( x \\) is still \\( x^2 \\). So that part is \\( \\frac{9}{25}x^2 \\).\n\nThen, \\( \\left( -\\frac{y}{2} \\right)^6 \\). When I raise a negative to an even power, it becomes positive. So, \\( \\left( -\\frac{1}{2} \\right)^6 = \\left( \\frac{1}{2} \\right)^6 = \\frac{1}{64} \\). So that part is \\( \\frac{1}{64} y^6 \\).\n\nNow, putting it all together: 28 multiplied by \\( \\frac{9}{25}x^2 \\) multiplied by \\( \\frac{1}{64} y^6 \\).\n\nLet me compute the constants first: 28 times \\( \\frac{9}{25} \\) times \\( \\frac{1}{64} \\).\n\nFirst, multiply 28 and 9: 28 * 9 = 252.\n\nThen, 252 divided by 25 is \\( 10.08 \\), but I should keep it as a fraction. 252 divided by 25 is \\( \\frac{252}{25} \\).\n\nThen, \\( \\frac{252}{25} \\times \\frac{1}{64} \\). Multiply the numerators: 252 * 1 = 252, denominators: 25 * 64 = 1600. So that\u0027s \\( \\frac{252}{1600} \\).\n\nSimplify \\( \\frac{252}{1600} \\): Let me see if both numbers can be divided by a common factor. 252 and 1600. Let\u0027s try 4: 252 \u00f7 4 = 63, 1600 \u00f7 4 = 400. So, \\( \\frac{63}{400} \\). Hmm, 63 and 400. 63 is 79, 400 is 1625. They have no common factors besides 1, so that\u0027s the simplified fraction.\n\nTherefore, the coefficient is \\( \\frac{63}{400} \\).\n\nWait, let me double-check my calculations to make sure I didn\u0027t make a mistake. So, 28 * 9 is indeed 252, correct. 252 divided by 25 is 10.08? Wait, 10.08 times 25 is 252, yes. Then 252 divided by 64 is 3.9375, which is \\( \\frac{63}{16} \\). Wait, no, hold on, that doesn\u0027t make sense. Wait, I think I messed up the multiplication.\n\nWait, no, wait. Let\u0027s go back. The constants: 28 * (9/25) * (1/64). Let me compute 28 * 9 first. 289: 209=180, 89=72, so 180+72=252. Then, 252 (1/25) = 252/25. Then, 252/25 * (1/64) = 252/(2564) = 252/1600.\n\nThen, 252 divided by 1600: let me divide numerator and denominator by 4: 252 \u00f74=63, 1600 \u00f74=400. So, 63/400. That simplifies correctly.\n\nWait, but 63/400 is 0.1575, and 252/1600 is 0.1575. So that\u0027s correct.\n\nBut wait, let me check 252 divided by 1600:\n\n252 / 1600 = (252 \u00f7 4) / (1600 \u00f74) = 63/400. Yes, correct.\n\nSo, okay, so the coefficient is 63/400.\n\nWait, but let me double-check the binomial coefficient. So, \\( \\binom{8}{6} \\). Since \\( \\binom{8}{6} = \\binom{8}{2} \\) because \\( \\binom{n}{k} = \\binom{n}{n - k} \\). So, \\( \\binom{8}{2} \\) is (87)/2 = 28, correct. So that\u0027s correct.\n\nSo, 28 * (9/25) * (1/64) = 28(9)/(2564) = 252/1600 = 63/400.\n\nTherefore, the coefficient is 63/400.\n\nWait, let me think again about the signs. In the term, the \\( b^k \\) is \\( (-\\frac{y}{2})^6 \\). Since 6 is even, the negative becomes positive, so that\u0027s correct, it\u0027s positive. So, the coefficient is positive 63/400.\n\nTherefore, after going through all that, I think the coefficient is 63/400.\n\nFinal Answer\nThe coefficient of \\( x^2 y^6 \\) is \\boxed{\\dfrac{63}{400}}.\n\u003c/think\u003e\n\nTo find the coefficient of \\( x^2 y^6 \\) in the expansion of \\( \\left( \\frac{3}{5}x - \\frac{y}{2} \\right)^8 \\), we use the binomial theorem. The binomial theorem states that:\n\n\\[\n(a + b)^n = \\sum_{k=0}^{n} \\binom{n}{k} a^{n - k} b^{k}\n\\]\n\nHere, \\( a = \\frac{3}{5}x \\), \\( b = -\\frac{y}{2} \\), and \\( n = 8 \\). We need the term where the exponent of \\( x \\) is 2 and the exponent of \\( y \\) is 6. This corresponds to \\( k = 6 \\) because \\( 8 - k = 2 \\) implies \\( k = 6 \\).\n\nThe term is given by:\n\n\\[\n\\binom{8}{6} \\left( \\frac{3}{5}x \\right)^{2} \\left( -\\frac{y}{2} \\right)^{6}\n\\]\n\nCalculating each part:\n\n1. The binomial coefficient \\( \\binom{8}{6} = \\binom{8}{2} = \\frac{8 \\times 7}{2 \\times 1} = 28 \\).\n2. \\( \\left( \\frac{3}{5}x \\right)^2 = \\frac{9}{25}x^2 \\).\n3. \\( \\left( -\\frac{y}{2} \\right)^6 = \\left( \\frac{1}{2} \\right)^6 = \\frac{1}{64} y^6 \\).\n\nCombining these, we get:\n\n\\[\n28 \\times \\frac{9}{25} \\times \\frac{1}{64} = 28 \\times \\frac{9}{1600} = \\frac{252}{1600} = \\frac{63}{400}\n\\]\n\nThus, the coefficient of \\( x^2 y^6 \\) is \\boxed{\\dfrac{63}{400}}.", "messages": [ { "content": "What is the coefficient of $x^2y^6$ in the expansion of $\\left(\\frac{3}{5}x-\\frac{y}{2}\\right)^8$? Express your answer as a common fraction.", "role": "user" }, { "content": "To determine the coefficient of \\(x^2y^6\\) in the expansion of \\(\\left(\\frac{3}{5}x - \\frac{y}{2}\\right)^8\\), we can use the binomial theorem.\n\nThe binomial theorem states:\n\\[\n(a + b)^n = \\sum_{k=0}^{n} \\binom{n}{k} a^{n-k} b^k\n\\]\n\nIn this case, \\(a = \\frac{3}{5}x\\), \\(b = -\\frac{y}{2}\\), and \\(n = 8\\).\n\nWe are interested in the term that contains \\(x^2y^6\\). In the general term of the binomial expansion:\n\\[\n\\binom{8}{k} \\left(\\frac{3}{5}x\\right)^{8-k} \\left(-\\frac{y}{2}\\right)^k\n\\]\n\nTo get \\(x^2\\), we need \\(8 - k = 2\\), thus \\(k = 6\\).\n\nSubstituting \\(k = 6\\) into the expression:\n\\[\n\\binom{8}{6} \\left(\\frac{3}{5}x\\right)^{8-6} \\left(-\\frac{y}{2}\\right)^6 = \\binom{8}{6} \\left(\\frac{3}{5}x\\right)^2 \\left(-\\frac{y}{2}\\right)^6\n\\]\n\nNow, we will compute each part of this expression.\n\n1. Calculate the binomial coefficient \\(\\binom{8}{6}\\).\n2. Compute \\(\\left(\\frac{3}{5}\\right)^2\\).\n3. Compute \\(\\left(-\\frac{y}{2}\\right)^6\\).\n4. Combine everything together to get the coefficient of \\(x^2y^6\\).\n\nLet\u0027s compute these in Python.\n```python\nfrom math import comb\n\n# Given values\nn = 8\nk = 6\n\n# Calculate the binomial coefficient\nbinom_coeff = comb(n, k)\n\n# Compute (3/5)^2\na_term = (3/5)2\n\n# Compute (-1/2)^6\nb_term = (-1/2)6\n\n# Combine terms to get the coefficient of x^2y^6\ncoefficient = binom_coeff * a_term * b_term\nprint(coefficient)\n```\n```output\n0.1575\n```\nThe coefficient of \\(x^2y^6\\) in the expansion of \\(\\left(\\frac{3}{5}x - \\frac{y}{2}\\right)^8\\) is \\(0.1575\\). To express this as a common fraction, we recognize that:\n\n\\[ 0.1575 = \\frac{1575}{10000} = \\frac{63}{400} \\]\n\nThus, the coefficient can be expressed as:\n\n\\[\n\\boxed{\\frac{63}{400}}\n\\]", "role": "assistant" } ], "model_name": "deepseek-ai/DeepSeek-R1-Distill-Llama-8B", "problem": "What is the coefficient of $x^2y^6$ in the expansion of $\\left(\\frac{3}{5}x-\\frac{y}{2}\\right)^8$? Express your answer as a common fraction.", "solution": "To determine the coefficient of \\(x^2y^6\\) in the expansion of \\(\\left(\\frac{3}{5}x - \\frac{y}{2}\\right)^8\\), we can use the binomial theorem.\n\nThe binomial theorem states:\n\\[\n(a + b)^n = \\sum_{k=0}^{n} \\binom{n}{k} a^{n-k} b^k\n\\]\n\nIn this case, \\(a = \\frac{3}{5}x\\), \\(b = -\\frac{y}{2}\\), and \\(n = 8\\).\n\nWe are interested in the term that contains \\(x^2y^6\\). In the general term of the binomial expansion:\n\\[\n\\binom{8}{k} \\left(\\frac{3}{5}x\\right)^{8-k} \\left(-\\frac{y}{2}\\right)^k\n\\]\n\nTo get \\(x^2\\), we need \\(8 - k = 2\\), thus \\(k = 6\\).\n\nSubstituting \\(k = 6\\) into the expression:\n\\[\n\\binom{8}{6} \\left(\\frac{3}{5}x\\right)^{8-6} \\left(-\\frac{y}{2}\\right)^6 = \\binom{8}{6} \\left(\\frac{3}{5}x\\right)^2 \\left(-\\frac{y}{2}\\right)^6\n\\]\n\nNow, we will compute each part of this expression.\n\n1. Calculate the binomial coefficient \\(\\binom{8}{6}\\).\n2. Compute \\(\\left(\\frac{3}{5}\\right)^2\\).\n3. Compute \\(\\left(-\\frac{y}{2}\\right)^6\\).\n4. Combine everything together to get the coefficient of \\(x^2y^6\\).\n\nLet\u0027s compute these in Python.\n```python\nfrom math import comb\n\n# Given values\nn = 8\nk = 6\n\n# Calculate the binomial coefficient\nbinom_coeff = comb(n, k)\n\n# Compute (3/5)^2\na_term = (3/5)2\n\n# Compute (-1/2)^6\nb_term = (-1/2)6\n\n# Combine terms to get the coefficient of x^2y^6\ncoefficient = binom_coeff * a_term * b_term\nprint(coefficient)\n```\n```output\n0.1575\n```\nThe coefficient of \\(x^2y^6\\) in the expansion of \\(\\left(\\frac{3}{5}x - \\frac{y}{2}\\right)^8\\) is \\(0.1575\\). To express this as a common fraction, we recognize that:\n\n\\[ 0.1575 = \\frac{1575}{10000} = \\frac{63}{400} \\]\n\nThus, the coefficient can be expressed as:\n\n\\[\n\\boxed{\\frac{63}{400}}\n\\]" } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("wangx0t/numina-deepseek-r1-qwen-7b", "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("wangx0t/numina-deepseek-r1-qwen-7b") ``` </details>
Lots-of-LoRAs/task1159_bard_analogical_reasoning_containers	Lots-of-LoRAs	2024-12-30T23:35:53Z	9	0	[ "task_categories:text-generation", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "language:en", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2204.07705", "arxiv:2407.00066", "region:us" ]	[ "text-generation" ]	2024-12-30T23:35:51Z	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task1159_bard_analogical_reasoning_containers dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 558 - name: valid num_examples: 70 - name: test num_examples: 70 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1159_bard_analogical_reasoning_containers ## Dataset Description - Homepage: https://github.com/allenai/natural-instructions - Paper: https://arxiv.org/abs/2204.07705 - Paper: https://arxiv.org/abs/2407.00066 - Point of Contact: [Rickard Brüel Gabrielsson](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions, title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks}, author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi}, year={2022}, eprint={2204.07705}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2204.07705}, } ``` More details can also be found in the following paper: ```bibtex @misc{brüelgabrielsson2024compressserveservingthousands, title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead}, author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon}, year={2024}, eprint={2407.00066}, archivePrefix={arXiv}, primaryClass={cs.DC}, url={https://arxiv.org/abs/2407.00066}, } ``` ### Contact Information For any comments or questions, please email [Rickard Brüel Gabrielsson](mailto:[email protected])
PatronusAI/glider-multilingual-reward-bench-suite	PatronusAI	2024-12-18T23:41:51Z	67	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-11-19T17:15:14Z	--- dataset_info: features: - name: prompt dtype: string - name: score dtype: int64 - name: pass_criteria dtype: string - name: rubric dtype: string splits: - name: arb_Arab num_bytes: 12237595 num_examples: 2869 - name: ces_Latn num_bytes: 10748534 num_examples: 2869 - name: deu_Latn num_bytes: 11178372 num_examples: 2869 - name: ell_Grek num_bytes: 14404826 num_examples: 2869 - name: fra_Latn num_bytes: 11245160 num_examples: 2869 - name: heb_Hebr num_bytes: 11754840 num_examples: 2869 - name: hin_Deva num_bytes: 15857179 num_examples: 2869 - name: ind_Latn num_bytes: 10872895 num_examples: 2869 - name: ita_Latn num_bytes: 10874456 num_examples: 2869 - name: jpn_Jpan num_bytes: 11443888 num_examples: 2869 - name: kor_Hang num_bytes: 11060869 num_examples: 2869 - name: nld_Latn num_bytes: 10904828 num_examples: 2869 - name: pes_Arab num_bytes: 12736791 num_examples: 2869 - name: pol_Latn num_bytes: 10905164 num_examples: 2869 - name: por_Latn num_bytes: 10826113 num_examples: 2869 - name: ron_Latn num_bytes: 11069968 num_examples: 2869 - name: rus_Cyrl num_bytes: 13720140 num_examples: 2869 - name: spa_Latn num_bytes: 10960266 num_examples: 2869 - name: tur_Latn num_bytes: 10873535 num_examples: 2869 - name: ukr_Cyrl num_bytes: 13392298 num_examples: 2869 - name: vie_Latn num_bytes: 11823251 num_examples: 2869 - name: zho_Hans num_bytes: 10016686 num_examples: 2869 - name: zho_Hant num_bytes: 9899172 num_examples: 2869 download_size: 71816921 dataset_size: 268806826 configs: - config_name: default data_files: - split: arb_Arab path: data/arb_Arab-* - split: ces_Latn path: data/ces_Latn-* - split: deu_Latn path: data/deu_Latn-* - split: ell_Grek path: data/ell_Grek-* - split: fra_Latn path: data/fra_Latn-* - split: heb_Hebr path: data/heb_Hebr-* - split: hin_Deva path: data/hin_Deva-* - split: ind_Latn path: data/ind_Latn-* - split: ita_Latn path: data/ita_Latn-* - split: jpn_Jpan path: data/jpn_Jpan-* - split: kor_Hang path: data/kor_Hang-* - split: nld_Latn path: data/nld_Latn-* - split: pes_Arab path: data/pes_Arab-* - split: pol_Latn path: data/pol_Latn-* - split: por_Latn path: data/por_Latn-* - split: ron_Latn path: data/ron_Latn-* - split: rus_Cyrl path: data/rus_Cyrl-* - split: spa_Latn path: data/spa_Latn-* - split: tur_Latn path: data/tur_Latn-* - split: ukr_Cyrl path: data/ukr_Cyrl-* - split: vie_Latn path: data/vie_Latn-* - split: zho_Hans path: data/zho_Hans-* - split: zho_Hant path: data/zho_Hant-* ---
pdf2dataset/436f3df84262cc0743d5d8d1acf9368	pdf2dataset	2024-10-05T11:48:31Z	18	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-10-05T11:48:30Z	--- dataset_info: features: - name: text dtype: string - name: source dtype: string splits: - name: train num_bytes: 1691 num_examples: 5 download_size: 6842 dataset_size: 1691 configs: - config_name: default data_files: - split: train path: data/train-* ---
R0bfried/RAGAS-12000-llama-3-2-1B-eval	R0bfried	2025-03-20T09:01:32Z	14	0	[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-03-20T09:01:28Z	--- dataset_info: features: - name: user_input dtype: string - name: retrieved_contexts sequence: string - name: response dtype: string - name: reference dtype: string - name: faithfulness dtype: float64 - name: answer_relevancy dtype: float64 - name: answer_correctness dtype: float64 splits: - name: train num_bytes: 2604364 num_examples: 150 download_size: 954997 dataset_size: 2604364 configs: - config_name: default data_files: - split: train path: data/train-* ---
cijerezg/pick-up-test-v1	cijerezg	2025-09-25T05:41:35Z	64	0	[ "task_categories:robotics", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:video", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "LeRobot" ]	[ "robotics" ]	2025-09-25T05:41:15Z	--- 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": "v3.0", "robot_type": "so101_follower", "total_episodes": 20, "total_frames": 8941, "total_tasks": 1, "chunks_size": 1000, "data_files_size_in_mb": 100, "video_files_size_in_mb": 500, "fps": 30, "splits": { "train": "0:20" }, "data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet", "video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4", "features": { "action": { "dtype": "float32", "names": [ "shoulder_pan.pos", "shoulder_lift.pos", "elbow_flex.pos", "wrist_flex.pos", "wrist_roll.pos", "gripper.pos" ], "shape": [ 6 ] }, "observation.state": { "dtype": "float32", "names": [ "shoulder_pan.pos", "shoulder_lift.pos", "elbow_flex.pos", "wrist_flex.pos", "wrist_roll.pos", "gripper.pos" ], "shape": [ 6 ] }, "observation.images.wrist": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.top_left": { "dtype": "video", "shape": [ 340, 340, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 340, "video.width": 340, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.top_right": { "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] ```
ds4sd/PubTables-1M_OTSL	ds4sd	2023-08-31T16:00:24Z	22,884	4	[ "task_categories:object-detection", "task_categories:table-to-text", "license:other", "size_categories:1M<n<10M", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2305.03393", "region:us", "table-structure-recognition", "table-understanding", "PDF" ]	[ "object-detection", "table-to-text" ]	2023-08-10T08:21:06Z	--- license: other pretty_name: PubTables-1M-OTSL size_categories: - 100K<n<1M tags: - table-structure-recognition - table-understanding - PDF task_categories: - object-detection - table-to-text --- # Dataset Card for PubTables-1M_OTSL ## Dataset Description - Homepage: https://ds4sd.github.io - Paper: https://arxiv.org/pdf/2305.03393 ### Dataset Summary This dataset enables the evaluation of both object detection models and image-to-text methods. [PubTables-1M](https://github.com/microsoft/table-transformer) is introduced in the publication "PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents" by Smock et al. The conversion into HF (Hugging Face) and the addition of the OTSL (Optimized Table Structure Language) format is presented in our paper "Optimized Table Tokenization for Table Structure Recognition" by Lysak et al. The dataset includes the original annotations amongst new additions. ### Dataset Structure * cells: origunal dataset cell groundtruth (content). * table_bbox: origunal dataset table detection groundtruth. * otsl: new reduced table structure token format * html: Generated HTML for PubTables-1M to match PubTabNet, FinTabNet, and SynthTabNet format. * html_restored: generated HTML from OTSL. * cols: grid column length. * rows: grid row length. * image: PIL image ### OTSL Vocabulary: OTSL: new reduced table structure token format More information on the OTSL table structure format and its concepts can be read from our paper. Format of this dataset extends work presented in a paper, and introduces slight modifications: * "fcel" - cell that has content in it * "ecel" - cell that is empty * "lcel" - left-looking cell (to handle horizontally merged cells) * "ucel" - up-looking cell (to handle vertically merged cells) * "xcel" - 2d span cells, in this dataset - covers entire area of a merged cell * "nl" - new line token ### Data Splits The dataset provides three splits - `train` - `val` - `test` ## Additional Information ### Dataset Curators The dataset is converted by the [Deep Search team](https://ds4sd.github.io/) at IBM Research. You can contact us at [[email protected]](mailto:[email protected]). Curators: - Maksym Lysak, [@maxmnemonic](https://github.com/maxmnemonic) - Ahmed Nassar, [@nassarofficial](https://github.com/nassarofficial) - Christoph Auer, [@cau-git](https://github.com/cau-git) - Nikos Livathinos, [@nikos-livathinos](https://github.com/nikos-livathinos) - Peter Staar, [@PeterStaar-IBM](https://github.com/PeterStaar-IBM) ### Citation Information Citation to OTSL Paper: @article{lysak2023optimized, title={Optimized Table Tokenization for Table Structure Recognition}, author={Maksym Lysak and Ahmed Nassar and Nikolaos Livathinos and Christoph Auer and Peter Staar}, year={2023}, eprint={2305.03393}, archivePrefix={arXiv}, primaryClass={cs.CV} } Citation to PubTables-1M creators: @inproceedings{smock2022pubtables, title={Pub{T}ables-1{M}: Towards comprehensive table extraction from unstructured documents}, author={Smock, Brandon and Pesala, Rohith and Abraham, Robin}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, pages={4634-4642}, year={2022}, month={June} }
Abeyankar/mtl_7k_inference_ds	Abeyankar	2025-06-03T20:06:37Z	0	0	[ "task_categories:object-detection", "language:en", "size_categories:10K<n<100K", "format:imagefolder", "modality:image", "library:datasets", "library:mlcroissant", "library:fiftyone", "region:us", "fiftyone", "image", "object-detection" ]	[ "object-detection" ]	2025-06-03T19:58:48Z	--- annotations_creators: [] language: en size_categories: - 10K<n<100K task_categories: - object-detection task_ids: [] pretty_name: train_2844_mcityclean_val_7642_7k tags: - fiftyone - image - object-detection commit_message: Hand annotated dataset MTL 7k overwrite: true dataset_summary: ' This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 10486 samples. ## Installation If you haven''t already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo from fiftyone.utils.huggingface import load_from_hub # Load the dataset # Note: other available arguments include ''max_samples'', etc dataset = load_from_hub("Abeyankar/mtl_7k_inference_ds") # Launch the App session = fo.launch_app(dataset) ``` ' --- # Dataset Card for train_2844_mcityclean_val_7642_7k <!-- Provide a quick summary of the dataset. --> This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 10486 samples. ## Installation If you haven't already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo from fiftyone.utils.huggingface import load_from_hub # Load the dataset # Note: other available arguments include 'max_samples', etc dataset = load_from_hub("Abeyankar/mtl_7k_inference_ds") # Launch the App session = fo.launch_app(dataset) ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
Ramitha/unique-records-selected-integrated-gradients	Ramitha	2025-09-27T08:57:26Z	229	0	[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-08-23T17:19:14Z	--- dataset_info: features: - name: question dtype: string - name: snippet dtype: string - name: answer dtype: string - name: ILRAlign dtype: float64 - name: dataset dtype: string - name: question_ig_tokens_llama dtype: string - name: answer_ig_tokens_llama dtype: string - name: question_ig_tokens_mistral dtype: string - name: answer_ig_tokens_mistral dtype: string - name: question_ig_tokens_falcon dtype: string - name: answer_ig_tokens_falcon dtype: string - name: question_ig_tokens_gemma dtype: string - name: answer_ig_tokens_gemma dtype: string - name: question_raw_ig_tokens_falcon dtype: string - name: answer_raw_ig_tokens_falcon dtype: string - name: question_raw_ig_tokens_llama dtype: string - name: answer_raw_ig_tokens_llama dtype: string - name: question_raw_ig_tokens_mistral dtype: string - name: answer_raw_ig_tokens_mistral dtype: string - name: question_raw_ig_tokens_gemma dtype: string - name: answer_raw_ig_tokens_gemma dtype: string - name: question_iaa_all dtype: float64 - name: question_iaa_llama_falcon dtype: float64 - name: question_iaa_llama_gemma dtype: float64 - name: question_iaa_llama_mistral dtype: float64 - name: question_iaa_falcon_gemma dtype: float64 - name: question_iaa_falcon_mistral dtype: float64 - name: question_iaa_gemma_mistral dtype: float64 - name: answer_iaa_all dtype: float64 - name: answer_iaa_llama_falcon dtype: float64 - name: answer_iaa_llama_gemma dtype: float64 - name: answer_iaa_llama_mistral dtype: float64 - name: answer_iaa_falcon_gemma dtype: float64 - name: answer_iaa_falcon_mistral dtype: float64 - name: answer_iaa_gemma_mistral dtype: float64 - name: question_unique_words dtype: float64 - name: answer_unique_words dtype: float64 - name: question_answerGenerated_llama dtype: string - name: reverse_answer_answerGenerated_llama dtype: string - name: question_answerGenerated_falcon dtype: string - name: reverse_answer_answerGenerated_falcon dtype: string - name: question_answerGenerated_gemma dtype: string - name: reverse_answer_answerGenerated_gemma dtype: string - name: question_answerGenerated_mistral dtype: string - name: reverse_answer_answerGenerated_mistral dtype: string - name: ILRAlign_without_context_llama dtype: float64 - name: ILRAlign_with_context_llama dtype: float64 - name: ILRAlign_without_context_falcon dtype: float64 - name: ILRAlign_with_context_falcon dtype: float64 - name: ILRAlign_without_context_gemma dtype: float64 - name: ILRAlign_with_context_gemma dtype: float64 - name: ILRAlign_without_context_mistral dtype: float64 - name: ILRAlign_with_context_mistral dtype: float64 - name: ILRAlign_with_problem_context_only_mistral dtype: float64 - name: ILRAlign_with_answer_context_only_mistral dtype: float64 - name: ILRAlign_with_problem_context_only_llama dtype: float64 - name: ILRAlign_with_answer_context_only_llama dtype: float64 - name: ILRAlign_with_problem_context_only_falcon dtype: float64 - name: ILRAlign_with_answer_context_only_falcon dtype: float64 - name: ILRAlign_with_problem_context_only_gemma dtype: float64 - name: ILRAlign_with_answer_context_only_gemma dtype: float64 splits: - name: rawcases num_bytes: 2063463 num_examples: 54 download_size: 1254341 dataset_size: 2063463 configs: - config_name: default data_files: - split: rawcases path: data/rawcases-* ---
trnguyenai01/FullyIndicators_distillation	trnguyenai01	2025-04-21T03:06:27Z	18	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-21T03:06:06Z	--- dataset_info: features: - name: reports dtype: string - name: labels dtype: string splits: - name: train num_bytes: 243978594 num_examples: 15350 download_size: 44782472 dataset_size: 243978594 configs: - config_name: default data_files: - split: train path: data/train-* ---
cchoi1/humaneval-datagen-qwen1.5b_best_att_50_sol_50_20250226_184052	cchoi1	2025-02-27T05:19:20Z	18	0	[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-02-27T05:19:18Z	--- dataset_info: features: - name: problem_id dtype: string - name: prompt dtype: string - name: canonical_solution dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: chosen_attack dtype: string - name: chosen_attack_explanation dtype: string - name: chosen_solution dtype: string - name: chosen_solution_explanation dtype: string - name: chosen_solve_rate dtype: float64 - name: rejected_attack dtype: string - name: rejected_attack_explanation dtype: string - name: rejected_solution dtype: string - name: rejected_solution_explanation dtype: string - name: rejected_solve_rate dtype: float64 splits: - name: train num_bytes: 464817 num_examples: 156 download_size: 45399 dataset_size: 464817 configs: - config_name: default data_files: - split: train path: data/train-* ---
kenken6696/ALCUNA_meta_affirmative_understood_unfamiliar_for_fix_middle_train	kenken6696	2024-12-22T14:26:06Z	55	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-12-22T14:26:02Z	--- dataset_info: features: - name: type dtype: string - name: form dtype: string - name: property_name dtype: string - name: related_property_name dtype: string - name: question dtype: string - name: answer dtype: string - name: sentence dtype: string - name: meta_tag dtype: string - name: meta_sentence dtype: string splits: - name: train num_bytes: 879893.8397722652 num_examples: 2213 - name: test num_bytes: 97810.16022773486 num_examples: 246 download_size: 388387 dataset_size: 977704.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
yoonholee/completions_easy-8k-med16k_HMMT2025	yoonholee	2025-05-13T23:04:46Z	0	0	[ "region:us" ]	[]	2025-05-13T23:04:44Z	--- 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: 10195068 num_examples: 30 download_size: 4017936 dataset_size: 10195068 configs: - config_name: default data_files: - split: train path: data/train-* ---
interhin/fora-eval	interhin	2024-10-18T13:25:20Z	18	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-10-18T13:08:15Z	--- dataset_info: features: - name: repo_id dtype: string - name: file_path dtype: string - name: content dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 15988254 num_examples: 4377 download_size: 4209542 dataset_size: 15988254 configs: - config_name: default data_files: - split: train path: data/train-* ---
gbyuvd/sabrlo-chem-selfies-training	gbyuvd	2025-09-26T17:52:29Z	37	0	[ "license:cc-by-3.0", "size_categories:100K<n<1M", "format:csv", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-09-26T17:39:59Z	--- license: cc-by-3.0 --- # Valid Bioactives and Natural Product SELFIES ~1M valid SELFIES with seq_len<=25 using FastChemTokenizerSelfies, built and curated from ChemBL34 (Zdrazil et al. 2023), COCONUTDB (Sorokina et al. 2021), and Supernatural3 (Gallo et al. 2023) dataset. ## Processing The original dataset was processed by filtering max_seq_len<=25 for training ChemMiniQ3-SAbRLo. The cleaned dataset was then split into 6 approximately equal chunks for training in limited compute. Curated by: gbyuvd ## References ### BibTeX #### COCONUTDB ```bibtex @article{sorokina2021coconut, title={COCONUT online: Collection of Open Natural Products database}, author={Sorokina, Maria and Merseburger, Peter and Rajan, Kohulan and Yirik, Mehmet Aziz and Steinbeck, Christoph}, journal={Journal of Cheminformatics}, volume={13}, number={1}, pages={2}, year={2021}, doi={10.1186/s13321-020-00478-9} } ``` #### ChemBL34 ```bibtex @article{zdrazil2023chembl, title={The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods}, author={Zdrazil, Barbara and Felix, Eloy and Hunter, Fiona and Manners, Emma J and Blackshaw, James and Corbett, Sybilla and de Veij, Marleen and Ioannidis, Harris and Lopez, David Mendez and Mosquera, Juan F and Magarinos, Maria Paula and Bosc, Nicolas and Arcila, Ricardo and Kizil{\"o}ren, Tevfik and Gaulton, Anna and Bento, A Patr{\'i}cia and Adasme, Melissa F and Monecke, Peter and Landrum, Gregory A and Leach, Andrew R}, journal={Nucleic Acids Research}, year={2023}, volume={gkad1004}, doi={10.1093/nar/gkad1004} } @misc{chembl34, title={ChemBL34}, year={2023}, doi={10.6019/CHEMBL.database.34} } ``` #### SuperNatural3 ```bibtex @article{Gallo2023, author = {Gallo, K and Kemmler, E and Goede, A and Becker, F and Dunkel, M and Preissner, R and Banerjee, P}, title = {{SuperNatural 3.0-a database of natural products and natural product-based derivatives}}, journal = {Nucleic Acids Research}, year = {2023}, month = jan, day = {6}, volume = {51}, number = {D1}, pages = {D654-D659}, doi = {10.1093/nar/gkac1008} } ```
jchun/so100_pickplace_small_20250322_160953	jchun	2025-03-23T00:44:11Z	25	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", "pickplace" ]	[ "robotics" ]	2025-03-22T23:03:54Z	--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - pickplace 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": 4, "total_frames": 3323, "total_tasks": 1, "total_videos": 12, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:4" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 12 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper", "cv_shoulder_pan", "cv_shoulder_lift", "cv_elbow_flex", "cv_wrist_flex", "cv_wrist_roll", "cv_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 12 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper", "cv_shoulder_pan", "cv_shoulder_lift", "cv_elbow_flex", "cv_wrist_flex", "cv_wrist_roll", "cv_gripper" ] }, "observation.images.main": { "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.cv": { "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.webcam": { "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] ```
Turbo-AI/train-multi-negatives-v2	Turbo-AI	2024-11-17T14:19:35Z	17	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-11-17T14:19:12Z	--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: relevant list: - name: id dtype: int64 - name: text dtype: string - name: not_relevant list: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 918366297 num_examples: 118956 download_size: 321049144 dataset_size: 918366297 configs: - config_name: default data_files: - split: train path: data/train-* ---
rishitdagli/lrmm_full	rishitdagli	2025-06-07T01:56:55Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-07T01:43:06Z	--- dataset_info: features: - name: inputs sequence: sequence: float32 - name: outputs sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 34504560000 num_examples: 10000 download_size: 34238220403 dataset_size: 34504560000 configs: - config_name: default data_files: - split: train path: data/train-* ---
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_32dc5455-b053-4c95-94dd-2400e610d4e9	argilla-internal-testing	2024-10-11T09:24:56Z	21	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-10-11T09:24:55Z	--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1454 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---
bwang0911/reasoning_pairs_s2orc	bwang0911	2025-03-28T09:33:30Z	16	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-03-28T09:33:24Z	--- dataset_info: features: - name: title dtype: string - name: body dtype: string - name: valid dtype: bool - name: reason dtype: string splits: - name: train num_bytes: 106573680 num_examples: 100000 download_size: 61811728 dataset_size: 106573680 configs: - config_name: default data_files: - split: train path: data/train-* ---
ThatsGroes/synthetic-from-text-mathing-short-tasks-swedish	ThatsGroes	2025-01-31T09:30:43Z	21	0	[ "language:sv", "license:mit", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2401.00368", "region:us" ]	[]	2025-01-25T09:51:51Z	--- dataset_info: features: - name: response dtype: string - name: model dtype: string - name: prompt list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 50422736 num_examples: 50000 download_size: 6177357 dataset_size: 50422736 configs: - config_name: default data_files: - split: train path: data/train-* license: mit language: - sv --- # Thanks to Arrow Denmark and Nvidia for sponsoring the compute used to generate this dataset The purpose of this dataset is to pre- or post-train embedding models for text matching tasks on short texts. The dataset consists of 100,000 samples generated with gemma-2-27b-it. The column "prompt" shows the prompt given to the LLM and "response" shows the LLM output. Each sample in the dataset was generated from a seed task randomly sampled from https://huggingface.co/datasets/ThatsGroes/text-matching-short-tasks-processed The data generation process described in this paper was followed: https://arxiv.org/pdf/2401.00368 Compute sponsored by Arrow Denmark and Nvidia through Danish Data Science Community.
tmpmodelsave/beta01llamasft_math_ift_balanced_dpo_moredata_400tmp10	tmpmodelsave	2025-01-22T08:38:43Z	15	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-01-22T08:38:41Z	--- dataset_info: features: - name: idx dtype: int64 - name: gt dtype: string - name: prompt dtype: string - name: level dtype: string - name: type dtype: string - name: solution dtype: string - name: my_solu sequence: string - name: pred sequence: string - name: rewards sequence: bool splits: - name: train num_bytes: 43372827 num_examples: 15000 download_size: 15914121 dataset_size: 43372827 configs: - config_name: default data_files: - split: train path: data/train-* ---
thusinh1969/EraX-Cosent_LanhGPTV2_30NOV2024_4.4M	thusinh1969	2024-12-01T01:56:45Z	23	0	[ "size_categories:1M<n<10M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-12-01T01:39:11Z	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 - name: similarity dtype: float64 - name: type dtype: string - name: hard dtype: int64 splits: - name: train num_bytes: 21363010272.562458 num_examples: 4399028 - name: test num_bytes: 194252096.3500342 num_examples: 40000 - name: eval num_bytes: 48563024.08750855 num_examples: 10000 download_size: 11579474915 dataset_size: 21605825393.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: eval path: data/eval-* ---
james-1111/x_dataset_0303241	james-1111	2025-06-24T11:55:57Z	1,077	0	[ "task_categories:text-classification", "task_categories:token-classification", "task_categories:question-answering", "task_categories:summarization", "task_categories:text-generation", "task_ids:sentiment-analysis", "task_ids:topic-classification", "task_ids:named-entity-recognition", "task_ids:language-modeling", "task_ids:text-scoring", "task_ids:multi-class-classification", "task_ids:multi-label-classification", "task_ids:extractive-qa", "task_ids:news-articles-summarization", "multilinguality:multilingual", "source_datasets:original", "license:mit", "size_categories:10M<n<100M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[ "text-classification", "token-classification", "question-answering", "summarization", "text-generation" ]	2025-01-25T07:11:23Z	--- license: mit multilinguality: - multilingual source_datasets: - original task_categories: - text-classification - token-classification - question-answering - summarization - text-generation task_ids: - sentiment-analysis - topic-classification - named-entity-recognition - language-modeling - text-scoring - multi-class-classification - multi-label-classification - extractive-qa - news-articles-summarization --- # Bittensor Subnet 13 X (Twitter) Dataset <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/bittensor.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/macrocosmos-black.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> ## Dataset Description - Repository: james-1111/x_dataset_0303241 - Subnet: Bittensor Subnet 13 - Miner Hotkey: 5HMi2tcDTWxckR86mn6d4cmM3dCGwoRdA4PvQNLcnSqjw86k ### Miner Data Compliance Agreement In uploading this dataset, I am agreeing to the [Macrocosmos Miner Data Compliance Policy](https://github.com/macrocosm-os/data-universe/blob/add-miner-policy/docs/miner_policy.md). ### Dataset Summary This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed data from X (formerly Twitter). The data is continuously updated by network miners, providing a real-time stream of tweets for various analytical and machine learning tasks. For more information about the dataset, please visit the [official repository](https://github.com/macrocosm-os/data-universe). ### Supported Tasks The versatility of this dataset allows researchers and data scientists to explore various aspects of social media dynamics and develop innovative applications. Users are encouraged to leverage this data creatively for their specific research or business needs. For example: - Sentiment Analysis - Trend Detection - Content Analysis - User Behavior Modeling ### Languages Primary language: Datasets are mostly English, but can be multilingual due to decentralized ways of creation. ## Dataset Structure ### Data Instances Each instance represents a single tweet with the following fields: ### Data Fields - `text` (string): The main content of the tweet. - `label` (string): Sentiment or topic category of the tweet. - `tweet_hashtags` (list): A list of hashtags used in the tweet. May be empty if no hashtags are present. - `datetime` (string): The date when the tweet was posted. - `username_encoded` (string): An encoded version of the username to maintain user privacy. - `url_encoded` (string): An encoded version of any URLs included in the tweet. May be empty if no URLs are present. ### Data Splits This dataset is continuously updated and does not have fixed splits. Users should create their own splits based on their requirements and the data's timestamp. ## Dataset Creation ### Source Data Data is collected from public tweets on X (Twitter), adhering to the platform's terms of service and API usage guidelines. ### Personal and Sensitive Information All usernames and URLs are encoded to protect user privacy. The dataset does not intentionally include personal or sensitive information. ## Considerations for Using the Data ### Social Impact and Biases Users should be aware of potential biases inherent in X (Twitter) data, including demographic and content biases. This dataset reflects the content and opinions expressed on X and should not be considered a representative sample of the general population. ### Limitations - Data quality may vary due to the decentralized nature of collection and preprocessing. - The dataset may contain noise, spam, or irrelevant content typical of social media platforms. - Temporal biases may exist due to real-time collection methods. - The dataset is limited to public tweets and does not include private accounts or direct messages. - Not all tweets contain hashtags or URLs. ## Additional Information ### Licensing Information The dataset is released under the MIT license. The use of this dataset is also subject to X Terms of Use. ### Citation Information If you use this dataset in your research, please cite it as follows: ``` @misc{james-11112025datauniversex_dataset_0303241, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={james-1111}, year={2025}, url={https://huggingface.co/datasets/james-1111/x_dataset_0303241}, } ``` ### 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: 5415872 - Date Range: 2025-01-02T00:00:00Z to 2025-06-14T00:00:00Z - Last Updated: 2025-06-24T11:55:56Z ### Data Distribution - Tweets with hashtags: 4.82% - Tweets without hashtags: 95.18% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. \| Rank \| Topic \| Total Count \| Percentage \| \|------\|-------\|-------------\|-------------\| \| 1 \| NULL \| 1112737 \| 80.99% \| \| 2 \| #riyadh \| 24082 \| 1.75% \| \| 3 \| #マテムり \| 15966 \| 1.16% \| \| 4 \| #pbbcollab6thduoeviction \| 11023 \| 0.80% \| \| 5 \| #tiktok \| 8638 \| 0.63% \| \| 6 \| #箱根駅伝 \| 8147 \| 0.59% \| \| 7 \| #thameposeriesep9 \| 7605 \| 0.55% \| \| 8 \| #wtcfinal2025 \| 6398 \| 0.47% \| \| 9 \| #first_showcase \| 6311 \| 0.46% \| \| 10 \| #ad \| 5465 \| 0.40% \| ## Update History \| Date \| New Instances \| Total Instances \| \|------\|---------------\|-----------------\| \| 2025-01-25T07:07:31Z \| 453526 \| 453526 \| \| 2025-01-25T07:07:59Z \| 453526 \| 907052 \| \| 2025-01-25T07:08:28Z \| 453526 \| 1360578 \| \| 2025-01-25T07:08:56Z \| 446896 \| 1807474 \| \| 2025-01-25T07:09:24Z \| 446896 \| 2254370 \| \| 2025-01-25T07:09:52Z \| 446896 \| 2701266 \| \| 2025-01-25T07:10:21Z \| 446896 \| 3148162 \| \| 2025-01-25T07:10:51Z \| 446896 \| 3595058 \| \| 2025-01-25T07:11:21Z \| 446896 \| 4041954 \| \| 2025-01-25T07:11:51Z \| 446896 \| 4488850 \| \| 2025-02-18T03:41:26Z \| 467290 \| 4956140 \| \| 2025-06-24T11:55:56Z \| 459732 \| 5415872 \|
aliazn/mathchatsync-temp	aliazn	2025-05-16T21:10:23Z	0	0	[ "region:us" ]	[]	2025-05-16T21:10:13Z	--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 377089336 num_examples: 144978 download_size: 159608385 dataset_size: 377089336 configs: - config_name: default data_files: - split: train path: data/train-* ---
JunxiongWang/MATH_SFT	JunxiongWang	2025-04-15T18:08:20Z	92	0	[ "size_categories:10M<n<100M", "format:parquet", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-15T18:05:29Z	--- dataset_info: - config_name: tokenized_llama31 features: - name: input_ids sequence: int32 - name: labels sequence: int64 - name: attention_mask sequence: int8 splits: - name: train num_examples: 19089792 configs: - config_name: tokenized_llama31 data_files: - split: train path: tokenized_llama31/train-* ---
mlfoundations-dev/instruction_filtering_scale_up_math_base_embedding_filter_mean_8K	mlfoundations-dev	2025-03-07T20:44:31Z	98	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-03-07T20:31:09Z	--- dataset_info: features: - name: instruction_seed dtype: string - name: source dtype: string - name: embeddings sequence: float64 - name: mean_positive_score dtype: float64 - name: mean_negative_score dtype: float64 - name: difference_score dtype: float64 - name: reasoning dtype: string - name: deepseek_solution dtype: string - name: __original_row_idx dtype: int64 - name: final_reasoning_trace dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 636139670 num_examples: 8000 download_size: 290540569 dataset_size: 636139670 configs: - config_name: default data_files: - split: train path: data/train-* ---
shuohsuan/grasp_data_090_099	shuohsuan	2025-06-16T08:53:26Z	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" ]	[ "robotics" ]	2025-06-16T07:25:13Z	--- 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": 3, "total_frames": 2488, "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": [ 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.top": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.wrist": { "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] ```
mingye94/trial0_fixed_5rules_rm_training_data	mingye94	2025-01-21T02:26:49Z	19	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-01-21T02:26:49Z	--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 2351567 num_examples: 1000 download_size: 1336836 dataset_size: 2351567 configs: - config_name: default data_files: - split: train path: data/train-* ---
perfectName/rse_test	perfectName	2025-06-20T11:25:56Z	0	0	[ "license:apache-2.0", "region:us" ]	[]	2025-06-20T11:25:15Z	--- license: apache-2.0 ---
TOBEAI/KOR_Merged_data	TOBEAI	2024-10-02T07:28:27Z	20	0	[ "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-10-02T00:43:07Z	--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 1316148258.1879513 num_examples: 1702522 download_size: 739948626 dataset_size: 1316148258.1879513 configs: - config_name: default data_files: - split: train path: data/train-* ---
OpenFinAL/FINGPT_QA_meta3B-train-dataset	OpenFinAL	2025-06-07T20:50:25Z	0	0	[ "region:us" ]	[]	2025-06-07T20:50:22Z	--- dataset_info: features: - name: Question dtype: string - name: Answer dtype: string splits: - name: train num_bytes: 16959815.1 num_examples: 15399 download_size: 10216321 dataset_size: 16959815.1 configs: - config_name: default data_files: - split: train path: data/train-* ---
DataTonic/climate-guard-thinking_data_nocomment_qwen_toxic_agent	DataTonic	2025-02-14T07:33:51Z	20	0	[ "task_categories:text-classification", "task_categories:zero-shot-classification", "language:en", "license:mit", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "climate" ]	[ "text-classification", "zero-shot-classification" ]	2025-02-09T14:54:23Z	--- dataset_info: features: - name: quote dtype: string - name: label dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1487214 num_examples: 1548 download_size: 742242 dataset_size: 1487214 configs: - config_name: default data_files: - split: train path: data/train-* license: mit task_categories: - text-classification - zero-shot-classification language: - en tags: - climate --- # Toxic Agent - Phi4 Synthetic Data : Magpie-like Climate Disinformation Dataset ## Dataset Description ### Overview This dataset contains synthetic climate change-related statements, including various forms of climate disinformation and denial. It was created by generating variations and transformations of real climate-related statements, producing a diverse set of synthetic examples across different categories of climate disinformation from the [climate-guard-synthetic_data_qwen_toxic_agent](https://huggingface.co/datasets/Tonic/climate-guard-synthetic_data_qwen_toxic_agent) dataset. Total examples from `qwen2.5:14b-instruct-q4_K_M`: 1,548 entries ### Labels The dataset includes the following categories of climate-related statements: 1. `1_not_happening`: Denial of climate change occurrence 2. `2_not_human`: Denial of human influence on climate 3. `3_not_bad`: Minimization of climate change impacts 4. `4_solutions_harmful_unnecessary`: Opposition to climate solutions 5. `5_science_is_unreliable`: Questioning climate science 6. `6_proponents_biased`: Criticism of climate scientists 7. `7_fossil_fuels_needed`: Pro-fossil fuel advocacy ### Data Fields - `quote`: The synthetic climate-related statement (text) - `label`: The category of climate disinformation (string) ### Data Splits This dataset is provided as a single split containing all examples. ## Intended Uses This dataset is intended for: - Research on climate disinformation detection - Development of content moderation systems - Study of climate denial narratives - Training classification models for climate-related content ## Limitations & Ethical Considerations - This dataset contains synthetic examples of climate disinformation - Should not be used to spread or promote climate denial - Intended for research and educational purposes only - May contain biases from the generation process ## Citation & Attribution If you use this dataset, please cite: ```bibtex @dataset{synthetic_climate_disinfo_2024, author = {[Joseph Pollack]}, title = {Toxic Agent - Qwen Synthetic Data 2}, year = {2025}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/datasets/DataTonic/climate-guard-thinking_data_nocomment_qwen_toxic_agent} } ``` ## Dataset Creation ### Data Generation Process The dataset was created using: 1. Base examples from the QuotaClimat/frugalaichallenge-text-train dataset 2. Generation of variations using "qwen2.5:14b-instruct-q4_K_M" 3. Preservation of original label categories 4. Multiple generation strategies for diversity #### Prompts ```python self.unified_prompt = """follow the following instructions : {quote} {category_instructions}, DO NOT PROVIDE ANY ADDITIONAL COMMENT produce final answer:""" ``` ```python category_instructions = { '1_not_happening': "based on the above, produce a statement that denies the occurrence of global warming and its effects. The statement should argue against evidence of melting ice, extreme weather, or rising sea levels. It may point to cold weather as counter-evidence:", '2_not_human': "based on the above, produce a statement that denies human responsibility in climate change. The focus should be on arguing that greenhouse gases from human activities are not causing climate change. Emphasize natural climate variability:", '3_not_bad': "based on the above, Produce a statement that minimizes or denies negative impacts of climate change. The statement should suggest that climate change impacts will be minimal or even beneficial to society and the environment:", '4_solutions_harmful_unnecessary': "based on the above, produce a statement arguing that climate solutions are harmful or unnecessary. Focus on the potential negative economic or social impacts of climate policies and why they shouldn't be implemented:", '5_science_is_unreliable': "based on the above, Emphasize uncertainties in climate research, suggest the science is unsound, unreliable, or biased. Question methodologies and conclusions:", '6_proponents_biased': "based on the above, Produce a statement attacking climate scientists and activists. Portray them as alarmist, biased, wrong, hypocritical, corrupt, or politically motivated. Question their credibility and intentions:", '7_fossil_fuels_needed': "based on the above, Generate a statement promoting the necessity of fossil fuels. Emphasize their importance for economic growth, prosperity, and maintaining living standards. Argue why they cannot be replaced:" } ``` ### Preprocessing - Removed irrelevant examples - Compose the prompt according to the above - Maintained label consistency ## Dataset Structure ```python DatasetDict({ 'train': Dataset({ features: ['quote', 'label'], num_rows: 1548 }) }) ``` ## Additional Information ### Dataset Curators [Tonic](https://huggingface.co/Tonic) / [Datatonic](https://huggingface.co/DataTonic) ### Licensing Information This dataset is released under the MIT License. ### Feedback & Contributions For questions, feedback, or contributions, please: 1. Open an issue on the dataset repository 2. Contact the dataset maintainers 3. Submit a pull request with improvements ## Example Usage ```python from datasets import load_dataset # Load the dataset dataset = load_dataset("Tonic/climate-guard-thinking_data_nocomment_qwen_toxic_agent") # Access examples for example in dataset['train'].select(range(3)): print(f"Quote: {example['quote']}") print(f"Label: {example['label']}") print("---") ``` ## Version History - v1.0.0 (2024-02-08): Initial release with 1,548 synthetic examples ## Acknowledgements - Based on the QuotaClimat/frugalaichallenge-text-train dataset - Generated using RunPod infrastructure - Developed for climate disinformation research
villekuosmanen/agilex_put_paper_cup_jsh	villekuosmanen	2025-02-13T05:17:47Z	24	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" ]	[ "robotics" ]	2025-02-13T05:17:27Z	--- 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": "v2.0", "robot_type": "arx5_bimanual", "total_episodes": 20, "total_frames": 4643, "total_tasks": 1, "total_videos": 60, "total_chunks": 1, "chunks_size": 1000, "fps": 25, "splits": { "train": "0:20" }, "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": [ 14 ] }, "observation.state": { "dtype": "float32", "shape": [ 14 ] }, "observation.effort": { "dtype": "float32", "shape": [ 14 ] }, "observation.images.cam_high": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 25.0, "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_left_wrist": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 25.0, "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_right_wrist": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 25.0, "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] ```
gcp-acp/flipkart-dataprep	gcp-acp	2024-11-21T00:37:24Z	31	0	[ "license:cc-by-sa-4.0", "region:us" ]	[]	2024-11-21T00:37:22Z	--- license: cc-by-sa-4.0 --- - Generated prompt data, [Built with Llama 3.1](https://www.llama.com/llama3_1/license/) - [Data Preparation](https://github.com/GoogleCloudPlatform/accelerated-platforms/tree/main/docs/use-cases/model-fine-tuning-pipeline#data-preparation) - [Raw Data](https://www.kaggle.com/datasets/PromptCloudHQ/flipkart-products/data)
dgambettaphd/D_llm2_gen10_run0_X_doc1000_synt64_tot128_SYNLAST	dgambettaphd	2025-04-28T10:24:41Z	18	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-28T10:24:38Z	--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: dataset dtype: string - name: gen dtype: int64 - name: synt dtype: int64 - name: TPP dtype: float64 - name: MPP dtype: float64 - name: FTP dtype: float64 splits: - name: train num_bytes: 7829037 num_examples: 14000 download_size: 4616053 dataset_size: 7829037 configs: - config_name: default data_files: - split: train path: data/train-* ---
luca0621/multi_turn_soda_seperate_rewards_trajectory_normalized	luca0621	2025-06-10T11:10:00Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-10T11:09:52Z	--- dataset_info: features: - name: context dtype: string - name: query dtype: string - name: response dtype: string - name: pref dtype: int64 - name: reward dtype: float64 - name: original_reward dtype: float64 - name: whole_dialogue dtype: string - name: turn_index dtype: int64 - name: dialogue_length dtype: int64 - name: trajectory_return dtype: float64 - name: original_trajectory_return dtype: float64 splits: - name: train num_bytes: 83628998 num_examples: 67108 - name: test num_bytes: 11316680 num_examples: 16778 download_size: 41498459 dataset_size: 94945678 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
rlhn/default-100K	rlhn	2025-03-19T20:09:02Z	18	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-03-19T20:08:17Z	--- 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: subset dtype: string splits: - name: train num_bytes: 1778484241 num_examples: 96167 download_size: 1026965735 dataset_size: 1778484241 configs: - config_name: default data_files: - split: train path: data/train-* ---
ZixuanKe/economy_fineweb_phi3.5_unsup_chunk_2	ZixuanKe	2024-10-24T00:17:33Z	18	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-10-22T23:51:25Z	--- dataset_info: features: - name: text dtype: string - name: topic dtype: string - name: title dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: special_tokens_mask sequence: int8 - name: chunk dtype: int64 - name: fineweb_justification dtype: string - name: fineweb_score dtype: int64 splits: - name: train num_bytes: 94124434 num_examples: 1445 download_size: 36108144 dataset_size: 94124434 configs: - config_name: default data_files: - split: train path: data/train-* ---
alea-institute/kl3m-filter-data-dotgov-www.aoc.gov	alea-institute	2025-02-03T22:18:44Z	15	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-02-03T22:18:42Z	--- 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: 21131384 num_examples: 1936 download_size: 5115965 dataset_size: 21131384 configs: - config_name: default data_files: - split: train path: data/train-* ---
Pamela153/swe-gym-test-getmoto	Pamela153	2025-09-24T22:53:40Z	49	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-09-24T22:30:12Z	--- dataset_info: features: - name: instance_id dtype: string - name: hints_text dtype: string - name: patch dtype: string - name: test_patch dtype: string - name: created_at dtype: string - name: problem_statement dtype: string - name: repo dtype: string - name: base_commit dtype: string - name: version dtype: string - name: PASS_TO_PASS sequence: string - name: FAIL_TO_PASS sequence: string - name: eval_script dtype: string splits: - name: train num_bytes: 805074 num_examples: 25 download_size: 260205 dataset_size: 805074 configs: - config_name: default data_files: - split: train path: data/train-* ---
mengdili/Marco-7B-Pruned-K-16-k-8-epoch-1-log-noclipping-nonorm_math_test_8192_normal_K-1	mengdili	2025-04-13T13:29:34Z	18	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-13T13:29:30Z	--- dataset_info: features: - name: problem dtype: string - name: ground_truth_solution dtype: string - name: ground_truth_answer dtype: string - name: pre_generated_steps sequence: string - name: pre_generated_answer dtype: string - name: pre_generated_verifier_score dtype: float64 - name: prompt list: - name: content dtype: string - name: role dtype: string - name: solution dtype: string - name: answer dtype: string splits: - name: train num_bytes: 1901530 num_examples: 458 download_size: 902208 dataset_size: 1901530 configs: - config_name: default data_files: - split: train path: data/train-* ---
jxie/droid	jxie	2024-11-15T23:14:19Z	17	0	[ "size_categories:100K<n<1M", "format:parquet", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-11-15T19:23:57Z	--- dataset_info: features: - name: video dtype: video splits: - name: train num_bytes: 16142033.0 num_examples: 276699 download_size: 416277616456 dataset_size: 16142033.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
automated-analytics/gretel-pii-coarse-grained-chatml	automated-analytics	2025-06-09T03:08:26Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-09T03:08:09Z	--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 188028367 num_examples: 50000 - name: validation num_bytes: 18793571 num_examples: 5000 - name: test num_bytes: 18807450 num_examples: 5000 download_size: 14633055 dataset_size: 225629388 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
zjhhhh/baseline_0	zjhhhh	2025-09-28T02:59:01Z	30	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-09-28T02:58:57Z	--- dataset_info: features: - name: prompt dtype: string - name: requirements dtype: string - name: selection_response_1 dtype: string - name: selection_response_2 dtype: string - name: selection_response_3 dtype: string - name: base_response_1 dtype: string - name: base_response_2 dtype: string - name: current_response_1 dtype: string - name: current_response_2 dtype: string - name: selection_1_mean dtype: float64 - name: selection_1_majority dtype: float64 - name: base_1_mean dtype: float64 - name: base_1_majority dtype: float64 - name: current_1_mean dtype: float64 - name: current_1_majority dtype: float64 - name: selection_2_mean dtype: float64 - name: selection_2_majority dtype: float64 - name: base_2_mean dtype: float64 - name: base_2_majority dtype: float64 - name: current_2_mean dtype: float64 - name: current_2_majority dtype: float64 - name: selection_3_mean dtype: float64 - name: selection_3_majority dtype: float64 splits: - name: train num_bytes: 46001070 num_examples: 2000 download_size: 23456998 dataset_size: 46001070 configs: - config_name: default data_files: - split: train path: data/train-* ---
CompassioninMachineLearning/2krows_20percentours_80percentalpaca	CompassioninMachineLearning	2025-05-14T18:55:25Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-14T18:55:23Z	--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 1059189.4611528823 num_examples: 1795 - name: test num_bytes: 118015.5388471178 num_examples: 200 download_size: 706274 dataset_size: 1177205.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
burman-ai/wikitext2-v1	burman-ai	2025-04-21T07:43:20Z	20	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-21T07:43:15Z	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 11061717 num_examples: 36718 download_size: 6342468 dataset_size: 11061717 configs: - config_name: default data_files: - split: train path: data/train-* ---
Anonyme162325/ultrachat_200k-Mistral-7B-Instruct-v0.3-with-thanks	Anonyme162325	2025-09-23T14:49:55Z	54	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-09-23T14:49:51Z	--- dataset_info: features: - name: conversation_with_thanks dtype: string - name: conversation_without_thanks dtype: string splits: - name: train num_bytes: 115156614 num_examples: 10000 download_size: 58010659 dataset_size: 115156614 configs: - config_name: default data_files: - split: train path: data/train-* ---

danthepol/mnlp-m2-rag-eval

danthepol

2025-05-23T19:18:27Z

0

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

[]

2025-05-23T19:16:12Z

0

--- dataset_info: features: - name: question dtype: string - name: context dtype: string - name: dataset dtype: string splits: - name: test num_bytes: 4918807 num_examples: 1000 download_size: 2712384 dataset_size: 4918807 configs: - config_name: default data_files: - split: test path: data/test-* ---

owengretzinger/mlh-top-hackers-2020-2023

owengretzinger

2025-03-27T15:11:01Z

16

0

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

[]

2025-03-27T15:01:33Z

0

--- license: mit --- Between 2020-2023 MLH published a top 50 hackers list. I scraped this data from top hacker pages. The data is available in both JSON and CSV files. Source code: https://github.com/owengretzinger/mlh-top-hackers-scrape

GeeveGeorge/miko-story-checkpoint-35

GeeveGeorge

2024-10-01T14:37:16Z

16

0

[ "format:parquet", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-10-01T14:37:15Z

0

--- dataset_info: features: - name: user dtype: 'null' - name: assistant dtype: 'null' splits: - name: train num_bytes: 0 num_examples: 0 download_size: 726 dataset_size: 0 configs: - config_name: default data_files: - split: train path: data/train-* ---

withmartian/cs3_dataset_synonyms

withmartian

2025-01-09T05:32:16Z

47

0

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

[]

2025-01-09T05:32:05Z

0

--- dataset_info: features: - name: command_set dtype: int64 - name: table_name dtype: string - name: create_statement dtype: string - name: english_prompt dtype: string - name: sql_statement dtype: string - name: table_fields dtype: string - name: select dtype: string - name: order_by dtype: string splits: - name: train num_bytes: 112705709.625 num_examples: 76500 - name: validation num_bytes: 19889242.875 num_examples: 13500 - name: test num_bytes: 14732772.5 num_examples: 10000 download_size: 42707420 dataset_size: 147327725.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-GR-revised_NoQuant_16_32_0.01_64_BestF1

ferrazzipietro

2024-11-28T17:42:50Z

16

0

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

[]

2024-11-28T17:42:47Z

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: 172478 num_examples: 94 - name: test num_bytes: 1556265 num_examples: 738 download_size: 311863 dataset_size: 1728743 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ai-chem/Cytotox

ai-chem

2025-06-02T13:37:31Z

69

0

[ "license:mit", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "doi:10.57967/hf/5581", "region:us" ]

[]

2025-05-11T19:12:58Z

0

--- dataset_info: features: - name: sn dtype: int64 - name: material dtype: string - name: shape dtype: string - name: coat_functional_group dtype: string - name: synthesis_method dtype: string - name: surface_charge dtype: string - name: size_in_medium_nm dtype: float64 - name: zeta_in_medium_mv dtype: float64 - name: no_of_cells_cells_well dtype: float64 - name: human_animal dtype: string - name: cell_source dtype: string - name: cell_tissue dtype: string - name: cell_morphology dtype: string - name: cell_age dtype: string - name: time_hr dtype: int64 - name: concentration dtype: float64 - name: test dtype: string - name: test_indicator dtype: string - name: viability_% dtype: float64 - name: doi dtype: string - name: article_list dtype: int64 - name: core_nm dtype: float64 - name: hydrodynamic_nm dtype: float64 - name: potential_mv dtype: float64 - name: cell_type dtype: string - name: journal_name dtype: string - name: publisher dtype: string - name: year dtype: int64 - name: title dtype: string - name: journal_is_oa dtype: bool - name: is_oa dtype: string - name: oa_status dtype: string - name: pdf dtype: string - name: access dtype: int64 splits: - name: train num_bytes: 2557938 num_examples: 5476 download_size: 172810 dataset_size: 2557938 configs: - config_name: default data_files: - split: train path: data/train-* license: mit --- Information about the dataset is detailed in the documentation: https://ai-chem.github.io/ChemX/overview/datasets_description.html You can find the Croissant file in our GitHub repository: https://github.com/ai-chem/ChemX/tree/main/datasets/croissants

emradar/0.1_augmented_implicit_hate_speech_dataset

emradar

2025-05-25T20:26:01Z

0

[ "task_categories:text-classification", "language:en", "license:mit", "size_categories:10K<n<100K", "format:arrow", "modality:text", "library:datasets", "library:mlcroissant", "region:us", "hate-speech" ]

[ "text-classification" ]

2025-05-25T19:13:32Z

0

--- license: mit task_categories: - text-classification language: - en tags: - hate-speech --- # Description This is a public release of the dataset described in ElSherief et al. (2022) and augmented by Adar and Wiberg (2025). This dataset card is a work in progress and will be improved over time. ## Contributions Dataset augmented by [@emradar](https://github.com/emradar) and [@Wiberacci](https://github.com/Wiberacci). ## References ElSherief, M., Ziems, C., Muchlinski, D., Anupindi, V., Seybolt, J., De Choudhury, M., & Yang, D. (2021). Latent Hatred: A Benchmark for Understanding Implicit Hate Speech. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP). ## Copyright MIT License Copyright (c) 2023 SALT Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

dwb2023/gdelt-event-2025-v3

dwb2023

2025-03-21T18:18:30Z

18

0

[ "license:cc-by-4.0", "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-03-21T17:40:46Z

0

--- license: cc-by-4.0 --- # Dataset Card for dwb2023/gdelt-event-2025-v3 This dataset contains global event records from the GDELT (Global Database of Events, Language, and Tone) Project, capturing real-world events and their characteristics across the globe through news media coverage. ## Dataset Details ### Dataset Description The GDELT Event Database is a comprehensive repository of human societal-scale behavior and beliefs across all countries of the world, connecting every person, organization, location, count, theme, news source, and event across the planet into a single massive network. The database records what happens in every corner of the world, containing over a quarter-billion event records in over 300 categories. - **Curated by:** The GDELT Project - **Funded by:** Google Ideas, supported by Google Cloud Platform - **Language(s) (NLP):** Multi-language source data, processed into standardized English format - **License:** All GDELT event data is available for free download and use with proper attribution - **Updates:** Every 15 minutes, 24/7 ### Dataset Sources - **Repository:** http://gdeltproject.org/ - **Paper:** Leetaru, Kalev and Schrodt, Philip. (2013). "GDELT: Global Data on Events, Language, and Tone, 1979-2012." International Studies Association Annual Conference, April 2013. San Francisco, CA. - **Documentation:** - http://data.gdeltproject.org/documentation/GDELT-Event_Codebook-V2.0.pdf - https://www.gdeltproject.org/data/documentation/CAMEO.Manual.1.1b3.pdf ## Uses ### Direct Use - Monitoring global events and conflicts in real-time - Analyzing international relations and diplomatic activities - Tracking social movements and protests - Studying media coverage patterns - Research in political science, international relations, and social sciences - Crisis early warning systems - Geopolitical risk assessment ### Out-of-Scope Use - Real-time emergency response (due to potential reporting delays) - Individual-level surveillance or tracking - Definitive source of truth for events (should be cross-referenced with other sources) - Prediction of future events without careful consideration of limitations ## Dataset Structure The dataset consists of tab-delimited files with 61 fields per event record, including: 1. Event Identification - GlobalEventID: Unique identifier for each event - DATEADDED: Timestamp in YYYYMMDDHHMMSS format - Day, MonthYear, Year: Various date formats 2. Actor Information (for both Actor1 and Actor2) - ActorCode: CAMEO-coded actor identifier - ActorName: Name of the actor - ActorCountryCode: 3-character country code - Various actor attribute codes (ethnic, religious, type) 3. Event Details - EventCode: CAMEO action code - EventBaseCode: Root event category - QuadClass: Primary event classification (Verbal/Material Cooperation/Conflict) - GoldsteinScale: Event impact score (-10 to +10) - AvgTone: Average tone of coverage (-100 to +100) 4. Geographic Information - Multiple geographic fields for each actor and action - Includes country codes, feature IDs, and lat/long coordinates ## Dataset Creation ### Curation Rationale GDELT was created to capture and analyze global human society in real-time, providing a platform for understanding global behavior through media coverage. It processes news media from around the world to identify and code events using the CAMEO (Conflict and Mediation Event Observations) coding system. ### Curation Method - Prefect based python extract script: https://gist.github.com/donbr/704789a6131bb4a92c9810185c63a16a ### Source Data #### Data Collection and Processing - Continuous monitoring of news media worldwide - Automated processing using natural language processing - Event coding using CAMEO taxonomy - Geographic coding using full-text geocoding - 15-minute update cycle - Machine translation for non-English sources #### Who are the source data producers? Primary sources include: - International news media - Web news - Broadcast transcripts - Print media - Various online platforms ### Personal and Sensitive Information While the dataset primarily focuses on public events and public figures, it may contain: - Names of public figures and officials - Locations of public events - Public statements and actions - Media coverage details ## Bias, Risks, and Limitations 1. Media Bias - Over-representation of English-language media - Varying media coverage across regions - Event selection bias based on news worthiness 2. Technical Limitations - Machine coding errors - Translation inaccuracies - Geographic coding challenges - Duplicate event reporting 3. Coverage Gaps - Limited coverage in media-restricted regions - Potential missed events in less-covered areas - Varying detail levels across events ### Recommendations 1. Users should: - Cross-reference critical events with other sources - Consider media bias in coverage - Account for regional differences in coverage - Use appropriate statistical methods for aggregation - Be aware of potential duplicates and coding errors 2. Best Practices: - Aggregate data over appropriate time periods - Consider confidence scores in analysis - Use multiple GDELT fields for validation - Account for regional and temporal variations in coverage ## Citation **BibTeX:** ```bibtex @inproceedings{leetaru2013gdelt, title={GDELT: Global Data on Events, Language, and Tone, 1979-2012}, author={Leetaru, Kalev and Schrodt, Philip}, booktitle={International Studies Association Annual Conference}, year={2013}, address={San Francisco, CA} } ``` **APA:** Leetaru, K., & Schrodt, P. (2013). GDELT: Global Data on Events, Language, and Tone, 1979-2012. Paper presented at the International Studies Association Annual Conference, San Francisco, CA. ## Dataset Card Contact dwb2023

magnifi/Phi3_intent_v66_1_w_unknown_upper_lower

magnifi

2025-09-29T10:00:09Z

35

0

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

[]

2025-09-24T18:32:27Z

0

--- dataset_info: features: - name: Query dtype: string - name: true_intent dtype: string splits: - name: train num_bytes: 3143608.0 num_examples: 43362 - name: validation num_bytes: 25818.0 num_examples: 383 download_size: 1215798 dataset_size: 3169426.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

Marta102/MNLP_M3_dpo_dataset_updated

Marta102

2025-06-04T13:29:37Z

0

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

[]

2025-06-04T12:18:15Z

0

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: dataset dtype: string splits: - name: train num_bytes: 348868872 num_examples: 112239 - name: validation num_bytes: 61680674 num_examples: 19809 download_size: 228049045 dataset_size: 410549546 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

ali-sh07/PhenoBench_images_semantics

ali-sh07

2025-05-17T02:31:49Z

0

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

[]

2025-05-16T23:43:35Z

0

--- dataset_info: features: - name: image dtype: image - name: label dtype: image splits: - name: train num_bytes: 3926148196.654 num_examples: 1407 - name: val num_bytes: 2019915541.0 num_examples: 772 download_size: 5665498678 dataset_size: 5946063737.653999 configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* ---

kevin017/bioS_inverse_QA_all_small_all_answer

kevin017

2025-04-10T10:05:25Z

16

0

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

[]

2025-04-10T10:05:19Z

0

--- dataset_info: features: - name: question dtype: string - name: all_answers sequence: string splits: - name: train num_bytes: 75370 num_examples: 350 - name: test num_bytes: 75370 num_examples: 350 download_size: 44360 dataset_size: 150740 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

mlfoundations-dev/embedding_apply_tiger_math

mlfoundations-dev

2025-02-26T23:08:40Z

39

0

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

[]

2025-02-26T23:08:32Z

0

--- dataset_info: features: - name: instruction_seed dtype: string - name: source dtype: string - name: embeddings sequence: float64 - name: mean_positive_score dtype: float64 - name: mean_negative_score dtype: float64 - name: difference_score dtype: float64 splits: - name: train num_bytes: 34283406.4 num_examples: 4000 download_size: 27034393 dataset_size: 34283406.4 configs: - config_name: default data_files: - split: train path: data/train-* ---

ZiyiXia/SWE-bench__style-3__fs-bm25

ZiyiXia

2025-05-06T08:24:59Z

0

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

[]

2025-05-06T08:24:53Z

0

--- dataset_info: features: - name: instance_id dtype: string - name: text dtype: string - name: repo dtype: string - name: base_commit dtype: string - name: problem_statement dtype: string - name: hints_text dtype: string - name: created_at dtype: string - name: patch dtype: string - name: test_patch dtype: string - name: version dtype: string - name: FAIL_TO_PASS dtype: string - name: PASS_TO_PASS dtype: string - name: environment_setup_commit dtype: string splits: - name: test num_bytes: 8430964 num_examples: 6 download_size: 3885049 dataset_size: 8430964 configs: - config_name: default data_files: - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-GR-revised_NoQuant_32_64_0.05_64_BestF1

ferrazzipietro

2024-11-28T17:47:34Z

16

0

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

[]

2024-11-28T17:47:26Z

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: 172478 num_examples: 94 - name: test num_bytes: 1556265 num_examples: 738 download_size: 312438 dataset_size: 1728743 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

TOBEAI/KOR_Merged_data_v3

TOBEAI

2024-10-28T02:11:47Z

20

0

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

[]

2024-10-28T02:10:54Z

0

--- dataset_info: features: - name: system dtype: string - name: user dtype: string - name: assistant dtype: string splits: - name: train num_bytes: 1392182088 num_examples: 1740818 download_size: 773822365 dataset_size: 1392182088 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_b56b5e5d-d682-4f17-b00a-b14b2f7285ec

argilla-internal-testing

2024-10-11T09:46:11Z

18

0

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

[]

2024-10-11T09:46:10Z

0

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1454 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_7d9c8002-6b5d-448c-a671-5eaef0a95f27

argilla-internal-testing

2024-12-10T10:59:48Z

14

0

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

[]

2024-12-10T10:59:47Z

0

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

macavaney/msmarco-passage.splade-lg.cache

macavaney

2025-02-08T11:39:59Z

37

0

[ "task_categories:text-retrieval", "region:us", "pyterrier", "pyterrier-artifact", "pyterrier-artifact.indexer_cache", "pyterrier-artifact.indexer_cache.lz4pickle" ]

[ "text-retrieval" ]

2025-02-08T11:27:44Z

0

--- # pretty_name: "" # Example: "MS MARCO Terrier Index" tags: - pyterrier - pyterrier-artifact - pyterrier-artifact.indexer_cache - pyterrier-artifact.indexer_cache.lz4pickle task_categories: - text-retrieval viewer: false --- # msmarco-passage.splade-lg.cache ## Description *TODO: What is the artifact?* ## Usage ```python # Load the artifact import pyterrier as pt artifact = pt.Artifact.from_hf('macavaney/msmarco-passage.splade-lg.cache') # TODO: Show how you use the artifact ``` ## Benchmarks *TODO: Provide benchmarks for the artifact.* ## Reproduction ```python # TODO: Show how you constructed the artifact. ``` ## Metadata ``` { "type": "indexer_cache", "format": "lz4pickle", "record_count": 8841823 } ```

somosnlp-hackathon-2025/gastronomia-hispana-dpo

somosnlp-hackathon-2025

2025-06-02T00:53:04Z

99

0

[ "language:es", "license:mit", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "dpo", "food", "recipes" ]

[]

2025-05-29T17:34:22Z

0

--- license: mit language: - es tags: - dpo - food - recipes pretty_name: Recetas en Español DPO size_categories: - 1K<n<10K --- # Gastronomía Hispana DPO ## Descripción del Dataset Este dataset contiene pares de preferencias para el entrenamiento de modelos de lenguaje especializados en gastronomía hispana utilizando la técnica DPO (Direct Preference Optimization). Los datos incluyen conversaciones sobre cocina internacional con un enfoque particular en recetas, ingredientes, técnicas culinarias y tradiciones gastronómicas del mundo hispano. ## Estructura del Dataset El dataset contiene las siguientes columnas: - **`chosen`**: Lista de mensajes de la conversación preferida (formato ChatML) - **`rejected`**: Lista de mensajes de la conversación rechazada (formato ChatML) - **`recipe_id`**: Identificador único de la receta (1-471) - **`recipe_name`**: Nombre de la receta o plato - **`category`**: Categoría del contenido ### Categorías Disponibles - `ingredients`: Preguntas sobre ingredientes, sustituciones y propiedades - `cooking_techniques`: Técnicas de cocción, temperaturas y métodos - `basic_recipe`: Recetas básicas y preparaciones - `cultural_context`: Contexto cultural e histórico de los platos ## Estadísticas del Dataset - **Total de ejemplos**: ~470 pares de preferencias - **Recetas únicas**: 471 - **Idioma**: Español - **Formato**: Conversaciones multi-turno ## Uso del Dataset ### Instalación ```python from datasets import load_dataset # Cargar el dataset dataset = load_dataset("somosnlp-hackathon-2025/gastronomia-hispana-dpo") ``` ### Ejemplo de Uso ```python # Acceder a un ejemplo example = dataset['train'][0] print("Conversación elegida:") for message in example['chosen']: print(f"{message['role']}: {message['content'][:100]}...") print(f"\nReceta: {example['recipe_name']}") print(f"Categoría: {example['category']}") ``` ### Entrenamiento DPO Este dataset está diseñado para ser usado con bibliotecas como TRL (Transformer Reinforcement Learning) para entrenar modelos con DPO: ```python from trl import DPOTrainer from transformers import AutoTokenizer, AutoModelForCausalLM # Preparar datos para DPO def format_dataset(example): return { "prompt": example["chosen"][0]["content"], "chosen": example["chosen"][1]["content"], "rejected": example["rejected"][1]["content"], } formatted_dataset = dataset.map(format_dataset) # Entrenar con DPOTrainer trainer = DPOTrainer( model=model, tokenizer=tokenizer, train_dataset=formatted_dataset["train"], # ... otros parámetros ) ``` ## Características del Dataset ### Roles del Sistema El dataset incluye diferentes roles especializados: - **Experto en ingredientes**: Conocimiento sobre ingredientes internacionales, propiedades y sustituciones - **Maestro culinario**: Especialista en técnicas de cocción y métodos tradicionales - **Chef instructor**: Explicaciones paso a paso de recetas y técnicas - **Historiador gastronómico**: Contexto cultural y origen de platos tradicionales ### Calidad de las Respuestas - **Respuestas preferidas**: Detalladas, culturalmente informadas y técnicamente precisas - **Respuestas rechazadas**: Más básicas, menos contexto cultural o información limitada - **Enfoque educativo**: Las respuestas incluyen contexto cultural, técnicas tradicionales y consejos prácticos ## Aplicaciones Este dataset es útil para: - **Entrenamiento de chatbots culinarios** especializados en gastronomía hispana - **Modelos de recomendación** de recetas y técnicas de cocina - **Sistemas de tutoría** para enseñanza de cocina - **Investigación en NLP** aplicado al dominio gastronómico - **Preservación del conocimiento culinario** tradicional ## Limitaciones - El dataset se centra principalmente en gastronomía hispana e internacional - Las respuestas están en español - El conocimiento está limitado a la fecha de creación (enero 2025) - Algunas recetas pueden requerir ingredientes específicos de ciertas regiones ## Consideraciones Éticas - Las recetas y técnicas representan tradiciones culturales que deben ser respetadas - Se recomienda verificar la información culinaria antes de aplicarla en contextos comerciales - El dataset respeta la diversidad cultural de la gastronomía internacional ## Licencia [Incluir información de licencia apropiada] ## Citación Si utilizas este dataset en tu investigación, por favor cita: ```bibtex @dataset{gastronomia_hispana_dpo_2025, title={Gastronomía Hispana DPO: Dataset for Culinary Instruction Following}, author={SomosNLP Hackathon 2025}, year={2025}, url={https://huggingface.co/datasets/somosnlp-hackathon-2025/gastronomia-hispana-dpo} } ``` ## Contribuciones Este dataset fue creado como parte del Hackathon SomosNLP 2025. Agradecemos las contribuciones de la comunidad para expandir y mejorar el conocimiento gastronómico representado. ## Contacto Para preguntas sobre el dataset, por favor abre un issue en este repositorio o contacta al equipo de SomosNLP.

shanchen/combine1k_ds

shanchen

2025-04-26T19:35:40Z

35

0

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

[]

2025-04-26T19:35:37Z

0

--- dataset_info: features: - name: source_dataset dtype: string - name: text dtype: string splits: - name: train num_bytes: 23008137.0 num_examples: 1000 download_size: 8932967 dataset_size: 23008137.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

hpprc/honyaku

hpprc

2024-11-20T09:06:40Z

18

2

[ "task_categories:translation", "language:ja", "language:en", "license:cc-by-sa-4.0", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[ "translation" ]

2024-11-20T04:42:28Z

0

--- language: - ja - en license: cc-by-sa-4.0 size_categories: - n<1K task_categories: - translation pretty_name: honyaku dataset_info: - config_name: passage features: - name: id dtype: int64 - name: en dtype: string - name: ja dtype: string - name: url_en dtype: string - name: url_ja dtype: string splits: - name: train num_bytes: 34839 num_examples: 33 download_size: 29554 dataset_size: 34839 - config_name: sentence features: - name: id dtype: int64 - name: en dtype: string - name: ja dtype: string - name: url_en dtype: string - name: url_ja dtype: string splits: - name: train num_bytes: 44734 num_examples: 104 download_size: 26899 dataset_size: 44734 configs: - config_name: passage data_files: - split: train path: passage/train-* - config_name: sentence data_files: - split: train path: sentence/train-* --- 英語Wikipedia記事の冒頭複数文を抽出し、人手で日本語翻訳した文章レベル対訳データセットです。日英対訳コーパスはライセンスが厳しいものが多く、自由に利用できる高品質なパッセージレベルの対訳データセットが少なかったため作成しました。翻訳は大体を[hpprc](https://x.com/hpp_ricecake)が、数件を[yano氏](https://x.com/yano0_c)が行いました。 `passage`サブセットは文章レベルの翻訳(対応する文ごとに改行区切り)を、`sentence`サブセットは文ごとの対訳(こちらは代名詞の翻訳の齟齬など誤った翻訳になっている可能性がある)を収載したデータセットです。 ## 翻訳方針 DeepLやGoogle翻訳など、既存の翻訳ツールは翻訳結果を機械学習モデルの入力として使用することを禁じています。本データセットは機械学習用途にも利用できる寛容なライセンスの元で公開したかったため、安全のため、できるだけこれらのツールを使用せずに英日翻訳を行いました。その代わり、ライセンスの寛容なLLMによる翻訳結果を参考に翻訳を行いました。具体的には、日本語における性能が高くライセンスがApache 2.0で配布されている[CALM3-22B-Chat](https://huggingface.co/cyberagent/calm3-22b-chat)および[Qwen 2.5 32B](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct)を利用しました。翻訳対象としたテキストは、邦訳にある程度の合意が取れそうなものを厳選して利用しました。具体的には、まず日本語Wikipediaの記事から一般性の高いもの(人名や地名っぽさが過ぎるもの以外)をランダムに選出して、英語に対応する記事が存在するかを調べました。英語に対応する記事が存在する日本語記事は、内容が大きく異なることが多いため翻訳としては利用できませんが、邦訳の参考としては非常に有用なので、そのようなペアを選んで、対応する英語記事の冒頭の数文を抽出しました。翻訳については、できるだけ日本語テキストがそれ単体として成り立つように作成しました。例えば、英語での発音記号が記載されているテキストについては、元の英語表記を残さないと発音記号単体では意味不明となってしまうので、そのような部分を残しました。日本語においてテクニカルタームが定まっている場合は、できるだけそれを反映した翻訳となるように留意しました。そのほかにもテキスト中に定型の翻訳が存在しないか確認する作業を全事例に対して行いました。そのため、翻訳には1件あたり15分程度を要しました。 ## ライセンス翻訳元テキストが英語Wikipediaであること、そして人手で翻訳を行なっていることから、本データセットはCC-BY-SA 4.0ライセンスでの配布とします。 ## Note 翻訳結果に誤りや修正事項がある場合、遠慮なくお教えいただけますと幸いです。

math-extraction-comp/princeton-nlp__Mistral-7B-Base-SFT-SLiC-HF

math-extraction-comp

2025-01-11T16:17:17Z

54

0

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

[]

2025-01-11T16:17:14Z

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: harness_extracted_answer dtype: string - name: harness_score dtype: float64 - name: qwen_extracted_answer dtype: string - name: qwen_score dtype: float64 - name: lighteval-0f21c935_extracted_answer dtype: string - name: lighteval-0f21c935_score dtype: float64 splits: - name: train num_bytes: 3227346 num_examples: 1324 download_size: 1323081 dataset_size: 3227346 configs: - config_name: default data_files: - split: train path: data/train-* ---

qfq/MetaMathQANoGSM8k

qfq

2024-10-28T21:21:01Z

20

0

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

[]

2024-10-28T21:20:56Z

0

--- dataset_info: features: - name: type dtype: string - name: problem dtype: string - name: original_question dtype: string - name: solution dtype: string - name: answer dtype: string splits: - name: train num_bytes: 146011835.67088607 num_examples: 155000 download_size: 62945481 dataset_size: 146011835.67088607 configs: - config_name: default data_files: - split: train path: data/train-* ---

mlfoundations-dev/olympiads_figures

mlfoundations-dev

2025-01-23T20:30:40Z

14

0

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

[]

2025-01-23T14:09:35Z

0

--- dataset_info: features: - name: source dtype: string - name: problem dtype: string - name: solution dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 11830252.961538998 num_examples: 4075 download_size: 9350602 dataset_size: 11830252.961538998 configs: - config_name: default data_files: - split: train path: data/train-* ---

AfriMM/AFRICaption_filtered

AfriMM

2025-03-25T05:50:39Z

10

0

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

[]

2025-03-25T05:50:25Z

0

--- dataset_info: features: - name: image_id dtype: string - name: caption dtype: string splits: - name: afr num_bytes: 691661 num_examples: 8089 - name: amh num_bytes: 1002994 num_examples: 8042 - name: bem num_bytes: 322159 num_examples: 3523 - name: cjk num_bytes: 113865 num_examples: 1181 - name: dik num_bytes: 88243 num_examples: 975 - name: dyu num_bytes: 12547 num_examples: 156 - name: ewe num_bytes: 26016 num_examples: 289 - name: fuv num_bytes: 45757 num_examples: 496 - name: hau num_bytes: 747641 num_examples: 7943 - name: ibo num_bytes: 826094 num_examples: 8078 - name: kik num_bytes: 67534 num_examples: 654 - name: kab num_bytes: 10841 num_examples: 121 - name: kam num_bytes: 33640 num_examples: 348 - name: kon num_bytes: 91343 num_examples: 927 - name: kmb num_bytes: 15833 num_examples: 180 - name: lua num_bytes: 103114 num_examples: 1075 - name: lug num_bytes: 340830 num_examples: 3710 - name: lin num_bytes: 131232 num_examples: 1269 - name: kin num_bytes: 652547 num_examples: 7088 - name: yor num_bytes: 902706 num_examples: 7526 download_size: 3497744 dataset_size: 6226597 configs: - config_name: default data_files: - split: afr path: data/afr-* - split: amh path: data/amh-* - split: bem path: data/bem-* - split: cjk path: data/cjk-* - split: dik path: data/dik-* - split: dyu path: data/dyu-* - split: ewe path: data/ewe-* - split: fuv path: data/fuv-* - split: hau path: data/hau-* - split: ibo path: data/ibo-* - split: kik path: data/kik-* - split: kab path: data/kab-* - split: kam path: data/kam-* - split: kon path: data/kon-* - split: kmb path: data/kmb-* - split: lua path: data/lua-* - split: lug path: data/lug-* - split: lin path: data/lin-* - split: kin path: data/kin-* - split: yor path: data/yor-* ---

andreina-covi/vqasynth_sample_spatial

andreina-covi

2024-10-24T15:12:32Z

69

0

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

[]

2024-10-16T02:57:37Z

0

--- dataset_info: features: - name: image dtype: image - name: messages sequence: 'null' splits: - name: train num_bytes: 1363377.0 num_examples: 10 download_size: 1364998 dataset_size: 1363377.0 configs: - config_name: default data_files: - split: train path: data/train-* tags: - vqasynth - remyx ---

Shalini731/ICONS_25_data_short

Shalini731

2025-02-05T17:02:27Z

15

0

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

[]

2025-02-05T17:02:24Z

0

--- dataset_info: features: - name: image dtype: image - name: prompt dtype: string splits: - name: train num_bytes: 352596.0 num_examples: 25 download_size: 354360 dataset_size: 352596.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

haorandai/png_bicycle_gaussian_noise_5samples_epsilon0.1

haorandai

2024-10-08T21:20:01Z

19

0

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

[]

2024-10-08T21:20:00Z

0

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1100851.0 num_examples: 10 download_size: 1102557 dataset_size: 1100851.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

RabotniKuma/Fast-Math-R1-GRPO

RabotniKuma

2025-04-15T01:55:46Z

74

1

[ "license:apache-2.0", "size_categories:1K<n<10K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-04-15T01:30:44Z

0

--- license: apache-2.0 --- We extracted the answers from [the 2nd stage SFT data of Light-R1](https://huggingface.co/datasets/qihoo360/Light-R1-SFTData).

lighteval/RULER-8192-SmolLM3-11T-32k-v1-remote-code

lighteval

2025-06-20T16:57:26Z

0

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

[]

2025-06-20T16:57:14Z

0

--- dataset_info: features: - name: index dtype: int64 - name: input dtype: string - name: outputs sequence: string - name: length dtype: int64 splits: - name: fwe num_bytes: 10481423 num_examples: 500 - name: niah_single_1 num_bytes: 14822605 num_examples: 500 - name: qa_2 num_bytes: 15858056 num_examples: 500 - name: niah_multikey_1 num_bytes: 17675160 num_examples: 500 - name: niah_multivalue num_bytes: 17699318 num_examples: 500 - name: niah_multikey_3 num_bytes: 7378000 num_examples: 500 - name: niah_single_3 num_bytes: 17599964 num_examples: 500 - name: niah_single_2 num_bytes: 17576096 num_examples: 500 - name: qa_1 num_bytes: 15574631 num_examples: 500 - name: niah_multikey_2 num_bytes: 14262757 num_examples: 500 - name: niah_multiquery num_bytes: 17754657 num_examples: 500 - name: cwe num_bytes: 11307523 num_examples: 500 download_size: 65357941 dataset_size: 177990190 configs: - config_name: default data_files: - split: fwe path: data/fwe-* - split: niah_single_1 path: data/niah_single_1-* - split: qa_2 path: data/qa_2-* - split: niah_multikey_1 path: data/niah_multikey_1-* - split: niah_multivalue path: data/niah_multivalue-* - split: niah_multikey_3 path: data/niah_multikey_3-* - split: niah_single_3 path: data/niah_single_3-* - split: niah_single_2 path: data/niah_single_2-* - split: qa_1 path: data/qa_1-* - split: niah_multikey_2 path: data/niah_multikey_2-* - split: niah_multiquery path: data/niah_multiquery-* - split: cwe path: data/cwe-* ---

Asap7772/prm800k_4_shot_formatted

Asap7772

2024-10-23T00:06:08Z

20

0

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

[]

2024-10-23T00:06:03Z

0

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 62286782 num_examples: 12000 download_size: 27228532 dataset_size: 62286782 configs: - config_name: default data_files: - split: train path: data/train-* ---

OmkaRane/Major_Legal3

OmkaRane

2025-03-12T10:15:21Z

15

0

[ "format:parquet", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-03-12T10:15:18Z

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-* --- # Dataset Card for "Major_Legal3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

kh4dien/mc-german

kh4dien

2025-01-24T01:02:37Z

54

0

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

[]

2025-01-24T01:02:36Z

0

--- dataset_info: features: - name: question dtype: string - name: correct dtype: string - name: incorrect dtype: string splits: - name: train num_bytes: 326045.99022736336 num_examples: 5145 download_size: 161034 dataset_size: 326045.99022736336 configs: - config_name: default data_files: - split: train path: data/train-* ---

dvgodoy/CUAD_v1_Contract_Understanding_PDF

dvgodoy

2025-01-29T18:32:20Z

97

0

[ "language:en", "license:cc-by-4.0", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2103.06268", "region:us", "PDF" ]

[]

2025-01-20T14:29:20Z

0

--- dataset_info: features: - name: file_name dtype: string - name: pdf_bytes_base64 dtype: binary - name: text dtype: string splits: - name: train num_bytes: 159348380 num_examples: 509 download_size: 138630085 dataset_size: 159348380 configs: - config_name: default data_files: - split: train path: data/train-* tags: - PDF size_categories: - n<1K license: cc-by-4.0 language: - en --- # Dataset Card for Contract Understanding Atticus Dataset (CUAD) PDF **This dataset contains the PDFs and the full text of 509 commercial legal contracts from the original [CUAD](https://www.atticusprojectai.org/cuad) dataset.** One of the original 510 contracts was removed due to being a scanned copy. The extracted text was cleaned using [`clean-text`](https://github.com/jfilter/clean-text). The PDFs were encoded in base64 and added as the `pdf_bytes_base64` feature. You can easily and quickly load it: ```python dataset = load_dataset("dvgodoy/CUAD_v1_Contract_Understanding_PDF") ``` ``` Dataset({ features: ['file_name', 'pdf_bytes_base64', 'text'], num_rows: 509 }) ``` ## Decoding PDFs To handle the PDFs, you will need to decode the `pdf_bytes_base64` feature and load it into a PDF object using your favorite PDF library (e.g. `pdfplumber`): ```python import io import pdfplumber # load the bytes into your favorite PDF library e.g., `pdfplumber` for encoded in mini_batch['pdf_bytes_base64']: bytes_content = io.BytesIO(base64.b64decode(encoded)) pdf_obj = pdfplumber.open(bytes_content) # process the PDFs # ... # CLOSE the objects after you've used them bytes_content.close() pdf_obj.close() ``` You can use any other library/package to load the PDF, just make sure it can open a PDF from bytes. ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Additional Information](#additional-information) - [Credits](#credits) - [Licensing Information](#licensing-information) ## Dataset Description - **Homepage:** [Contract Understanding Atticus Dataset (CUAD)](https://www.atticusprojectai.org/cuad) - **Repository:** https://github.com/TheAtticusProject/cuad - **Paper:** [CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review](https://arxiv.org/abs/2103.06268) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The original CUAD v1 dataset contained 510 commercial legals contracts. **This version contains 509 contracts, as one of those contracts was removed due to being a scanned copy.** ## Dataset Structure ### Data Instances A sample from the training set is provided below : ``` { 'file_name': '2ThemartComInc_19990826_10-12G_EX-10.10_6700288_EX-10.10_Co-Branding Agreement_ Agency Agreement.pdf', 'pdf_bytes_base64': b'SlZCRVJpMHhMalF...', 'text': 'CO-BRANDING AND ADVERTISING AGREEMENT\nTHIS CO-BRANDING AND ADVERTISING AGREEMENT...' } ``` ## Dataset Creation ### Curation Rationale CUAD v1 is a corpus of 13,000+ labels in 510 commercial legal contracts with rich expert annotations curated for AI training purposes. The dataset has been manually labeled under the supervision of experienced attorneys to identify 41 types of legal clauses in commercial contracts that are considered important in contract review in connection with a corporate transaction, including mergers & acquisitions, corporate finance, investments & IPOs. ### Source Data #### Initial Data Collection and Normalization The beta version of CUAD was released in October 2020 under the name AOK v.1 with 200 contracts. CUAD v1 has 510 contracts and includes both PDF and TXT versions of the full contracts, one master CSV file and 27 Excel files corresponding to one or more types of legal clauses to ensure ease of use by both developers and attorneys. ## Additional Information ### Credits ``` @article{hendrycks2021cuad, title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review}, author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball}, journal={NeurIPS}, year={2021} } ``` ### Licensing Information - [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)

svjack/Product_Posters_Singer_DESC

svjack

2025-06-15T14:51:03Z

0

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

[]

2025-06-15T14:50:58Z

0

--- dataset_info: features: - name: product_category dtype: string - name: poster_prompt dtype: string - name: final_prompt dtype: string splits: - name: train num_bytes: 54202 num_examples: 50 download_size: 30844 dataset_size: 54202 configs: - config_name: default data_files: - split: train path: data/train-* ---

Thermostatic/CommonVoice-17.0-Spanish-Filtered

Thermostatic

2025-06-20T01:03:10Z

0

[ "license:cc0-1.0", "size_categories:100K<n<1M", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-06-20T00:57:10Z

0

--- license: cc0-1.0 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: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string splits: - name: train num_bytes: 13030072526.0 num_examples: 336846 - name: validation num_bytes: 766524228.0 num_examples: 15857 - name: test num_bytes: 744404420.0 num_examples: 15857 download_size: 14292272484 dataset_size: 14541001174.0 ---

candywal/code_rule_violation

candywal

2025-04-19T21:29:44Z

21

0

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

[]

2025-04-19T21:29:43Z

0

--- dataset_info: features: - name: user dtype: string - name: assistant dtype: string - name: classification dtype: string - name: explanation dtype: string - name: rule dtype: string splits: - name: train num_bytes: 3498524 num_examples: 1328 download_size: 1351865 dataset_size: 3498524 configs: - config_name: default data_files: - split: train path: data/train-* ---

gokceKy/earthquake

gokceKy

2024-11-15T14:00:11Z

18

0

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

[]

2024-11-09T23:59:15Z

0

--- dataset_info: features: - name: image dtype: image - name: label dtype: image - name: pixel_values dtype: image - name: labels dtype: image splits: - name: train num_bytes: 9888970.0 num_examples: 75 - name: test num_bytes: 880548.0 num_examples: 8 download_size: 10247557 dataset_size: 10769518.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

ljnlonoljpiljm/docci

ljnlonoljpiljm

2024-12-17T04:42:15Z

61

0

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

[]

2024-12-15T19:18:43Z

0

--- dataset_info: features: - name: uuid dtype: string - name: url dtype: string - name: image dtype: image - name: caption dtype: string - name: detailed_caption dtype: string - name: tags sequence: string - name: dataset dtype: string - name: points sequence: - name: uuid dtype: string - name: x dtype: float32 - name: y dtype: float32 - name: label dtype: string - name: objects sequence: - name: uuid dtype: string - name: x_min dtype: float32 - name: y_min dtype: float32 - name: x_max dtype: float32 - name: y_max dtype: float32 - name: label dtype: string - name: image_width dtype: int32 - name: image_height dtype: int32 - name: aesthetic_score dtype: float32 - name: sensitivity_score dtype: float32 splits: - name: train num_bytes: 7550643050.0 num_examples: 14647 download_size: 7542100387 dataset_size: 7550643050.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

zkpbeats/reddit_ds_100415

zkpbeats

2025-05-22T10:06:16Z

1,867

0

[ "task_categories:text-classification", "task_categories:token-classification", "task_categories:question-answering", "task_categories:summarization", "task_categories:text-generation", "task_ids:sentiment-analysis", "task_ids:topic-classification", "task_ids:named-entity-recognition", "task_ids:language-modeling", "task_ids:text-scoring", "task_ids:multi-class-classification", "task_ids:multi-label-classification", "task_ids:extractive-qa", "task_ids:news-articles-summarization", "multilinguality:multilingual", "source_datasets:original", "license:mit", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[ "text-classification", "token-classification", "question-answering", "summarization", "text-generation" ]

2025-04-03T12:12:07Z

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 Reddit Dataset <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/bittensor.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/macrocosmos-black.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> ## Dataset Description - **Repository:** zkpbeats/reddit_ds_100415 - **Subnet:** Bittensor Subnet 13 - **Miner Hotkey:** 5H3AggXAqErtsYWdn5A2cnf2MhkVS45HzqyErD3VxoDGWuxC ### Dataset Summary This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed Reddit data. The data is continuously updated by network miners, providing a real-time stream of Reddit content for various analytical and machine learning tasks. For more information about the dataset, please visit the [official repository](https://github.com/macrocosm-os/data-universe). ### Supported Tasks The versatility of this dataset allows researchers and data scientists to explore various aspects of social media dynamics and develop innovative applications. Users are encouraged to leverage this data creatively for their specific research or business needs. For example: - Sentiment Analysis - Topic Modeling - Community Analysis - Content Categorization ### Languages Primary language: Datasets are mostly English, but can be multilingual due to decentralized ways of creation. ## Dataset Structure ### Data Instances Each instance represents a single Reddit post or comment with the following fields: ### Data Fields - `text` (string): The main content of the Reddit post or comment. - `label` (string): Sentiment or topic category of the content. - `dataType` (string): Indicates whether the entry is a post or a comment. - `communityName` (string): The name of the subreddit where the content was posted. - `datetime` (string): The date when the content was posted or commented. - `username_encoded` (string): An encoded version of the username to maintain user privacy. - `url_encoded` (string): An encoded version of any URLs included in the content. ### Data Splits This dataset is continuously updated and does not have fixed splits. Users should create their own splits based on their requirements and the data's timestamp. ## Dataset Creation ### Source Data Data is collected from public posts and comments on Reddit, adhering to the platform's terms of service and API usage guidelines. ### Personal and Sensitive Information All usernames and URLs are encoded to protect user privacy. The dataset does not intentionally include personal or sensitive information. ## Considerations for Using the Data ### Social Impact and Biases Users should be aware of potential biases inherent in Reddit data, including demographic and content biases. This dataset reflects the content and opinions expressed on Reddit and should not be considered a representative sample of the general population. ### Limitations - Data quality may vary due to the nature of media sources. - The dataset may contain noise, spam, or irrelevant content typical of social media platforms. - Temporal biases may exist due to real-time collection methods. - The dataset is limited to public subreddits and does not include private or restricted communities. ## Additional Information ### Licensing Information The dataset is released under the MIT license. The use of this dataset is also subject to Reddit Terms of Use. ### Citation Information If you use this dataset in your research, please cite it as follows: ``` @misc{zkpbeats2025datauniversereddit_ds_100415, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={zkpbeats}, year={2025}, url={https://huggingface.co/datasets/zkpbeats/reddit_ds_100415}, } ``` ### 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:** 2046436 - **Date Range:** 2025-04-14T00:00:00Z to 2025-05-22T00:00:00Z - **Last Updated:** 2025-05-22T10:06:14Z ### Data Distribution - Posts: 2.20% - Comments: 97.45% ### Top 10 Subreddits For full statistics, please refer to the `stats.json` file in the repository. | Rank | Topic | Total Count | Percentage | |------|-------|-------------|-------------| | 1 | r/worldnews | 119635 | 5.87% | | 2 | r/Millennials | 105681 | 5.18% | | 3 | r/mildlyinteresting | 80343 | 3.94% | | 4 | r/redscarepod | 44502 | 2.18% | | 5 | r/mexico | 43052 | 2.11% | | 6 | r/india | 39499 | 1.94% | | 7 | r/Grimdank | 38994 | 1.91% | | 8 | r/ShitAmericansSay | 38710 | 1.90% | | 9 | r/wallstreetbets | 38257 | 1.88% | | 10 | r/BravoRealHousewives | 36118 | 1.77% | ## Update History | Date | New Instances | Total Instances | |------|---------------|-----------------| | 2025-04-14T14:31:44Z | 6673 | 6673 | | 2025-04-16T08:55:17Z | 513 | 7186 | | 2025-04-18T17:12:54Z | 6171 | 13357 | | 2025-04-18T19:38:03Z | 6914 | 20271 | | 2025-04-18T22:02:52Z | 7520 | 27791 | | 2025-04-19T00:26:05Z | 5532 | 33323 | | 2025-04-19T02:49:42Z | 6645 | 39968 | | 2025-04-19T05:16:23Z | 4202 | 44170 | | 2025-04-19T07:42:07Z | 4649 | 48819 | | 2025-04-19T10:07:00Z | 2362 | 51181 | | 2025-04-19T12:29:29Z | 2887 | 54068 | | 2025-04-19T14:52:07Z | 5581 | 59649 | | 2025-04-19T17:14:54Z | 4835 | 64484 | | 2025-04-19T19:38:30Z | 5905 | 70389 | | 2025-04-19T22:01:12Z | 6387 | 76776 | | 2025-04-20T00:25:18Z | 6282 | 83058 | | 2025-04-20T02:49:26Z | 3961 | 87019 | | 2025-04-20T05:12:31Z | 4729 | 91748 | | 2025-04-20T07:35:25Z | 4478 | 96226 | | 2025-04-20T09:57:13Z | 2655 | 98881 | | 2025-04-20T12:19:06Z | 4021 | 102902 | | 2025-04-20T14:41:14Z | 5906 | 108808 | | 2025-04-20T17:04:41Z | 8013 | 116821 | | 2025-04-20T19:27:42Z | 5631 | 122452 | | 2025-04-20T21:51:21Z | 6411 | 128863 | | 2025-04-21T00:13:38Z | 5824 | 134687 | | 2025-04-21T02:36:17Z | 4309 | 138996 | | 2025-04-21T05:01:15Z | 4852 | 143848 | | 2025-04-21T07:23:24Z | 2730 | 146578 | | 2025-04-21T09:47:13Z | 2943 | 149521 | | 2025-04-21T12:10:27Z | 4326 | 153847 | | 2025-04-21T14:33:50Z | 5710 | 159557 | | 2025-04-21T16:57:09Z | 7218 | 166775 | | 2025-04-21T19:20:09Z | 8127 | 174902 | | 2025-04-21T21:43:20Z | 6225 | 181127 | | 2025-04-22T00:08:03Z | 6598 | 187725 | | 2025-04-22T02:30:40Z | 5490 | 193215 | | 2025-04-22T04:53:06Z | 4215 | 197430 | | 2025-04-22T07:19:07Z | 3912 | 201342 | | 2025-04-22T09:42:40Z | 4005 | 205347 | | 2025-04-22T12:05:12Z | 4493 | 209840 | | 2025-04-22T14:28:03Z | 4894 | 214734 | | 2025-04-22T16:50:41Z | 8216 | 222950 | | 2025-04-22T19:14:24Z | 6801 | 229751 | | 2025-04-22T21:37:05Z | 5624 | 235375 | | 2025-04-23T00:00:02Z | 4731 | 240106 | | 2025-04-23T02:22:21Z | 6388 | 246494 | | 2025-04-23T04:45:31Z | 5112 | 251606 | | 2025-04-23T07:08:24Z | 3077 | 254683 | | 2025-04-23T09:31:32Z | 4866 | 259549 | | 2025-04-23T10:47:48Z | 1339 | 260888 | | 2025-04-23T13:10:54Z | 3924 | 264812 | | 2025-04-23T15:33:55Z | 7979 | 272791 | | 2025-04-23T17:56:36Z | 6625 | 279416 | | 2025-04-23T20:19:21Z | 6475 | 285891 | | 2025-04-23T22:42:32Z | 7214 | 293105 | | 2025-04-24T01:05:22Z | 6323 | 299428 | | 2025-04-24T03:27:38Z | 6420 | 305848 | | 2025-04-24T05:49:47Z | 5245 | 311093 | | 2025-04-24T08:12:36Z | 3189 | 314282 | | 2025-04-24T10:34:53Z | 3231 | 317513 | | 2025-04-24T12:57:32Z | 5343 | 322856 | | 2025-04-24T15:20:06Z | 8967 | 331823 | | 2025-04-24T17:42:55Z | 6654 | 338477 | | 2025-04-24T20:05:36Z | 8008 | 346485 | | 2025-04-24T22:29:45Z | 8446 | 354931 | | 2025-04-25T00:52:49Z | 6185 | 361116 | | 2025-04-25T03:15:40Z | 6436 | 367552 | | 2025-04-25T05:38:44Z | 4778 | 372330 | | 2025-04-25T08:02:25Z | 4177 | 376507 | | 2025-04-25T10:26:00Z | 3792 | 380299 | | 2025-04-25T12:48:47Z | 3961 | 384260 | | 2025-04-25T15:10:50Z | 7941 | 392201 | | 2025-04-25T17:33:47Z | 9420 | 401621 | | 2025-04-25T19:57:24Z | 7643 | 409264 | | 2025-04-25T22:20:14Z | 7954 | 417218 | | 2025-04-26T00:42:45Z | 6541 | 423759 | | 2025-04-26T03:05:10Z | 4562 | 428321 | | 2025-04-26T05:27:09Z | 6095 | 434416 | | 2025-04-26T07:48:52Z | 3612 | 438028 | | 2025-04-26T10:10:51Z | 3086 | 441114 | | 2025-04-26T12:33:17Z | 3439 | 444553 | | 2025-04-26T15:00:29Z | 7397 | 451950 | | 2025-04-26T17:22:53Z | 7048 | 458998 | | 2025-04-26T19:47:35Z | 6964 | 465962 | | 2025-04-26T22:10:58Z | 6460 | 472422 | | 2025-04-27T00:33:42Z | 7338 | 479760 | | 2025-04-27T02:55:53Z | 6334 | 486094 | | 2025-04-27T05:18:25Z | 6222 | 492316 | | 2025-04-27T07:40:51Z | 5159 | 497475 | | 2025-04-27T10:03:05Z | 2964 | 500439 | | 2025-04-27T12:25:30Z | 4022 | 504461 | | 2025-04-27T14:48:21Z | 7933 | 512394 | | 2025-04-27T17:13:44Z | 7546 | 519940 | | 2025-04-27T19:36:15Z | 7962 | 527902 | | 2025-04-27T21:58:30Z | 5000 | 532902 | | 2025-04-28T00:21:59Z | 5738 | 538640 | | 2025-04-28T02:45:06Z | 6255 | 544895 | | 2025-04-28T05:07:08Z | 4881 | 549776 | | 2025-04-28T07:29:45Z | 4842 | 554618 | | 2025-04-28T09:52:39Z | 4563 | 559181 | | 2025-04-28T12:15:39Z | 3516 | 562697 | | 2025-04-28T14:38:12Z | 7026 | 569723 | | 2025-04-28T17:01:05Z | 6173 | 575896 | | 2025-04-28T19:23:58Z | 6904 | 582800 | | 2025-04-28T21:47:11Z | 7422 | 590222 | | 2025-04-29T00:09:34Z | 5669 | 595891 | | 2025-04-29T02:32:31Z | 5035 | 600926 | | 2025-04-29T04:55:25Z | 6811 | 607737 | | 2025-04-29T07:17:27Z | 4580 | 612317 | | 2025-04-29T09:40:18Z | 3572 | 615889 | | 2025-04-29T12:04:13Z | 5189 | 621078 | | 2025-04-29T14:26:42Z | 6134 | 627212 | | 2025-04-29T16:48:49Z | 5725 | 632937 | | 2025-04-29T19:12:12Z | 8269 | 641206 | | 2025-04-29T21:35:08Z | 7741 | 648947 | | 2025-04-29T23:57:26Z | 7825 | 656772 | | 2025-04-30T02:19:53Z | 4065 | 660837 | | 2025-04-30T04:42:16Z | 5086 | 665923 | | 2025-04-30T07:04:32Z | 4704 | 670627 | | 2025-04-30T09:28:04Z | 3608 | 674235 | | 2025-04-30T11:50:14Z | 5846 | 680081 | | 2025-04-30T14:12:22Z | 7098 | 687179 | | 2025-04-30T16:34:44Z | 8904 | 696083 | | 2025-04-30T18:59:01Z | 5544 | 701627 | | 2025-04-30T21:21:06Z | 6703 | 708330 | | 2025-04-30T23:43:26Z | 7272 | 715602 | | 2025-05-01T02:07:20Z | 6315 | 721917 | | 2025-05-01T04:30:14Z | 5673 | 727590 | | 2025-05-01T06:54:49Z | 4787 | 732377 | | 2025-05-01T09:17:54Z | 4126 | 736503 | | 2025-05-01T11:40:22Z | 2985 | 739488 | | 2025-05-01T14:02:56Z | 6913 | 746401 | | 2025-05-01T16:26:00Z | 7966 | 754367 | | 2025-05-01T18:48:30Z | 6874 | 761241 | | 2025-05-01T21:12:58Z | 8503 | 769744 | | 2025-05-01T23:35:27Z | 6871 | 776615 | | 2025-05-02T01:57:54Z | 6292 | 782907 | | 2025-05-02T04:20:13Z | 7125 | 790032 | | 2025-05-02T06:43:10Z | 5111 | 795143 | | 2025-05-02T09:05:25Z | 4295 | 799438 | | 2025-05-02T11:27:56Z | 3304 | 802742 | | 2025-05-02T13:52:15Z | 7162 | 809904 | | 2025-05-02T16:14:29Z | 8007 | 817911 | | 2025-05-02T18:36:41Z | 7826 | 825737 | | 2025-05-02T20:59:13Z | 6862 | 832599 | | 2025-05-02T23:21:43Z | 6934 | 839533 | | 2025-05-03T01:44:23Z | 6926 | 846459 | | 2025-05-03T04:06:58Z | 6269 | 852728 | | 2025-05-03T06:30:10Z | 5893 | 858621 | | 2025-05-03T08:52:13Z | 4328 | 862949 | | 2025-05-03T11:14:30Z | 2742 | 865691 | | 2025-05-03T13:37:06Z | 5906 | 871597 | | 2025-05-03T15:59:34Z | 6432 | 878029 | | 2025-05-03T18:22:12Z | 6930 | 884959 | | 2025-05-03T20:44:37Z | 7420 | 892379 | | 2025-05-03T23:06:52Z | 7364 | 899743 | | 2025-05-04T01:29:49Z | 5757 | 905500 | | 2025-05-04T03:51:49Z | 5738 | 911238 | | 2025-05-04T06:13:45Z | 4217 | 915455 | | 2025-05-04T08:35:53Z | 3333 | 918788 | | 2025-05-04T10:58:18Z | 3020 | 921808 | | 2025-05-04T13:20:37Z | 3502 | 925310 | | 2025-05-04T15:43:13Z | 6520 | 931830 | | 2025-05-04T18:05:44Z | 7524 | 939354 | | 2025-05-04T20:28:05Z | 7386 | 946740 | | 2025-05-04T22:50:26Z | 6014 | 952754 | | 2025-05-05T01:12:56Z | 8336 | 961090 | | 2025-05-05T03:35:18Z | 5919 | 967009 | | 2025-05-05T05:57:34Z | 4551 | 971560 | | 2025-05-05T08:19:41Z | 4397 | 975957 | | 2025-05-05T10:42:17Z | 2881 | 978838 | | 2025-05-05T13:04:28Z | 3834 | 982672 | | 2025-05-05T15:26:54Z | 6271 | 988943 | | 2025-05-05T17:49:31Z | 6878 | 995821 | | 2025-05-05T20:12:02Z | 8960 | 1004781 | | 2025-05-05T22:34:32Z | 7150 | 1011931 | | 2025-05-06T00:57:02Z | 6813 | 1018744 | | 2025-05-06T03:19:26Z | 8478 | 1027222 | | 2025-05-06T05:41:35Z | 4649 | 1031871 | | 2025-05-06T08:04:13Z | 3824 | 1035695 | | 2025-05-06T10:26:38Z | 3650 | 1039345 | | 2025-05-06T12:48:51Z | 6481 | 1045826 | | 2025-05-06T15:11:09Z | 6622 | 1052448 | | 2025-05-06T17:33:58Z | 8696 | 1061144 | | 2025-05-06T19:57:06Z | 7521 | 1068665 | | 2025-05-06T22:20:12Z | 7746 | 1076411 | | 2025-05-07T00:43:15Z | 7306 | 1083717 | | 2025-05-07T03:06:05Z | 6757 | 1090474 | | 2025-05-07T05:28:28Z | 6072 | 1096546 | | 2025-05-07T07:50:33Z | 4436 | 1100982 | | 2025-05-07T10:12:43Z | 5652 | 1106634 | | 2025-05-07T12:34:44Z | 3694 | 1110328 | | 2025-05-07T14:57:11Z | 8226 | 1118554 | | 2025-05-07T17:19:48Z | 9114 | 1127668 | | 2025-05-07T19:42:30Z | 9541 | 1137209 | | 2025-05-07T22:04:58Z | 9539 | 1146748 | | 2025-05-08T00:27:37Z | 6664 | 1153412 | | 2025-05-08T02:50:19Z | 9841 | 1163253 | | 2025-05-08T05:12:27Z | 5400 | 1168653 | | 2025-05-08T07:34:47Z | 3427 | 1172080 | | 2025-05-08T09:57:17Z | 4060 | 1176140 | | 2025-05-08T12:20:24Z | 5275 | 1181415 | | 2025-05-08T14:43:07Z | 8854 | 1190269 | | 2025-05-08T17:05:46Z | 9541 | 1199810 | | 2025-05-08T19:28:22Z | 9742 | 1209552 | | 2025-05-08T21:51:45Z | 6099 | 1215651 | | 2025-05-09T00:14:14Z | 5912 | 1221563 | | 2025-05-09T02:36:51Z | 6879 | 1228442 | | 2025-05-09T04:59:11Z | 7747 | 1236189 | | 2025-05-09T07:21:38Z | 4184 | 1240373 | | 2025-05-09T09:44:15Z | 4371 | 1244744 | | 2025-05-09T12:06:36Z | 3647 | 1248391 | | 2025-05-09T14:29:16Z | 5890 | 1254281 | | 2025-05-09T16:53:09Z | 7717 | 1261998 | | 2025-05-09T19:15:46Z | 7368 | 1269366 | | 2025-05-09T21:38:19Z | 7879 | 1277245 | | 2025-05-10T00:00:58Z | 6570 | 1283815 | | 2025-05-10T02:23:31Z | 5730 | 1289545 | | 2025-05-10T04:45:51Z | 5362 | 1294907 | | 2025-05-10T07:08:08Z | 4617 | 1299524 | | 2025-05-10T09:30:21Z | 3101 | 1302625 | | 2025-05-10T11:52:51Z | 3653 | 1306278 | | 2025-05-10T14:14:50Z | 6043 | 1312321 | | 2025-05-10T16:36:54Z | 8235 | 1320556 | | 2025-05-10T18:58:59Z | 8370 | 1328926 | | 2025-05-10T21:21:14Z | 6141 | 1335067 | | 2025-05-10T23:44:04Z | 6265 | 1341332 | | 2025-05-11T02:07:00Z | 7315 | 1348647 | | 2025-05-11T04:29:19Z | 5421 | 1354068 | | 2025-05-11T06:51:58Z | 5463 | 1359531 | | 2025-05-11T09:14:38Z | 3980 | 1363511 | | 2025-05-11T11:37:39Z | 4431 | 1367942 | | 2025-05-11T13:59:50Z | 6181 | 1374123 | | 2025-05-11T16:22:14Z | 7736 | 1381859 | | 2025-05-11T18:45:19Z | 6791 | 1388650 | | 2025-05-11T21:08:49Z | 6579 | 1395229 | | 2025-05-11T23:32:14Z | 7701 | 1402930 | | 2025-05-12T01:54:40Z | 6786 | 1409716 | | 2025-05-12T04:16:44Z | 6802 | 1416518 | | 2025-05-12T06:39:01Z | 3979 | 1420497 | | 2025-05-12T09:03:11Z | 4558 | 1425055 | | 2025-05-12T11:25:38Z | 4377 | 1429432 | | 2025-05-12T13:48:02Z | 6324 | 1435756 | | 2025-05-12T16:10:47Z | 9365 | 1445121 | | 2025-05-12T18:33:55Z | 11055 | 1456176 | | 2025-05-12T20:57:18Z | 9381 | 1465557 | | 2025-05-12T23:19:54Z | 8918 | 1474475 | | 2025-05-13T01:43:10Z | 7599 | 1482074 | | 2025-05-13T04:05:16Z | 7517 | 1489591 | | 2025-05-13T06:27:34Z | 5505 | 1495096 | | 2025-05-13T08:50:58Z | 3874 | 1498970 | | 2025-05-13T11:13:21Z | 3143 | 1502113 | | 2025-05-13T13:36:09Z | 5539 | 1507652 | | 2025-05-13T15:59:58Z | 9643 | 1517295 | | 2025-05-13T18:22:45Z | 6962 | 1524257 | | 2025-05-13T20:46:29Z | 6749 | 1531006 | | 2025-05-13T23:09:32Z | 9450 | 1540456 | | 2025-05-14T01:32:19Z | 7048 | 1547504 | | 2025-05-14T03:54:58Z | 8498 | 1556002 | | 2025-05-14T06:17:04Z | 4854 | 1560856 | | 2025-05-14T08:39:47Z | 3784 | 1564640 | | 2025-05-14T11:02:47Z | 3460 | 1568100 | | 2025-05-14T13:25:01Z | 5587 | 1573687 | | 2025-05-14T15:47:30Z | 8370 | 1582057 | | 2025-05-14T18:10:21Z | 8253 | 1590310 | | 2025-05-14T20:33:17Z | 7316 | 1597626 | | 2025-05-14T22:56:47Z | 6516 | 1604142 | | 2025-05-15T01:19:36Z | 5547 | 1609689 | | 2025-05-15T03:42:05Z | 8617 | 1618306 | | 2025-05-15T06:04:30Z | 5333 | 1623639 | | 2025-05-15T08:28:22Z | 4609 | 1628248 | | 2025-05-15T10:51:36Z | 4085 | 1632333 | | 2025-05-15T13:15:28Z | 6781 | 1639114 | | 2025-05-15T15:38:39Z | 9008 | 1648122 | | 2025-05-15T15:39:15Z | 9008 | 1657130 | | 2025-05-15T18:02:16Z | 7464 | 1664594 | | 2025-05-15T20:27:30Z | 7837 | 1672431 | | 2025-05-15T22:52:22Z | 6106 | 1678537 | | 2025-05-16T01:15:56Z | 7440 | 1685977 | | 2025-05-16T03:40:49Z | 5423 | 1691400 | | 2025-05-16T06:03:43Z | 4286 | 1695686 | | 2025-05-16T08:29:29Z | 4233 | 1699919 | | 2025-05-16T10:52:28Z | 2931 | 1702850 | | 2025-05-16T13:17:57Z | 5866 | 1708716 | | 2025-05-16T15:43:13Z | 7311 | 1716027 | | 2025-05-16T18:06:29Z | 8174 | 1724201 | | 2025-05-16T20:29:58Z | 7819 | 1732020 | | 2025-05-16T22:54:39Z | 6752 | 1738772 | | 2025-05-17T01:18:03Z | 6382 | 1745154 | | 2025-05-17T03:40:48Z | 6051 | 1751205 | | 2025-05-17T06:03:19Z | 3672 | 1754877 | | 2025-05-17T08:25:46Z | 3530 | 1758407 | | 2025-05-17T10:50:23Z | 4177 | 1762584 | | 2025-05-17T13:15:07Z | 4038 | 1766622 | | 2025-05-17T15:38:41Z | 6297 | 1772919 | | 2025-05-17T18:01:58Z | 4948 | 1777867 | | 2025-05-17T20:24:30Z | 5059 | 1782926 | | 2025-05-17T22:47:29Z | 6712 | 1789638 | | 2025-05-18T01:10:48Z | 5350 | 1794988 | | 2025-05-18T03:32:58Z | 5936 | 1800924 | | 2025-05-18T05:55:36Z | 4483 | 1805407 | | 2025-05-18T08:19:35Z | 4392 | 1809799 | | 2025-05-18T10:42:00Z | 3959 | 1813758 | | 2025-05-18T13:04:32Z | 4301 | 1818059 | | 2025-05-18T15:27:47Z | 5957 | 1824016 | | 2025-05-18T17:51:32Z | 7226 | 1831242 | | 2025-05-18T20:14:39Z | 5501 | 1836743 | | 2025-05-18T22:39:03Z | 5990 | 1842733 | | 2025-05-19T01:01:47Z | 6209 | 1848942 | | 2025-05-19T03:24:31Z | 5727 | 1854669 | | 2025-05-19T05:47:09Z | 4786 | 1859455 | | 2025-05-19T08:11:58Z | 4459 | 1863914 | | 2025-05-19T10:35:14Z | 3981 | 1867895 | | 2025-05-19T12:58:03Z | 3385 | 1871280 | | 2025-05-19T15:20:57Z | 5702 | 1876982 | | 2025-05-19T17:44:21Z | 7794 | 1884776 | | 2025-05-19T20:08:26Z | 9671 | 1894447 | | 2025-05-19T22:31:29Z | 8001 | 1902448 | | 2025-05-20T00:54:35Z | 6063 | 1908511 | | 2025-05-20T03:18:15Z | 7571 | 1916082 | | 2025-05-20T05:42:05Z | 4629 | 1920711 | | 2025-05-20T08:05:38Z | 4244 | 1924955 | | 2025-05-20T10:29:07Z | 3897 | 1928852 | | 2025-05-20T12:53:19Z | 4809 | 1933661 | | 2025-05-20T15:17:08Z | 6091 | 1939752 | | 2025-05-20T17:40:47Z | 7407 | 1947159 | | 2025-05-20T20:03:39Z | 10047 | 1957206 | | 2025-05-20T22:26:07Z | 8196 | 1965402 | | 2025-05-21T00:48:58Z | 5330 | 1970732 | | 2025-05-21T03:11:28Z | 6717 | 1977449 | | 2025-05-21T05:34:18Z | 4849 | 1982298 | | 2025-05-21T07:57:03Z | 3652 | 1985950 | | 2025-05-21T10:19:15Z | 3610 | 1989560 | | 2025-05-21T12:42:34Z | 3372 | 1992932 | | 2025-05-21T15:05:07Z | 6436 | 1999368 | | 2025-05-21T17:27:46Z | 6563 | 2005931 | | 2025-05-21T19:50:35Z | 8433 | 2014364 | | 2025-05-21T22:13:20Z | 5952 | 2020316 | | 2025-05-22T00:36:19Z | 5190 | 2025506 | | 2025-05-22T02:58:36Z | 6844 | 2032350 | | 2025-05-22T05:21:02Z | 6449 | 2038799 | | 2025-05-22T07:43:09Z | 3942 | 2042741 | | 2025-05-22T10:06:14Z | 3695 | 2046436 |

samabena/dreambooth-hackathon-images

samabena

2025-04-17T18:58:05Z

17

0

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

[]

2025-04-17T17:32:33Z

0

--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 3440294.0 num_examples: 14 download_size: 3270864 dataset_size: 3440294.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

neginashz/ins-medqa-dataset_winst

neginashz

2024-12-11T13:35:40Z

13

0

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

[]

2024-12-11T13:35:40Z

0

--- dataset_info: features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 9544494 num_examples: 10178 download_size: 5250508 dataset_size: 9544494 configs: - config_name: default data_files: - split: train path: data/train-* ---

nickfuryavg/Audio_Sample_1

nickfuryavg

2024-11-01T07:52:53Z

18

0

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

[]

2024-11-01T07:52:49Z

0

--- dataset_info: features: - name: job_id dtype: int64 - name: english_sentence dtype: string - name: english_audio_bytes dtype: string - name: hindi_sentence dtype: string - name: hindi_audio_bytes dtype: string splits: - name: train num_bytes: 17706348 num_examples: 50 download_size: 12249570 dataset_size: 17706348 configs: - config_name: default data_files: - split: train path: data/train-* ---

cauaveiga/moss_test

cauaveiga

2024-10-27T11:36:30Z

21

0

[ "task_categories:robotics", "region:us", "LeRobot", "moss", "tutorial" ]

[ "robotics" ]

2024-10-27T11:26:20Z

0

--- task_categories: - robotics tags: - LeRobot - moss - tutorial --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).

pclucas14/nqa-RAG-64_1_24

pclucas14

2024-12-04T23:08:44Z

15

0

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

[]

2024-12-04T23:08:42Z

0

--- dataset_info: features: - name: text sequence: sequence: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 5935246 num_examples: 66 download_size: 2094096 dataset_size: 5935246 configs: - config_name: default data_files: - split: train path: data/train-* ---

SnehaPriyaaMP/llama-pre-finetuned-Apr-21-2025-Updated

SnehaPriyaaMP

2025-04-21T13:34:12Z

19

0

[ "size_categories:n<1K", "modality:text", "region:us" ]

[]

2025-04-21T13:34:02Z

0

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 114289 num_examples: 70 download_size: 26451 dataset_size: 114289 configs: - config_name: default data_files: - split: train path: data/train-* ---

SayantanJoker/Hindi_1000hr_Train_Subset_44100Hz_quality_metadata

SayantanJoker

2025-04-13T10:31:09Z

70

0

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

[]

2025-04-13T10:31:08Z

0

--- dataset_info: features: - name: text dtype: string - name: file_name dtype: string - 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: stoi dtype: float64 - name: si-sdr dtype: float64 - name: pesq dtype: float64 - name: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string - name: sdr_noise dtype: string - name: pesq_speech_quality dtype: string splits: - name: train num_bytes: 1307557 num_examples: 3843 download_size: 255513 dataset_size: 1307557 configs: - config_name: default data_files: - split: train path: data/train-* ---

mlfoundations-dev/GeneralThought-feb25_10k

mlfoundations-dev

2025-03-21T22:28:30Z

13

0

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

[]

2025-03-21T22:28:18Z

0

--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 120276586.92596735 num_examples: 10000 download_size: 52837405 dataset_size: 120276586.92596735 configs: - config_name: default data_files: - split: train path: data/train-* ---

kh4dien/talktuner-openai

kh4dien

2025-02-18T23:49:17Z

20

0

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

[]

2025-02-13T05:50:19Z

0

--- dataset_info: features: - name: label dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: age num_bytes: 6957752.147239264 num_examples: 2880 - name: education num_bytes: 13866627.816924142 num_examples: 4263 - name: gender num_bytes: 4336468.098876405 num_examples: 1762 - name: socioeconomic num_bytes: 5369022.74964904 num_examples: 2124 download_size: 14962444 dataset_size: 30529870.812688854 configs: - config_name: default data_files: - split: age path: data/age-* - split: education path: data/education-* - split: gender path: data/gender-* - split: socioeconomic path: data/socioeconomic-* ---

Asap7772/d1shs0ap-hard-hintgen-qwen3-4b-lr1e6-shard5

Asap7772

2025-05-10T07:47:20Z

0

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

[]

2025-05-10T07:47:14Z

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: 71420403 num_examples: 1586 download_size: 31277734 dataset_size: 71420403 configs: - config_name: default data_files: - split: train path: data/train-* ---

IAlsace/affiche_gestes_barrieres

IAlsace

2025-03-29T22:12:24Z

29

0

[ "task_categories:translation", "multilinguality:multilingual", "language:gsw", "language:fra", "region:us", "welche", "polatt" ]

[ "translation" ]

2025-01-12T14:16:14Z

0

--- language: - gsw - fra multilinguality: - multilingual viewer: false task_categories: - translation tags: - welche - polatt --- > [!NOTE] > Dataset origin: https://www.olcalsace.org/fr/affiches-cartes-et-signaletique ## Description Les gestes à adopter pour se protéger et protéger les autres du coronavirus. Affiches conçues par le gouvernement dans le cadre de la lutte contre la Covid-19 et traduite par l'OLCA.

wangx0t/numina-deepseek-r1-qwen-7b

wangx0t

2025-01-30T13:12:36Z

35

0

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

[]

2025-01-30T12:56:24Z

0

--- size_categories: n<1K dataset_info: features: - name: problem dtype: string - name: solution dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: generation dtype: string - name: distilabel_metadata struct: - name: raw_input_text_generation_0 list: - name: content dtype: string - name: role dtype: string - name: raw_output_text_generation_0 dtype: string - name: statistics_text_generation_0 struct: - name: input_tokens dtype: int64 - name: output_tokens dtype: int64 - name: model_name dtype: string splits: - name: train num_bytes: 751350 num_examples: 40 download_size: 277645 dataset_size: 751350 configs: - config_name: default data_files: - split: train path: data/train-* tags: - synthetic - distilabel - rlaif --- <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 numina-deepseek-r1-qwen-7b 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/wangx0t/numina-deepseek-r1-qwen-7b/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/wangx0t/numina-deepseek-r1-qwen-7b/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "distilabel_metadata": { "raw_input_text_generation_0": [ { "content": "You will be given a problem. Please reason step by step, and put your final answer within \boxed{}:\nWhat is the coefficient of $x^2y^6$ in the expansion of $\\left(\\frac{3}{5}x-\\frac{y}{2}\\right)^8$? Express your answer as a common fraction.", "role": "user" } ], "raw_output_text_generation_0": "\u003cthink\u003e\nOkay, so I need to find the coefficient of \$ x^2 y^6 \$ in the expansion of \$ \\left( \\frac{3}{5}x - \\frac{y}{2} \\right)^8 \$. Hmm, I remember that when expanding binomials raised to a power, the binomial theorem is useful here. Let me recall the binomial theorem.\n\nThe binomial theorem states that \$ (a + b)^n = \\sum_{k=0}^{n} \\binom{n}{k} a^{n - k} b^{k} \$. So in this case, \$ a \$ is \$ \\frac{3}{5}x \$ and \$ b \$ is \$ -\\frac{y}{2} \$. The exponent \$ n \$ is 8.\n\nI need to find the term where the exponents of \$ x \$ is 2 and the exponent of \$ y \$ is 6. Let me think: Since each term in the expansion is \$ \\binom{8}{k} \\left( \\frac{3}{5}x \\right)^{8 - k} \\left( -\\frac{y}{2} \\right)^k \$. \n\nSo, for the term to have \$ x^2 y^6 \$, the exponent on \$ x \$ should be 2, which means \$ 8 - k = 2 \$. Solving for \$ k \$, that gives \$ k = 6 \$. \n\nLet me verify that: if \$ k = 6 \$, then the exponent on \$ y \$ is \$ 6 \$, which is exactly what we need. So, that term will be the one we\u0027re interested in.\n\nNow, let me write out that term:\n\n\$ \\binom{8}{6} \\left( \\frac{3}{5}x \\right)^{8 - 6} \\left( -\\frac{y}{2} \\right)^6 \$\n\nSimplifying the exponents first: \$ 8 - 6 = 2 \$, so:\n\n\$ \\binom{8}{6} \\left( \\frac{3}{5}x \\right)^2 \\left( -\\frac{y}{2} \\right)^6 \$\n\nCalculating each part separately. First, \$ \\binom{8}{6} \$. I know that \$ \\binom{8}{6} \$ is equal to \$ \\binom{8}{2} \$ because of the symmetry in the binomial coefficients, right? \$ \\binom{8}{6} = \\binom{8}{2} \$. And \$ \\binom{8}{2} = \\frac{8 \\times 7}{2 \\times 1} = 28 \$. So that part is 28.\n\nNext, \$ \\left( \\frac{3}{5}x \\right)^2 \$. Squaring \$ \\frac{3}{5} \$ gives \$ \\frac{9}{25} \$, and squaring \$ x \$ is still \$ x^2 \$. So that part is \$ \\frac{9}{25}x^2 \$.\n\nThen, \$ \\left( -\\frac{y}{2} \\right)^6 \$. When I raise a negative to an even power, it becomes positive. So, \$ \\left( -\\frac{1}{2} \\right)^6 = \\left( \\frac{1}{2} \\right)^6 = \\frac{1}{64} \$. So that part is \$ \\frac{1}{64} y^6 \$.\n\nNow, putting it all together: 28 multiplied by \$ \\frac{9}{25}x^2 \$ multiplied by \$ \\frac{1}{64} y^6 \$.\n\nLet me compute the constants first: 28 times \$ \\frac{9}{25} \$ times \$ \\frac{1}{64} \$.\n\nFirst, multiply 28 and 9: 28 * 9 = 252.\n\nThen, 252 divided by 25 is \$ 10.08 \$, but I should keep it as a fraction. 252 divided by 25 is \$ \\frac{252}{25} \$.\n\nThen, \$ \\frac{252}{25} \\times \\frac{1}{64} \$. Multiply the numerators: 252 * 1 = 252, denominators: 25 * 64 = 1600. So that\u0027s \$ \\frac{252}{1600} \$.\n\nSimplify \$ \\frac{252}{1600} \$: Let me see if both numbers can be divided by a common factor. 252 and 1600. Let\u0027s try 4: 252 \u00f7 4 = 63, 1600 \u00f7 4 = 400. So, \$ \\frac{63}{400} \$. Hmm, 63 and 400. 63 is 7*9, 400 is 16*25. They have no common factors besides 1, so that\u0027s the simplified fraction.\n\nTherefore, the coefficient is \$ \\frac{63}{400} \$.\n\nWait, let me double-check my calculations to make sure I didn\u0027t make a mistake. So, 28 * 9 is indeed 252, correct. 252 divided by 25 is 10.08? Wait, 10.08 times 25 is 252, yes. Then 252 divided by 64 is 3.9375, which is \$ \\frac{63}{16} \$. Wait, no, hold on, that doesn\u0027t make sense. Wait, I think I messed up the multiplication.\n\nWait, no, wait. Let\u0027s go back. The constants: 28 * (9/25) * (1/64). Let me compute 28 * 9 first. 28*9: 20*9=180, 8*9=72, so 180+72=252. Then, 252 * (1/25) = 252/25. Then, 252/25 * (1/64) = 252/(25*64) = 252/1600.\n\nThen, 252 divided by 1600: let me divide numerator and denominator by 4: 252 \u00f74=63, 1600 \u00f74=400. So, 63/400. That simplifies correctly.\n\nWait, but 63/400 is 0.1575, and 252/1600 is 0.1575. So that\u0027s correct.\n\nBut wait, let me check 252 divided by 1600:\n\n252 / 1600 = (252 \u00f7 4) / (1600 \u00f74) = 63/400. Yes, correct.\n\nSo, okay, so the coefficient is 63/400.\n\nWait, but let me double-check the binomial coefficient. So, \$ \\binom{8}{6} \$. Since \$ \\binom{8}{6} = \\binom{8}{2} \$ because \$ \\binom{n}{k} = \\binom{n}{n - k} \$. So, \$ \\binom{8}{2} \$ is (8*7)/2 = 28, correct. So that\u0027s correct.\n\nSo, 28 * (9/25) * (1/64) = 28*(9)/(25*64) = 252/1600 = 63/400.\n\nTherefore, the coefficient is 63/400.\n\nWait, let me think again about the signs. In the term, the \$ b^k \$ is \$ (-\\frac{y}{2})^6 \$. Since 6 is even, the negative becomes positive, so that\u0027s correct, it\u0027s positive. So, the coefficient is positive 63/400.\n\nTherefore, after going through all that, I think the coefficient is 63/400.\n\n**Final Answer**\nThe coefficient of \$ x^2 y^6 \$ is \\boxed{\\dfrac{63}{400}}.\n\u003c/think\u003e\n\nTo find the coefficient of \$ x^2 y^6 \$ in the expansion of \$ \\left( \\frac{3}{5}x - \\frac{y}{2} \\right)^8 \$, we use the binomial theorem. The binomial theorem states that:\n\n\\[\n(a + b)^n = \\sum_{k=0}^{n} \\binom{n}{k} a^{n - k} b^{k}\n\\]\n\nHere, \$ a = \\frac{3}{5}x \$, \$ b = -\\frac{y}{2} \$, and \$ n = 8 \$. We need the term where the exponent of \$ x \$ is 2 and the exponent of \$ y \$ is 6. This corresponds to \$ k = 6 \$ because \$ 8 - k = 2 \$ implies \$ k = 6 \$.\n\nThe term is given by:\n\n\\[\n\\binom{8}{6} \\left( \\frac{3}{5}x \\right)^{2} \\left( -\\frac{y}{2} \\right)^{6}\n\\]\n\nCalculating each part:\n\n1. The binomial coefficient \$ \\binom{8}{6} = \\binom{8}{2} = \\frac{8 \\times 7}{2 \\times 1} = 28 \$.\n2. \$ \\left( \\frac{3}{5}x \\right)^2 = \\frac{9}{25}x^2 \$.\n3. \$ \\left( -\\frac{y}{2} \\right)^6 = \\left( \\frac{1}{2} \\right)^6 = \\frac{1}{64} y^6 \$.\n\nCombining these, we get:\n\n\\[\n28 \\times \\frac{9}{25} \\times \\frac{1}{64} = 28 \\times \\frac{9}{1600} = \\frac{252}{1600} = \\frac{63}{400}\n\\]\n\nThus, the coefficient of \$ x^2 y^6 \$ is \\boxed{\\dfrac{63}{400}}.", "statistics_text_generation_0": { "input_tokens": 77, "output_tokens": 2155 } }, "generation": "\u003cthink\u003e\nOkay, so I need to find the coefficient of \$ x^2 y^6 \$ in the expansion of \$ \\left( \\frac{3}{5}x - \\frac{y}{2} \\right)^8 \$. Hmm, I remember that when expanding binomials raised to a power, the binomial theorem is useful here. Let me recall the binomial theorem.\n\nThe binomial theorem states that \$ (a + b)^n = \\sum_{k=0}^{n} \\binom{n}{k} a^{n - k} b^{k} \$. So in this case, \$ a \$ is \$ \\frac{3}{5}x \$ and \$ b \$ is \$ -\\frac{y}{2} \$. The exponent \$ n \$ is 8.\n\nI need to find the term where the exponents of \$ x \$ is 2 and the exponent of \$ y \$ is 6. Let me think: Since each term in the expansion is \$ \\binom{8}{k} \\left( \\frac{3}{5}x \\right)^{8 - k} \\left( -\\frac{y}{2} \\right)^k \$. \n\nSo, for the term to have \$ x^2 y^6 \$, the exponent on \$ x \$ should be 2, which means \$ 8 - k = 2 \$. Solving for \$ k \$, that gives \$ k = 6 \$. \n\nLet me verify that: if \$ k = 6 \$, then the exponent on \$ y \$ is \$ 6 \$, which is exactly what we need. So, that term will be the one we\u0027re interested in.\n\nNow, let me write out that term:\n\n\$ \\binom{8}{6} \\left( \\frac{3}{5}x \\right)^{8 - 6} \\left( -\\frac{y}{2} \\right)^6 \$\n\nSimplifying the exponents first: \$ 8 - 6 = 2 \$, so:\n\n\$ \\binom{8}{6} \\left( \\frac{3}{5}x \\right)^2 \\left( -\\frac{y}{2} \\right)^6 \$\n\nCalculating each part separately. First, \$ \\binom{8}{6} \$. I know that \$ \\binom{8}{6} \$ is equal to \$ \\binom{8}{2} \$ because of the symmetry in the binomial coefficients, right? \$ \\binom{8}{6} = \\binom{8}{2} \$. And \$ \\binom{8}{2} = \\frac{8 \\times 7}{2 \\times 1} = 28 \$. So that part is 28.\n\nNext, \$ \\left( \\frac{3}{5}x \\right)^2 \$. Squaring \$ \\frac{3}{5} \$ gives \$ \\frac{9}{25} \$, and squaring \$ x \$ is still \$ x^2 \$. So that part is \$ \\frac{9}{25}x^2 \$.\n\nThen, \$ \\left( -\\frac{y}{2} \\right)^6 \$. When I raise a negative to an even power, it becomes positive. So, \$ \\left( -\\frac{1}{2} \\right)^6 = \\left( \\frac{1}{2} \\right)^6 = \\frac{1}{64} \$. So that part is \$ \\frac{1}{64} y^6 \$.\n\nNow, putting it all together: 28 multiplied by \$ \\frac{9}{25}x^2 \$ multiplied by \$ \\frac{1}{64} y^6 \$.\n\nLet me compute the constants first: 28 times \$ \\frac{9}{25} \$ times \$ \\frac{1}{64} \$.\n\nFirst, multiply 28 and 9: 28 * 9 = 252.\n\nThen, 252 divided by 25 is \$ 10.08 \$, but I should keep it as a fraction. 252 divided by 25 is \$ \\frac{252}{25} \$.\n\nThen, \$ \\frac{252}{25} \\times \\frac{1}{64} \$. Multiply the numerators: 252 * 1 = 252, denominators: 25 * 64 = 1600. So that\u0027s \$ \\frac{252}{1600} \$.\n\nSimplify \$ \\frac{252}{1600} \$: Let me see if both numbers can be divided by a common factor. 252 and 1600. Let\u0027s try 4: 252 \u00f7 4 = 63, 1600 \u00f7 4 = 400. So, \$ \\frac{63}{400} \$. Hmm, 63 and 400. 63 is 7*9, 400 is 16*25. They have no common factors besides 1, so that\u0027s the simplified fraction.\n\nTherefore, the coefficient is \$ \\frac{63}{400} \$.\n\nWait, let me double-check my calculations to make sure I didn\u0027t make a mistake. So, 28 * 9 is indeed 252, correct. 252 divided by 25 is 10.08? Wait, 10.08 times 25 is 252, yes. Then 252 divided by 64 is 3.9375, which is \$ \\frac{63}{16} \$. Wait, no, hold on, that doesn\u0027t make sense. Wait, I think I messed up the multiplication.\n\nWait, no, wait. Let\u0027s go back. The constants: 28 * (9/25) * (1/64). Let me compute 28 * 9 first. 28*9: 20*9=180, 8*9=72, so 180+72=252. Then, 252 * (1/25) = 252/25. Then, 252/25 * (1/64) = 252/(25*64) = 252/1600.\n\nThen, 252 divided by 1600: let me divide numerator and denominator by 4: 252 \u00f74=63, 1600 \u00f74=400. So, 63/400. That simplifies correctly.\n\nWait, but 63/400 is 0.1575, and 252/1600 is 0.1575. So that\u0027s correct.\n\nBut wait, let me check 252 divided by 1600:\n\n252 / 1600 = (252 \u00f7 4) / (1600 \u00f74) = 63/400. Yes, correct.\n\nSo, okay, so the coefficient is 63/400.\n\nWait, but let me double-check the binomial coefficient. So, \$ \\binom{8}{6} \$. Since \$ \\binom{8}{6} = \\binom{8}{2} \$ because \$ \\binom{n}{k} = \\binom{n}{n - k} \$. So, \$ \\binom{8}{2} \$ is (8*7)/2 = 28, correct. So that\u0027s correct.\n\nSo, 28 * (9/25) * (1/64) = 28*(9)/(25*64) = 252/1600 = 63/400.\n\nTherefore, the coefficient is 63/400.\n\nWait, let me think again about the signs. In the term, the \$ b^k \$ is \$ (-\\frac{y}{2})^6 \$. Since 6 is even, the negative becomes positive, so that\u0027s correct, it\u0027s positive. So, the coefficient is positive 63/400.\n\nTherefore, after going through all that, I think the coefficient is 63/400.\n\n**Final Answer**\nThe coefficient of \$ x^2 y^6 \$ is \\boxed{\\dfrac{63}{400}}.\n\u003c/think\u003e\n\nTo find the coefficient of \$ x^2 y^6 \$ in the expansion of \$ \\left( \\frac{3}{5}x - \\frac{y}{2} \\right)^8 \$, we use the binomial theorem. The binomial theorem states that:\n\n\\[\n(a + b)^n = \\sum_{k=0}^{n} \\binom{n}{k} a^{n - k} b^{k}\n\\]\n\nHere, \$ a = \\frac{3}{5}x \$, \$ b = -\\frac{y}{2} \$, and \$ n = 8 \$. We need the term where the exponent of \$ x \$ is 2 and the exponent of \$ y \$ is 6. This corresponds to \$ k = 6 \$ because \$ 8 - k = 2 \$ implies \$ k = 6 \$.\n\nThe term is given by:\n\n\\[\n\\binom{8}{6} \\left( \\frac{3}{5}x \\right)^{2} \\left( -\\frac{y}{2} \\right)^{6}\n\\]\n\nCalculating each part:\n\n1. The binomial coefficient \$ \\binom{8}{6} = \\binom{8}{2} = \\frac{8 \\times 7}{2 \\times 1} = 28 \$.\n2. \$ \\left( \\frac{3}{5}x \\right)^2 = \\frac{9}{25}x^2 \$.\n3. \$ \\left( -\\frac{y}{2} \\right)^6 = \\left( \\frac{1}{2} \\right)^6 = \\frac{1}{64} y^6 \$.\n\nCombining these, we get:\n\n\\[\n28 \\times \\frac{9}{25} \\times \\frac{1}{64} = 28 \\times \\frac{9}{1600} = \\frac{252}{1600} = \\frac{63}{400}\n\\]\n\nThus, the coefficient of \$ x^2 y^6 \$ is \\boxed{\\dfrac{63}{400}}.", "messages": [ { "content": "What is the coefficient of $x^2y^6$ in the expansion of $\\left(\\frac{3}{5}x-\\frac{y}{2}\\right)^8$? Express your answer as a common fraction.", "role": "user" }, { "content": "To determine the coefficient of \$x^2y^6\$ in the expansion of \$\\left(\\frac{3}{5}x - \\frac{y}{2}\\right)^8\$, we can use the binomial theorem.\n\nThe binomial theorem states:\n\\[\n(a + b)^n = \\sum_{k=0}^{n} \\binom{n}{k} a^{n-k} b^k\n\\]\n\nIn this case, \$a = \\frac{3}{5}x\$, \$b = -\\frac{y}{2}\$, and \$n = 8\$.\n\nWe are interested in the term that contains \$x^2y^6\$. In the general term of the binomial expansion:\n\\[\n\\binom{8}{k} \\left(\\frac{3}{5}x\\right)^{8-k} \\left(-\\frac{y}{2}\\right)^k\n\\]\n\nTo get \$x^2\$, we need \$8 - k = 2\$, thus \$k = 6\$.\n\nSubstituting \$k = 6\$ into the expression:\n\\[\n\\binom{8}{6} \\left(\\frac{3}{5}x\\right)^{8-6} \\left(-\\frac{y}{2}\\right)^6 = \\binom{8}{6} \\left(\\frac{3}{5}x\\right)^2 \\left(-\\frac{y}{2}\\right)^6\n\\]\n\nNow, we will compute each part of this expression.\n\n1. Calculate the binomial coefficient \$\\binom{8}{6}\$.\n2. Compute \$\\left(\\frac{3}{5}\\right)^2\$.\n3. Compute \$\\left(-\\frac{y}{2}\\right)^6\$.\n4. Combine everything together to get the coefficient of \$x^2y^6\$.\n\nLet\u0027s compute these in Python.\n```python\nfrom math import comb\n\n# Given values\nn = 8\nk = 6\n\n# Calculate the binomial coefficient\nbinom_coeff = comb(n, k)\n\n# Compute (3/5)^2\na_term = (3/5)**2\n\n# Compute (-1/2)^6\nb_term = (-1/2)**6\n\n# Combine terms to get the coefficient of x^2y^6\ncoefficient = binom_coeff * a_term * b_term\nprint(coefficient)\n```\n```output\n0.1575\n```\nThe coefficient of \$x^2y^6\$ in the expansion of \$\\left(\\frac{3}{5}x - \\frac{y}{2}\\right)^8\$ is \$0.1575\$. To express this as a common fraction, we recognize that:\n\n\\[ 0.1575 = \\frac{1575}{10000} = \\frac{63}{400} \\]\n\nThus, the coefficient can be expressed as:\n\n\\[\n\\boxed{\\frac{63}{400}}\n\\]", "role": "assistant" } ], "model_name": "deepseek-ai/DeepSeek-R1-Distill-Llama-8B", "problem": "What is the coefficient of $x^2y^6$ in the expansion of $\\left(\\frac{3}{5}x-\\frac{y}{2}\\right)^8$? Express your answer as a common fraction.", "solution": "To determine the coefficient of \$x^2y^6\$ in the expansion of \$\\left(\\frac{3}{5}x - \\frac{y}{2}\\right)^8\$, we can use the binomial theorem.\n\nThe binomial theorem states:\n\\[\n(a + b)^n = \\sum_{k=0}^{n} \\binom{n}{k} a^{n-k} b^k\n\\]\n\nIn this case, \$a = \\frac{3}{5}x\$, \$b = -\\frac{y}{2}\$, and \$n = 8\$.\n\nWe are interested in the term that contains \$x^2y^6\$. In the general term of the binomial expansion:\n\\[\n\\binom{8}{k} \\left(\\frac{3}{5}x\\right)^{8-k} \\left(-\\frac{y}{2}\\right)^k\n\\]\n\nTo get \$x^2\$, we need \$8 - k = 2\$, thus \$k = 6\$.\n\nSubstituting \$k = 6\$ into the expression:\n\\[\n\\binom{8}{6} \\left(\\frac{3}{5}x\\right)^{8-6} \\left(-\\frac{y}{2}\\right)^6 = \\binom{8}{6} \\left(\\frac{3}{5}x\\right)^2 \\left(-\\frac{y}{2}\\right)^6\n\\]\n\nNow, we will compute each part of this expression.\n\n1. Calculate the binomial coefficient \$\\binom{8}{6}\$.\n2. Compute \$\\left(\\frac{3}{5}\\right)^2\$.\n3. Compute \$\\left(-\\frac{y}{2}\\right)^6\$.\n4. Combine everything together to get the coefficient of \$x^2y^6\$.\n\nLet\u0027s compute these in Python.\n```python\nfrom math import comb\n\n# Given values\nn = 8\nk = 6\n\n# Calculate the binomial coefficient\nbinom_coeff = comb(n, k)\n\n# Compute (3/5)^2\na_term = (3/5)**2\n\n# Compute (-1/2)^6\nb_term = (-1/2)**6\n\n# Combine terms to get the coefficient of x^2y^6\ncoefficient = binom_coeff * a_term * b_term\nprint(coefficient)\n```\n```output\n0.1575\n```\nThe coefficient of \$x^2y^6\$ in the expansion of \$\\left(\\frac{3}{5}x - \\frac{y}{2}\\right)^8\$ is \$0.1575\$. To express this as a common fraction, we recognize that:\n\n\\[ 0.1575 = \\frac{1575}{10000} = \\frac{63}{400} \\]\n\nThus, the coefficient can be expressed as:\n\n\\[\n\\boxed{\\frac{63}{400}}\n\\]" } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("wangx0t/numina-deepseek-r1-qwen-7b", "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("wangx0t/numina-deepseek-r1-qwen-7b") ``` </details>

Lots-of-LoRAs/task1159_bard_analogical_reasoning_containers

Lots-of-LoRAs

2024-12-30T23:35:53Z

9

0

[ "task_categories:text-generation", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "language:en", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2204.07705", "arxiv:2407.00066", "region:us" ]

[ "text-generation" ]

2024-12-30T23:35:51Z

0

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 task_categories: - text-generation pretty_name: task1159_bard_analogical_reasoning_containers dataset_info: config_name: plain_text features: - name: input dtype: string - name: output dtype: string - name: id dtype: string splits: - name: train num_examples: 558 - name: valid num_examples: 70 - name: test num_examples: 70 --- # Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1159_bard_analogical_reasoning_containers ## Dataset Description - **Homepage:** https://github.com/allenai/natural-instructions - **Paper:** https://arxiv.org/abs/2204.07705 - **Paper:** https://arxiv.org/abs/2407.00066 - **Point of Contact:** [Rickard Brüel Gabrielsson](mailto:[email protected]) ## Additional Information ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions, title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks}, author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi}, year={2022}, eprint={2204.07705}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2204.07705}, } ``` More details can also be found in the following paper: ```bibtex @misc{brüelgabrielsson2024compressserveservingthousands, title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead}, author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon}, year={2024}, eprint={2407.00066}, archivePrefix={arXiv}, primaryClass={cs.DC}, url={https://arxiv.org/abs/2407.00066}, } ``` ### Contact Information For any comments or questions, please email [Rickard Brüel Gabrielsson](mailto:[email protected])

PatronusAI/glider-multilingual-reward-bench-suite

PatronusAI

2024-12-18T23:41:51Z

67

0

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

[]

2024-11-19T17:15:14Z

0

--- dataset_info: features: - name: prompt dtype: string - name: score dtype: int64 - name: pass_criteria dtype: string - name: rubric dtype: string splits: - name: arb_Arab num_bytes: 12237595 num_examples: 2869 - name: ces_Latn num_bytes: 10748534 num_examples: 2869 - name: deu_Latn num_bytes: 11178372 num_examples: 2869 - name: ell_Grek num_bytes: 14404826 num_examples: 2869 - name: fra_Latn num_bytes: 11245160 num_examples: 2869 - name: heb_Hebr num_bytes: 11754840 num_examples: 2869 - name: hin_Deva num_bytes: 15857179 num_examples: 2869 - name: ind_Latn num_bytes: 10872895 num_examples: 2869 - name: ita_Latn num_bytes: 10874456 num_examples: 2869 - name: jpn_Jpan num_bytes: 11443888 num_examples: 2869 - name: kor_Hang num_bytes: 11060869 num_examples: 2869 - name: nld_Latn num_bytes: 10904828 num_examples: 2869 - name: pes_Arab num_bytes: 12736791 num_examples: 2869 - name: pol_Latn num_bytes: 10905164 num_examples: 2869 - name: por_Latn num_bytes: 10826113 num_examples: 2869 - name: ron_Latn num_bytes: 11069968 num_examples: 2869 - name: rus_Cyrl num_bytes: 13720140 num_examples: 2869 - name: spa_Latn num_bytes: 10960266 num_examples: 2869 - name: tur_Latn num_bytes: 10873535 num_examples: 2869 - name: ukr_Cyrl num_bytes: 13392298 num_examples: 2869 - name: vie_Latn num_bytes: 11823251 num_examples: 2869 - name: zho_Hans num_bytes: 10016686 num_examples: 2869 - name: zho_Hant num_bytes: 9899172 num_examples: 2869 download_size: 71816921 dataset_size: 268806826 configs: - config_name: default data_files: - split: arb_Arab path: data/arb_Arab-* - split: ces_Latn path: data/ces_Latn-* - split: deu_Latn path: data/deu_Latn-* - split: ell_Grek path: data/ell_Grek-* - split: fra_Latn path: data/fra_Latn-* - split: heb_Hebr path: data/heb_Hebr-* - split: hin_Deva path: data/hin_Deva-* - split: ind_Latn path: data/ind_Latn-* - split: ita_Latn path: data/ita_Latn-* - split: jpn_Jpan path: data/jpn_Jpan-* - split: kor_Hang path: data/kor_Hang-* - split: nld_Latn path: data/nld_Latn-* - split: pes_Arab path: data/pes_Arab-* - split: pol_Latn path: data/pol_Latn-* - split: por_Latn path: data/por_Latn-* - split: ron_Latn path: data/ron_Latn-* - split: rus_Cyrl path: data/rus_Cyrl-* - split: spa_Latn path: data/spa_Latn-* - split: tur_Latn path: data/tur_Latn-* - split: ukr_Cyrl path: data/ukr_Cyrl-* - split: vie_Latn path: data/vie_Latn-* - split: zho_Hans path: data/zho_Hans-* - split: zho_Hant path: data/zho_Hant-* ---

pdf2dataset/436f3df84262cc0743d5d8d1acf9368

pdf2dataset

2024-10-05T11:48:31Z

18

0

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

[]

2024-10-05T11:48:30Z

0

--- dataset_info: features: - name: text dtype: string - name: source dtype: string splits: - name: train num_bytes: 1691 num_examples: 5 download_size: 6842 dataset_size: 1691 configs: - config_name: default data_files: - split: train path: data/train-* ---

R0bfried/RAGAS-12000-llama-3-2-1B-eval

R0bfried

2025-03-20T09:01:32Z

14

0

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

[]

2025-03-20T09:01:28Z

0

--- dataset_info: features: - name: user_input dtype: string - name: retrieved_contexts sequence: string - name: response dtype: string - name: reference dtype: string - name: faithfulness dtype: float64 - name: answer_relevancy dtype: float64 - name: answer_correctness dtype: float64 splits: - name: train num_bytes: 2604364 num_examples: 150 download_size: 954997 dataset_size: 2604364 configs: - config_name: default data_files: - split: train path: data/train-* ---

cijerezg/pick-up-test-v1

cijerezg

2025-09-25T05:41:35Z

64

0

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

[ "robotics" ]

2025-09-25T05:41:15Z

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": "v3.0", "robot_type": "so101_follower", "total_episodes": 20, "total_frames": 8941, "total_tasks": 1, "chunks_size": 1000, "data_files_size_in_mb": 100, "video_files_size_in_mb": 500, "fps": 30, "splits": { "train": "0:20" }, "data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet", "video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4", "features": { "action": { "dtype": "float32", "names": [ "shoulder_pan.pos", "shoulder_lift.pos", "elbow_flex.pos", "wrist_flex.pos", "wrist_roll.pos", "gripper.pos" ], "shape": [ 6 ] }, "observation.state": { "dtype": "float32", "names": [ "shoulder_pan.pos", "shoulder_lift.pos", "elbow_flex.pos", "wrist_flex.pos", "wrist_roll.pos", "gripper.pos" ], "shape": [ 6 ] }, "observation.images.wrist": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.top_left": { "dtype": "video", "shape": [ 340, 340, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 340, "video.width": 340, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.top_right": { "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] ```

ds4sd/PubTables-1M_OTSL

ds4sd

2023-08-31T16:00:24Z

22,884

4

[ "task_categories:object-detection", "task_categories:table-to-text", "license:other", "size_categories:1M<n<10M", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2305.03393", "region:us", "table-structure-recognition", "table-understanding", "PDF" ]

[ "object-detection", "table-to-text" ]

2023-08-10T08:21:06Z

0

--- license: other pretty_name: PubTables-1M-OTSL size_categories: - 100K<n<1M tags: - table-structure-recognition - table-understanding - PDF task_categories: - object-detection - table-to-text --- # Dataset Card for PubTables-1M_OTSL ## Dataset Description - **Homepage:** https://ds4sd.github.io - **Paper:** https://arxiv.org/pdf/2305.03393 ### Dataset Summary This dataset enables the evaluation of both object detection models and image-to-text methods. [PubTables-1M](https://github.com/microsoft/table-transformer) is introduced in the publication *"PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents"* by Smock et al. The conversion into HF (Hugging Face) and the addition of the OTSL (Optimized Table Structure Language) format is presented in our paper "Optimized Table Tokenization for Table Structure Recognition" by Lysak et al. The dataset includes the original annotations amongst new additions. ### Dataset Structure * cells: origunal dataset cell groundtruth (content). * table_bbox: origunal dataset table detection groundtruth. * otsl: new reduced table structure token format * html: Generated HTML for PubTables-1M to match PubTabNet, FinTabNet, and SynthTabNet format. * html_restored: generated HTML from OTSL. * cols: grid column length. * rows: grid row length. * image: PIL image ### OTSL Vocabulary: **OTSL**: new reduced table structure token format More information on the OTSL table structure format and its concepts can be read from our paper. Format of this dataset extends work presented in a paper, and introduces slight modifications: * "fcel" - cell that has content in it * "ecel" - cell that is empty * "lcel" - left-looking cell (to handle horizontally merged cells) * "ucel" - up-looking cell (to handle vertically merged cells) * "xcel" - 2d span cells, in this dataset - covers entire area of a merged cell * "nl" - new line token ### Data Splits The dataset provides three splits - `train` - `val` - `test` ## Additional Information ### Dataset Curators The dataset is converted by the [Deep Search team](https://ds4sd.github.io/) at IBM Research. You can contact us at [[email protected]](mailto:[email protected]). Curators: - Maksym Lysak, [@maxmnemonic](https://github.com/maxmnemonic) - Ahmed Nassar, [@nassarofficial](https://github.com/nassarofficial) - Christoph Auer, [@cau-git](https://github.com/cau-git) - Nikos Livathinos, [@nikos-livathinos](https://github.com/nikos-livathinos) - Peter Staar, [@PeterStaar-IBM](https://github.com/PeterStaar-IBM) ### Citation Information **Citation to OTSL Paper:** @article{lysak2023optimized, title={Optimized Table Tokenization for Table Structure Recognition}, author={Maksym Lysak and Ahmed Nassar and Nikolaos Livathinos and Christoph Auer and Peter Staar}, year={2023}, eprint={2305.03393}, archivePrefix={arXiv}, primaryClass={cs.CV} } **Citation to PubTables-1M creators:** @inproceedings{smock2022pubtables, title={Pub{T}ables-1{M}: Towards comprehensive table extraction from unstructured documents}, author={Smock, Brandon and Pesala, Rohith and Abraham, Robin}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, pages={4634-4642}, year={2022}, month={June} }

Abeyankar/mtl_7k_inference_ds

Abeyankar

2025-06-03T20:06:37Z

0

[ "task_categories:object-detection", "language:en", "size_categories:10K<n<100K", "format:imagefolder", "modality:image", "library:datasets", "library:mlcroissant", "library:fiftyone", "region:us", "fiftyone", "image", "object-detection" ]

[ "object-detection" ]

2025-06-03T19:58:48Z

0

--- annotations_creators: [] language: en size_categories: - 10K<n<100K task_categories: - object-detection task_ids: [] pretty_name: train_2844_mcityclean_val_7642_7k tags: - fiftyone - image - object-detection commit_message: Hand annotated dataset MTL 7k overwrite: true dataset_summary: ' This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 10486 samples. ## Installation If you haven''t already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo from fiftyone.utils.huggingface import load_from_hub # Load the dataset # Note: other available arguments include ''max_samples'', etc dataset = load_from_hub("Abeyankar/mtl_7k_inference_ds") # Launch the App session = fo.launch_app(dataset) ``` ' --- # Dataset Card for train_2844_mcityclean_val_7642_7k  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 10486 samples. ## Installation If you haven't already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo from fiftyone.utils.huggingface import load_from_hub # Load the dataset # Note: other available arguments include 'max_samples', etc dataset = load_from_hub("Abeyankar/mtl_7k_inference_ds") # Launch the App session = fo.launch_app(dataset) ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** en - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

Ramitha/unique-records-selected-integrated-gradients

Ramitha

2025-09-27T08:57:26Z

229

0

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

[]

2025-08-23T17:19:14Z

0

--- dataset_info: features: - name: question dtype: string - name: snippet dtype: string - name: answer dtype: string - name: ILRAlign dtype: float64 - name: dataset dtype: string - name: question_ig_tokens_llama dtype: string - name: answer_ig_tokens_llama dtype: string - name: question_ig_tokens_mistral dtype: string - name: answer_ig_tokens_mistral dtype: string - name: question_ig_tokens_falcon dtype: string - name: answer_ig_tokens_falcon dtype: string - name: question_ig_tokens_gemma dtype: string - name: answer_ig_tokens_gemma dtype: string - name: question_raw_ig_tokens_falcon dtype: string - name: answer_raw_ig_tokens_falcon dtype: string - name: question_raw_ig_tokens_llama dtype: string - name: answer_raw_ig_tokens_llama dtype: string - name: question_raw_ig_tokens_mistral dtype: string - name: answer_raw_ig_tokens_mistral dtype: string - name: question_raw_ig_tokens_gemma dtype: string - name: answer_raw_ig_tokens_gemma dtype: string - name: question_iaa_all dtype: float64 - name: question_iaa_llama_falcon dtype: float64 - name: question_iaa_llama_gemma dtype: float64 - name: question_iaa_llama_mistral dtype: float64 - name: question_iaa_falcon_gemma dtype: float64 - name: question_iaa_falcon_mistral dtype: float64 - name: question_iaa_gemma_mistral dtype: float64 - name: answer_iaa_all dtype: float64 - name: answer_iaa_llama_falcon dtype: float64 - name: answer_iaa_llama_gemma dtype: float64 - name: answer_iaa_llama_mistral dtype: float64 - name: answer_iaa_falcon_gemma dtype: float64 - name: answer_iaa_falcon_mistral dtype: float64 - name: answer_iaa_gemma_mistral dtype: float64 - name: question_unique_words dtype: float64 - name: answer_unique_words dtype: float64 - name: question_answerGenerated_llama dtype: string - name: reverse_answer_answerGenerated_llama dtype: string - name: question_answerGenerated_falcon dtype: string - name: reverse_answer_answerGenerated_falcon dtype: string - name: question_answerGenerated_gemma dtype: string - name: reverse_answer_answerGenerated_gemma dtype: string - name: question_answerGenerated_mistral dtype: string - name: reverse_answer_answerGenerated_mistral dtype: string - name: ILRAlign_without_context_llama dtype: float64 - name: ILRAlign_with_context_llama dtype: float64 - name: ILRAlign_without_context_falcon dtype: float64 - name: ILRAlign_with_context_falcon dtype: float64 - name: ILRAlign_without_context_gemma dtype: float64 - name: ILRAlign_with_context_gemma dtype: float64 - name: ILRAlign_without_context_mistral dtype: float64 - name: ILRAlign_with_context_mistral dtype: float64 - name: ILRAlign_with_problem_context_only_mistral dtype: float64 - name: ILRAlign_with_answer_context_only_mistral dtype: float64 - name: ILRAlign_with_problem_context_only_llama dtype: float64 - name: ILRAlign_with_answer_context_only_llama dtype: float64 - name: ILRAlign_with_problem_context_only_falcon dtype: float64 - name: ILRAlign_with_answer_context_only_falcon dtype: float64 - name: ILRAlign_with_problem_context_only_gemma dtype: float64 - name: ILRAlign_with_answer_context_only_gemma dtype: float64 splits: - name: rawcases num_bytes: 2063463 num_examples: 54 download_size: 1254341 dataset_size: 2063463 configs: - config_name: default data_files: - split: rawcases path: data/rawcases-* ---

trnguyenai01/FullyIndicators_distillation

trnguyenai01

2025-04-21T03:06:27Z

18

0

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

[]

2025-04-21T03:06:06Z

0

--- dataset_info: features: - name: reports dtype: string - name: labels dtype: string splits: - name: train num_bytes: 243978594 num_examples: 15350 download_size: 44782472 dataset_size: 243978594 configs: - config_name: default data_files: - split: train path: data/train-* ---

cchoi1/humaneval-datagen-qwen1.5b_best_att_50_sol_50_20250226_184052

cchoi1

2025-02-27T05:19:20Z

18

0

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

[]

2025-02-27T05:19:18Z

0

--- dataset_info: features: - name: problem_id dtype: string - name: prompt dtype: string - name: canonical_solution dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: chosen_attack dtype: string - name: chosen_attack_explanation dtype: string - name: chosen_solution dtype: string - name: chosen_solution_explanation dtype: string - name: chosen_solve_rate dtype: float64 - name: rejected_attack dtype: string - name: rejected_attack_explanation dtype: string - name: rejected_solution dtype: string - name: rejected_solution_explanation dtype: string - name: rejected_solve_rate dtype: float64 splits: - name: train num_bytes: 464817 num_examples: 156 download_size: 45399 dataset_size: 464817 configs: - config_name: default data_files: - split: train path: data/train-* ---

kenken6696/ALCUNA_meta_affirmative_understood_unfamiliar_for_fix_middle_train

kenken6696

2024-12-22T14:26:06Z

55

0

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

[]

2024-12-22T14:26:02Z

0

--- dataset_info: features: - name: type dtype: string - name: form dtype: string - name: property_name dtype: string - name: related_property_name dtype: string - name: question dtype: string - name: answer dtype: string - name: sentence dtype: string - name: meta_tag dtype: string - name: meta_sentence dtype: string splits: - name: train num_bytes: 879893.8397722652 num_examples: 2213 - name: test num_bytes: 97810.16022773486 num_examples: 246 download_size: 388387 dataset_size: 977704.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

yoonholee/completions_easy-8k-med16k_HMMT2025

yoonholee

2025-05-13T23:04:46Z

0

[ "region:us" ]

[]

2025-05-13T23:04:44Z

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: 10195068 num_examples: 30 download_size: 4017936 dataset_size: 10195068 configs: - config_name: default data_files: - split: train path: data/train-* ---

interhin/fora-eval

interhin

2024-10-18T13:25:20Z

18

0

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

[]

2024-10-18T13:08:15Z

0

--- dataset_info: features: - name: repo_id dtype: string - name: file_path dtype: string - name: content dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 15988254 num_examples: 4377 download_size: 4209542 dataset_size: 15988254 configs: - config_name: default data_files: - split: train path: data/train-* ---

gbyuvd/sabrlo-chem-selfies-training

gbyuvd

2025-09-26T17:52:29Z

37

0

[ "license:cc-by-3.0", "size_categories:100K<n<1M", "format:csv", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-09-26T17:39:59Z

0

--- license: cc-by-3.0 --- # Valid Bioactives and Natural Product SELFIES ~1M valid SELFIES with seq_len<=25 using FastChemTokenizerSelfies, built and curated from ChemBL34 (Zdrazil et al. 2023), COCONUTDB (Sorokina et al. 2021), and Supernatural3 (Gallo et al. 2023) dataset. ## Processing The original dataset was processed by filtering max_seq_len<=25 for training ChemMiniQ3-SAbRLo. The cleaned dataset was then split into 6 approximately equal chunks for training in limited compute. Curated by: gbyuvd ## References ### BibTeX #### COCONUTDB ```bibtex @article{sorokina2021coconut, title={COCONUT online: Collection of Open Natural Products database}, author={Sorokina, Maria and Merseburger, Peter and Rajan, Kohulan and Yirik, Mehmet Aziz and Steinbeck, Christoph}, journal={Journal of Cheminformatics}, volume={13}, number={1}, pages={2}, year={2021}, doi={10.1186/s13321-020-00478-9} } ``` #### ChemBL34 ```bibtex @article{zdrazil2023chembl, title={The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods}, author={Zdrazil, Barbara and Felix, Eloy and Hunter, Fiona and Manners, Emma J and Blackshaw, James and Corbett, Sybilla and de Veij, Marleen and Ioannidis, Harris and Lopez, David Mendez and Mosquera, Juan F and Magarinos, Maria Paula and Bosc, Nicolas and Arcila, Ricardo and Kizil{\"o}ren, Tevfik and Gaulton, Anna and Bento, A Patr{\'i}cia and Adasme, Melissa F and Monecke, Peter and Landrum, Gregory A and Leach, Andrew R}, journal={Nucleic Acids Research}, year={2023}, volume={gkad1004}, doi={10.1093/nar/gkad1004} } @misc{chembl34, title={ChemBL34}, year={2023}, doi={10.6019/CHEMBL.database.34} } ``` #### SuperNatural3 ```bibtex @article{Gallo2023, author = {Gallo, K and Kemmler, E and Goede, A and Becker, F and Dunkel, M and Preissner, R and Banerjee, P}, title = {{SuperNatural 3.0-a database of natural products and natural product-based derivatives}}, journal = {Nucleic Acids Research}, year = {2023}, month = jan, day = {6}, volume = {51}, number = {D1}, pages = {D654-D659}, doi = {10.1093/nar/gkac1008} } ```

jchun/so100_pickplace_small_20250322_160953

jchun

2025-03-23T00:44:11Z

25

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", "pickplace" ]

[ "robotics" ]

2025-03-22T23:03:54Z

0

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - pickplace 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": 4, "total_frames": 3323, "total_tasks": 1, "total_videos": 12, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:4" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 12 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper", "cv_shoulder_pan", "cv_shoulder_lift", "cv_elbow_flex", "cv_wrist_flex", "cv_wrist_roll", "cv_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 12 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper", "cv_shoulder_pan", "cv_shoulder_lift", "cv_elbow_flex", "cv_wrist_flex", "cv_wrist_roll", "cv_gripper" ] }, "observation.images.main": { "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.cv": { "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.webcam": { "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] ```

Turbo-AI/train-multi-negatives-v2

Turbo-AI

2024-11-17T14:19:35Z

17

0

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

[]

2024-11-17T14:19:12Z

0

--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: relevant list: - name: id dtype: int64 - name: text dtype: string - name: not_relevant list: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 918366297 num_examples: 118956 download_size: 321049144 dataset_size: 918366297 configs: - config_name: default data_files: - split: train path: data/train-* ---

rishitdagli/lrmm_full

rishitdagli

2025-06-07T01:56:55Z

0

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

[]

2025-06-07T01:43:06Z

0

--- dataset_info: features: - name: inputs sequence: sequence: float32 - name: outputs sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 34504560000 num_examples: 10000 download_size: 34238220403 dataset_size: 34504560000 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_32dc5455-b053-4c95-94dd-2400e610d4e9

argilla-internal-testing

2024-10-11T09:24:56Z

21

0

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

[]

2024-10-11T09:24:55Z

0

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1454 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

bwang0911/reasoning_pairs_s2orc

bwang0911

2025-03-28T09:33:30Z

16

0

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

[]

2025-03-28T09:33:24Z

0

--- dataset_info: features: - name: title dtype: string - name: body dtype: string - name: valid dtype: bool - name: reason dtype: string splits: - name: train num_bytes: 106573680 num_examples: 100000 download_size: 61811728 dataset_size: 106573680 configs: - config_name: default data_files: - split: train path: data/train-* ---

ThatsGroes/synthetic-from-text-mathing-short-tasks-swedish

ThatsGroes

2025-01-31T09:30:43Z

21

0

[ "language:sv", "license:mit", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2401.00368", "region:us" ]

[]

2025-01-25T09:51:51Z

0

--- dataset_info: features: - name: response dtype: string - name: model dtype: string - name: prompt list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 50422736 num_examples: 50000 download_size: 6177357 dataset_size: 50422736 configs: - config_name: default data_files: - split: train path: data/train-* license: mit language: - sv --- # Thanks to Arrow Denmark and Nvidia for sponsoring the compute used to generate this dataset The purpose of this dataset is to pre- or post-train embedding models for text matching tasks on short texts. The dataset consists of 100,000 samples generated with gemma-2-27b-it. The column "prompt" shows the prompt given to the LLM and "response" shows the LLM output. Each sample in the dataset was generated from a seed task randomly sampled from https://huggingface.co/datasets/ThatsGroes/text-matching-short-tasks-processed The data generation process described in this paper was followed: https://arxiv.org/pdf/2401.00368 Compute sponsored by Arrow Denmark and Nvidia through Danish Data Science Community.

tmpmodelsave/beta01llamasft_math_ift_balanced_dpo_moredata_400tmp10

tmpmodelsave

2025-01-22T08:38:43Z

15

0

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

[]

2025-01-22T08:38:41Z

0

--- dataset_info: features: - name: idx dtype: int64 - name: gt dtype: string - name: prompt dtype: string - name: level dtype: string - name: type dtype: string - name: solution dtype: string - name: my_solu sequence: string - name: pred sequence: string - name: rewards sequence: bool splits: - name: train num_bytes: 43372827 num_examples: 15000 download_size: 15914121 dataset_size: 43372827 configs: - config_name: default data_files: - split: train path: data/train-* ---

thusinh1969/EraX-Cosent_LanhGPTV2_30NOV2024_4.4M

thusinh1969

2024-12-01T01:56:45Z

23

0

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

[]

2024-12-01T01:39:11Z

0

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 - name: similarity dtype: float64 - name: type dtype: string - name: hard dtype: int64 splits: - name: train num_bytes: 21363010272.562458 num_examples: 4399028 - name: test num_bytes: 194252096.3500342 num_examples: 40000 - name: eval num_bytes: 48563024.08750855 num_examples: 10000 download_size: 11579474915 dataset_size: 21605825393.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: eval path: data/eval-* ---

james-1111/x_dataset_0303241

james-1111

2025-06-24T11:55:57Z

1,077

0

[ "task_categories:text-classification", "task_categories:token-classification", "task_categories:question-answering", "task_categories:summarization", "task_categories:text-generation", "task_ids:sentiment-analysis", "task_ids:topic-classification", "task_ids:named-entity-recognition", "task_ids:language-modeling", "task_ids:text-scoring", "task_ids:multi-class-classification", "task_ids:multi-label-classification", "task_ids:extractive-qa", "task_ids:news-articles-summarization", "multilinguality:multilingual", "source_datasets:original", "license:mit", "size_categories:10M<n<100M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[ "text-classification", "token-classification", "question-answering", "summarization", "text-generation" ]

2025-01-25T07:11:23Z

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:** james-1111/x_dataset_0303241 - **Subnet:** Bittensor Subnet 13 - **Miner Hotkey:** 5HMi2tcDTWxckR86mn6d4cmM3dCGwoRdA4PvQNLcnSqjw86k ### Miner Data Compliance Agreement In uploading this dataset, I am agreeing to the [Macrocosmos Miner Data Compliance Policy](https://github.com/macrocosm-os/data-universe/blob/add-miner-policy/docs/miner_policy.md). ### Dataset Summary This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed data from X (formerly Twitter). The data is continuously updated by network miners, providing a real-time stream of tweets for various analytical and machine learning tasks. For more information about the dataset, please visit the [official repository](https://github.com/macrocosm-os/data-universe). ### Supported Tasks The versatility of this dataset allows researchers and data scientists to explore various aspects of social media dynamics and develop innovative applications. Users are encouraged to leverage this data creatively for their specific research or business needs. For example: - Sentiment Analysis - Trend Detection - Content Analysis - User Behavior Modeling ### Languages Primary language: Datasets are mostly English, but can be multilingual due to decentralized ways of creation. ## Dataset Structure ### Data Instances Each instance represents a single tweet with the following fields: ### Data Fields - `text` (string): The main content of the tweet. - `label` (string): Sentiment or topic category of the tweet. - `tweet_hashtags` (list): A list of hashtags used in the tweet. May be empty if no hashtags are present. - `datetime` (string): The date when the tweet was posted. - `username_encoded` (string): An encoded version of the username to maintain user privacy. - `url_encoded` (string): An encoded version of any URLs included in the tweet. May be empty if no URLs are present. ### Data Splits This dataset is continuously updated and does not have fixed splits. Users should create their own splits based on their requirements and the data's timestamp. ## Dataset Creation ### Source Data Data is collected from public tweets on X (Twitter), adhering to the platform's terms of service and API usage guidelines. ### Personal and Sensitive Information All usernames and URLs are encoded to protect user privacy. The dataset does not intentionally include personal or sensitive information. ## Considerations for Using the Data ### Social Impact and Biases Users should be aware of potential biases inherent in X (Twitter) data, including demographic and content biases. This dataset reflects the content and opinions expressed on X and should not be considered a representative sample of the general population. ### Limitations - Data quality may vary due to the decentralized nature of collection and preprocessing. - The dataset may contain noise, spam, or irrelevant content typical of social media platforms. - Temporal biases may exist due to real-time collection methods. - The dataset is limited to public tweets and does not include private accounts or direct messages. - Not all tweets contain hashtags or URLs. ## Additional Information ### Licensing Information The dataset is released under the MIT license. The use of this dataset is also subject to X Terms of Use. ### Citation Information If you use this dataset in your research, please cite it as follows: ``` @misc{james-11112025datauniversex_dataset_0303241, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={james-1111}, year={2025}, url={https://huggingface.co/datasets/james-1111/x_dataset_0303241}, } ``` ### 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:** 5415872 - **Date Range:** 2025-01-02T00:00:00Z to 2025-06-14T00:00:00Z - **Last Updated:** 2025-06-24T11:55:56Z ### Data Distribution - Tweets with hashtags: 4.82% - Tweets without hashtags: 95.18% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. | Rank | Topic | Total Count | Percentage | |------|-------|-------------|-------------| | 1 | NULL | 1112737 | 80.99% | | 2 | #riyadh | 24082 | 1.75% | | 3 | #マテムり | 15966 | 1.16% | | 4 | #pbbcollab6thduoeviction | 11023 | 0.80% | | 5 | #tiktok | 8638 | 0.63% | | 6 | #箱根駅伝 | 8147 | 0.59% | | 7 | #thameposeriesep9 | 7605 | 0.55% | | 8 | #wtcfinal2025 | 6398 | 0.47% | | 9 | #first_showcase | 6311 | 0.46% | | 10 | #ad | 5465 | 0.40% | ## Update History | Date | New Instances | Total Instances | |------|---------------|-----------------| | 2025-01-25T07:07:31Z | 453526 | 453526 | | 2025-01-25T07:07:59Z | 453526 | 907052 | | 2025-01-25T07:08:28Z | 453526 | 1360578 | | 2025-01-25T07:08:56Z | 446896 | 1807474 | | 2025-01-25T07:09:24Z | 446896 | 2254370 | | 2025-01-25T07:09:52Z | 446896 | 2701266 | | 2025-01-25T07:10:21Z | 446896 | 3148162 | | 2025-01-25T07:10:51Z | 446896 | 3595058 | | 2025-01-25T07:11:21Z | 446896 | 4041954 | | 2025-01-25T07:11:51Z | 446896 | 4488850 | | 2025-02-18T03:41:26Z | 467290 | 4956140 | | 2025-06-24T11:55:56Z | 459732 | 5415872 |

aliazn/mathchatsync-temp

aliazn

2025-05-16T21:10:23Z

0

[ "region:us" ]

[]

2025-05-16T21:10:13Z

0

--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 377089336 num_examples: 144978 download_size: 159608385 dataset_size: 377089336 configs: - config_name: default data_files: - split: train path: data/train-* ---

JunxiongWang/MATH_SFT

JunxiongWang

2025-04-15T18:08:20Z

92

0

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

[]

2025-04-15T18:05:29Z

0

--- dataset_info: - config_name: tokenized_llama31 features: - name: input_ids sequence: int32 - name: labels sequence: int64 - name: attention_mask sequence: int8 splits: - name: train num_examples: 19089792 configs: - config_name: tokenized_llama31 data_files: - split: train path: tokenized_llama31/train-* ---

mlfoundations-dev/instruction_filtering_scale_up_math_base_embedding_filter_mean_8K

mlfoundations-dev

2025-03-07T20:44:31Z

98

0

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

[]

2025-03-07T20:31:09Z

0

--- dataset_info: features: - name: instruction_seed dtype: string - name: source dtype: string - name: embeddings sequence: float64 - name: mean_positive_score dtype: float64 - name: mean_negative_score dtype: float64 - name: difference_score dtype: float64 - name: reasoning dtype: string - name: deepseek_solution dtype: string - name: __original_row_idx dtype: int64 - name: final_reasoning_trace dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 636139670 num_examples: 8000 download_size: 290540569 dataset_size: 636139670 configs: - config_name: default data_files: - split: train path: data/train-* ---

shuohsuan/grasp_data_090_099

shuohsuan

2025-06-16T08:53:26Z

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" ]

[ "robotics" ]

2025-06-16T07:25:13Z

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": 3, "total_frames": 2488, "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": [ 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.top": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.wrist": { "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] ```

mingye94/trial0_fixed_5rules_rm_training_data

mingye94

2025-01-21T02:26:49Z

19

0

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

[]

2025-01-21T02:26:49Z

0

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 2351567 num_examples: 1000 download_size: 1336836 dataset_size: 2351567 configs: - config_name: default data_files: - split: train path: data/train-* ---

perfectName/rse_test

perfectName

2025-06-20T11:25:56Z

0

[ "license:apache-2.0", "region:us" ]

[]

2025-06-20T11:25:15Z

0

--- license: apache-2.0 ---

TOBEAI/KOR_Merged_data

TOBEAI

2024-10-02T07:28:27Z

20

0

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

[]

2024-10-02T00:43:07Z

0

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 1316148258.1879513 num_examples: 1702522 download_size: 739948626 dataset_size: 1316148258.1879513 configs: - config_name: default data_files: - split: train path: data/train-* ---

OpenFinAL/FINGPT_QA_meta3B-train-dataset

OpenFinAL

2025-06-07T20:50:25Z

0

[ "region:us" ]

[]

2025-06-07T20:50:22Z

0

--- dataset_info: features: - name: Question dtype: string - name: Answer dtype: string splits: - name: train num_bytes: 16959815.1 num_examples: 15399 download_size: 10216321 dataset_size: 16959815.1 configs: - config_name: default data_files: - split: train path: data/train-* ---

DataTonic/climate-guard-thinking_data_nocomment_qwen_toxic_agent

DataTonic

2025-02-14T07:33:51Z

20

0

[ "task_categories:text-classification", "task_categories:zero-shot-classification", "language:en", "license:mit", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "climate" ]

[ "text-classification", "zero-shot-classification" ]

2025-02-09T14:54:23Z

0

--- dataset_info: features: - name: quote dtype: string - name: label dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1487214 num_examples: 1548 download_size: 742242 dataset_size: 1487214 configs: - config_name: default data_files: - split: train path: data/train-* license: mit task_categories: - text-classification - zero-shot-classification language: - en tags: - climate --- # Toxic Agent - Phi4 Synthetic Data : Magpie-like Climate Disinformation Dataset ## Dataset Description ### Overview This dataset contains synthetic climate change-related statements, including various forms of climate disinformation and denial. It was created by generating variations and transformations of real climate-related statements, producing a diverse set of synthetic examples across different categories of climate disinformation from the [climate-guard-synthetic_data_qwen_toxic_agent](https://huggingface.co/datasets/Tonic/climate-guard-synthetic_data_qwen_toxic_agent) dataset. Total examples from `qwen2.5:14b-instruct-q4_K_M`: 1,548 entries ### Labels The dataset includes the following categories of climate-related statements: 1. `1_not_happening`: Denial of climate change occurrence 2. `2_not_human`: Denial of human influence on climate 3. `3_not_bad`: Minimization of climate change impacts 4. `4_solutions_harmful_unnecessary`: Opposition to climate solutions 5. `5_science_is_unreliable`: Questioning climate science 6. `6_proponents_biased`: Criticism of climate scientists 7. `7_fossil_fuels_needed`: Pro-fossil fuel advocacy ### Data Fields - `quote`: The synthetic climate-related statement (text) - `label`: The category of climate disinformation (string) ### Data Splits This dataset is provided as a single split containing all examples. ## Intended Uses This dataset is intended for: - Research on climate disinformation detection - Development of content moderation systems - Study of climate denial narratives - Training classification models for climate-related content ## Limitations & Ethical Considerations - This dataset contains synthetic examples of climate disinformation - Should not be used to spread or promote climate denial - Intended for research and educational purposes only - May contain biases from the generation process ## Citation & Attribution If you use this dataset, please cite: ```bibtex @dataset{synthetic_climate_disinfo_2024, author = {[Joseph Pollack]}, title = {Toxic Agent - Qwen Synthetic Data 2}, year = {2025}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/datasets/DataTonic/climate-guard-thinking_data_nocomment_qwen_toxic_agent} } ``` ## Dataset Creation ### Data Generation Process The dataset was created using: 1. Base examples from the QuotaClimat/frugalaichallenge-text-train dataset 2. Generation of variations using "qwen2.5:14b-instruct-q4_K_M" 3. Preservation of original label categories 4. Multiple generation strategies for diversity #### Prompts ```python self.unified_prompt = """follow the following instructions : {quote} {category_instructions}, DO NOT PROVIDE ANY ADDITIONAL COMMENT produce final answer:""" ``` ```python category_instructions = { '1_not_happening': "based on the above, produce a statement that denies the occurrence of global warming and its effects. The statement should argue against evidence of melting ice, extreme weather, or rising sea levels. It may point to cold weather as counter-evidence:", '2_not_human': "based on the above, produce a statement that denies human responsibility in climate change. The focus should be on arguing that greenhouse gases from human activities are not causing climate change. Emphasize natural climate variability:", '3_not_bad': "based on the above, Produce a statement that minimizes or denies negative impacts of climate change. The statement should suggest that climate change impacts will be minimal or even beneficial to society and the environment:", '4_solutions_harmful_unnecessary': "based on the above, produce a statement arguing that climate solutions are harmful or unnecessary. Focus on the potential negative economic or social impacts of climate policies and why they shouldn't be implemented:", '5_science_is_unreliable': "based on the above, Emphasize uncertainties in climate research, suggest the science is unsound, unreliable, or biased. Question methodologies and conclusions:", '6_proponents_biased': "based on the above, Produce a statement attacking climate scientists and activists. Portray them as alarmist, biased, wrong, hypocritical, corrupt, or politically motivated. Question their credibility and intentions:", '7_fossil_fuels_needed': "based on the above, Generate a statement promoting the necessity of fossil fuels. Emphasize their importance for economic growth, prosperity, and maintaining living standards. Argue why they cannot be replaced:" } ``` ### Preprocessing - Removed irrelevant examples - Compose the prompt according to the above - Maintained label consistency ## Dataset Structure ```python DatasetDict({ 'train': Dataset({ features: ['quote', 'label'], num_rows: 1548 }) }) ``` ## Additional Information ### Dataset Curators [Tonic](https://huggingface.co/Tonic) / [Datatonic](https://huggingface.co/DataTonic) ### Licensing Information This dataset is released under the MIT License. ### Feedback & Contributions For questions, feedback, or contributions, please: 1. Open an issue on the dataset repository 2. Contact the dataset maintainers 3. Submit a pull request with improvements ## Example Usage ```python from datasets import load_dataset # Load the dataset dataset = load_dataset("Tonic/climate-guard-thinking_data_nocomment_qwen_toxic_agent") # Access examples for example in dataset['train'].select(range(3)): print(f"Quote: {example['quote']}") print(f"Label: {example['label']}") print("---") ``` ## Version History - v1.0.0 (2024-02-08): Initial release with 1,548 synthetic examples ## Acknowledgements - Based on the QuotaClimat/frugalaichallenge-text-train dataset - Generated using RunPod infrastructure - Developed for climate disinformation research

villekuosmanen/agilex_put_paper_cup_jsh

villekuosmanen

2025-02-13T05:17:47Z

24

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" ]

[ "robotics" ]

2025-02-13T05:17:27Z

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": "v2.0", "robot_type": "arx5_bimanual", "total_episodes": 20, "total_frames": 4643, "total_tasks": 1, "total_videos": 60, "total_chunks": 1, "chunks_size": 1000, "fps": 25, "splits": { "train": "0:20" }, "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": [ 14 ] }, "observation.state": { "dtype": "float32", "shape": [ 14 ] }, "observation.effort": { "dtype": "float32", "shape": [ 14 ] }, "observation.images.cam_high": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 25.0, "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_left_wrist": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 25.0, "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_right_wrist": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 25.0, "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] ```

gcp-acp/flipkart-dataprep

gcp-acp

2024-11-21T00:37:24Z

31

0

[ "license:cc-by-sa-4.0", "region:us" ]

[]

2024-11-21T00:37:22Z

0

--- license: cc-by-sa-4.0 --- - Generated prompt data, [Built with Llama 3.1](https://www.llama.com/llama3_1/license/) - [Data Preparation](https://github.com/GoogleCloudPlatform/accelerated-platforms/tree/main/docs/use-cases/model-fine-tuning-pipeline#data-preparation) - [Raw Data](https://www.kaggle.com/datasets/PromptCloudHQ/flipkart-products/data)

dgambettaphd/D_llm2_gen10_run0_X_doc1000_synt64_tot128_SYNLAST

dgambettaphd

2025-04-28T10:24:41Z

18

0

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

[]

2025-04-28T10:24:38Z

0

--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: dataset dtype: string - name: gen dtype: int64 - name: synt dtype: int64 - name: TPP dtype: float64 - name: MPP dtype: float64 - name: FTP dtype: float64 splits: - name: train num_bytes: 7829037 num_examples: 14000 download_size: 4616053 dataset_size: 7829037 configs: - config_name: default data_files: - split: train path: data/train-* ---

luca0621/multi_turn_soda_seperate_rewards_trajectory_normalized

luca0621

2025-06-10T11:10:00Z

0

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

[]

2025-06-10T11:09:52Z

0

--- dataset_info: features: - name: context dtype: string - name: query dtype: string - name: response dtype: string - name: pref dtype: int64 - name: reward dtype: float64 - name: original_reward dtype: float64 - name: whole_dialogue dtype: string - name: turn_index dtype: int64 - name: dialogue_length dtype: int64 - name: trajectory_return dtype: float64 - name: original_trajectory_return dtype: float64 splits: - name: train num_bytes: 83628998 num_examples: 67108 - name: test num_bytes: 11316680 num_examples: 16778 download_size: 41498459 dataset_size: 94945678 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

rlhn/default-100K

rlhn

2025-03-19T20:09:02Z

18

0

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

[]

2025-03-19T20:08:17Z

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: subset dtype: string splits: - name: train num_bytes: 1778484241 num_examples: 96167 download_size: 1026965735 dataset_size: 1778484241 configs: - config_name: default data_files: - split: train path: data/train-* ---

ZixuanKe/economy_fineweb_phi3.5_unsup_chunk_2

ZixuanKe

2024-10-24T00:17:33Z

18

0

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

[]

2024-10-22T23:51:25Z

0

--- dataset_info: features: - name: text dtype: string - name: topic dtype: string - name: title dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: special_tokens_mask sequence: int8 - name: chunk dtype: int64 - name: fineweb_justification dtype: string - name: fineweb_score dtype: int64 splits: - name: train num_bytes: 94124434 num_examples: 1445 download_size: 36108144 dataset_size: 94124434 configs: - config_name: default data_files: - split: train path: data/train-* ---

alea-institute/kl3m-filter-data-dotgov-www.aoc.gov

alea-institute

2025-02-03T22:18:44Z

15

0

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

[]

2025-02-03T22:18:42Z

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: 21131384 num_examples: 1936 download_size: 5115965 dataset_size: 21131384 configs: - config_name: default data_files: - split: train path: data/train-* ---

Pamela153/swe-gym-test-getmoto

Pamela153

2025-09-24T22:53:40Z

49

0

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

[]

2025-09-24T22:30:12Z

0

--- dataset_info: features: - name: instance_id dtype: string - name: hints_text dtype: string - name: patch dtype: string - name: test_patch dtype: string - name: created_at dtype: string - name: problem_statement dtype: string - name: repo dtype: string - name: base_commit dtype: string - name: version dtype: string - name: PASS_TO_PASS sequence: string - name: FAIL_TO_PASS sequence: string - name: eval_script dtype: string splits: - name: train num_bytes: 805074 num_examples: 25 download_size: 260205 dataset_size: 805074 configs: - config_name: default data_files: - split: train path: data/train-* ---

mengdili/Marco-7B-Pruned-K-16-k-8-epoch-1-log-noclipping-nonorm_math_test_8192_normal_K-1

mengdili

2025-04-13T13:29:34Z

18

0

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

[]

2025-04-13T13:29:30Z

0

--- dataset_info: features: - name: problem dtype: string - name: ground_truth_solution dtype: string - name: ground_truth_answer dtype: string - name: pre_generated_steps sequence: string - name: pre_generated_answer dtype: string - name: pre_generated_verifier_score dtype: float64 - name: prompt list: - name: content dtype: string - name: role dtype: string - name: solution dtype: string - name: answer dtype: string splits: - name: train num_bytes: 1901530 num_examples: 458 download_size: 902208 dataset_size: 1901530 configs: - config_name: default data_files: - split: train path: data/train-* ---

jxie/droid

jxie

2024-11-15T23:14:19Z

17

0

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

[]

2024-11-15T19:23:57Z

0

--- dataset_info: features: - name: video dtype: video splits: - name: train num_bytes: 16142033.0 num_examples: 276699 download_size: 416277616456 dataset_size: 16142033.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

automated-analytics/gretel-pii-coarse-grained-chatml

automated-analytics

2025-06-09T03:08:26Z

0

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

[]

2025-06-09T03:08:09Z

0

--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 188028367 num_examples: 50000 - name: validation num_bytes: 18793571 num_examples: 5000 - name: test num_bytes: 18807450 num_examples: 5000 download_size: 14633055 dataset_size: 225629388 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

zjhhhh/baseline_0

zjhhhh

2025-09-28T02:59:01Z

30

0

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

[]

2025-09-28T02:58:57Z

0

--- dataset_info: features: - name: prompt dtype: string - name: requirements dtype: string - name: selection_response_1 dtype: string - name: selection_response_2 dtype: string - name: selection_response_3 dtype: string - name: base_response_1 dtype: string - name: base_response_2 dtype: string - name: current_response_1 dtype: string - name: current_response_2 dtype: string - name: selection_1_mean dtype: float64 - name: selection_1_majority dtype: float64 - name: base_1_mean dtype: float64 - name: base_1_majority dtype: float64 - name: current_1_mean dtype: float64 - name: current_1_majority dtype: float64 - name: selection_2_mean dtype: float64 - name: selection_2_majority dtype: float64 - name: base_2_mean dtype: float64 - name: base_2_majority dtype: float64 - name: current_2_mean dtype: float64 - name: current_2_majority dtype: float64 - name: selection_3_mean dtype: float64 - name: selection_3_majority dtype: float64 splits: - name: train num_bytes: 46001070 num_examples: 2000 download_size: 23456998 dataset_size: 46001070 configs: - config_name: default data_files: - split: train path: data/train-* ---

CompassioninMachineLearning/2krows_20percentours_80percentalpaca

CompassioninMachineLearning

2025-05-14T18:55:25Z

0

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

[]

2025-05-14T18:55:23Z

0

--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 1059189.4611528823 num_examples: 1795 - name: test num_bytes: 118015.5388471178 num_examples: 200 download_size: 706274 dataset_size: 1177205.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

burman-ai/wikitext2-v1

burman-ai

2025-04-21T07:43:20Z

20

0

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

[]

2025-04-21T07:43:15Z

0

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 11061717 num_examples: 36718 download_size: 6342468 dataset_size: 11061717 configs: - config_name: default data_files: - split: train path: data/train-* ---

Anonyme162325/ultrachat_200k-Mistral-7B-Instruct-v0.3-with-thanks

Anonyme162325

2025-09-23T14:49:55Z

54

0

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

[]

2025-09-23T14:49:51Z

0

--- dataset_info: features: - name: conversation_with_thanks dtype: string - name: conversation_without_thanks dtype: string splits: - name: train num_bytes: 115156614 num_examples: 10000 download_size: 58010659 dataset_size: 115156614 configs: - config_name: default data_files: - split: train path: data/train-* ---