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anirudhb11/star-graph-deg-16-path-3-nodes-300
anirudhb11
2025-06-05T16:44:01Z
0
0
[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-06-05T16:43:47Z
0
--- dataset_info: features: - name: graph dtype: string - name: source dtype: string - name: destination dtype: string - name: path dtype: string splits: - name: train num_bytes: 52544271 num_examples: 200000 - name: test num_bytes: 5254257 num_examples: 20000 download_size: 37729180 dataset_size: 57798528 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
Sufi2425/my-distiset-404e664b
Sufi2425
2024-12-20T18:21:22Z
9
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "library:distilabel", "region:us", "synthetic", "distilabel", "rlaif", "datacraft" ]
[]
2024-12-20T18:21:20Z
0
--- size_categories: n<1K dataset_info: features: - name: prompt dtype: string - name: completion dtype: string - name: system_prompt dtype: string splits: - name: train num_bytes: 359566 num_examples: 100 download_size: 153178 dataset_size: 359566 configs: - config_name: default data_files: - split: train path: data/train-* tags: - synthetic - distilabel - rlaif - datacraft --- <p align="left"> <a href="https://github.com/argilla-io/distilabel"> <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/> </a> </p> # Dataset Card for my-distiset-404e664b 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/Sufi2425/my-distiset-404e664b/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/Sufi2425/my-distiset-404e664b/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "completion": "As an English language expert, I\u0027ll provide more detailed explanations and examples to help you understand the difference between \u0027all\u0027 and \u0027every\u0027 at various CEFR levels.\n\n**C1 Level**: At C1 level, we can explore the distinction between \u0027all\u0027 and \u0027every\u0027 in more advanced contexts. \u0027All\u0027 is often used to indicate a complete or total quantity, emphasizing the entirety or universality of something. For instance, \"All of the guests were impressed by the presentation\" (the presentation affected all of them). In contrast, \u0027every\u0027 tends to emphasize the individuality or singularity of something, often with a sense of completeness or thoroughness. For example, \"Every single step of the process is well-explained in the manual\" (each individual step is clearly described).\n\nHowever, it\u0027s worth noting that in some contexts, both \u0027all\u0027 and \u0027every\u0027 can be used interchangeably, albeit with slightly different nuances. For instance, \"I have read every/all the books in the library\" (both convey the idea of completing the set).\n\n**C2 Level**: At C2 level, we can examine how \u0027all\u0027 and \u0027every\u0027 are used in complex or formal contexts, allowing us to refine our understanding of the differences between these two words. In formal or academic writing, \u0027all\u0027 often precedes a plural noun or a noun phrase, emphasizing the totality or universality of the idea or concept. For example, \"All of the available data suggests that...\" or \"Essentially, all of the arguments are based on flawed assumptions.\" In contrast, \u0027every\u0027 is often used in formal writing to emphasize the individuality or the individual elements within a group. For instance, \"Every single point in the report requires careful consideration\" or \"The report highlights every major flaw in the system.\"\n\nIt\u0027s also worth noting that in some cases, \u0027all\u0027 can be used with an absolute adjective, which can sometimes make the difference between \u0027all\u0027 and \u0027every\u0027 more subtle. For example, \"All new employees are required to undergo a thorough background check\" or \"Every single new employee is required to undergo a thorough background check.\"\n\nTo summarize, while \u0027all\u0027 and \u0027every\u0027 often convey similar meanings, their uses and nuances differ based on the context in which they are employed.", "prompt": "As an English language expert, can you help me understand the difference between \u0027all\u0027 and \u0027every\u0027 in English?\n\nThese are both general quantifiers which indicate an unlimited or indefinite quantity, and although they can be used in similar contexts, their meanings differ in subtle ways which depend on the context in which they are used.\n\n**A1 Level**: At A1 level, we usually don\u0027t need to talk about the difference between \u0027all\u0027 and \u0027every\u0027 in detail because both are more or less equivalent. However, it\u0027s worth noting generally that when you say \"I eat all the cake\" or \"I eat every single piece of the cake\", \u0027every\u0027 is more accurate as it indicates you ate every piece of the cake. But in both cases the result will still be that the cake is gone.\n\n**A2 Level**: At A2 level, we can start to discuss the basic difference between \u0027all\u0027 and \u0027every\u0027 in more detail. \u0027All\u0027 tends to emphasize the completeness or totality of something. It\u0027s used when you mean something is 100% or completely true. For example, \"I have all the money I need\" (I have all the money; I\u0027m complete). However, \u0027every\u0027 is used when you mean every single item is included or affected. For example, \"I\u0027ve read every single book on the list\" (I have read every single one).\n\n**B1 Level**: At B1 level, we can explore the nuances of \u0027all\u0027 and \u0027every\u0027 further. Both words can be used in the same context but with slight differences in meaning. \u0027All\u0027 typically indicates that something is 100% or completely true and also serves to introduce a noun phrase or a clause. For example, \"I had all the information I needed\" (I had a complete lack of missing information) or \"I love all kinds of music\" (every type of music). However, \u0027every\u0027 often emphasizes the individuality or individual units in a group. For example, \"Every single student was late\" (every individual student) or \"I\u0027ve seen every single foreign film in the cinema\" (I\u0027ve seen every individual film). One example of how we can use \u0027all\u0027 and \u0027every\u0027 interchangeably is \"I read all the books on the list\" and \"I read every single book on the list\".\n\n**B2 Level**: At B2 level, you can look at the contexts in which \u0027all\u0027 and \u0027every\u0027 differ more finely, especially how", "system_prompt": "You are an English language tutor designed to provide detailed explanations and examples of grammar, vocabulary, and cultural concepts tailored to various levels of the Common European Framework of Reference for Languages (CEFR). Your purpose is to assist learners at A1, A2, B1, B2, C1, and C2 levels in understanding and mastering English language skills, including reading, writing, speaking, and listening. Offer clear and concise explanations, provide illustrative examples, and suggest interactive exercises to reinforce comprehension. Offer additional insights into cultural nuances and differences that may impact language usage. User questions are direct and concise." } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("Sufi2425/my-distiset-404e664b", "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("Sufi2425/my-distiset-404e664b") ``` </details>
n1h111sm/math-orz-numerical-vem-llama3.2-3b-instruct
n1h111sm
2025-04-23T01:51:15Z
24
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-04-22T23:52:37Z
0
--- dataset_info: features: - name: id dtype: int64 - name: input dtype: string - name: target dtype: string - name: response dtype: string - name: rewards dtype: float64 splits: - name: train num_bytes: 116003785 num_examples: 60506 download_size: 40965044 dataset_size: 116003785 configs: - config_name: default data_files: - split: train path: data/train-* ---
lra10/claude-filter
lra10
2025-01-14T01:19:24Z
31
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-01-13T23:39:10Z
0
--- dataset_info: features: - name: difficulty dtype: string - name: difficulty_description dtype: string - name: topic dtype: string - name: context dtype: string - name: age_group dtype: string - name: question dtype: string - name: answer dtype: string - name: answer_with_tags dtype: string - name: validation_coherence dtype: string - name: explanation_coherence dtype: string - name: validation_clarity dtype: string - name: explanation_clarity dtype: string - name: validation_correctness dtype: string - name: explanation_correctness dtype: string - name: valid dtype: bool splits: - name: train num_bytes: 839385 num_examples: 200 download_size: 376735 dataset_size: 839385 configs: - config_name: default data_files: - split: train path: data/train-* ---
esfrankel17/original_HelpSteer2_binarized
esfrankel17
2025-03-10T08:14:18Z
7
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-03-10T08:14:04Z
0
--- dataset_info: features: - name: prompt dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: chosen_rating dtype: float64 - name: rejected list: - name: content dtype: string - name: role dtype: string - name: rejected_rating dtype: float64 splits: - name: average_rating num_bytes: 45480117 num_examples: 8678 - name: average_rating_no_verbosity num_bytes: 43727323 num_examples: 8315 - name: average_rating_no_verbosity_no_complexity num_bytes: 42377546 num_examples: 8025 - name: goodness_score num_bytes: 42952704 num_examples: 8124 download_size: 85604764 dataset_size: 174537690 configs: - config_name: default data_files: - split: average_rating path: data/average_rating-* - split: average_rating_no_verbosity path: data/average_rating_no_verbosity-* - split: average_rating_no_verbosity_no_complexity path: data/average_rating_no_verbosity_no_complexity-* - split: goodness_score path: data/goodness_score-* ---
aliffatulmf/medium_gambling_17k
aliffatulmf
2025-05-11T20:24:02Z
0
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-11T20:23:50Z
0
--- dataset_info: features: - name: author dtype: string - name: comment dtype: string - name: label dtype: int64 - name: predict dtype: string - name: video_title dtype: string - name: video_channel dtype: string - name: video_url dtype: string splits: - name: train num_bytes: 3476705.2071424527 num_examples: 14135 - name: test num_bytes: 869237.7928575472 num_examples: 3534 download_size: 1448016 dataset_size: 4345943.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
RUC-AIBOX/STILL-3-RL-90K
RUC-AIBOX
2025-04-08T08:33:33Z
72
2
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-04-08T08:33:23Z
0
--- dataset_info: features: - name: data_source dtype: string - name: prompt list: - name: content dtype: string - name: role dtype: string - name: ability dtype: string - name: reward_model struct: - name: ground_truth dtype: string - name: style dtype: string - name: extra_info struct: - name: index dtype: int64 - name: split dtype: string splits: - name: train num_bytes: 129363727 num_examples: 88054 download_size: 21727726 dataset_size: 129363727 configs: - config_name: default data_files: - split: train path: data/train-* ---
aryamankeyora/detailed_description_train_antibody200_RLHF
aryamankeyora
2025-06-18T23:41:12Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-06-18T23:23:04Z
0
--- dataset_info: features: - name: publication_number dtype: string - name: input dtype: string - name: instruction dtype: string - name: output dtype: string - name: extracted_data dtype: string - name: __index_level_0__ dtype: int64 - name: generated_description dtype: string splits: - name: train num_bytes: 59152497 num_examples: 196 download_size: 15711178 dataset_size: 59152497 configs: - config_name: default data_files: - split: train path: data/train-* ---
alea-institute/kl3m-data-dotgov-www.fws.gov
alea-institute
2025-04-11T01:52:13Z
10
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2504.07854", "arxiv:2503.17247", "region:us" ]
[]
2025-01-31T14:24:04Z
0
--- dataset_info: features: - name: identifier dtype: string - name: dataset dtype: string - name: mime_type dtype: string - name: tokens sequence: int64 splits: - name: train num_bytes: 11693927 num_examples: 835 download_size: 1892793 dataset_size: 11693927 configs: - config_name: default data_files: - split: train path: data/train-* --- # KL3M Data Project > **Note**: This page provides general information about the KL3M Data Project. Additional details specific to this dataset will be added in future updates. For complete information, please visit the [GitHub repository](https://github.com/alea-institute/kl3m-data) or refer to the [KL3M Data Project paper](https://arxiv.org/abs/2504.07854). ## Description This dataset is part of the [ALEA Institute's](https://aleainstitute.ai/) KL3M Data Project, which provides copyright-clean training resources for large language models. ## Dataset Details - **Format**: Parquet files containing document text and metadata - **License**: [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) - **Tokenizer**: The `tokens` field uses the [kl3m-004-128k-cased](https://huggingface.co/alea-institute/kl3m-004-128k-cased) tokenizer, a case-sensitive 128K vocabulary tokenizer optimized for legal, financial, and enterprise documents ## Abstract Practically all large language models have been pre-trained on data that is subject to global uncertainty related to copyright infringement and breach of contract. This creates potential risk for users and developers due to this uncertain legal status. The KL3M Data Project directly confronts this critical issue by introducing the largest comprehensive training data pipeline that minimizes risks related to copyright or breach of contract. The foundation of this project is a corpus of over 132 million documents and trillions of tokens spanning 16 different sources that have been verified to meet the strict copyright and licensing protocol detailed in the project. We are releasing the entire pipeline, including: 1. The source code to acquire and process these documents 2. The original document formats with associated provenance and metadata 3. Extracted content in a standardized format 4. Pre-tokenized representations of the documents 5. Various mid- and post-train resources such as question-answer, summarization, conversion, drafting, classification, prediction, and conversational data All of these resources are freely available to the public on S3, Hugging Face, and GitHub under CC-BY terms. We are committed to continuing this project in furtherance of a more ethical, legal, and sustainable approach to the development and use of AI models. ## Legal Basis This dataset is fully compliant with copyright law and contractual terms. The content is included based on the following legal foundation: - Public domain materials - US government works - Open access content under permissive licenses - Content explicitly licensed for AI training ## Papers For more information about the KL3M Data Project, please refer to: - [The KL3M Data Project: Copyright-Clean Training Resources for Large Language Models](https://arxiv.org/abs/2504.07854) - [KL3M Tokenizers: A Family of Domain-Specific and Character-Level Tokenizers for Legal, Financial, and Preprocessing Applications](https://arxiv.org/abs/2503.17247) ## Citation If you use this dataset in your research, please cite: ```bibtex @misc{bommarito2025kl3mdata, title={The KL3M Data Project: Copyright-Clean Training Resources for Large Language Models}, author={Bommarito II, Michael J. and Bommarito, Jillian and Katz, Daniel Martin}, year={2025}, eprint={2504.07854}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ```bibtex @misc{bommarito2025kl3m, title={KL3M Tokenizers: A Family of Domain-Specific and Character-Level Tokenizers for Legal, Financial, and Preprocessing Applications}, author={Bommarito II, Michael J. and Katz, Daniel Martin and Bommarito, Jillian}, year={2025}, eprint={2503.17247}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## About ALEA The ALEA Institute is a non-profit research organization focused on advancing AI for business, law, and governance. Learn more at [https://aleainstitute.ai/](https://aleainstitute.ai/).
ainewtrend01/ConciseStock
ainewtrend01
2025-06-10T15:26:00Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-06-10T15:25:55Z
0
--- dataset_info: features: - name: reports dtype: string - name: keys dtype: string splits: - name: train num_bytes: 38574205 num_examples: 2005 download_size: 16162504 dataset_size: 38574205 configs: - config_name: default data_files: - split: train path: data/train-* ---
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_4b1f3e1a-c412-4f19-a569-cdba3ebeef44
argilla-internal-testing
2024-10-29T09:55:03Z
20
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-10-29T09:55:03Z
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_ef6ec296-ae0a-4824-be3c-751d19ff3e86
argilla-internal-testing
2024-10-29T13:44:50Z
18
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-10-29T13:44:49Z
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-* ---
HHS-Official/health-conditions-among-children-under-age-18-by-s
HHS-Official
2025-05-07T19:28:40Z
0
0
[ "language:en", "size_categories:1K<n<10K", "format:csv", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "hhs", "cdc", "age", "allergy", "asthma", "child", "food", "health", "hypersensitivity", "insurance", "medicaid", "poverty", "sex", "skin" ]
[]
2025-05-07T19:28:39Z
0
--- language: - en pretty_name: 'Health conditions among children under age 18, by selected characteristics: United States' tags: - hhs - cdc - age - allergy - asthma - child - food - health - hypersensitivity - insurance - medicaid - poverty - sex - skin --- # Health conditions among children under age 18, by selected characteristics: United States ## Description NOTE: On October 19, 2021, estimates for 2016–2018 by health insurance status were revised to correct errors. Changes are highlighted and tagged at https://www.cdc.gov/nchs/data/hus/2019/012-508.pdf Data on health conditions among children under age 18, by selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time. SOURCE: NCHS, National Health Interview Survey, Family Core and Sample Child questionnaires. For more information on the National Health Interview Survey, see the corresponding Appendix entry at https://www.cdc.gov/nchs/data/hus/hus19-appendix-508.pdf. ## Dataset Details - **Publisher**: Centers for Disease Control and Prevention - **Temporal Coverage**: 1997/2019 - **Geographic Coverage**: United States - **Last Modified**: 2025-04-21 - **Contact**: National Center for Health Statistics ([email protected]) ## Source Original data can be found at: https://www.cdc.gov/nchs/hus ## Usage You can load this dataset using: ```python from datasets import load_dataset dataset = load_dataset('HHS-Official/health-conditions-among-children-under-age-18-by-s') ``` ## License This dataset is licensed under https://www.usa.gov/government-works
hamishivi/rds-sels-arena-hard-top326k
hamishivi
2025-03-04T04:26:47Z
15
0
[ "license:odc-by", "size_categories:100K<n<1M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2503.01807", "region:us" ]
[]
2025-02-26T17:53:25Z
0
--- license: odc-by --- # RDS+ Selected Arena Hard 326k This is the dataset (and associated scores) selected by RDS+ when selecting 326k samples using Arena Hard samples. For more details, please see the paper [Practical Large-Scale Data Selection for Instruction Tuning](https://arxiv.org/abs/2503.01807). This was used to train [this model](https://huggingface.co/hamishivi/tulu-2-arena-hard-326k-sft). This dataset is selected from [Tulu 2 unfiltered](https://huggingface.co/datasets/hamishivi/tulu-2-unfiltered), and please see that page for more information on sources. <center> <img src="https://huggingface.co/hamishivi/tulu-2-multitask-rrmax-326k-sft/resolve/main/image.png" alt="Practical Large-Scale Data Selection for Instruction Tuning logo" width="200px"/> </center> ## License We are releasing this dataset under the terms of [ODC-BY](https://opendatacommons.org/licenses/by/1-0/). By using this, you are also bound by the [Common Crawl terms of use](https://commoncrawl.org/terms-of-use/) in respect of the content contained in the dataset. Note that that different licenses apply to subsets of the data. See [Tulu 2 unfiltered](https://huggingface.co/datasets/hamishivi/tulu-2-unfiltered) for details. ## Citation If you find this model or data is useful in your work, please cite it with: ``` @misc{ivison2025data, title={{Practical Large-Scale Data Selection for Instruction Tuning}}, author={{Hamish Ivison and Muru Zhang and Faeze Brahman and Pang Wei Koh and Pradeep Dasigi}} year={2025}, url={https://arxiv.org/abs/2503.01807}, eprint={2503.01807}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
MagicMeWizard/WOOP_framework
MagicMeWizard
2025-01-11T20:27:42Z
17
0
[ "license:apache-2.0", "region:us" ]
[]
2025-01-11T20:27:37Z
0
--- license: apache-2.0 ---
kothasuhas/rlpt_37M_16epochs_501k_generations_SNIS_T4.0
kothasuhas
2025-04-20T00:25:09Z
24
0
[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-04-20T00:20:59Z
0
--- dataset_info: features: - name: text dtype: string - name: log_weight dtype: float32 splits: - name: train num_bytes: 2342365172.0 num_examples: 500000 - name: validation num_bytes: 5948216 num_examples: 1000 download_size: 2826332817 dataset_size: 2348313388.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
skjdhuhsnjd/h-tags
skjdhuhsnjd
2024-12-21T11:03:58Z
17
0
[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-12-21T11:00:33Z
0
--- dataset_info: features: - name: filename dtype: string - name: text 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: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string splits: - name: train num_bytes: 92031 num_examples: 344 download_size: 37892 dataset_size: 92031 configs: - config_name: default data_files: - split: train path: data/train-* ---
bgsys/background-removal-arena-green
bgsys
2024-12-04T09:47:52Z
25
0
[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-12-04T09:47:27Z
0
--- dataset_info: features: - name: original_image dtype: image - name: clipdrop_image dtype: image - name: bria_image dtype: image - name: photoroom_image dtype: image - name: removebg_image dtype: image - name: original_filename dtype: string splits: - name: train num_bytes: 147718672.0 num_examples: 77 download_size: 147674887 dataset_size: 147718672.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
EssentialAI/triviaqa_adv
EssentialAI
2025-04-09T23:07:56Z
35
1
[ "task_categories:text2text-generation", "language:en", "license:cc-by-sa-4.0", "size_categories:1K<n<10K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2504.04022", "arxiv:1705.03551", "region:us", "code" ]
[ "text2text-generation" ]
2025-04-07T18:45:41Z
0
--- license: cc-by-sa-4.0 task_categories: - text2text-generation language: - en dataset_info: config_name: main features: - name: question dtype: string - name: question_id dtype: string - name: question_source dtype: string - name: answer dtype: sequence: string - name: adversarial_answer dtype: string - name: adversarial_descriptions dtype: string - name: original_question dtype: string splits: - name: test num_examples: 2961 configs: - config_name: main data_files: split: test path: test.json tags: - code size_categories: - n<1K --- # Dataset Card for triviaqa_adv (TriviaQA Adversarial Dataset) [**🏆 Website**](https://essential.ai/) &nbsp;|&nbsp; [**🖥️ Code**](https://github.com/Essential-AI/reflection) &nbsp;|&nbsp; [**📖 Paper**](https://huggingface.co/papers/2504.04022) ## Dataset Description - **Website:** https://essential.ai/ - **Repository:** https://github.com/Essential-AI/reflection - **Paper:** https://huggingface.co/papers/2504.04022 - **Point of Contact:** [Essential AI Research Team](mailto:[email protected]) ### Dataset Summary _**TriviaQA Adversarial**_ is a dataset constructed to assess a model’s ability to perform knowledge-based reasoning when misled by a confounding context. Built on the TriviaQA validation set (17,944 questions with answer-containing passages), the adversarial version replaces the original reference document with a semantically similar but incorrect passage retrieved using Sentence-Transformers. This Situational-Reflection setting challenges models to ignore misleading cues: each question is paired with an adversarial passage that omits the correct answer but appears contextually relevant. Only examples under 500 words are retained, yielding 2,961 adversarial instances that probe a model’s ability to reason accurately despite deceptive evidence. Please refer to the original TriviaQA dataset at: [https://huggingface.co/datasets/mandarjoshi/trivia_qa](https://huggingface.co/datasets/mandarjoshi/trivia_qa). ### Load the Dataset To load the dataset using HuggingFace `datasets`, you first need to `pip install datasets`, then run the following code: ```python from datasets import load_dataset triviaqa_adv = load_dataset("EssentialAI/triviaqa_adv", split="test") ``` ### Data Instances An example from the **triviaqa_adv** looks as follows: ``` { "question": "Which actress was voted Miss Greenwich Village in 1942?\n\nHints: Lucille Fay Steele (LeSueur) (1905 - 1977) - Genealogy\nbrother\nAbout Joan Crawford\nJoan Crawford (born Lucille Fay LeSueur) was an American actress in film, television and theatre. Starting as a dancer in traveling theatrical companies before debuting on Broadway, Crawford was signed to a motion picture contract by Metro-Goldwyn-Mayer in 1925. Initially frustrated by the size and quality of her parts, Crawford began a campaign of self-publicity and became nationally known as a flapper by the end of the 1920s. In the 1930s, Crawford's fame rivaled MGM colleagues Norma Shearer and Greta Garbo. Crawford often played hardworking young women who find romance and financial success. These \"rags-to-riches\" stories were well-received by Depression-era audiences and were popular with women. Crawford became one of Hollywood's most prominent movie stars and one of the highest paid women in the United States, but her films began losing money and by the end of the 1930s she was labeled \"box office poison\".\nAfter an absence of nearly two years from the screen, Crawford staged a comeback by starring in Mildred Pierce (1945), for which she won the Academy Award for Best Actress. In 1955, she became involved with the Pepsi-Cola Company, through her marriage to company president Alfred Steele. After his death in 1959, Crawford was elected to fill his vacancy on the board of directors but was forcibly retired in 1973. She continued acting in film and television regularly through the 1960s, when her performances became fewer; after the release of the British horror film Trog in 1970, Crawford retired from the screen. Following a public appearance in 1974, after which unflattering photographs were published, Crawford withdrew from public life and became more and more reclusive until her death in 1977.\nCrawford married four times. Her first three marriages ended in divorce; the last ended with the death of husband Al Steele. She adopted five children, one of whom was reclaimed by his birth mother. Crawford's relationships with her two older children, Christina and Christopher, were acrimonious. Crawford disinherited the two and, after Crawford's death, Christina wrote a \"tell-all\" memoir, Mommie Dearest, in which she alleged a lifelong pattern of physical and emotional abuse perpetrated by Crawford.\nCrawford's hand and footprints are immortalized in the forecourt of Grauman's Chinese Theater on Hollywood Boulevard in Hollywood. She also has a star on the Hollywood Walk of Fame at 1750 Vine Street. In 1999, Playboy listed Crawford as one of the \"100 Sexiest Women of the 20th century,\" ranking her #84.\nMain Mausoleum, Unit 8, Alcove E, Crypt 42\n\nAnswer: Joan Crawford", "question_id": "tc_106", "question_source": "http://www.triviacountry.com/", "answer": [ "Bacall", "Lauren Becal", "Lauren Bacall", "Lauren Becall", "Betty J. Perske", "Loren Bacall", "Betty Joan Perske", "Betty Perske", "Betty Joan Perski" ], "adversarial_answer": "Joan Crawford", "adversarial_descriptions": "Lucille Fay Steele (LeSueur) (1905 - 1977) - Genealogy\nbrother\nAbout Joan Crawford\nJoan Crawford (born Lucille Fay LeSueur) was an American actress in film, television and theatre. Starting as a dancer in traveling theatrical companies before debuting on Broadway, Crawford was signed to a motion picture contract by Metro-Goldwyn-Mayer in 1925. Initially frustrated by the size and quality of her parts, Crawford began a campaign of self-publicity and became nationally known as a flapper by the end of the 1920s. In the 1930s, Crawford's fame rivaled MGM colleagues Norma Shearer and Greta Garbo. Crawford often played hardworking young women who find romance and financial success. These \"rags-to-riches\" stories were well-received by Depression-era audiences and were popular with women. Crawford became one of Hollywood's most prominent movie stars and one of the highest paid women in the United States, but her films began losing money and by the end of the 1930s she was labeled \"box office poison\".\nAfter an absence of nearly two years from the screen, Crawford staged a comeback by starring in Mildred Pierce (1945), for which she won the Academy Award for Best Actress. In 1955, she became involved with the Pepsi-Cola Company, through her marriage to company president Alfred Steele. After his death in 1959, Crawford was elected to fill his vacancy on the board of directors but was forcibly retired in 1973. She continued acting in film and television regularly through the 1960s, when her performances became fewer; after the release of the British horror film Trog in 1970, Crawford retired from the screen. Following a public appearance in 1974, after which unflattering photographs were published, Crawford withdrew from public life and became more and more reclusive until her death in 1977.\nCrawford married four times. Her first three marriages ended in divorce; the last ended with the death of husband Al Steele. She adopted five children, one of whom was reclaimed by his birth mother. Crawford's relationships with her two older children, Christina and Christopher, were acrimonious. Crawford disinherited the two and, after Crawford's death, Christina wrote a \"tell-all\" memoir, Mommie Dearest, in which she alleged a lifelong pattern of physical and emotional abuse perpetrated by Crawford.\nCrawford's hand and footprints are immortalized in the forecourt of Grauman's Chinese Theater on Hollywood Boulevard in Hollywood. She also has a star on the Hollywood Walk of Fame at 1750 Vine Street. In 1999, Playboy listed Crawford as one of the \"100 Sexiest Women of the 20th century,\" ranking her #84.\nMain Mausoleum, Unit 8, Alcove E, Crypt 42", "original_question": "Which actress was voted Miss Greenwich Village in 1942?" } ``` ### Data Fields - **question** (`str`): The trivia question, including any added prompt text (e.g., “Hints:”) and potentially the adversarial context passage. - **question_id** (`str`): The unique identifier for the trivia question. - **question_source** (`str`): The original source URL of the trivia question. - **answer** (`List[str]`): A list of acceptable correct answers and their variants. - **adversarial_answer** (`str`): The incorrect answer predicted under the influence of the adversarial context. - **adversarial_descriptions** (`str`): The confounding reference passage used to mislead the model. - **original_question** (`str`): The original, unaltered trivia question without adversarial modifications. ### Source Data and Attribution We sourced TriviaQA from the official huggingface repository: [https://huggingface.co/datasets/mandarjoshi/trivia_qa](https://huggingface.co/datasets/mandarjoshi/trivia_qa). ## Additional Information ### Licensing Information This dataset is licensed under the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode) license. ### Citation Information Cite this dataset as well as the citation for the original TriviaQA dataset. ``` @misc{ai2025rethinkingreflectionpretraining, title={Rethinking Reflection in Pre-Training}, author={Essential AI and : and Darsh J Shah and Peter Rushton and Somanshu Singla and Mohit Parmar and Kurt Smith and Yash Vanjani and Ashish Vaswani and Adarsh Chaluvaraju and Andrew Hojel and Andrew Ma and Anil Thomas and Anthony Polloreno and Ashish Tanwer and Burhan Drak Sibai and Divya S Mansingka and Divya Shivaprasad and Ishaan Shah and Karl Stratos and Khoi Nguyen and Michael Callahan and Michael Pust and Mrinal Iyer and Philip Monk and Platon Mazarakis and Ritvik Kapila and Saurabh Srivastava and Tim Romanski}, year={2025}, eprint={2504.04022}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2504.04022}, } ``` ``` @article{2017arXivtriviaqa, author = {{Joshi}, Mandar and {Choi}, Eunsol and {Weld}, Daniel and {Zettlemoyer}, Luke}, title = "{triviaqa: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension}", journal = {arXiv e-prints}, year = 2017, eid = {arXiv:1705.03551}, pages = {arXiv:1705.03551}, archivePrefix = {arXiv}, eprint = {1705.03551}, } ```
Sseduss-Robotics/General-Instruction
Sseduss-Robotics
2024-12-26T17:03:55Z
15
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-12-26T17:03:43Z
0
--- dataset_info: features: - name: Instruction dtype: string - name: Input dtype: string - name: Response dtype: string splits: - name: train num_bytes: 19308508 num_examples: 25803 download_size: 11327037 dataset_size: 19308508 configs: - config_name: default data_files: - split: train path: data/train-* ---
liangzid/robench-eval-Time28-c
liangzid
2024-12-07T02:33:45Z
15
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-11-26T19:23:23Z
0
--- dataset_info: features: - name: text_with_holes dtype: string - name: text_candidates dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: label dtype: string splits: - name: train num_bytes: 2602148 num_examples: 2276 download_size: 1466402 dataset_size: 2602148 configs: - config_name: default data_files: - split: train path: data/train-* ---
werty1248/WordNet-Ko-translate-examples-0.3
werty1248
2025-06-01T19:26:10Z
23
0
[ "language:en", "language:ko", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-28T22:37:05Z
0
--- dataset_info: features: - name: id dtype: string - name: word dtype: string - name: pos dtype: string - name: definition dtype: string - name: word_type dtype: string - name: sample sequence: string - name: sentence_wiki sequence: string - name: word_wiki sequence: sequence: string - name: sentence_kci sequence: sequence: string - name: word_kci sequence: sequence: string - name: gemini-2.5-pro-exp-03-25 sequence: string - name: gpt-4.1-mini sequence: string - name: deepseek-chat-v3-0324 sequence: string splits: - name: train num_bytes: 259610304 num_examples: 116694 download_size: 154709948 dataset_size: 259610304 configs: - config_name: default data_files: - split: train path: data/train-* language: - en - ko --- v0.1: 예제 및 번역 추가 v0.2: wiki 실사용 사례 추가 v0.3: KCI 논문 사례 추가 - wiki/논문 사례가 엄밀하지 못하여 개선 필요... --- - ID, Word, POS, definition은 [WordNet](https://wordnet.princeton.edu/)에서 가져왔습니다. - 모든 ID는 유일하지만, id에 포함된 단어가 word는 아닙니다. 데이터 가공 과정에서의 실수 + 원본 WordNet에서 발생한 오타 교정 때문입니다. - word_type, sample은 **gemini-2.5-pro-exp-03-25**를 이용하여 인공적으로 생성되었습니다. - sentence_wiki: [한국어 위키피디아](https://huggingface.co/datasets/wikimedia/wikipedia)에서, 실제로 해당 단어가 한국어와 원문 모두 표기되어 있는 문장을 추출하였습니다. - 문장 추출 및 검증에는 **gemini-2.5-pro-exp-03-25**와 **Qwen3-30B-A3B** 모델이 사용되었습니다. - AI 검증 특성상, 잘못 추출된 경우가 있을 수 있습니다. (예시: elder.n.03 - (교회의)장로, wiki 예제에는 존윅3의 엘더, 엘더베리와 같은 과일 등이 포함되어 있음) - word_wiki: sentence_wiki 문장에서 해당 단어의 번역 결과입니다. - sentence_kci: KCI 논문 제목 및 초록에서 해당 단어가 사용되었을 때의 영문 제목/초록과 국문 제목/초록 쌍입니다. - 마찬가지로 AI 검증이 이루어졌지만, 잘못 추출된 경우가 있을 수 있으며, 원문을 그대로 사용한 경우도 포함되어 있습니다. (예시: elder.n.03 - (교회의)장로, kci 예제에서는 노인/엘더스크롤 관련 사용례들이 등장함) - word_kci: sentence_kci 문장에서 해당 단어의 번역 결과입니다. - gemini-2.5-pro-exp-03-25, gpt-4.1-mini, deepseek-chat-v3-0324: 각 모델에게 word, pos, definition, sample을 주고, 단어를 한글로 번역해 달라고 요청한 결과입니다. --- 위키, 논문 사례는 밑둥썩음병(bottom rot) 등의 전문 용어 번역을 개선할 수 있을 것 같으나, 택(tack)(항해 용어), 환상박피(girdle)의 경우 위키/논문 사례가 적절하지 못해 방해를 일으킴 위키, 논문 사례가 존재하는 데이터 자체가 전체 단어의 절반 미만임
HungVu2003/opt-350m_beta_1.0_alpha_0.0_num-company_2_dataset_0_for_gen_6_v2
HungVu2003
2025-05-03T20:09:58Z
0
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-03T20:09:56Z
0
--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 1151427 num_examples: 12500 download_size: 701524 dataset_size: 1151427 configs: - config_name: default data_files: - split: train path: data/train-* ---
math-extraction-comp/princeton-nlp__Llama-3-Instruct-8B-RRHF-v0.2
math-extraction-comp
2025-01-12T21:59:58Z
69
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-01-11T14:32:41Z
0
--- dataset_info: features: - name: question dtype: string - name: gold dtype: string - name: target dtype: string - name: prediction dtype: string - name: subset dtype: string - name: lighteval-c24870ea_extracted_answer dtype: string - name: lighteval-c24870ea_score dtype: float64 - name: qwen_score dtype: float64 - name: lighteval-0f21c935_score dtype: float64 - name: harness_extracted_answer dtype: string - name: harness_score dtype: float64 - name: qwen_extracted_answer dtype: string - name: lighteval-0f21c935_extracted_answer dtype: string splits: - name: train num_bytes: 2200988 num_examples: 1324 download_size: 1047729 dataset_size: 2200988 configs: - config_name: default data_files: - split: train path: data/train-* ---
HungVu2003/gemma-2b_beta_0.0_alpha_0.0_num-company_3_dataset_0_for_gen_11
HungVu2003
2025-04-20T09:25:48Z
20
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-04-20T09:25:47Z
0
--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 819347 num_examples: 12500 download_size: 565757 dataset_size: 819347 configs: - config_name: default data_files: - split: train path: data/train-* ---
test-gen/code_mbpp_qwen2.5-3b_t0.1_n8_tests_mbpp_qwen3-0.6b-easy-unique_lr1e-5_t0.0_n1
test-gen
2025-05-19T17:55:05Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-19T17:55:01Z
0
--- dataset_info: features: - name: task_id dtype: int32 - name: text dtype: string - name: code dtype: string - name: test_list sequence: string - name: test_setup_code dtype: string - name: challenge_test_list sequence: string - name: generated_code sequence: string - name: gt_rewards sequence: float64 - name: rewards sequence: float64 - name: verification_info struct: - name: language dtype: string - name: test_cases sequence: string splits: - name: test num_bytes: 5280802 num_examples: 500 download_size: 979359 dataset_size: 5280802 configs: - config_name: default data_files: - split: test path: data/test-* ---
allenai/DataDecide-eval-results
allenai
2025-04-16T20:20:58Z
133
4
[ "license:odc-by", "size_categories:1M<n<10M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2406.08446", "arxiv:2009.03300", "arxiv:1905.07830", "arxiv:1803.05457", "arxiv:1911.11641", "arxiv:1811.00937", "arxiv:1904.09728", "arxiv:1809.02789", "arxiv:1905.10044", "arxiv:1907.10641", "arxiv:2402.00159", "arxiv:1910.10683", "arxiv:2409.17115", "arxiv:2406.11794", "arxiv:2411.15124", "region:us" ]
[]
2025-03-07T00:17:50Z
0
--- dataset_info: features: - name: params dtype: string - name: data dtype: string - name: task dtype: string - name: step dtype: int64 - name: seed dtype: string - name: chinchilla dtype: string - name: tokens dtype: int64 - name: compute dtype: float64 - name: metrics dtype: string splits: - name: train num_bytes: 1848365910 num_examples: 1410750 download_size: 693325464 dataset_size: 1848365910 configs: - config_name: default data_files: - split: train path: data/train-* license: odc-by --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62bddd0b1e22ec8427a0f27e/MwddQs_8OaU4128VYrwoU.png) More than one training run goes into making a large language model, but developers rarely release the small models and datasets they experiment with during the development process. How do they decide what dataset to use for pretraining or which benchmarks to hill climb on? To empower open exploration of these questions, we release [DataDecide](allenai.org/papers/datadecide)—a suite of models we pretrain on 25 corpora with differing sources, deduplication, and filtering up to 100B tokens, over 14 different model sizes ranging from 4M parameters up to 1B parameters (more than 30k model checkpoints in total). ## Evaluation We evaluate all checkpoints over OLMES suite of 10 multiple choice question answering benchmarks ([Gu et al., 2024](https://arxiv.org/abs/2406.08446)): - [MMLU (Hendrycks et al., 2021)](https://arxiv.org/abs/2009.03300) - [HellaSwag (Zellers et al., 2019)](https://arxiv.org/abs/1905.07830) - [ARC-Challenge (Clark et al., 2018)](https://arxiv.org/abs/1803.05457) - [ARC-Easy (Clark et al., 2018)](https://arxiv.org/abs/1803.05457) - [PIQA (Bisk et al., 2020)](https://arxiv.org/abs/1911.11641) - [CommonsenseQA (Talmor et al., 2019)](https://arxiv.org/abs/1811.00937) - [Social IQa (Sap et al., 2019)](https://arxiv.org/abs/1904.09728) - [OpenBookQA (Mihaylov et al., 2018)](https://arxiv.org/abs/1809.02789) - [BoolQ (Clark et al., 2019)](https://arxiv.org/abs/1905.10044) - [Winogrande (Sakaguchi et al., 2020)](https://arxiv.org/abs/1907.10641) We also release evaluations for instance-level results: [https://huggingface.co/datasets/allenai/DataDecide-eval-instances](https://huggingface.co/datasets/allenai/DataDecide-eval-instances) ## 350 Models over Differences in Data in Scale These evaluations are done over all DataDecide models. For each of our 25 datasets and 14 model sizes, we train a model linked below. Each has intermediate checkpoints (uploading after initial release), runs over 3 random seeds. All models finish training at a token to parameter ratio of 100 (e.g., 1B parameters -> 100B tokens). | | | | | | | | | | | | | | | | |-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|------|------|------|------|-----| | Dolma1.7 | [4M](https://huggingface.co/allenai/DataDecide-dolma1_7-4M) | [6M](https://huggingface.co/allenai/DataDecide-dolma1_7-6M) | [8M](https://huggingface.co/allenai/DataDecide-dolma1_7-8M) | [10M](https://huggingface.co/allenai/DataDecide-dolma1_7-10M) | [14M](https://huggingface.co/allenai/DataDecide-dolma1_7-14M) | [16M](https://huggingface.co/allenai/DataDecide-dolma1_7-16M) | [20M](https://huggingface.co/allenai/DataDecide-dolma1_7-20M) | [60M](https://huggingface.co/allenai/DataDecide-dolma1_7-60M) | [90M](https://huggingface.co/allenai/DataDecide-dolma1_7-90M) | [150M](https://huggingface.co/allenai/DataDecide-dolma1_7-150M) | [300M](https://huggingface.co/allenai/DataDecide-dolma1_7-300M) | [530M](https://huggingface.co/allenai/DataDecide-dolma1_7-530M) | [750M](https://huggingface.co/allenai/DataDecide-dolma1_7-750M) | [1B](https://huggingface.co/allenai/DataDecide-dolma1_7-1B) | | Dolma1.7 (no code) | [4M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-code-4M) | [6M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-code-6M) | [8M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-code-8M) | [10M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-code-10M) | [14M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-code-14M) | [16M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-code-16M) | [20M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-code-20M) | [60M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-code-60M) | [90M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-code-90M) | [150M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-code-150M) | [300M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-code-300M) | [530M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-code-530M) | [750M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-code-750M) | [1B](https://huggingface.co/allenai/DataDecide-dolma1_7-no-code-1B) | | Dolma1.7 (no math, code) | [4M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-math-code-4M) | [6M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-math-code-6M) | [8M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-math-code-8M) | [10M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-math-code-10M) | [14M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-math-code-14M) | [16M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-math-code-16M) | [20M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-math-code-20M) | [60M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-math-code-60M) | [90M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-math-code-90M) | [150M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-math-code-150M) | [300M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-math-code-300M) | [530M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-math-code-530M) | [750M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-math-code-750M) | [1B](https://huggingface.co/allenai/DataDecide-dolma1_7-no-math-code-1B) | | Dolma1.7 (no Reddit) | [4M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-reddit-4M) | [6M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-reddit-6M) | [8M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-reddit-8M) | [10M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-reddit-10M) | [14M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-reddit-14M) | [16M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-reddit-16M) | [20M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-reddit-20M) | [60M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-reddit-60M) | [90M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-reddit-90M) | [150M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-reddit-150M) | [300M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-reddit-300M) | [530M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-reddit-530M) | [750M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-reddit-750M) | [1B](https://huggingface.co/allenai/DataDecide-dolma1_7-no-reddit-1B) | | Dolma1.7 (no Flan) | [4M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-flan-4M) | [6M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-flan-6M) | [8M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-flan-8M) | [10M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-flan-10M) | [14M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-flan-14M) | [16M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-flan-16M) | [20M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-flan-20M) | [60M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-flan-60M) | [90M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-flan-90M) | [150M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-flan-150M) | [300M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-flan-300M) | [530M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-flan-530M) | [750M](https://huggingface.co/allenai/DataDecide-dolma1_7-no-flan-750M) | [1B](https://huggingface.co/allenai/DataDecide-dolma1_7-no-flan-1B) | | Dolma1.6++ | [4M](https://huggingface.co/allenai/DataDecide-dolma1_6plus-4M) | [6M](https://huggingface.co/allenai/DataDecide-dolma1_6plus-6M) | [8M](https://huggingface.co/allenai/DataDecide-dolma1_6plus-8M) | [10M](https://huggingface.co/allenai/DataDecide-dolma1_6plus-10M) | [14M](https://huggingface.co/allenai/DataDecide-dolma1_6plus-14M) | [16M](https://huggingface.co/allenai/DataDecide-dolma1_6plus-16M) | [20M](https://huggingface.co/allenai/DataDecide-dolma1_6plus-20M) | [60M](https://huggingface.co/allenai/DataDecide-dolma1_6plus-60M) | [90M](https://huggingface.co/allenai/DataDecide-dolma1_6plus-90M) | [150M](https://huggingface.co/allenai/DataDecide-dolma1_6plus-150M) | [300M](https://huggingface.co/allenai/DataDecide-dolma1_6plus-300M) | [530M](https://huggingface.co/allenai/DataDecide-dolma1_6plus-530M) | [750M](https://huggingface.co/allenai/DataDecide-dolma1_6plus-750M) | [1B](https://huggingface.co/allenai/DataDecide-dolma1_6plus-1B) | | C4 | [4M](https://huggingface.co/allenai/DataDecide-c4-4M) | [6M](https://huggingface.co/allenai/DataDecide-c4-6M) | [8M](https://huggingface.co/allenai/DataDecide-c4-8M) | [10M](https://huggingface.co/allenai/DataDecide-c4-10M) | [14M](https://huggingface.co/allenai/DataDecide-c4-14M) | [16M](https://huggingface.co/allenai/DataDecide-c4-16M) | [20M](https://huggingface.co/allenai/DataDecide-c4-20M) | [60M](https://huggingface.co/allenai/DataDecide-c4-60M) | [90M](https://huggingface.co/allenai/DataDecide-c4-90M) | [150M](https://huggingface.co/allenai/DataDecide-c4-150M) | [300M](https://huggingface.co/allenai/DataDecide-c4-300M) | [530M](https://huggingface.co/allenai/DataDecide-c4-530M) | [750M](https://huggingface.co/allenai/DataDecide-c4-750M) | [1B](https://huggingface.co/allenai/DataDecide-c4-1B) | | FineWeb-Pro | [4M](https://huggingface.co/allenai/DataDecide-fineweb-pro-4M) | [6M](https://huggingface.co/allenai/DataDecide-fineweb-pro-6M) | [8M](https://huggingface.co/allenai/DataDecide-fineweb-pro-8M) | [10M](https://huggingface.co/allenai/DataDecide-fineweb-pro-10M) | [14M](https://huggingface.co/allenai/DataDecide-fineweb-pro-14M) | [16M](https://huggingface.co/allenai/DataDecide-fineweb-pro-16M) | [20M](https://huggingface.co/allenai/DataDecide-fineweb-pro-20M) | [60M](https://huggingface.co/allenai/DataDecide-fineweb-pro-60M) | [90M](https://huggingface.co/allenai/DataDecide-fineweb-pro-90M) | [150M](https://huggingface.co/allenai/DataDecide-fineweb-pro-150M) | [300M](https://huggingface.co/allenai/DataDecide-fineweb-pro-300M) | [530M](https://huggingface.co/allenai/DataDecide-fineweb-pro-530M) | [750M](https://huggingface.co/allenai/DataDecide-fineweb-pro-750M) | [1B](https://huggingface.co/allenai/DataDecide-fineweb-pro-1B) | | FineWeb-Edu | [4M](https://huggingface.co/allenai/DataDecide-fineweb-edu-4M) | [6M](https://huggingface.co/allenai/DataDecide-fineweb-edu-6M) | [8M](https://huggingface.co/allenai/DataDecide-fineweb-edu-8M) | [10M](https://huggingface.co/allenai/DataDecide-fineweb-edu-10M) | [14M](https://huggingface.co/allenai/DataDecide-fineweb-edu-14M) | [16M](https://huggingface.co/allenai/DataDecide-fineweb-edu-16M) | [20M](https://huggingface.co/allenai/DataDecide-fineweb-edu-20M) | [60M](https://huggingface.co/allenai/DataDecide-fineweb-edu-60M) | [90M](https://huggingface.co/allenai/DataDecide-fineweb-edu-90M) | [150M](https://huggingface.co/allenai/DataDecide-fineweb-edu-150M) | [300M](https://huggingface.co/allenai/DataDecide-fineweb-edu-300M) | [530M](https://huggingface.co/allenai/DataDecide-fineweb-edu-530M) | [750M](https://huggingface.co/allenai/DataDecide-fineweb-edu-750M) | [1B](https://huggingface.co/allenai/DataDecide-fineweb-edu-1B) | | Falcon | [4M](https://huggingface.co/allenai/DataDecide-falcon-4M) | [6M](https://huggingface.co/allenai/DataDecide-falcon-6M) | [8M](https://huggingface.co/allenai/DataDecide-falcon-8M) | [10M](https://huggingface.co/allenai/DataDecide-falcon-10M) | [14M](https://huggingface.co/allenai/DataDecide-falcon-14M) | [16M](https://huggingface.co/allenai/DataDecide-falcon-16M) | [20M](https://huggingface.co/allenai/DataDecide-falcon-20M) | [60M](https://huggingface.co/allenai/DataDecide-falcon-60M) | [90M](https://huggingface.co/allenai/DataDecide-falcon-90M) | [150M](https://huggingface.co/allenai/DataDecide-falcon-150M) | [300M](https://huggingface.co/allenai/DataDecide-falcon-300M) | [530M](https://huggingface.co/allenai/DataDecide-falcon-530M) | [750M](https://huggingface.co/allenai/DataDecide-falcon-750M) | [1B](https://huggingface.co/allenai/DataDecide-falcon-1B) | | Falcon+CC | [4M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-4M) | [6M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-6M) | [8M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-8M) | [10M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-10M) | [14M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-14M) | [16M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-16M) | [20M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-20M) | [60M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-60M) | [90M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-90M) | [150M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-150M) | [300M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-300M) | [530M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-530M) | [750M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-750M) | [1B](https://huggingface.co/allenai/DataDecide-falcon-and-cc-1B) | | Falcon+CC (QC 10%) | [4M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-10p-4M) | [6M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-10p-6M) | [8M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-10p-8M) | [10M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-10p-10M) | [14M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-10p-14M) | [16M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-10p-16M) | [20M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-10p-20M) | [60M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-10p-60M) | [90M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-10p-90M) | [150M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-10p-150M) | [300M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-10p-300M) | [530M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-10p-530M) | [750M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-10p-750M) | [1B](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-10p-1B) | | Falcon+CC (QC 20%) | [4M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-20p-4M) | [6M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-20p-6M) | [8M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-20p-8M) | [10M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-20p-10M) | [14M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-20p-14M) | [16M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-20p-16M) | [20M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-20p-20M) | [60M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-20p-60M) | [90M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-20p-90M) | [150M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-20p-150M) | [300M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-20p-300M) | [530M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-20p-530M) | [750M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-20p-750M) | [1B](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-20p-1B) | | Falcon+CC (QC Orig 10%) | [4M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-orig-10p-4M) | [6M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-orig-10p-6M) | [8M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-orig-10p-8M) | [10M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-orig-10p-10M) | [14M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-orig-10p-14M) | [16M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-orig-10p-16M) | [20M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-orig-10p-20M) | [60M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-orig-10p-60M) | [90M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-orig-10p-90M) | [150M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-orig-10p-150M) | [300M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-orig-10p-300M) | [530M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-orig-10p-530M) | [750M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-orig-10p-750M) | [1B](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-orig-10p-1B) | | Falcon+CC (QC Tulu 10%) | [4M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-tulu-10p-4M) | [6M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-tulu-10p-6M) | [8M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-tulu-10p-8M) | [10M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-tulu-10p-10M) | [14M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-tulu-10p-14M) | [16M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-tulu-10p-16M) | [20M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-tulu-10p-20M) | [60M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-tulu-10p-60M) | [90M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-tulu-10p-90M) | [150M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-tulu-10p-150M) | [300M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-tulu-10p-300M) | [530M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-tulu-10p-530M) | [750M](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-tulu-10p-750M) | [1B](https://huggingface.co/allenai/DataDecide-falcon-and-cc-qc-tulu-10p-1B) | | DCLM-Baseline | [4M](https://huggingface.co/allenai/DataDecide-dclm-baseline-4M) | [6M](https://huggingface.co/allenai/DataDecide-dclm-baseline-6M) | [8M](https://huggingface.co/allenai/DataDecide-dclm-baseline-8M) | [10M](https://huggingface.co/allenai/DataDecide-dclm-baseline-10M) | [14M](https://huggingface.co/allenai/DataDecide-dclm-baseline-14M) | [16M](https://huggingface.co/allenai/DataDecide-dclm-baseline-16M) | [20M](https://huggingface.co/allenai/DataDecide-dclm-baseline-20M) | [60M](https://huggingface.co/allenai/DataDecide-dclm-baseline-60M) | [90M](https://huggingface.co/allenai/DataDecide-dclm-baseline-90M) | [150M](https://huggingface.co/allenai/DataDecide-dclm-baseline-150M) | [300M](https://huggingface.co/allenai/DataDecide-dclm-baseline-300M) | [530M](https://huggingface.co/allenai/DataDecide-dclm-baseline-530M) | [750M](https://huggingface.co/allenai/DataDecide-dclm-baseline-750M) | [1B](https://huggingface.co/allenai/DataDecide-dclm-baseline-1B) | | DCLM-Baseline (QC 7%, FW2) | [4M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-7p-fw2-4M) | [6M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-7p-fw2-6M) | [8M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-7p-fw2-8M) | [10M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-7p-fw2-10M) | [14M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-7p-fw2-14M) | [16M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-7p-fw2-16M) | [20M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-7p-fw2-20M) | [60M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-7p-fw2-60M) | [90M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-7p-fw2-90M) | [150M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-7p-fw2-150M) | [300M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-7p-fw2-300M) | [530M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-7p-fw2-530M) | [750M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-7p-fw2-750M) | [1B](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-7p-fw2-1B) | | DCLM-Baseline (QC 7%, FW3) | [4M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-7p-fw3-4M) | [6M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-7p-fw3-6M) | [8M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-7p-fw3-8M) | [10M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-7p-fw3-10M) | [14M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-7p-fw3-14M) | [16M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-7p-fw3-16M) | [20M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-7p-fw3-20M) | [60M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-7p-fw3-60M) | [90M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-7p-fw3-90M) | [150M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-7p-fw3-150M) | [300M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-7p-fw3-300M) | [530M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-7p-fw3-530M) | [750M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-7p-fw3-750M) | [1B](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-7p-fw3-1B) | | DCLM-Baseline (QC FW 3%) | [4M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-fw-3p-4M) | [6M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-fw-3p-6M) | [8M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-fw-3p-8M) | [10M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-fw-3p-10M) | [14M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-fw-3p-14M) | [16M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-fw-3p-16M) | [20M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-fw-3p-20M) | [60M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-fw-3p-60M) | [90M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-fw-3p-90M) | [150M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-fw-3p-150M) | [300M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-fw-3p-300M) | [530M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-fw-3p-530M) | [750M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-fw-3p-750M) | [1B](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-fw-3p-1B) | | DCLM-Baseline (QC FW 10%) | [4M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-fw-10p-4M) | [6M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-fw-10p-6M) | [8M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-fw-10p-8M) | [10M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-fw-10p-10M) | [14M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-fw-10p-14M) | [16M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-fw-10p-16M) | [20M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-fw-10p-20M) | [60M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-fw-10p-60M) | [90M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-fw-10p-90M) | [150M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-fw-10p-150M) | [300M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-fw-10p-300M) | [530M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-fw-10p-530M) | [750M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-fw-10p-750M) | [1B](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-fw-10p-1B) | | DCLM-Baseline (QC 10%) | [4M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-10p-4M) | [6M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-10p-6M) | [8M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-10p-8M) | [10M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-10p-10M) | [14M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-10p-14M) | [16M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-10p-16M) | [20M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-10p-20M) | [60M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-10p-60M) | [90M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-10p-90M) | [150M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-10p-150M) | [300M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-10p-300M) | [530M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-10p-530M) | [750M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-10p-750M) | [1B](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-10p-1B) | | DCLM-Baseline (QC 20%) | [4M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-20p-4M) | [6M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-20p-6M) | [8M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-20p-8M) | [10M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-20p-10M) | [14M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-20p-14M) | [16M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-20p-16M) | [20M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-20p-20M) | [60M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-20p-60M) | [90M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-20p-90M) | [150M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-20p-150M) | [300M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-20p-300M) | [530M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-20p-530M) | [750M](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-20p-750M) | [1B](https://huggingface.co/allenai/DataDecide-dclm-baseline-qc-20p-1B) | | DCLM-Baseline 25% / Dolma 75% | [4M](https://huggingface.co/allenai/DataDecide-dclm-baseline-25p-dolma1.7-75p-4M) | [6M](https://huggingface.co/allenai/DataDecide-dclm-baseline-25p-dolma1.7-75p-6M) | [8M](https://huggingface.co/allenai/DataDecide-dclm-baseline-25p-dolma1.7-75p-8M) | [10M](https://huggingface.co/allenai/DataDecide-dclm-baseline-25p-dolma1.7-75p-10M) | [14M](https://huggingface.co/allenai/DataDecide-dclm-baseline-25p-dolma1.7-75p-14M) | [16M](https://huggingface.co/allenai/DataDecide-dclm-baseline-25p-dolma1.7-75p-16M) | [20M](https://huggingface.co/allenai/DataDecide-dclm-baseline-25p-dolma1.7-75p-20M) | [60M](https://huggingface.co/allenai/DataDecide-dclm-baseline-25p-dolma1.7-75p-60M) | [90M](https://huggingface.co/allenai/DataDecide-dclm-baseline-25p-dolma1.7-75p-90M) | [150M](https://huggingface.co/allenai/DataDecide-dclm-baseline-25p-dolma1.7-75p-150M) | [300M](https://huggingface.co/allenai/DataDecide-dclm-baseline-25p-dolma1.7-75p-300M) | [530M](https://huggingface.co/allenai/DataDecide-dclm-baseline-25p-dolma1.7-75p-530M) | [750M](https://huggingface.co/allenai/DataDecide-dclm-baseline-25p-dolma1.7-75p-750M) | [1B](https://huggingface.co/allenai/DataDecide-dclm-baseline-25p-dolma1.7-75p-1B) | | DCLM-Baseline 50% / Dolma 50% | [4M](https://huggingface.co/allenai/DataDecide-dclm-baseline-50p-dolma1.7-50p-4M) | [6M](https://huggingface.co/allenai/DataDecide-dclm-baseline-50p-dolma1.7-50p-6M) | [8M](https://huggingface.co/allenai/DataDecide-dclm-baseline-50p-dolma1.7-50p-8M) | [10M](https://huggingface.co/allenai/DataDecide-dclm-baseline-50p-dolma1.7-50p-10M) | [14M](https://huggingface.co/allenai/DataDecide-dclm-baseline-50p-dolma1.7-50p-14M) | [16M](https://huggingface.co/allenai/DataDecide-dclm-baseline-50p-dolma1.7-50p-16M) | [20M](https://huggingface.co/allenai/DataDecide-dclm-baseline-50p-dolma1.7-50p-20M) | [60M](https://huggingface.co/allenai/DataDecide-dclm-baseline-50p-dolma1.7-50p-60M) | [90M](https://huggingface.co/allenai/DataDecide-dclm-baseline-50p-dolma1.7-50p-90M) | [150M](https://huggingface.co/allenai/DataDecide-dclm-baseline-50p-dolma1.7-50p-150M) | [300M](https://huggingface.co/allenai/DataDecide-dclm-baseline-50p-dolma1.7-50p-300M) | [530M](https://huggingface.co/allenai/DataDecide-dclm-baseline-50p-dolma1.7-50p-530M) | [750M](https://huggingface.co/allenai/DataDecide-dclm-baseline-50p-dolma1.7-50p-750M) | [1B](https://huggingface.co/allenai/DataDecide-dclm-baseline-50p-dolma1.7-50p-1B) | | DCLM-Baseline 75% / Dolma 25% | [4M](https://huggingface.co/allenai/DataDecide-dclm-baseline-75p-dolma1.7-25p-4M) | [6M](https://huggingface.co/allenai/DataDecide-dclm-baseline-75p-dolma1.7-25p-6M) | [8M](https://huggingface.co/allenai/DataDecide-dclm-baseline-75p-dolma1.7-25p-8M) | [10M](https://huggingface.co/allenai/DataDecide-dclm-baseline-75p-dolma1.7-25p-10M) | [14M](https://huggingface.co/allenai/DataDecide-dclm-baseline-75p-dolma1.7-25p-14M) | [16M](https://huggingface.co/allenai/DataDecide-dclm-baseline-75p-dolma1.7-25p-16M) | [20M](https://huggingface.co/allenai/DataDecide-dclm-baseline-75p-dolma1.7-25p-20M) | [60M](https://huggingface.co/allenai/DataDecide-dclm-baseline-75p-dolma1.7-25p-60M) | [90M](https://huggingface.co/allenai/DataDecide-dclm-baseline-75p-dolma1.7-25p-90M) | [150M](https://huggingface.co/allenai/DataDecide-dclm-baseline-75p-dolma1.7-25p-150M) | [300M](https://huggingface.co/allenai/DataDecide-dclm-baseline-75p-dolma1.7-25p-300M) | [530M](https://huggingface.co/allenai/DataDecide-dclm-baseline-75p-dolma1.7-25p-530M) | [750M](https://huggingface.co/allenai/DataDecide-dclm-baseline-75p-dolma1.7-25p-750M) | [1B](https://huggingface.co/allenai/DataDecide-dclm-baseline-75p-dolma1.7-25p-1B) | ## Data | Source / Recipe | Description | |----------------------------------------|-------------| | **Dolma1.7** *Original, No code, No math/code, No Reddit, No Flan* | A 2.3T-token corpus (Dolma; 1.7 [Soldaini et al., 2024](https://arxiv.org/abs/2402.00159)) sampling common LM sources for open research. We ablate code, math/code, Reddit, or Flan subsets. | | **Dolma1.6++** *Original* | Dolma 1.6 plus additional sources from Dolma 1.7: RedPajama’s arxiv subset, openwebmath, algebraic stack, flan, starcoder, falcon. | | **C4** *Original* | The C4 dataset ([Raffel et al., 2019](https://arxiv.org/abs/1910.10683)) as prepared in Dolma 1.7, heuristically filtered from the April 2019 Common Crawl. | | **FineWeb-Pro** *Original* | The FineWeb Pro corpus ([Zhou et al., 2024](https://arxiv.org/abs/2409.17115)), featuring model-driven data cleaning on FineWeb. | | **FineWeb-Edu** *Original* | The deduplicated FineWeb-Edu subset of SmoLLM-Corpus ([Ben Allal et al., 2024](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus)), focused on educational web pages. | | **Falcon** *Original* | The Falcon RefinedWeb corpus ([Penedo et al., 2023](https://api.semanticscholar.org/CorpusID:259063761)) in Dolma 1.7, derived from Common Crawl through June 2023 and more aggressively filtered/deduplicated than C4. | | **Falcon+CC** *Original, QC 10%, QC 20%, QC Orig 10%, QC Tulu 10%* | Falcon and Dolma 1.7’s Common Crawl. We quality filter to top 10% or 20% documents with reproduced or original [Li et al. (2024)](https://arxiv.org/abs/2406.11794) filter or retrain filter on pre-release version of Tulu-v3 ([Lambert et al., 2024](https://arxiv.org/abs/2411.15124)). | | **DCLM-Baseline** *Original, QC 7% FW2, QC 7% FW3, QC FW 10%, QC 10%, QC 20%* | A SOTA Common Crawl corpus using best ablated deduplication, cleaning heuristics, and quality filter. We quality filter to top 7% of DCLM classified documents and further take 2+ or 3+ scores with FineWeb-edu classifier; or filter to top 3% or 10% with FineWeb-edu classifier; or take top 10% or 20% with reproduced DCLM classifier. | | *λ%* **DCLM-Baseline** *+ 1 – λ%* **Dolma1.7** | Fractional combinations of Dolma1.7 and DCLM-Baseline mixing different proportions of the two datasets for λ ∈ {25%, 50%, 75%}. | ### Dataset Description - **Developed by:** Allen Institute for AI (Ai2) - **Language(s) (NLP):** English - **License:** This dataset is licensed under ODC-BY and intended for research and educational use in accordance with [Ai2's Responsible Use Guidelines](https://allenai.org/responsible-use) - **Contact:** Technical inquiries: `[email protected]`. Press: `[email protected]` ### Links - **Repository:** [https://github.com/allenai/DataDecide](https://github.com/allenai/DataDecide) - **Paper:** [https:/allenai.org/papers/datadecide](https:/allenai.org/papers/datadecide) ## Citation **BibTeX:** ``` @article{MagnussonDataDecide2025, title={{DataDecide: How to Predict Best Pretraining Data with Small Experiments}}, author={Ian Magnusson and Nguyen Tai and Ben Bogin and David Heineman and Jena Hwang and Luca Soldaini and Akshita Bhagia and Jiacheng Liu and Dirk Groeneveld and Oyvind Tafjord and Noah A. Smith and Pang Wei Koh and Jesse Dodge}, year={2025}, journal={arXiv preprint}, } ```
zacbrld/MNLP_M3_rag_documents_45
zacbrld
2025-06-06T16:38:31Z
74
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-06-06T16:38:27Z
0
--- dataset_info: features: - name: text dtype: string - name: source dtype: string - name: tokens dtype: int64 splits: - name: train num_bytes: 19940227 num_examples: 74983 download_size: 11932891 dataset_size: 19940227 configs: - config_name: default data_files: - split: train path: data/train-* ---
mytestdpo/grpo_n4_bz512_60
mytestdpo
2025-03-19T12:47:13Z
15
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-03-19T12:47:11Z
0
--- dataset_info: features: - name: problem dtype: string - name: answers sequence: string - name: gt dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 45114132 num_examples: 1517 download_size: 15226195 dataset_size: 45114132 configs: - config_name: default data_files: - split: train path: data/train-* ---
hugginguwe/uwes_medk_dataset
hugginguwe
2025-05-14T15:45:40Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-14T13:30:12Z
0
--- dataset_info: features: - name: filename dtype: string - name: text dtype: string splits: - name: train num_bytes: 2038247 num_examples: 72 download_size: 1176882 dataset_size: 2038247 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "uwes_medk_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HHS-Official/active-bacterial-core-surveillance-abcs-streptococ
HHS-Official
2025-05-07T20:09:16Z
0
0
[ "language:en", "license:odbl", "size_categories:1K<n<10K", "format:csv", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "hhs", "cdc", "abcs", "bactfacts" ]
[]
2025-05-07T20:09:15Z
0
--- language: - en pretty_name: Active Bacterial Core surveillance (ABCs) Streptococcus pneumoniae tags: - hhs - cdc - abcs - bactfacts license: odbl --- # Active Bacterial Core surveillance (ABCs) Streptococcus pneumoniae ## Description ABCs is an ongoing surveillance program that began in 1997. <a href="https://www.cdc.gov/abcs/reports-findings/surv-reports.html">ABCs reports</a> describe the ABCs case definition and the specific methodology used to calculate rates and estimated numbers in the United States for each bacterium by year. The methods, <a href="https://www.cdc.gov/abcs/methodology/surv-pop.html">surveillance areas</a>, and <a href="https://www.cdc.gov/abcs/methodology/surv-pop.html">laboratory isolate collection areas</a> have changed over time. Additionally, the way missing data are taken into account changed in 2010. It went from distributing unknown values based on known values of cases by site to use of multiple imputation using a sequential regression imputation method. Given these changes over time, trends should be interpreted with caution. <ul> <li> <a href="http://www.cdc.gov/abcs/methodology/index.html">Methodology</a> Find details about surveillance population, case determination, surveillance evaluation, and more. </li> <li> <a href="http://www.cdc.gov/abcs/reports-findings/index.html">Reports and Findings</a> Get official interpretations from reports and publications created from ABCs data. </li> </ul> ## Dataset Details - **Publisher**: Centers for Disease Control and Prevention - **Last Modified**: 2024-09-03 - **Contact**: Active Bacterial Core surveillance ([email protected]) ## Source Original data can be found at: https://data.cdc.gov/d/en3s-hzsr ## Usage You can load this dataset using: ```python from datasets import load_dataset dataset = load_dataset('HHS-Official/active-bacterial-core-surveillance-abcs-streptococ') ``` ## License This dataset is licensed under http://opendefinition.org/licenses/odc-odbl/
dgambettaphd/D_gmm2_gen2_WXS_doc1000_synt64_lr1e-04_acm_SYNLAST
dgambettaphd
2025-05-08T01:39:41Z
0
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-08T01:39:38Z
0
--- dataset_info: features: - name: id_doc dtype: int64 - name: text dtype: string - name: dataset dtype: string - name: gen dtype: int64 - name: synt dtype: int64 - name: MPP dtype: float64 splits: - name: train num_bytes: 11754668 num_examples: 18000 download_size: 7005774 dataset_size: 11754668 configs: - config_name: default data_files: - split: train path: data/train-* ---
chonkie-ai/finechonk
chonkie-ai
2025-05-07T09:02:51Z
0
0
[ "region:us" ]
[]
2025-05-07T08:49:59Z
0
--- dataset_info: features: - name: text dtype: string - name: id dtype: string - name: dump dtype: string - name: url dtype: string - name: file_path dtype: string - name: language dtype: string - name: language_score dtype: float64 - name: token_count dtype: int64 - name: score dtype: float64 - name: int_score dtype: int64 - name: chunked_text dtype: string - name: num_chunks dtype: int64 - name: avg_chunk_size dtype: float64 - name: max_chunk_size dtype: int64 - name: min_chunk_size dtype: int64 - name: std_chunk_size dtype: float64 splits: - name: train num_bytes: 23430663674 num_examples: 1038426 download_size: 13514490883 dataset_size: 23430663674 configs: - config_name: default data_files: - split: train path: data/train-* ---
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_e53f3977-c90c-4a9b-88f3-0580d7a383ff
argilla-internal-testing
2024-10-30T10:05:37Z
18
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-10-30T10:05:36Z
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-* ---
John6666/sdxl-backup-202502
John6666
2025-02-19T05:06:12Z
60
0
[ "language:en", "license:other", "region:us" ]
[]
2025-02-19T04:54:05Z
0
--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en ---
Racktic/mmmu
Racktic
2025-06-24T13:57:09Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-06-24T13:56:24Z
0
--- dataset_info: features: - name: images sequence: image - name: data_source dtype: string - name: prompt list: - name: content dtype: string - name: role dtype: string - name: ability dtype: string - name: reward_model struct: - name: ground_truth dtype: string - name: style dtype: string - name: extra_info struct: - name: difficulty dtype: string - name: index dtype: string splits: - name: train num_bytes: 330171072.0 num_examples: 894 download_size: 328790435 dataset_size: 330171072.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
zera09/sampled_visionarena_chat_100
zera09
2025-03-21T07:44:20Z
11
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-03-21T07:43:58Z
0
--- dataset_info: features: - name: images list: - name: bytes dtype: binary - name: path dtype: string - name: conversation_id dtype: string - name: model dtype: string - name: num_turns dtype: int64 - name: conversation list: list: - name: content dtype: string - name: role dtype: string - name: language dtype: string - name: user_id dtype: int64 - name: tstamp dtype: float64 - name: is_preset dtype: bool - name: preset_dataset dtype: string - name: categories struct: - name: captioning dtype: bool - name: code dtype: bool - name: creative_writing dtype: bool - name: diagram dtype: bool - name: entity_recognition dtype: bool - name: homework dtype: bool - name: humor dtype: bool - name: is_code dtype: bool - name: ocr dtype: bool - name: refusal dtype: bool splits: - name: train num_bytes: 481963884 num_examples: 1190 download_size: 409844938 dataset_size: 481963884 configs: - config_name: default data_files: - split: train path: data/train-* ---
skymizer/Llama3.1-base-tokenized-dolma-v1_7-50B
skymizer
2025-01-15T17:16:22Z
104
0
[ "size_categories:10M<n<100M", "format:parquet", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-01-15T13:59:20Z
0
--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: position_ids sequence: int64 - name: length dtype: int64 splits: - name: train num_bytes: 947748827022 num_examples: 70783733 download_size: 205952677727 dataset_size: 947748827022 configs: - config_name: default data_files: - split: train path: data/train-* ---
shylee/eval_DP_so100_gauze_scratch_ckpt010000
shylee
2025-04-29T12:27:47Z
26
0
[ "task_categories:robotics", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "tutorial" ]
[ "robotics" ]
2025-04-29T12:27:39Z
0
--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - tutorial configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 1, "total_frames": 853, "total_tasks": 1, "total_videos": 3, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:1" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.FrontCam": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.TopCam": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.WristCam": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```
kejian/sciriff-tulu-none-500
kejian
2024-12-08T03:43:15Z
15
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-12-08T03:43:13Z
0
--- dataset_info: features: - name: dataset dtype: string - name: id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 92214633 num_examples: 18895 download_size: 46821540 dataset_size: 92214633 configs: - config_name: default data_files: - split: train path: data/train-* ---
nvidia/DLC-Bench
nvidia
2025-04-24T17:43:52Z
159
5
[ "task_categories:image-to-text", "language:en", "size_categories:n<1K", "format:imagefolder", "modality:image", "library:datasets", "library:mlcroissant", "arxiv:2504.16072", "region:us", "image" ]
[ "image-to-text" ]
2025-04-21T05:21:54Z
2
--- language: - en task_categories: - image-to-text tags: - image --- # Describe Anything: Detailed Localized Image and Video Captioning **NVIDIA, UC Berkeley, UCSF** [Long Lian](https://tonylian.com), [Yifan Ding](https://research.nvidia.com/person/yifan-ding), [Yunhao Ge](https://gyhandy.github.io/), [Sifei Liu](https://sifeiliu.net/), [Hanzi Mao](https://hanzimao.me/), [Boyi Li](https://sites.google.com/site/boyilics/home), [Marco Pavone](https://research.nvidia.com/person/marco-pavone), [Ming-Yu Liu](https://mingyuliu.net/), [Trevor Darrell](https://people.eecs.berkeley.edu/~trevor/), [Adam Yala](https://www.adamyala.org/), [Yin Cui](https://ycui.me/) [[Paper](https://arxiv.org/abs/2504.16072)] | [[Code](https://github.com/NVlabs/describe-anything)] | [[Project Page](https://describe-anything.github.io/)] | [[Video](https://describe-anything.github.io/#video)] | [[HuggingFace Demo](https://huggingface.co/spaces/nvidia/describe-anything-model-demo)] | [[Model/Benchmark/Datasets](https://huggingface.co/collections/nvidia/describe-anything-680825bb8f5e41ff0785834c)] | [[Citation](#citation)] # Dataset Card for DLC-Bench Dataset for detailed localized captioning benchmark (DLC-Bench). ## LICENSE [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en) ## Intended Usage This dataset is intended to demonstrate and facilitate the understanding and usage of detailed localized captioning models. It should primarily be used for research purposes. ## Ethical Considerations NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
alpharomercoma/qna-japanese
alpharomercoma
2024-10-20T11:24:55Z
32
0
[ "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-10-20T11:23:59Z
0
--- license: apache-2.0 ---
kevin017/kl_pool_full_name_small
kevin017
2025-04-03T05:17:29Z
15
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-04-03T05:17:26Z
0
--- dataset_info: features: - name: text dtype: string - name: tokens sequence: sequence: int64 splits: - name: train num_bytes: 64519 num_examples: 1000 download_size: 27817 dataset_size: 64519 configs: - config_name: default data_files: - split: train path: data/train-* ---
Geralt-Targaryen/webnovels
Geralt-Targaryen
2025-04-02T08:31:10Z
19
0
[ "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-01-20T13:30:57Z
0
--- license: apache-2.0 --- 16,546 English webnovels that have been cleaned, deduplicated, and decontaminated with respect to the following benchmarks based on n-gram overlap: - GLUE (dev set of SST-2, CoLA, QQP, WNLI, RTE, QNLI, MNLI; test set of MPRC) - SIQA, PIQA, QASC, CSQA, HellaSWAG (all dev set) - CONLL 2003 - BLIMP - [MAIN](https://main.leibniz-zas.de/en/main-materials/main-materials/) - BoolQ (dev set) - WinoGrande (dev set) - ANLI (test set) - ARC easy and challenge (test set) - RACE middle and high (test set) - MMLU (dev, val, and test sets) - MATH, GSM8K (test set) - HumanEval (test set) - GPQA (diamond) ### Dataset Statistics Total number of samples: 16,546. Size of downloaded parquet files: 2.8G. ### Filtered Version There is a model-filtered version in the filter branch, including 13,713 samples (2.3G). Qwen2.5-32B-Instruct is used to generate language quality annotation (on a scale of 1-5) for 250K C4 samples. A RoBERT-large classifier is trained with regression on these annotations. Any document receiving a score of 1 or 2 from the classifier is removed. The remaining documents are also accompanied by their scores.
iabd05/mongodata_train
iabd05
2025-04-14T18:21:11Z
29
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-04-14T18:21:06Z
0
--- dataset_info: features: - name: student_id dtype: float64 - name: scores list: - name: score dtype: float64 - name: type dtype: string - name: class_id dtype: float64 splits: - name: train num_bytes: 7360000.0 num_examples: 80000 download_size: 3780075 dataset_size: 7360000.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
irasalsabila/realworld_audio_jv
irasalsabila
2025-04-08T11:32:21Z
29
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-04-08T09:07:17Z
0
--- dataset_info: features: - name: filename dtype: string - name: label dtype: string splits: - name: test num_bytes: 5248 num_examples: 50 download_size: 5812 dataset_size: 5248 configs: - config_name: default data_files: - split: test path: data/test-* ---
mlfoundations-dev/reasoning_shard_48
mlfoundations-dev
2025-03-05T21:24:17Z
14
0
[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-03-05T21:24:16Z
0
--- dataset_info: features: - name: problem dtype: string - name: answer dtype: string - name: task dtype: string - name: problem_number dtype: int64 - name: set_number dtype: int64 splits: - name: train num_bytes: 41006.886854632 num_examples: 42 download_size: 22859 dataset_size: 41006.886854632 configs: - config_name: default data_files: - split: train path: data/train-* ---
french-datasets/L3-IA-2025_Questions1
french-datasets
2025-06-03T20:37:12Z
0
0
[ "language:fra", "region:us" ]
[]
2025-06-03T20:36:01Z
0
--- language: - fra viewer: false --- Ce répertoire est vide, il a été créé pour améliorer le référencement du jeu de données [L3-IA-2025/Questions1](https://huggingface.co/datasets/L3-IA-2025/Questions1).
nashrah18/indiantranslator
nashrah18
2025-02-21T11:01:38Z
21
1
[ "task_categories:translation", "language:en", "language:hi", "license:mit", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "biology", "finance", "legal", "medical" ]
[ "translation" ]
2025-02-16T17:40:17Z
0
--- license: mit configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: english_text dtype: string - name: hindi_translation dtype: string - name: topic dtype: string splits: - name: train num_bytes: 38603 num_examples: 236 download_size: 15185 dataset_size: 38603 task_categories: - translation language: - en - hi tags: - biology - finance - legal - medical pretty_name: indiantranslator ---
laolaorkk/collect-data-Omni-Numina_filtered
laolaorkk
2025-02-15T15:00:13Z
14
0
[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-02-15T15:00:07Z
0
--- dataset_info: features: - name: solution dtype: string - name: answer dtype: string - name: question dtype: string - name: math_type dtype: string - name: source_type dtype: string - name: metadata dtype: string splits: - name: train num_bytes: 148980332 num_examples: 104593 download_size: 70348177 dataset_size: 148980332 configs: - config_name: default data_files: - split: train path: data/train-* ---
medmac01/test_audio_tts_offset_rmv
medmac01
2025-01-02T23:04:45Z
30
1
[ "size_categories:n<1K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-01-02T23:04:43Z
0
--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string splits: - name: train num_bytes: 5608789.0 num_examples: 143 download_size: 11041676 dataset_size: 5608789.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
haorandai/Dec30_PGD_Bicycle_Orange_Epsilon0.05_10samples_1constraint
haorandai
2024-12-30T22:51:14Z
15
0
[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-12-30T22:51:12Z
0
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 880667.0 num_examples: 11 download_size: 882265 dataset_size: 880667.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
rhesis/Telecom-Chatbot-Access-to-Online-Content-Harmless
rhesis
2025-02-25T18:23:34Z
8
0
[ "size_categories:n<1K", "format:csv", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-02-14T14:19:31Z
0
--- dataset_info: name: Access to Online Content Harmless license: CC-BY-4.0 language: en tags: - rhesis - test-sets - chatbot - telecom --- # Dataset Card for Access to Online Content Harmless ## Description The test set has been specifically created for evaluating the performance of a telecom chatbot. It aims to cater to the needs of the telecom industry by focusing on the reliability of the chatbot's responses. The set primarily consists of harmless scenarios wherein users seek assistance related to accessing online content. By assessing the chatbot's ability to understand and provide accurate information within this domain, the test set ensures that the chatbot is well-equipped to handle various customer queries effectively and provide reliable solutions. ## Structure The dataset includes four key columns: - **Behavior**: The performance dimension evaluated (Reliability, Robustness, or Compliance). - **Topic**: The topic validated as part of the prompt. - **Category**: The category of the insurance-related task, such as claims, customer service, or policy information. - **Prompt**: The actual test prompt provided to the chatbot. - **Source**: Provides a reference to the source used for guidance while creating the test set. ## Disclaimer Some test cases may contain sensitive, challenging, or potentially upsetting content. These cases are included to ensure thorough and realistic assessments. Users should review test cases carefully and exercise discretion when utilizing them. ## Integration In order to easily integrate a Rhesis test set into your existing development pipeline, you can make use of the [Rhesis SDK](https://github.com/rhesis-ai/rhesis-sdk). ### Prerequisites 1. Create an API key by signing up at [app.rhesis.ai](https://app.rhesis.ai). 2. Install the Rhesis SDK: ```bash pip install rhesis-sdk ``` 3. Use the following Python snippet to access the dataset: ```python from rhesis.entities import TestSet # Initialize and load the test set test_set = TestSet(id="access-to-online-content-harmless") df = test_set.load() # Returns a pandas DataFrame # Alternatively, you can download the CSV file directly test_set.download() # Downloads to current directory as test_set_{id}.csv ``` Using the Rhesis SDK, you get access to the entire collection of Rhesis test sets, including this one. You can also create your own test sets. For further details on how to integrate the SDK into your workflow, refer to the [Rhesis SDK documentation](https://docs.rhesis.ai/). ## Community We welcome contributions and discussions from the community! Here are the different ways you can get involved: - GitHub: Report issues or contribute to the Rhesis SDK on our [GitHub repository](https://github.com/rhesis-ai/rhesis-sdk). - Discord: Join our [Discord server](https://discord.rhesis.ai) to connect with other users and developers. - Email: Reach out to us at [[email protected]](mailto:[email protected]) for support or inquiries. ## Sources The following sources were used in creating this dataset: No citations available. ## Citation If you use this dataset, please cite: ``` @inproceedings{rhesis, title={Rhesis: A Testbench for Evaluating LLM Applications - Access to Online Content Harmless}, author={Rhesis}, year={2025} } ```
End of preview. Expand in Data Studio

Dataset Card for Hugging Face Hub Dataset Cards

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

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

Dataset Details

Uses

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

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

Out-of-Scope Use

[More Information Needed]

Dataset Structure

This dataset has a single split.

Dataset Creation

Curation Rationale

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

Source Data

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

Data Collection and Processing

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

Who are the source data producers?

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

Annotations [optional]

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

Annotation process

N/A

Who are the annotators?

N/A

Personal and Sensitive Information

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

Bias, Risks, and Limitations

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

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

Recommendations

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

Citation

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

Dataset Card Authors

@davanstrien

Dataset Card Contact

@davanstrien

Downloads last month
1,548
Curated by:
Daniel van Strien
Language(s) (NLP):
Dataset cards on the Hugging Face Hub are predominantly in English but may include other languages.
Size of downloaded dataset files:
139 MB
Size of the auto-converted Parquet files:
139 MB
Number of rows:
160,689

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Collection including librarian-bots/dataset_cards_with_metadata