davanstrien/embedded-cards · Datasets at Hugging Face

datablations/oscar-filter-small

datablations

2022-11-24T11:45:03Z

12

0

null

[ "region:us" ]

2022-11-24T11:45:03Z

2022-11-24T11:44:37.000Z

2022-11-24T11:44:37

--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: meta struct: - name: annotations sequence: string - name: identification struct: - name: label dtype: string - name: prob dtype: float64 - name: line_identifications list: - name: label dtype: string - name: prob dtype: float64 - name: perplexity_score dtype: float64 - name: warc_headers struct: - name: content-length dtype: int64 - name: content-type dtype: string - name: warc-block-digest dtype: string - name: warc-date dtype: string - name: warc-identified-content-language dtype: string - name: warc-record-id dtype: string - name: warc-refers-to dtype: string - name: warc-target-uri dtype: string - name: warc-type dtype: string splits: - name: train num_bytes: 658480427 num_examples: 100000 download_size: 347756473 dataset_size: 658480427 --- # Dataset Card for "small-oscar" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.7069371938705444, -0.03961409255862236, 0.32713785767555237, -0.06372931599617004, -0.3681672513484955, -0.15735524892807007, 0.29250603914260864, -0.248735710978508, 1.1664137840270996, 0.3690994679927826, -0.6706997752189636, -0.4669804871082306, -0.7546698451042175, -0.13423164188861...

null

supermy/poetry

supermy

2022-11-24T23:06:59Z

12

0

null

[ "region:us" ]

2022-11-24T23:06:59Z

2022-11-24T13:23:19.000Z

2022-11-24T13:23:19

Entry not found

[ -0.3227647542953491, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965083122253, 0.7915717959403992, 0.07618629932403564, 0.7746022343635559, 0.2563222348690033, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

royam0820/baya-paintings-01

royam0820

2022-11-24T14:03:38Z

12

0

null

[ "license:afl-3.0", "region:us" ]

2022-11-24T14:03:38Z

2022-11-24T13:56:57.000Z

2022-11-24T13:56:57

--- license: afl-3.0 ---

[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...

null

jeapaul/europarl_bilingual_processed

jeapaul

2022-11-24T18:30:24Z

12

0

null

[ "region:us" ]

2022-11-24T18:30:24Z

2022-11-24T18:30:02.000Z

2022-11-24T18:30:02

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 281100121 num_examples: 1892723 download_size: 155904108 dataset_size: 281100121 --- # Dataset Card for "europarl_bilingual_processed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.5238747000694275, -0.38309088349342346, 0.23588129878044128, 0.4843233525753021, -0.14523230493068695, 0.06301659345626831, -0.07672297209501266, -0.24500872194766998, 0.8308473229408264, 0.6596837043762207, -0.9107206463813782, -0.9970584511756897, -0.6896096467971802, 0.04774861410260...

null

ashraq/tmdb-celeb-10k

ashraq

2022-11-24T18:48:43Z

12

0

null

[ "region:us" ]

2022-11-24T18:48:43Z

2022-11-24T18:47:53.000Z

2022-11-24T18:47:53

Entry not found

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null

teticio/imdb-posters-and-description-512

teticio

2022-11-24T23:39:16Z

12

0

null

[ "region:us" ]

2022-11-24T23:39:16Z

2022-11-24T19:05:17.000Z

2022-11-24T19:05:17

Entry not found

[ -0.3227647542953491, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965083122253, 0.7915717959403992, 0.07618629932403564, 0.7746022343635559, 0.2563222348690033, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

shreyasharma/proofs

shreyasharma

2022-11-24T21:45:28Z

12

0

null

[ "region:us" ]

2022-11-24T21:45:28Z

2022-11-24T21:45:22.000Z

2022-11-24T21:45:22

--- dataset_info: features: - name: intermediate_conclusions struct: - name: int1 dtype: string - name: int10 dtype: string - name: int11 dtype: string - name: int12 dtype: string - name: int13 dtype: string - name: int14 dtype: string - name: int15 dtype: string - name: int16 dtype: string - name: int17 dtype: string - name: int2 dtype: string - name: int3 dtype: string - name: int4 dtype: string - name: int5 dtype: string - name: int6 dtype: string - name: int7 dtype: string - name: int8 dtype: string - name: int9 dtype: string - name: step_proof dtype: string - name: triples struct: - name: sent1 dtype: string - name: sent10 dtype: string - name: sent11 dtype: string - name: sent12 dtype: string - name: sent13 dtype: string - name: sent14 dtype: string - name: sent15 dtype: string - name: sent16 dtype: string - name: sent17 dtype: string - name: sent2 dtype: string - name: sent3 dtype: string - name: sent4 dtype: string - name: sent5 dtype: string - name: sent6 dtype: string - name: sent7 dtype: string - name: sent8 dtype: string - name: sent9 dtype: string - name: hypothesis dtype: string - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 1296774 num_examples: 1313 download_size: 609276 dataset_size: 1296774 --- # Dataset Card for "proofs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.5178183317184448, -0.10430160909891129, 0.44106972217559814, 0.2956085503101349, -0.23853382468223572, -0.09676513075828552, 0.07267078757286072, -0.19556759297847748, 0.7234905958175659, 0.45367273688316345, -0.6009597182273865, -0.8233897686004639, -0.6894094944000244, -0.262038141489...

null

shreyasharma/proofs2

shreyasharma

2022-11-24T21:54:13Z

12

0

null

[ "region:us" ]

2022-11-24T21:54:13Z

2022-11-24T21:54:07.000Z

2022-11-24T21:54:07

--- dataset_info: features: - name: intermediate_conclusions struct: - name: int1 dtype: string - name: int10 dtype: string - name: int11 dtype: string - name: int12 dtype: string - name: int13 dtype: string - name: int14 dtype: string - name: int15 dtype: string - name: int16 dtype: string - name: int17 dtype: string - name: int2 dtype: string - name: int3 dtype: string - name: int4 dtype: string - name: int5 dtype: string - name: int6 dtype: string - name: int7 dtype: string - name: int8 dtype: string - name: int9 dtype: string - name: step_proof dtype: string - name: triples struct: - name: sent1 dtype: string - name: sent10 dtype: string - name: sent11 dtype: string - name: sent12 dtype: string - name: sent13 dtype: string - name: sent14 dtype: string - name: sent15 dtype: string - name: sent16 dtype: string - name: sent17 dtype: string - name: sent2 dtype: string - name: sent3 dtype: string - name: sent4 dtype: string - name: sent5 dtype: string - name: sent6 dtype: string - name: sent7 dtype: string - name: sent8 dtype: string - name: sent9 dtype: string - name: hypothesis dtype: string - name: question dtype: string - name: answer dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 1307278 num_examples: 1313 download_size: 609969 dataset_size: 1307278 --- # Dataset Card for "proofs2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.2850024402141571, 0.011128208599984646, 0.3676005005836487, 0.25731343030929565, -0.2994130253791809, -0.19007661938667297, 0.282234251499176, -0.201797217130661, 0.5868068933486938, 0.3588410019874573, -0.5654723644256592, -0.5529460310935974, -0.7796205878257751, -0.37427857518196106,...

null

teticio/imdb-posters-and-description-256

teticio

2022-11-25T00:37:31Z

12

0

null

[ "region:us" ]

2022-11-25T00:37:31Z

2022-11-25T00:31:23.000Z

2022-11-25T00:31:23

Entry not found

[ -0.3227649927139282, -0.225684255361557, 0.862226128578186, 0.43461498618125916, -0.5282987952232361, 0.7012963891029358, 0.7915717363357544, 0.07618629932403564, 0.7746025919914246, 0.2563219666481018, -0.7852816581726074, -0.2257382869720459, -0.9104480743408203, 0.5715669393539429, -0...

null

TTian/feedback-prize-tokenized-datasets

TTian

2022-11-25T02:38:47Z

12

0

null

[ "region:us" ]

2022-11-25T02:38:47Z

2022-11-25T02:38:32.000Z

2022-11-25T02:38:32

Entry not found

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null

nzh324/green

nzh324

2022-11-25T03:57:16Z

12

0

null

[ "license:mit", "region:us" ]

2022-11-25T03:57:16Z

2022-11-25T03:56:16.000Z

2022-11-25T03:56:16

--- license: mit ---

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null

nzh324/pink

nzh324

2022-11-25T03:58:24Z

12

0

null

[ "license:mit", "region:us" ]

2022-11-25T03:58:24Z

2022-11-25T03:57:43.000Z

2022-11-25T03:57:43

--- license: mit ---

[ -0.12853367626667023, -0.18616794049739838, 0.6529126763343811, 0.4943627417087555, -0.19319313764572144, 0.23607443273067474, 0.36071979999542236, 0.05056338757276535, 0.5793654322624207, 0.7400138974189758, -0.6508103013038635, -0.23783987760543823, -0.710224986076355, -0.047825977206230...

null

Abhilashvj/deliveryman

Abhilashvj

2022-11-25T05:15:24Z

12

0

null

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

2022-11-25T05:15:24Z

2022-11-25T05:14:14.000Z

2022-11-25T05:14:14

--- license: apache-2.0 ---

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null

Hiraa/Roman-urdu_reviews_and_summary

Hiraa

2022-11-25T09:06:46Z

12

0

null

[ "region:us" ]

2022-11-25T09:06:46Z

2022-11-25T09:00:47.000Z

2022-11-25T09:00:47

Entry not found

[ -0.3227647542953491, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965083122253, 0.7915717959403992, 0.07618629932403564, 0.7746022343635559, 0.2563222348690033, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

shreyasharma/proofs3

shreyasharma

2022-11-25T09:55:08Z

12

0

null

[ "region:us" ]

2022-11-25T09:55:08Z

2022-11-25T09:55:04.000Z

2022-11-25T09:55:04

--- dataset_info: features: - name: intermediate_conclusions struct: - name: int1 dtype: string - name: int10 dtype: string - name: int11 dtype: string - name: int12 dtype: string - name: int13 dtype: string - name: int14 dtype: string - name: int15 dtype: string - name: int16 dtype: string - name: int17 dtype: string - name: int2 dtype: string - name: int3 dtype: string - name: int4 dtype: string - name: int5 dtype: string - name: int6 dtype: string - name: int7 dtype: string - name: int8 dtype: string - name: int9 dtype: string - name: step_proof dtype: string - name: triples struct: - name: sent1 dtype: string - name: sent10 dtype: string - name: sent11 dtype: string - name: sent12 dtype: string - name: sent13 dtype: string - name: sent14 dtype: string - name: sent15 dtype: string - name: sent16 dtype: string - name: sent17 dtype: string - name: sent2 dtype: string - name: sent3 dtype: string - name: sent4 dtype: string - name: sent5 dtype: string - name: sent6 dtype: string - name: sent7 dtype: string - name: sent8 dtype: string - name: sent9 dtype: string - name: hypothesis dtype: string - name: question dtype: string - name: answer dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 2614556 num_examples: 2626 download_size: 1188057 dataset_size: 2614556 --- # Dataset Card for "proofs3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.43329334259033203, 0.04677458107471466, 0.528128445148468, 0.3508748412132263, -0.22898873686790466, -0.19437545537948608, 0.3371061682701111, -0.21716290712356567, 0.6331523060798645, 0.45073217153549194, -0.6035435795783997, -0.7302740216255188, -0.668983519077301, -0.1381047517061233...

null

Isma/librispeech_1000_seed_42

Isma

2022-11-28T14:52:52Z

12

0

null

[ "region:us" ]

2022-11-28T14:52:52Z

2022-11-28T14:51:41.000Z

2022-11-28T14:51:41

Entry not found

[ -0.3227647542953491, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965083122253, 0.7915717959403992, 0.07618629932403564, 0.7746022343635559, 0.2563222348690033, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

autoevaluate/autoeval-staging-eval-project-ecaf0dbc-43a3-4513-bbcf-d0f372522232-109106

autoevaluate

2022-11-29T14:08:27Z

12

0

null

[ "autotrain", "evaluation", "region:us" ]

2022-11-29T14:08:27Z

2022-11-29T14:07:50.000Z

2022-11-29T14:07:50

--- type: predictions tags: - autotrain - evaluation datasets: - glue eval_info: task: binary_classification model: autoevaluate/binary-classification metrics: ['matthews_correlation'] dataset_name: glue dataset_config: sst2 dataset_split: validation col_mapping: text: sentence target: label --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Binary Text Classification * Model: autoevaluate/binary-classification * Dataset: glue * Config: sst2 * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.

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null

dferndz/cSQuAD2

dferndz

2022-12-09T23:18:39Z

12

0

null

[ "task_categories:question-answering", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "language:en", "license:apache-2.0", "region:us" ]

2022-12-09T23:18:39Z

2022-11-30T00:49:11.000Z

2022-11-30T00:49:11

--- annotations_creators: - expert-generated language: - en language_creators: - other license: - apache-2.0 multilinguality: - monolingual pretty_name: cSQuAD2 size_categories: [] source_datasets: [] tags: [] task_categories: - question-answering task_ids: [] --- # Dataset Card for cSQuAD2 ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary A contrast set to evaluate models trained on SQUAD on out-of-domain data. ### Supported Tasks Evaluate question-answering ### Languages English ## Dataset Structure ### Data Instances Dataset contains 40 instances ### Data Fields | Field | Description | |----------|-------------------------------------------------- | id | Id of document containing context | | title | Title of the document | | context | The context of the question | | question | The question to answer | | answers | A list of possible answers from the context | | answer_start | The index in context where the answer starts | ### Data Splits A single `test` split is provided ## Dataset Creation Dataset was created from Wikipedia articles ## Additional Information ### Licensing Information Apache 2.0 license ### Citation Information TODO: add citations

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null

yuvalkirstain/laion-hd-subset

yuvalkirstain

2022-11-30T11:07:56Z

12

0

null

[ "region:us" ]

2022-11-30T11:07:56Z

2022-11-30T09:48:05.000Z

2022-11-30T09:48:05

--- dataset_info: features: - name: similarity dtype: float64 - name: hash dtype: int64 - name: punsafe dtype: float64 - name: pwatermark dtype: float64 - name: LANGUAGE dtype: string - name: caption dtype: string - name: url dtype: string - name: key dtype: string - name: status dtype: string - name: error_message dtype: 'null' - name: width dtype: int64 - name: height dtype: int64 - name: original_width dtype: int64 - name: original_height dtype: int64 - name: exif dtype: string - name: md5 dtype: string - name: path dtype: string - name: image dtype: image splits: - name: train num_bytes: 4395359106.2963705 num_examples: 13451 - name: test num_bytes: 496904910.53063023 num_examples: 1495 download_size: 4890190248 dataset_size: 4892264016.827001 --- # Dataset Card for "laion-hd-subset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.5386945009231567, -0.2610115110874176, 0.22680456936359406, 0.09531481564044952, -0.2095387876033783, 0.013276174664497375, 0.4428166449069977, 0.045999232679605484, 0.9906775951385498, 0.6558960676193237, -0.8690496683120728, -0.730083167552948, -0.48813191056251526, -0.312788873910903...

null

SauravMaheshkar/tox21_SRp53

SauravMaheshkar

2023-02-12T14:30:43Z

12

2

null

[ "task_categories:other", "task_categories:graph-ml", "annotations_creators:machine-generated", "language_creators:machine-generated", "bio", "bio-chem", "molnet", "molecule-net", "biophysics", "arxiv:1703.00564", "region:us" ]

2023-02-12T14:30:43Z

2022-11-30T10:33:29.000Z

2022-11-30T10:33:29

--- annotations_creators: - machine-generated language_creators: - machine-generated pretty_name: tox21_SRp53 tags: - bio - bio-chem - molnet - molecule-net - biophysics task_categories: - other - graph-ml task_ids: [] --- ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Additional Information](#additional-information) - [Citation Information](#citation-information) - [Contributions](#contributions) # Dataset Description - **Homepage: https://moleculenet.org/** - **Repository: https://github.com/deepchem/deepchem/tree/master** - **Paper: https://arxiv.org/abs/1703.00564** ## Dataset Summary `tox21_SRp53` is a dataset included in [MoleculeNet](https://moleculenet.org/). The "Toxicology in the 21st Century" (Tox21) initiative created a public database measuring toxicity of compounds, which has been used in the 2014 Tox21 Data Challenge. This dataset contains qualitative toxicity measurements for 8k compounds on 12 different targets, including nuclear receptors and stress response pathways. # Dataset Structure ## Data Fields Each split contains * `smiles`: the [SMILES](https://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system) representation of a molecule * `selfies`: the [SELFIES](https://github.com/aspuru-guzik-group/selfies) representation of a molecule * `target`: Measured results (Active/Inactive) for bioassays ## Data Splits The dataset is split into an 80/10/10 train/valid/test split using random split. # Additional Information ## Citation Information ``` @misc{https://doi.org/10.48550/arxiv.1703.00564, doi = {10.48550/ARXIV.1703.00564}, url = {https://arxiv.org/abs/1703.00564}, author = {Wu, Zhenqin and Ramsundar, Bharath and Feinberg, Evan N. and Gomes, Joseph and Geniesse, Caleb and Pappu, Aneesh S. and Leswing, Karl and Pande, Vijay}, keywords = {Machine Learning (cs.LG), Chemical Physics (physics.chem-ph), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Physical sciences, FOS: Physical sciences}, title = {MoleculeNet: A Benchmark for Molecular Machine Learning}, publisher = {arXiv}, year = {2017}, copyright = {arXiv.org perpetual, non-exclusive license} } ``` ## Contributions Thanks to [@SauravMaheshkar](https://github.com/SauravMaheshkar) and [@zanussbaum](https://github.com/zanussbaum) for adding this dataset

[ -0.26724809408187866, -0.04732227697968483, 0.3967911899089813, 0.23023183643817902, -0.15783371031284332, 0.02013002522289753, -0.18514342606067657, -0.16829949617385864, -0.04758699610829353, 0.39713048934936523, -0.7172518968582153, -0.9645119905471802, -0.5188170075416565, 0.3059370517...

null

AntonioTal/IMAGES

AntonioTal

2022-12-02T20:18:52Z

12

0

null

[ "region:us" ]

2022-12-02T20:18:52Z

2022-11-30T11:40:12.000Z

2022-11-30T11:40:12

Entry not found

[ -0.3227645754814148, -0.22568479180335999, 0.8622264862060547, 0.43461528420448303, -0.52829909324646, 0.7012971639633179, 0.7915720343589783, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104477167129517, 0.5715674161911011, -...

null

Asmar/visioniert

Asmar

2022-11-30T12:07:49Z

12

0

null

[ "region:us" ]

2022-11-30T12:07:49Z

2022-11-30T12:07:08.000Z

2022-11-30T12:07:08

Entry not found

[ -0.3227645754814148, -0.22568479180335999, 0.8622264862060547, 0.43461528420448303, -0.52829909324646, 0.7012971639633179, 0.7915720343589783, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104477167129517, 0.5715674161911011, -...

null

malteos/germeval2017

malteos

2022-11-30T13:49:08Z

12

0

null

[ "language:de", "region:us" ]

2022-11-30T13:49:08Z

2022-11-30T12:53:43.000Z

2022-11-30T12:53:43

--- language: - de --- # Germeval Task 2017: Shared Task on Aspect-based Sentiment in Social Media Customer Feedback In the connected, modern world, customer feedback is a valuable source for insights on the quality of products or services. This feedback allows other customers to benefit from the experiences of others and enables businesses to react on requests, complaints or recommendations. However, the more people use a product or service, the more feedback is generated, which results in the major challenge of analyzing huge amounts of feedback in an efficient, but still meaningful way. Thus, we propose a shared task on automatically analyzing customer reviews about “Deutsche Bahn” - the german public train operator with about two billion passengers each year. Example: > “RT @XXX: Da hört jemand in der Bahn so laut ‘700 Main Street’ durch seine Kopfhörer, dass ich mithören kann. :( :( :(“ As shown in the example, insights from reviews can be derived on different granularities. The review contains a general evaluation of the travel (The customer disliked the travel). Furthermore, the review evaluates a dedicated aspect of the train travel (“laut” → customer did not like the noise level). Consequently, we frame the task as aspect-based sentiment analysis with four sub tasks: ## Data format ``` ID <tab> Text <tab> Relevance <tab> Sentiment <tab> Aspect:Polarity (whitespace separated) ``` ## Links - http://ltdata1.informatik.uni-hamburg.de/germeval2017/ - https://sites.google.com/view/germeval2017-absa/ ## How to cite ```bibtex @inproceedings{germevaltask2017, title = {{GermEval 2017: Shared Task on Aspect-based Sentiment in Social Media Customer Feedback}}, author = {Michael Wojatzki and Eugen Ruppert and Sarah Holschneider and Torsten Zesch and Chris Biemann}, year = {2017}, booktitle = {Proceedings of the GermEval 2017 – Shared Task on Aspect-based Sentiment in Social Media Customer Feedback}, address={Berlin, Germany}, pages={1--12} } ```

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null

m-aliabbas/idrak_unsplitted_amy

m-aliabbas

2022-11-30T14:24:08Z

12

0

null

[ "region:us" ]

2022-11-30T14:24:08Z

2022-11-30T13:50:33.000Z

2022-11-30T13:50:33

Entry not found

[ -0.3227645754814148, -0.22568479180335999, 0.8622264862060547, 0.43461528420448303, -0.52829909324646, 0.7012971639633179, 0.7915720343589783, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104477167129517, 0.5715674161911011, -...

null

slnader/fcc-comments

slnader

2022-11-30T19:05:23Z

12

2

null

[ "task_categories:text-retrieval", "task_ids:document-retrieval", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10M<n<100M", "source_datasets:original", "language:en", "license:cc-by-nc-sa-4.0", "notice and comment", "regulatio...

2022-11-30T19:05:23Z

2022-11-30T17:38:32.000Z

2022-11-30T17:38:32

--- annotations_creators: - expert-generated language: - en language_creators: - found license: - cc-by-nc-sa-4.0 multilinguality: - monolingual pretty_name: fcc-comments size_categories: - 10M<n<100M source_datasets: - original tags: - notice and comment - regulation - government task_categories: - text-retrieval task_ids: - document-retrieval --- # Dataset Card for fcc-comments ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository: https://github.com/slnader/fcc-comments ** - **Paper: https://doi.org/10.1002/poi3.327 ** ### Dataset Summary Online comment floods during public consultations have posed unique governance challenges for regulatory bodies seeking relevant information on proposed regulations. How should regulatory bodies separate spam and fake comments from genuine submissions by the public, especially when fake comments are designed to imitate ordinary citizens? How can regulatory bodies achieve both breadth and depth in their citations to the comment corpus? What is the best way to select comments that represent the average submission and comments that supply highly specialized information? `fcc-comments` is an annotated version of the comment corpus from the Federal Communications Commission's (FCC) 2017 "Restoring Internet Freedom" proceeding. The source data were downloaded directly from the FCC's Electronic Comment Filing System (ECFS) between January and February of 2019 and include raw comment text and metadata on comment submissions. The comment data were processed to be in a consistent format (machine-readable pdf or plain text), and annotated with three types of information: whether the comment was cited in the agency's final order, the type of commenter (individual, interest group, business group), and whether the comment was associated with an in-person meeting. The release also includes query-term and document-term matrices to facilitate keyword searches on the comment corpus. An example of how these can be used with the bm25 algorithm can be found [here](https://github.com/slnader/fcc-comments/blob/main/process_comments/1_score_comments.py). ## Dataset Structure FCC relational database (fcc.pgsql): The core components of the database include a table for submission metadata, a table for attachment metadata, a table for filer metadata, and a table that contains comment text if submitted in express format. In addition to these core tables, there are several derived tables specific to the analyses in the paper, including which submissions and attachments were cited in the final order, which submissions were associated with in-person meetings, and which submissions were associated with interest groups. Full documentation of the tables can be found in fcc_database.md. Attachments (attachments.tar.gz): Attachments to submissions that could be converted to text via OCR and saved in machine-readable pdf format. The filenames are formatted as [submission_id]_[document_id].pdf, where submission_id and document_id are keys in the relational database. Search datasets (search.tar.gz): Objects to facilitate prototyping of search algorithms on the comment corpus. Contains the following elements: | Filename | description | | ----------- | ----------- | query_dtm.pickle | Query-term matrix (79x3986) in sparse csr format (rows are queries, columns are bigram keyword counts). query_text.pickle | Dictionary keyed by the paragraph number in the FCC’s Notice of Proposed Rulemaking. Values are the text of the query containing a call for comments. | search_dtms_express.pickle | Document-term matrix for express comments (3800691x3986) in sparse csr format (rows are comment pages, columns are bigram keyword counts). | search_index_express.pickle | Pandas dataframe containing unique id and total term length for express comments. | search_dtms.pickle | Document-term matrix for standard comment attachments (44655x3986) in sparse csr format (rows are comment pages, columns are bigram keyword counts). | search_index.pickle | Pandas dataframe containing unique id and total term length for standard comment attachments. | ### Data Fields The following tables are available in fcc.pgsql: - comments: plain text comments associated with submissions | column | type | description | | ----------- | ----------- | ----------- | | comment_id | character varying(64) | unique id for plain text comment | comment_text | text | raw text of plain text comment row_id | integer | row sequence for plain text comments - submissions: metadata for submissions | column | type | description | | ----------- | ----------- | ----------- | submission_id | character varying(20) | unique id for submission submission_type | character varying(100) | type of submission (e.g., comment, reply, statement) express_comment | numeric | 1 if express comment date_received | date | date submission was received contact_email | character varying(255) | submitter email address city | character varying(255) | submitter city address_line_1 | character varying(255) | submitter address line 1 address_line_2 | character varying(255) | submitter address line 2 state | character varying(255) | submitter state zip_code | character varying(50) | submitter zip comment_id | character varying(64) | unique id for plain text comment - filers: names of filers associated with submissions | column | type | description | | ----------- | ----------- | ----------- | submission_id | character varying(20) | unique id for submission filer_name | character varying(250) | name of filer associated with submission - documents: attachments associated with submissions | column | type | description | | ----------- | ----------- | ----------- | submission_id | character varying(20) | unique id for submission document_name | text | filename of attachment download_status | numeric | status of attachment download document_id | character varying(64) | unique id for attachment file_extension | character varying(4) | file extension for attachment - filers_cited: citations from final order | column | type | description | | ----------- | ----------- | ----------- | point | numeric | paragraph number in final order filer_name | character varying(250) | name of cited filer submission_type | character varying(12) | type of submission as indicated in final order page_numbers | text[] | cited page numbers cite_id | integer | unique id for citation filer_id | character varying(250) | id for cited filer - docs_cited: attachments associated with cited submissions | column | type | description | | ----------- | ----------- | ----------- | cite_id | numeric | unique id for citation submission_id | character varying(20) | unique id for submission document_id | character varying(64) | unique id for attachment - near_duplicates: lookup table for comment near-duplicates | column | type | description | | ----------- | ----------- | ----------- | target_document_id | unique id for target document duplicate_document_id | unique id for duplicate of target document - exact_duplicates: lookup table for comment exact duplicates | column | type | description | | ----------- | ----------- | ----------- | target_document_id | character varying(100) | unique id for target document duplicate_document_id | character varying(100) | unique id for duplicate of target document - in_person_exparte: submissions associated with ex parte meeting | column | type | description | | ----------- | ----------- | ----------- | submission_id | character varying(20) | unique id for submission - interest_groups: submissions associated with interest groups | column | type | description | | ----------- | ----------- | ----------- | submission_id | character varying(20) | unique id for submission business | numeric | 1 if business group, 0 otherwise ## Dataset Creation ### Curation Rationale The data were curated to perform information retrieval and summarization tasks as documented in https://doi.org/10.1002/poi3.327. ### Source Data #### Initial Data Collection and Normalization The data for this study come from the FCC's Electronic Comment Filing System (ECFS) system, accessed between January and February of 2019. I converted the API responses into a normalized, relational database containing information on 23,951,967 submissions. 23,938,686 "express" submissions contained a single plain text comment submitted directly through the comment form. 13,821 "standard" submissions contained one or more comment documents submitted as attachments in various file formats. While the FCC permitted any file format for attachments, I only consider documents attached in pdf, plain text, rich text, and Microsoft Word file formats, and I drop submitted documents that were simply copies of the FCC’s official documents (e.g., the NPRM itself). Using standard OCR software, I attempted to convert all attachments into plain text and saved them as machine-readable pdfs. #### Who are the source language producers? All submitters of public comments during the public comment period (but see note on fake comments in considerations). ### Annotations #### Annotation process - Citations: I consider citations from the main text of the FCC's final rule. I did not include citations to supporting documents not available through ECFS (e.g., court decisions), nor did I include citations to submissions from prior FCC proceedings. The direct citations to filed submissions are included in a series of 1,186 footnotes. The FCC’s citation format typically followed a relatively standard pattern: the name of the filer (e.g., Verizon), a description of the document (e.g., Comment), and at times a page number. I extracted citations from the text using regular expressions. Based on a random sample of paragraphs from the final order, the regular expressions identified 98% of eligible citations, while successfully excluding all non-citation text. In total, this produced 1,886 unique citations. I then identified which of the comments were cited. First, I identified all documents from the cited filer that had enough pages to contain the page number cited (if provided), and, where applicable, whose filename contained the moniker from the FCC’s citation (e.g., "Reply"). The majority of citations matched to only one possible comment submitted, and I identified the re- maining cited comments through manual review of the citations. In this way, I was able to tag documents associated with all but three citations. When the same cited document was submitted under multiple separate submissions, I tagged all versions of the document as being cited. - Commenter type: Comments are labeled as mass comments if 10 or more duplicate or near-duplicate copies were submitted by individual commenters. Near-duplicates were defined as comments with non-zero identical information scores. To identify the type of commenter for non-mass comments, I take advantage of the fact that the vast majority of organized groups preferred standard submissions over express submissions. Any non-mass comment submitted as an express comment was coded as coming from an individual. To distinguish between individuals and organizations that used standard submissions, I use a first name and surname database from the names dataset Python package to characterize filer names as belonging to individuals or organizations. I also use the domain of the submitter’s email address to re-categorize comments as coming from organizations if they were submitted on behalf of organizations by an individual. Government officials were identified by their .gov email addresses. I manually review this procedure for mischaracterizations. After obtaining a list of organization names, I manually code each one as belonging to a business group or a non-business group. Government officials writing in their official capacity were categorized as a non-business group. - In-person meetings: To identify which commenters held in-person meetings with the agency, I collect all comments labeled as an ex-parte submission in the EFCS. I manually review these submissions for mention of an in-person meeting. I label a commenter as having held an in-person meeting if they submitted at least one ex-parte document that mentioned an in-person meeting. #### Who are the annotators? Annotations are a combination of automated and manual review done by the author. ### Personal and Sensitive Information This dataset may contain personal and sensitive information, as there were no restrictions on what commenters could submit to the agency. This dataset also contains numerous examples of profanity and spam. These comments represent what the FCC decided was appropriate to share publicly on their own website. ## Considerations for Using the Data ### Discussion of Biases This proceeding was famous for the large number of "fake" comments (comments impersonating ordinary citizens) submitted to the agency (see [this report](https://ag.ny.gov/sites/default/files/oag-fakecommentsreport.pdf) by the NY AG for more information). As such, this comment corpus contains a mix of computer-generated and natural language, and there is currently no way to reliably separate mass comments submitted with the approval of the commenter and those submitted on behalf of the commenter without their knowledge. ## Additional Information ### Licensing Information CreativeCommons Attribution-NonCommercial-ShareAlike 4.0 International. ### Citation Information ``` @article{handan2022, title={Do fake online comments pose a threat to regulatory policymaking? Evidence from Internet regulation in the United States}, author={Handan-Nader, Cassandra}, journal={Policy \& Internet}, year={2022} } ```

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null

argilla/uber-reviews

argilla

2022-12-06T12:00:28Z

12

0

null

[ "task_categories:text-classification", "task_ids:sentiment-classification", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:unknown", "region:us" ]

2022-12-06T12:00:28Z

2022-12-06T11:47:18.000Z

2022-12-06T11:47:18

--- language: - en license: - unknown size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification dataset_info: features: - name: text dtype: string - name: inputs struct: - name: text dtype: string - name: prediction list: - name: label dtype: string - name: score dtype: float64 - name: prediction_agent dtype: string - name: annotation dtype: 'null' - name: annotation_agent dtype: 'null' - name: multi_label dtype: bool - name: explanation dtype: 'null' - name: id dtype: string - name: metadata dtype: 'null' - name: status dtype: string - name: event_timestamp dtype: timestamp[us] - name: metrics struct: - name: text_length dtype: int64 splits: - name: train num_bytes: 2761597 num_examples: 2347 download_size: 1691346 dataset_size: 2761597 --- # Dataset Card for "uber-reviews" ## Dataset Description - **Homepage:** Kaggle Challenge - **Repository:** https://www.kaggle.com/datasets/jschne61701/uber-rides-costumer-reviews-dataset - **Paper:** N.A. - **Leaderboard:** N.A. - **Point of Contact:** N.A. ### Dataset Summary Using Python's Beautiful Soup library and Scrappy framework, scraped date, star rating, and comment from all reviews from 2013 - 2019. ### Languages english ### Citation Information https://www.kaggle.com/datasets/jschne61701/uber-rides-costumer-reviews-dataset https://www.sitejabber.com/reviews/uber.com https://www.consumeraffairs.com/travel/uber.html https://www.kaggle.com/purvank/uber-rider-reviews-dataset ### Contributions Thanks to [@davidberenstein1957](https://github.com/davidberenstein1957) for adding this dataset.

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null

tilos/ASR-CCANTCSC

tilos

2022-12-07T21:39:11Z

12

0

null

[ "language:zh", "license:cc-by-nc-nd-4.0", "region:us" ]

2022-12-07T21:39:11Z

2022-12-07T11:13:27.000Z

2022-12-07T11:13:27

--- license: cc-by-nc-nd-4.0 pretty_name: ASR-CCANTCSC language: - zh dataset_info: features: - name: audio dtype: Audio - name: sentence dtype: string ---

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null

maratim/romanianspeech

maratim

2022-12-07T12:08:50Z

12

0

null

[ "region:us" ]

2022-12-07T12:08:50Z

2022-12-07T12:05:06.000Z

2022-12-07T12:05:06

Entry not found

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null

ywan111/dataset-test

ywan111

2022-12-07T12:18:19Z

12

0

null

[ "region:us" ]

2022-12-07T12:18:19Z

2022-12-07T12:15:48.000Z

2022-12-07T12:15:48

Entry not found

[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

autoevaluate/autoeval-staging-eval-autoevaluate__squad-sample-autoevaluate__squad-sample-778ba0-17436360

autoevaluate

2022-12-07T12:28:44Z

12

0

null

[ "autotrain", "evaluation", "region:us" ]

2022-12-07T12:28:44Z

2022-12-07T12:28:20.000Z

2022-12-07T12:28:20

--- type: predictions tags: - autotrain - evaluation datasets: - autoevaluate/squad-sample eval_info: task: extractive_question_answering model: autoevaluate/extractive-question-answering-not-evaluated metrics: [] dataset_name: autoevaluate/squad-sample dataset_config: autoevaluate--squad-sample dataset_split: test col_mapping: context: context question: question answers-text: answers.text answers-answer_start: answers.answer_start --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: autoevaluate/extractive-question-answering-not-evaluated * Dataset: autoevaluate/squad-sample * Config: autoevaluate--squad-sample * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.

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null

xusenlin/clue-ner

xusenlin

2022-12-07T14:22:37Z

12

7

null

[ "language:zh", "license:apache-2.0", "named entity recognition", "clue", "region:us" ]

2022-12-07T14:22:37Z

2022-12-07T13:14:03.000Z

2022-12-07T13:14:03

--- dataset_info: features: - name: text dtype: string - name: entities list: - name: id dtype: int64 - name: entity dtype: string - name: start_offset dtype: int64 - name: end_offset dtype: int64 - name: label dtype: string splits: - name: train num_bytes: 2443356 num_examples: 10748 - name: test num_bytes: 154492 num_examples: 1345 - name: validation num_bytes: 309106 num_examples: 1343 download_size: 1658426 dataset_size: 2906954 language: - zh tags: - named entity recognition - clue license: apache-2.0 --- # CLUE-NER 命名实体识别数据集字段说明 + `text`: 文本 + `entities`: 文本中包含的实体 + `id`: 实体 `id` + `entity`: 实体对应的字符串 + `start_offset`: 实体开始位置 + `end_offset`: 实体结束位置的下一位 + `label`: 实体对应的开始位置

[ -0.4908401370048523, -0.6941743493080139, 0.11775249987840652, 0.3810179531574249, -1.0162177085876465, 0.03100728616118431, 0.2675838768482208, -0.41077181696891785, 0.9416723251342773, 0.11332858353853226, -0.7723591327667236, -0.9075393676757812, -0.6635699272155762, 0.19504155218601227...

null

ola13/small-oscar-dedup

ola13

2022-12-07T15:48:57Z

12

0

null

[ "region:us" ]

2022-12-07T15:48:57Z

2022-12-07T13:44:16.000Z

2022-12-07T13:44:16

--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: url dtype: string - name: domain dtype: string - name: perplexity dtype: float64 - name: dup_ratio dtype: float64 - name: pairs sequence: sequence: int64 - name: repetitions sequence: binary - name: cluster sequence: sequence: int64 splits: - name: train num_bytes: 323557137 num_examples: 43200 download_size: 0 dataset_size: 323557137 --- # Dataset Card for "small-oscar-dedup" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.6796634793281555, -0.11920082569122314, 0.2930970788002014, -0.0364091582596302, -0.4273083209991455, -0.17712780833244324, 0.3144541084766388, -0.22153165936470032, 1.018802285194397, 0.46245884895324707, -0.6185622215270996, -0.5488615036010742, -0.8006286025047302, -0.133874177932739...

null

Tristan/olm-bookcorpus-tokenized-1024

Tristan

2022-12-07T19:33:36Z

12

0

null

[ "region:us" ]

2022-12-07T19:33:36Z

2022-12-07T19:28:08.000Z

2022-12-07T19:28:08

--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: special_tokens_mask sequence: int8 splits: - name: train num_bytes: 8534733804.0 num_examples: 1386409 download_size: 2291578601 dataset_size: 8534733804.0 --- # Dataset Card for "olm-bookcorpus-tokenized-1024" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.4007304906845093, -0.22370117902755737, -0.04232306405901909, 0.07134042680263519, -0.2512008845806122, -0.013094460591673851, 0.22184133529663086, -0.0029629277996718884, 0.6641333699226379, 0.7961339354515076, -0.668185830116272, -0.8444881439208984, -0.3950076103210449, -0.0687094330...

null

evageon/myaudio

evageon

2022-12-08T15:28:56Z

12

0

null

[ "region:us" ]

2022-12-08T15:28:56Z

2022-12-07T20:56:35.000Z

2022-12-07T20:56:35

--- pretty_name: MGB2 alt_glob: [] alt_sep: '' dataset_link: https://huggingface.co/datasets/malmarz/test_mgb2/resolve/main/mgb2.test.tar.gz dataset_name: mgb2_speech datasets_path: datasets file_type: wav header: null hf_path: '' json_key: null label_column_name: '' level: null lines: false local_dir: false new_columns: '' pal: false skiprows: 0 squad: false xml_columns: '' dataset_info: features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: test num_bytes: 384372257.17 num_examples: 3890 - name: train num_bytes: 1488884786.673 num_examples: 15559 download_size: 0 dataset_size: 1873257043.8430002 --- Please note this dataset is private ### Using the data You can stream the data data loader: ```python myaudio = load_dataset( "evageon/myaudio", use_auth_token=os.environ["HG_USER_TOKEN"], # replace this with your access token streaming=True) ``` Then you can iterate over the dataset ```python # replace test with validation or train depending on split you need print(next(iter(myaudio["test"]))) ``` outputs: ``` {'path': 'CD93A8FF-C3ED-4AD4-95A6-8363CCB93B90_spk-0001_seg-0024467:0025150.wav', 'audio': {'path': 'dataset/test/wav/CD93A8FF-C3ED-4AD4-95A6-8363CCB93B90_spk-0001_seg-0024467:0025150.wav', 'array': array([0.00662231, 0.00497437, 0.00518799, ..., 0.01150513, 0.00708008, 0.00296021]), 'sampling_rate': 16000}, 'text': 'خطرا على دول الخليج لماذا اعتبرت أن إيران اليوم والخطر الذي تشكله إيران مختلف'} ```

[ -0.3966018557548523, -0.5160664916038513, 0.22213369607925415, 0.19003397226333618, -0.128263920545578, 0.20020733773708344, -0.4569166302680969, 0.07743871212005615, 0.4706849157810211, 0.44717031717300415, -0.6632488369941711, -0.7606067061424255, -0.5709961652755737, 0.20207592844963074...

null

thennal/msc

thennal

2022-12-08T06:49:31Z

12

1

null

[ "task_categories:automatic-speech-recognition", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:ml", "license:cc-by-sa-4.0", "region:us" ]

2022-12-08T06:49:31Z

2022-12-08T06:19:56.000Z

2022-12-08T06:19:56

--- annotations_creators: - crowdsourced language: - ml language_creators: - crowdsourced license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: Swathanthra Malayalam Computing Malayalam Speech Corpus size_categories: - 1K<n<10K source_datasets: [] tags: [] task_categories: - automatic-speech-recognition task_ids: [] dataset_info: features: - name: speechid dtype: string - name: speaker_id dtype: string - name: review_score dtype: int64 - name: transcript dtype: string - name: category dtype: string - name: speaker_gender dtype: string - name: speaker_age dtype: string - name: audio dtype: audio: sampling_rate: 48000 splits: - name: train num_bytes: 581998721.306 num_examples: 1541 download_size: 422643542 dataset_size: 581998721.306 --- # SMC Malayalam Speech Corpus Malayalam Speech Corpus (MSC) is a repository of curated speech samples collected using MSC web application, released by Swathanthra Malayalam Computing. The official blog post and source data can be found at [https://blog.smc.org.in/malayalam-speech-corpus/](https://blog.smc.org.in/malayalam-speech-corpus/). ## Dataset Description - **Homepage:** [https://blog.smc.org.in/malayalam-speech-corpus/](https://blog.smc.org.in/malayalam-speech-corpus/) ### Dataset Summary The first version of Malayalam Speech Corpus contains 1541 speech samples from 75 contributors amounting to 1:38:16 hours of speech. It has 482 unique sentences, 1400 unique words, 553 unique syllables and 48 unique phonemes.

[ -0.42506739497184753, -0.44385531544685364, 0.1807435154914856, 0.3562546968460083, -0.3989783823490143, 0.31520506739616394, -0.048128146678209305, -0.03258460760116577, 0.5415960550308228, 0.43088746070861816, -0.7856222987174988, -0.5531346797943115, -0.46952274441719055, 0.221303701400...

null

nuprl/MultiPL-E-raw-data

nuprl

2022-12-20T18:40:05Z

12

0

null

[ "license:bsd-3-clause", "arxiv:2208.08227", "region:us" ]

2022-12-20T18:40:05Z

2022-12-11T19:07:19.000Z

2022-12-11T19:07:19

--- license: bsd-3-clause --- # MultiPL-E Evaluation Raw Data This is the raw data for the MultiPL-E paper: https://arxiv.org/abs/2208.08227

[ -0.32517582178115845, -0.4364900290966034, 0.24707752466201782, 0.3497063219547272, -0.1511189341545105, 0.16554835438728333, 0.22173793613910675, -0.056709498167037964, 0.3589414954185486, 0.4290607273578644, -0.7636997699737549, -0.32563087344169617, -0.36635443568229675, -0.092848390340...

null

amosr2002/NYCDATA

amosr2002

2022-12-14T16:36:21Z

12

0

null

[ "region:us" ]

2022-12-14T16:36:21Z

2022-12-14T16:30:31.000Z

2022-12-14T16:30:31

--- dataset_info: features: - name: name dtype: string - name: uuid dtype: string - name: status dtype: string - name: image dtype: image - name: label.annotations list: - name: id dtype: int32 - name: category_id dtype: int32 - name: label.segmentation_bitmap dtype: image splits: - name: train num_bytes: 5365744.0 num_examples: 10 download_size: 0 dataset_size: 5365744.0 --- # Dataset Card for "NYCDATA" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.4909600019454956, -0.23996716737747192, 0.295451819896698, 0.06791726499795914, -0.1374521255493164, 0.31571951508522034, 0.4012744128704071, -0.35118088126182556, 1.09690260887146, 0.3896866738796234, -0.4865715801715851, -0.8733740448951721, -0.4924921691417694, -0.27827519178390503, ...

null

my-projects/dataset1

my-projects

2022-12-14T19:15:42Z

12

0

null

[ "region:us" ]

2022-12-14T19:15:42Z

2022-12-14T19:15:35.000Z

2022-12-14T19:15:35

--- dataset_info: features: - name: solution sequence: int64 - name: coefficients sequence: int64 - name: discriminant dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 4814334 num_examples: 80000 - name: test num_bytes: 1204417 num_examples: 20000 download_size: 1554124 dataset_size: 6018751 --- # Dataset Card for "dataset1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.6457745432853699, -0.33117765188217163, 0.07624127715826035, 0.32439887523651123, -0.2809703052043915, -0.03685969486832619, 0.46780815720558167, -0.07035554945468903, 0.9785434603691101, 0.516433596611023, -1.0301873683929443, -0.7862607836723328, -0.6689369082450867, -0.28167438507080...

null

Mai321/ZeroTwo-PlugSuit

Mai321

2022-12-14T19:45:33Z

12

0

null

[ "license:openrail", "region:us" ]

2022-12-14T19:45:33Z

2022-12-14T19:42:56.000Z

2022-12-14T19:42:56

--- license: openrail ---

[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...

null

laion/laion2b-en-vit-l-14-embeddings

laion

2022-12-17T02:00:01Z

12

10

null

[ "region:us" ]

2022-12-17T02:00:01Z

2022-12-15T01:43:31.000Z

2022-12-15T01:43:31

Entry not found

[ -0.3227647542953491, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965083122253, 0.7915717959403992, 0.07618629932403564, 0.7746022343635559, 0.2563222348690033, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

bigcode/the-stack-metadata

bigcode

2023-03-16T13:58:24Z

12

3

null

[ "task_categories:text-generation", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:unknown", "language:code", "license:other", "arxiv:2211.15533", "region:us" ]

2023-03-16T13:58:24Z

2022-12-19T09:17:28.000Z

2022-12-19T09:17:28

--- annotations_creators: [] language_creators: - crowdsourced - expert-generated language: - code license: - other multilinguality: - multilingual pretty_name: The-Stack-Metadata size_categories: - unknown source_datasets: [] task_categories: - text-generation task_ids: [] extra_gated_prompt: |- ## Terms of Use for The Stack The Stack Metadata is a collection of additional information for and is part of The Stack dataset, - a collection of source code in over 300 programming languages. We ask that you read and acknowledge the following points before using the dataset: 1. The Stack is a collection of source code from repositories with various licenses. Any use of all or part of the code gathered in The Stack must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point. 2. The Stack is regularly updated to enact validated data removal requests. By clicking on "Access repository", you agree to update your own version of The Stack to the most recent usable version specified by the maintainers in [the following thread](https://huggingface.co/datasets/bigcode/the-stack/discussions/7). If you have questions about dataset versions and allowed uses, please also ask them in the dataset’s [community discussions](https://huggingface.co/datasets/bigcode/the-stack/discussions/new). We will also notify users via email when the latest usable version changes. 3. To host, share, or otherwise provide access to The Stack dataset, you must include [these Terms of Use](https://huggingface.co/datasets/bigcode/the-stack#terms-of-use-for-the-stack) and require users to agree to it. By clicking on "Access repository" below, you accept that your contact information (email address and username) can be shared with the dataset maintainers as well. extra_gated_fields: Email: text I have read the License and agree with its terms: checkbox --- # Dataset Card for The Stack Metadata ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Changelog](#changelog) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Usage Example](#usage-example) - [Dataset Creation](#dataset-creation) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Additional Information](#additional-information) - [Terms of Use for The Stack](#terms-of-use-for-the-stack) ## Dataset Description - **Homepage:** https://www.bigcode-project.org/ - **Repository:** https://github.com/bigcode-project - **Paper:** https://arxiv.org/abs/2211.15533 - **Leaderboard:** N/A - **Point of Contact:** contact@bigcode-project.org ### Changelog |Release|Description| |-|-| |v1.1| This is the first release of the metadata. It is for The Stack v1.1| |v1.2| Metadata dataset matching The Stack v1.2| ### Dataset Summary This is a set of additional information for repositories used for The Stack. It contains file paths, detected licenes as well as some other information for the repositories. ### Supported Tasks and Leaderboards The main task is to recreate repository structure from the files of The Stack. Also, the set can be used for computing statistics and custom filtering or aggregation operations on The Stack. ## Dataset Structure ### Data Fields ![set structure](images/structure.png) The set is split into buckets by repositories. There are 944 buckets. Additionally to the fields in the image, `ri` contains `min_repo_event_datetime` which is the ealiest date and time of an event for a repo after Jan 1 2015. ![set usage](images/usage.png) As an example of an aggregation operation on The Stack, the image above shows conceptually a selection of stars ( and issues and PR count) for a file. Each unique file can be part of multiple repositories. So, The Stack releases unique files and aggregates meta information (e.g stars) from all repositories it belongs to. For example, for max_stars_count we take the maximum number of stars from all repositories the file is part of. The meta data will allow you to reconstruct repository directory structures. For this, for each repository form `ri` tabele it is needed to take all its files from `fi` table, find them in The Stack by file's `hexsha` and save those files' content under its path for a repository from `fi` table. For speed it is preferable to index The Stack by hexsha first. ### Usage Example Restore folder structure for python files in numpy repository ```python import datasets from pathlib import Path from tqdm.auto import tqdm import pandas as pd # assuming metadata is cloned into the local folder /data/hf_repos/the-stack-metadata # the stack is cloned into the local folder /data/hf_repos/the-stack-v1.1 # destination folder is in /repo_workdir/numpy_restored the_stack_meta_path = Path('/data/hf_repos/the-stack-metadata') the_stack_path = Path('/data/hf_repos/the-stack-v1.1') repo_dst_root = Path('/repo_workdir/numpy_restored') repo_name = 'numpy/numpy' # Get bucket with numpy repo info # meta_bucket_path = None #for fn in tqdm(list((the_stack_meta_path/'data').glob('*/ri.parquet'))): # df = pd.read_parquet(fn) # if any(df['name'] == repo_name): # meta_bucket_path = fn # break meta_bucket_path = the_stack_meta_path / 'data/255_944' # Get repository id from repo name ri_id = pd.read_parquet( meta_bucket_path / 'ri.parquet' ).query( f'`name` == "{repo_name}"' )['id'].to_list()[0] # Get files information for the reopository files_info = pd.read_parquet( meta_bucket_path / 'fi.parquet' ).query( f'`ri_id` == {ri_id} and `size` != 0 and `is_deleted` == False' ) # Convert DF with files information to a dictionary by language and then file hexsha # there can be more than one file with the same hexsha in the repo so we gather # all instances per unique hexsha files_info_dict = { k: v[['hexsha', 'path']].groupby('hexsha').apply(lambda x: list(x['path'])).to_dict() for k, v in files_info.groupby('lang_ex') } # Load Python part of The Stack ds = datasets.load_dataset( str(the_stack_path/'data/python'), num_proc=10, ignore_verifications=True ) # Save file content of the python files in the numpy reposirotry in their appropriate locations def save_file_content(example, files_info_dict, repo_dst_root): if example['hexsha'] in files_info_dict: for el in files_info_dict[example['hexsha']]: path = repo_dst_root / el path.parent.mkdir(parents=True, exist_ok=True) path.write_text(example['content']) ds.map( save_file_content, fn_kwargs={'files_info_dict': files_info_dict['Python'], 'repo_dst_root': repo_dst_root}, num_proc=10 ) ``` ## Dataset Creation Please refer to [the section](https://huggingface.co/datasets/bigcode/the-stack#dataset-creation) in The Stack. ## Considerations for Using the Data Please refer to [the section](https://huggingface.co/datasets/bigcode/the-stack#considerations-for-using-the-data) in The Stack. ## Additional Information Please refer to [the section](https://huggingface.co/datasets/bigcode/the-stack#additional-information) in The Stack. ## Terms of Use for The Stack Please refer to [the section](https://huggingface.co/datasets/bigcode/the-stack#terms-of-use-for-the-stack) in The Stack.

[ -0.5771356225013733, -0.18708331882953644, 0.0888160914182663, 0.23876595497131348, -0.2861611545085907, -0.0569390207529068, 0.04967242851853371, -0.07762430608272552, 0.4950277507305145, 0.7887977957725525, -0.4692462384700775, -0.8657298684120178, -0.38229820132255554, 0.291829288005828...

null

akirasosa/laion-mini-ja

akirasosa

2022-12-28T04:33:54Z

12

0

null

[ "region:us" ]

2022-12-28T04:33:54Z

2022-12-28T04:11:03.000Z

2022-12-28T04:11:03

Entry not found

[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -...

null

taejunkim/beats

taejunkim

2022-12-28T06:50:44Z

12

0

null

[ "region:us" ]

2022-12-28T06:50:44Z

2022-12-28T06:50:28.000Z

2022-12-28T06:50:28

--- dataset_info: features: - name: mix_id dtype: string - name: beats sequence: float64 splits: - name: train num_bytes: 1479883 num_examples: 13 download_size: 1119868 dataset_size: 1479883 --- # Dataset Card for "beats" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.6293665170669556, -0.15773053467273712, 0.26815447211265564, 0.23729044198989868, -0.20448338985443115, 0.07778789848089218, 0.17924578487873077, -0.23576436936855316, 1.0927064418792725, 0.34017667174339294, -1.1057791709899902, -0.8568328619003296, -0.5412177443504333, -0.270925492048...

null

hellosimple/dataset-demo

hellosimple

2022-12-28T07:19:37Z

12

0

null

[ "license:mit", "region:us" ]

2022-12-28T07:19:37Z

2022-12-28T07:18:49.000Z

2022-12-28T07:18:49

--- license: mit ---

[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...

null

Marlenka/alpaco

Marlenka

2022-12-28T07:54:29Z

12

0

null

[ "region:us" ]

2022-12-28T07:54:29Z

2022-12-28T07:37:06.000Z

2022-12-28T07:37:06

Entry not found

[ -0.3227647542953491, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965083122253, 0.7915717959403992, 0.07618629932403564, 0.7746022343635559, 0.2563222348690033, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

ddd2jj/pawn2

ddd2jj

2022-12-28T07:46:38Z

12

0

null

[ "region:us" ]

2022-12-28T07:46:38Z

2022-12-28T07:41:42.000Z

2022-12-28T07:41:42

Entry not found

[ -0.3227647542953491, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965083122253, 0.7915717959403992, 0.07618629932403564, 0.7746022343635559, 0.2563222348690033, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

taejunkim/alignments

taejunkim

2022-12-28T07:49:17Z

12

0

null

[ "region:us" ]

2022-12-28T07:49:17Z

2022-12-28T07:48:57.000Z

2022-12-28T07:48:57

--- dataset_info: features: - name: mix_id dtype: string - name: track_id dtype: string - name: case_name dtype: string - name: feature dtype: string - name: metric dtype: string - name: key_change dtype: int64 - name: match_rate dtype: float64 - name: match_rate_raw dtype: float64 - name: matched_beats dtype: int64 - name: matched_beats_raw dtype: int64 - name: matched_time_mix dtype: float64 - name: matched_time_track dtype: float64 - name: mix_cue_in_beat dtype: float64 - name: mix_cue_out_beat dtype: float64 - name: track_cue_in_beat dtype: float64 - name: track_cue_out_beat dtype: float64 - name: mix_cue_in_time dtype: float64 - name: mix_cue_out_time dtype: float64 - name: track_cue_in_time dtype: float64 - name: track_cue_out_time dtype: float64 - name: cost dtype: float64 - name: __index_level_0__ dtype: int64 - name: wp sequence: sequence: int64 splits: - name: train num_bytes: 22961341 num_examples: 6600 download_size: 3089520 dataset_size: 22961341 --- # Dataset Card for "alignments" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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null

com0040/only

com0040

2022-12-28T11:50:21Z

12

0

null

[ "region:us" ]

2022-12-28T11:50:21Z

2022-12-28T08:22:40.000Z

2022-12-28T08:22:40

Entry not found

[ -0.3227647542953491, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965083122253, 0.7915717959403992, 0.07618629932403564, 0.7746022343635559, 0.2563222348690033, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

dvilasuero/banking_app

dvilasuero

2022-12-29T13:25:35Z

12

0

null

[ "region:us" ]

2022-12-29T13:25:35Z

2022-12-29T13:25:10.000Z

2022-12-29T13:25:10

Entry not found

[ -0.3227645754814148, -0.22568479180335999, 0.8622264862060547, 0.43461528420448303, -0.52829909324646, 0.7012971639633179, 0.7915720343589783, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104477167129517, 0.5715674161911011, -...

null

theatticusproject/cuad

theatticusproject

2023-01-02T22:36:46Z

12

1

null

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

2023-01-02T22:36:46Z

2023-01-02T21:54:27.000Z

2023-01-02T21:54:27

--- license: cc-by-4.0 ---

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null

TREC-AToMiC/AToMiC-Texts-v0.2

TREC-AToMiC

2023-02-14T21:30:37Z

12

0

null

[ "size_categories:100M<n<1B", "license:cc-by-sa-4.0", "arxiv:2103.01913", "region:us" ]

2023-02-14T21:30:37Z

2023-01-03T04:29:46.000Z

2023-01-03T04:29:46

--- dataset_info: features: - name: text_id dtype: string - name: page_url dtype: string - name: page_title dtype: string - name: section_title dtype: string - name: context_page_description dtype: string - name: context_section_description dtype: string - name: media sequence: string - name: hierachy sequence: string - name: category sequence: string - name: source_id dtype: string splits: - name: train num_bytes: 14378574060.336058 num_examples: 10134744 download_size: 6408012391 dataset_size: 14378574060.336058 license: cc-by-sa-4.0 size_categories: - 100M<n<1B --- # Dataset Card for "AToMiC-Texts-Mapped" ## Dataset Description - **Homepage:** [AToMiC homepage](https://trec-atomic.github.io/) - **Source:** [WIT](https://github.com/google-research-datasets/wit) - **Paper:** [WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning](https://arxiv.org/abs/2103.01913) ### Languages This dataset only contains English in Wikipedia (parsed from the 20221101 XML dump). ### Data Instances Each instance is a section of a Wikipedia page. We also provide its page-level information, and associated information such as categories and media. The `source_id` can be mapped back to the instance in the original [WIT instance](https://github.com/google-research-datasets/wit/blob/main/DATA.md). Notice that the WIT dataset is crawled from the earlier version of Wikipedia (2020-08-30). The WIT dataset is mapped to the new dump by pure BM25 matching with [Anserini](https://github.com/castorini/anserini). ### Intended Usage 1. Text collection for Image-to-Text retrieval 2. Language model pretraining 3. Document classification ### Licensing Information [CC BY-SA 4.0 international license](https://creativecommons.org/licenses/by-sa/4.0/) ### Citation Information TBA ### Acknowledgement Thanks to: [mwparserfromhell](https://github.com/earwig/mwparserfromhell) [Datasets](https://github.com/huggingface/datasets) [Anserini](https://github.com/castorini/anserini) [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.5326377153396606, -0.5886614918708801, 0.36725303530693054, -0.11899543553590775, -0.12919853627681732, -0.20468974113464355, -0.5011049509048462, -0.2944892942905426, 0.19713672995567322, 0.294734388589859, -0.6793920397758484, -0.8970690369606018, -0.3777366578578949, 0.20203685760498...

null

carlosejimenez/bookcorpus_filtered_len_17_simcse_retrieval_top32__source_tranch_18__target_tranch_10__from_120

carlosejimenez

2023-01-04T02:55:24Z

12

0

null

[ "region:us" ]

2023-01-04T02:55:24Z

2023-01-04T02:55:03.000Z

2023-01-04T02:55:03

Entry not found

[ -0.3227645754814148, -0.22568479180335999, 0.8622264862060547, 0.43461528420448303, -0.52829909324646, 0.7012971639633179, 0.7915720343589783, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104477167129517, 0.5715674161911011, -...

null

carlosejimenez/bookcorpus_filtered_len_17_simcse_retrieval_top32__source_tranch_17__target_tranch_13__from_120

carlosejimenez

2023-01-04T02:55:39Z

12

0

null

[ "region:us" ]

2023-01-04T02:55:39Z

2023-01-04T02:55:25.000Z

2023-01-04T02:55:25

Entry not found

[ -0.3227649927139282, -0.225684255361557, 0.862226128578186, 0.43461498618125916, -0.5282987952232361, 0.7012963891029358, 0.7915717363357544, 0.07618629932403564, 0.7746025919914246, 0.2563219666481018, -0.7852816581726074, -0.2257382869720459, -0.9104480743408203, 0.5715669393539429, -0...

null

carlosejimenez/bookcorpus_filtered_len_17_simcse_retrieval_top32__source_tranch_15__target_tranch_1__from_120

carlosejimenez

2023-01-04T02:56:29Z

12

0

null

[ "region:us" ]

2023-01-04T02:56:29Z

2023-01-04T02:56:13.000Z

2023-01-04T02:56:13

Entry not found

[ -0.3227649927139282, -0.225684255361557, 0.862226128578186, 0.43461498618125916, -0.5282987952232361, 0.7012963891029358, 0.7915717363357544, 0.07618629932403564, 0.7746025919914246, 0.2563219666481018, -0.7852816581726074, -0.2257382869720459, -0.9104480743408203, 0.5715669393539429, -0...

null

carlosejimenez/bookcorpus_filtered_len_17_simcse_retrieval_top32__source_tranch_16__target_tranch_9__from_120

carlosejimenez

2023-01-04T02:56:31Z

12

0

null

[ "region:us" ]

2023-01-04T02:56:31Z

2023-01-04T02:56:15.000Z

2023-01-04T02:56:15

Entry not found

[ -0.3227649927139282, -0.225684255361557, 0.862226128578186, 0.43461498618125916, -0.5282987952232361, 0.7012963891029358, 0.7915717363357544, 0.07618629932403564, 0.7746025919914246, 0.2563219666481018, -0.7852816581726074, -0.2257382869720459, -0.9104480743408203, 0.5715669393539429, -0...

null

Boadiwaa/heart_data

Boadiwaa

2023-01-04T16:18:19Z

12

0

null

[ "region:us" ]

2023-01-04T16:18:19Z

2023-01-04T16:17:26.000Z

2023-01-04T16:17:26

Entry not found

[ -0.3227649927139282, -0.225684255361557, 0.862226128578186, 0.43461498618125916, -0.5282987952232361, 0.7012963891029358, 0.7915717363357544, 0.07618629932403564, 0.7746025919914246, 0.2563219666481018, -0.7852816581726074, -0.2257382869720459, -0.9104480743408203, 0.5715669393539429, -0...

null

Umal-exvc/test-captioned-dataset

Umal-exvc

2023-01-04T16:23:44Z

12

0

null

[ "region:us" ]

2023-01-04T16:23:44Z

2023-01-04T16:23:40.000Z

2023-01-04T16:23:40

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 111187.0 num_examples: 5 download_size: 111705 dataset_size: 111187.0 --- # Dataset Card for "test-captioned-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.6395055055618286, -0.28833919763565063, 0.08057824522256851, 0.3144600987434387, -0.31555768847465515, 0.20413349568843842, 0.22837454080581665, -0.024589017033576965, 0.7099284529685974, 0.5649012327194214, -0.8694941997528076, -0.5757884979248047, -0.5737922787666321, -0.0667070895433...

null

kmewhort/tu-berlin-svgs

kmewhort

2023-01-10T19:20:44Z

12

1

null

[ "region:us" ]

2023-01-10T19:20:44Z

2023-01-04T16:34:42.000Z

2023-01-04T16:34:42

--- dataset_info: features: - name: label dtype: class_label: names: '0': airplane '1': alarm clock '2': angel '3': ant '4': apple '5': arm '6': armchair '7': ashtray '8': axe '9': backpack '10': banana '11': barn '12': baseball bat '13': basket '14': bathtub '15': bear (animal) '16': bed '17': bee '18': beer-mug '19': bell '20': bench '21': bicycle '22': binoculars '23': blimp '24': book '25': bookshelf '26': boomerang '27': bottle opener '28': bowl '29': brain '30': bread '31': bridge '32': bulldozer '33': bus '34': bush '35': butterfly '36': cabinet '37': cactus '38': cake '39': calculator '40': camel '41': camera '42': candle '43': cannon '44': canoe '45': car (sedan) '46': carrot '47': castle '48': cat '49': cell phone '50': chair '51': chandelier '52': church '53': cigarette '54': cloud '55': comb '56': computer monitor '57': computer-mouse '58': couch '59': cow '60': crab '61': crane (machine) '62': crocodile '63': crown '64': cup '65': diamond '66': dog '67': dolphin '68': donut '69': door '70': door handle '71': dragon '72': duck '73': ear '74': elephant '75': envelope '76': eye '77': eyeglasses '78': face '79': fan '80': feather '81': fire hydrant '82': fish '83': flashlight '84': floor lamp '85': flower with stem '86': flying bird '87': flying saucer '88': foot '89': fork '90': frog '91': frying-pan '92': giraffe '93': grapes '94': grenade '95': guitar '96': hamburger '97': hammer '98': hand '99': harp '100': hat '101': head '102': head-phones '103': hedgehog '104': helicopter '105': helmet '106': horse '107': hot air balloon '108': hot-dog '109': hourglass '110': house '111': human-skeleton '112': ice-cream-cone '113': ipod '114': kangaroo '115': key '116': keyboard '117': knife '118': ladder '119': laptop '120': leaf '121': lightbulb '122': lighter '123': lion '124': lobster '125': loudspeaker '126': mailbox '127': megaphone '128': mermaid '129': microphone '130': microscope '131': monkey '132': moon '133': mosquito '134': motorbike '135': mouse (animal) '136': mouth '137': mug '138': mushroom '139': nose '140': octopus '141': owl '142': palm tree '143': panda '144': paper clip '145': parachute '146': parking meter '147': parrot '148': pear '149': pen '150': penguin '151': person sitting '152': person walking '153': piano '154': pickup truck '155': pig '156': pigeon '157': pineapple '158': pipe (for smoking) '159': pizza '160': potted plant '161': power outlet '162': present '163': pretzel '164': pumpkin '165': purse '166': rabbit '167': race car '168': radio '169': rainbow '170': revolver '171': rifle '172': rollerblades '173': rooster '174': sailboat '175': santa claus '176': satellite '177': satellite dish '178': saxophone '179': scissors '180': scorpion '181': screwdriver '182': sea turtle '183': seagull '184': shark '185': sheep '186': ship '187': shoe '188': shovel '189': skateboard '190': skull '191': skyscraper '192': snail '193': snake '194': snowboard '195': snowman '196': socks '197': space shuttle '198': speed-boat '199': spider '200': sponge bob '201': spoon '202': squirrel '203': standing bird '204': stapler '205': strawberry '206': streetlight '207': submarine '208': suitcase '209': sun '210': suv '211': swan '212': sword '213': syringe '214': t-shirt '215': table '216': tablelamp '217': teacup '218': teapot '219': teddy-bear '220': telephone '221': tennis-racket '222': tent '223': tiger '224': tire '225': toilet '226': tomato '227': tooth '228': toothbrush '229': tractor '230': traffic light '231': train '232': tree '233': trombone '234': trousers '235': truck '236': trumpet '237': tv '238': umbrella '239': van '240': vase '241': violin '242': walkie talkie '243': wheel '244': wheelbarrow '245': windmill '246': wine-bottle '247': wineglass '248': wrist-watch '249': zebra - name: svg dtype: string splits: - name: train num_bytes: 82640829.32506625 num_examples: 15999 - name: test num_bytes: 20661498.674933746 num_examples: 4000 download_size: 65748314 dataset_size: 103302328.0 --- # Dataset Card for "tu-berlin-svgs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.7535617351531982, -0.15976256132125854, 0.2313520312309265, 0.2302398830652237, -0.42150554060935974, 0.02562454342842102, 0.45438721776008606, -0.11452003568410873, 0.7514451146125793, 0.2662273645401001, -0.7762050032615662, -0.9845258593559265, -0.6359640955924988, -0.289115220308303...

null

eBoreal/scrat-imgs

eBoreal

2023-01-04T18:19:14Z

12

0

null

[ "region:us" ]

2023-01-04T18:19:14Z

2023-01-04T18:18:37.000Z

2023-01-04T18:18:37

Entry not found

[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

fgomeza17/Sammy

fgomeza17

2023-01-04T19:49:05Z

12

0

null

[ "license:openrail", "region:us" ]

2023-01-04T19:49:05Z

2023-01-04T19:48:29.000Z

2023-01-04T19:48:29

--- license: openrail ---

[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...

null

uumlaut/VanGoghPaintings

uumlaut

2023-01-04T21:18:46Z

12

0

null

[ "region:us" ]

2023-01-04T21:18:46Z

2023-01-04T20:54:52.000Z

2023-01-04T20:54:52

Entry not found

[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

epaolinos/septuagint

epaolinos

2023-01-04T21:31:19Z

12

0

null

[ "region:us" ]

2023-01-04T21:31:19Z

2023-01-04T21:31:08.000Z

2023-01-04T21:31:08

--- dataset_info: features: - name: Book dtype: string - name: Chapter dtype: int64 - name: Verse Number dtype: int64 - name: Verse Text dtype: string - name: Genre dtype: string splits: - name: train num_bytes: 9101054 num_examples: 30568 download_size: 3421032 dataset_size: 9101054 --- # Dataset Card for "septuagint" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.27498766779899597, -0.3355090916156769, 0.11248943954706192, 0.18951325118541718, -0.47797879576683044, 0.08997127413749695, -0.06669862568378448, -0.22201977670192719, 1.1001300811767578, 0.5285810232162476, -0.6512641310691833, -0.7917959690093994, -0.7502202391624451, 0.0205996930599...

null

Amy12zz/dreambooth-hackathon-images

Amy12zz

2023-01-04T22:05:25Z

12

0

null

[ "region:us" ]

2023-01-04T22:05:25Z

2023-01-04T22:05:18.000Z

2023-01-04T22:05:18

--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 1047395.0 num_examples: 4 download_size: 1047434 dataset_size: 1047395.0 --- # Dataset Card for "dreambooth-hackathon-images" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.42646852135658264, -0.13476064801216125, 0.23689782619476318, 0.23072221875190735, -0.40631160140037537, 0.3039889335632324, 0.32873836159706116, -0.32934942841529846, 1.1760584115982056, 0.36833885312080383, -0.8234314322471619, -0.6366724967956543, -0.3446301519870758, -0.267739206552...

null

RobertLucian/avatar-10k

RobertLucian

2023-01-04T22:15:52Z

12

0

null

[ "license:gpl-3.0", "region:us" ]

2023-01-04T22:15:52Z

2023-01-04T22:12:33.000Z

2023-01-04T22:12:33

--- license: gpl-3.0 dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 771570808.685 num_examples: 10689 download_size: 646236257 dataset_size: 771570808.685 ---

[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...

null

ivelin/processed_sroie_donut_dataset_json2token

ivelin

2023-01-05T00:19:38Z

12

0

null

[ "region:us" ]

2023-01-05T00:19:38Z

2023-01-05T00:19:04.000Z

2023-01-05T00:19:04

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 586245601.0 num_examples: 626 download_size: 577293738 dataset_size: 586245601.0 --- # Dataset Card for "processed_sroie_donut_dataset_json2token" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ 0.0009039849974215031, -0.16903114318847656, 0.1913672536611557, 0.23318858444690704, -0.2850587069988251, 0.3050466775894165, 0.11789344996213913, -0.08738389611244202, 0.8015451431274414, 0.7641820907592773, -0.8002272844314575, -0.5041705965995789, -0.8640604019165039, -0.14679706096649...

null

Atallahw/clippy

Atallahw

2023-01-08T02:24:50Z

12

0

null

[ "region:us" ]

2023-01-08T02:24:50Z

2023-01-05T03:48:19.000Z

2023-01-05T03:48:19

Entry not found

[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -...

null

inkoziev/paraphrases

inkoziev

2023-01-14T13:37:24Z

12

2

null

[ "task_categories:sentence-similarity", "task_categories:text2text-generation", "task_ids:semantic-similarity-classification", "language_creators:expert-generated", "language:ru", "license:cc-by-nc-4.0", "region:us" ]

2023-01-14T13:37:24Z

2023-01-05T09:08:02.000Z

2023-01-05T09:08:02

--- license: cc-by-nc-4.0 language: - ru language_creators: - expert-generated task_categories: - sentence-similarity - text2text-generation task_ids: - semantic-similarity-classification --- # Датасет перефразировок коротких фраз (читчат+поэзия) В датасете содержатся правильные и некорректные перефразировки коротких диалоговых реплик ([проект диалоговой системы](https://github.com/Koziev/chatbot)) и фрагментов стихов ([проект генеративной поэзии](https://github.com/Koziev/verslibre)). Датасет представляет из себя список сэмплов-кортежей. Каждый сэмпл состоит из двух списков: ```paraphrases``` - примеры правильных перефразировок ```distractors``` - примеры неправильных перефразировок Датасет используется для создания моделей [детектора перефразировок sbert_synonymy](https://huggingface.co/inkoziev/sbert_synonymy) и [генеративного поэтического перефразировщика](https://huggingface.co/inkoziev/paraphraser). ## Disclaimer В датасете целенаправленно допускалась неконсервативность семантики перефразировок в определенных пределах. К примеру, правильными перефразировками считаются пары "_Помолчи_" и "_Дружище, не говори ни слова!_". Так как перефразировщик используется в проекте генеративной поэзии для создания датасетов, в нем есть некоторое количество метафоричных и достаточно вольных перефразировок. Эти особенности датасета могут сделать невозможным использование датасета и моделей на его основе в Ваших проектах. ## Другие датасеты перефразировок При обучении моделей вы можете совмещать этот датасет с данными из других датасетов перефразировок, например [tapaco](https://huggingface.co/datasets/tapaco).

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null

Joanne/katz1980_set_A

Joanne

2023-01-05T11:36:13Z

12

0

null

[ "region:us" ]

2023-01-05T11:36:13Z

2023-01-05T11:36:04.000Z

2023-01-05T11:36:04

Entry not found

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null

Joanne/katz1980_set_B

Joanne

2023-01-05T11:36:33Z

12

0

null

[ "region:us" ]

2023-01-05T11:36:33Z

2023-01-05T11:36:24.000Z

2023-01-05T11:36:24

Entry not found

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null

Someman/nepali-flag

Someman

2023-01-05T11:45:55Z

12

0

null

[ "license:mit", "region:us" ]

2023-01-05T11:45:55Z

2023-01-05T11:44:53.000Z

2023-01-05T11:44:53

--- license: mit ---

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null

Joanne/cardillo_2010_2017_lexical

Joanne

2023-01-05T11:49:15Z

12

0

null

[ "region:us" ]

2023-01-05T11:49:15Z

2023-01-05T11:49:05.000Z

2023-01-05T11:49:05

Entry not found

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null

Joanne/cardillo_2010_2017_random

Joanne

2023-01-05T11:49:26Z

12

0

null

[ "region:us" ]

2023-01-05T11:49:26Z

2023-01-05T11:49:17.000Z

2023-01-05T11:49:17

Entry not found

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null

Joanne/katz1980_random

Joanne

2023-01-05T11:50:28Z

12

0

null

[ "region:us" ]

2023-01-05T11:50:28Z

2023-01-05T11:50:18.000Z

2023-01-05T11:50:18

Entry not found

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null

Joanne/katz1980_lexical

Joanne

2023-01-05T11:51:50Z

12

0

null

[ "region:us" ]

2023-01-05T11:51:50Z

2023-01-05T11:51:40.000Z

2023-01-05T11:51:40

Entry not found

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null

vencortex/News

vencortex

2023-01-05T14:14:16Z

12

0

null

[ "region:us" ]

2023-01-05T14:14:16Z

2023-01-05T14:13:58.000Z

2023-01-05T14:13:58

--- dataset_info: features: - name: symbol dtype: string - name: publishedDate dtype: string - name: title dtype: string - name: image dtype: string - name: site dtype: string - name: text dtype: string - name: url dtype: string - name: type dtype: string splits: - name: train num_bytes: 834852911 num_examples: 1495869 download_size: 170603751 dataset_size: 834852911 --- # Dataset Card for "News" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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null

pyakymenko/test_6k

pyakymenko

2023-01-05T14:50:56Z

12

0

null

[ "region:us" ]

2023-01-05T14:50:56Z

2023-01-05T14:33:32.000Z

2023-01-05T14:33:32

--- dataset_info: features: - name: audio dtype: audio - name: sentence dtype: string splits: - name: train num_bytes: 475682224.444 num_examples: 6661 download_size: 473720429 dataset_size: 475682224.444 --- # Dataset Card for "test_6k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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null

bstrai/subj_multi

bstrai

2023-01-17T17:17:15Z

12

0

null

[ "region:us" ]

2023-01-17T17:17:15Z

2023-01-05T16:35:39.000Z

2023-01-05T16:35:39

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': objective '1': subjective - name: language dtype: string splits: - name: test num_bytes: 2914488 num_examples: 16000 - name: train num_bytes: 11518066 num_examples: 64000 download_size: 8870704 dataset_size: 14432554 --- # Dataset Card for "subj_multi" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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null

fuyulinh04/dataset_glstxt

fuyulinh04

2023-01-05T23:21:14Z

12

0

null

[ "region:us" ]

2023-01-05T23:21:14Z

2023-01-05T23:20:43.000Z

2023-01-05T23:20:43

--- dataset_info: features: - name: gloss dtype: string - name: text dtype: string splits: - name: train num_bytes: 11227076.8 num_examples: 73696 - name: test num_bytes: 2806769.2 num_examples: 18424 download_size: 8513566 dataset_size: 14033846.0 --- # Dataset Card for "dataset_glstxt" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.37770992517471313, -0.14748430252075195, 0.3798990845680237, 0.11246412247419357, -0.44321903586387634, 0.18284271657466888, 0.42760807275772095, -0.247656911611557, 0.7942173480987549, 0.34282249212265015, -0.8290347456932068, -0.6926448941230774, -0.6878188252449036, -0.43358078598976...

null

svjack/GLM-Open-Dialogue-Chinese-samples

svjack

2023-01-06T02:01:30Z

12

3

null

[ "region:us" ]

2023-01-06T02:01:30Z

2023-01-06T01:52:11.000Z

2023-01-06T01:52:11

Entry not found

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null

kxly/princess_tutu

kxly

2023-01-06T02:55:47Z

12

3

null

[ "language:en", "license:creativeml-openrail-m", "stable-diffusion", "text-to-image", "image-to-image", "region:us" ]

2023-01-06T02:55:47Z

2023-01-06T02:00:33.000Z

2023-01-06T02:00:33

--- language: - en license: creativeml-openrail-m thumbnail: >- https://huggingface.co/datasets/kxly/princess_tutu/blob/main/princess_tutu_showcase.png tags: - stable-diffusion - text-to-image - image-to-image inference: false pretty_name: Princess Tutu --- # Character Embedding - Princess Tutu/Ahiru ![princess_tutu_showcase.png](https://s3.amazonaws.com/moonup/production/uploads/1672973706523-6366fabccbf2cf32918c2830.png) ## Usage To use an embedding, download the .pt file and place it in "\stable-diffusion-webui\embeddings". In your prompt, write ```"princess_tutu-6500"```. ## License This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content 2. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)

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null

sanjin7/embedding_dataset_distilbert_base_uncased_ad_subwords

sanjin7

2023-01-16T11:12:24Z

12

0

null

[ "region:us" ]

2023-01-16T11:12:24Z

2023-01-08T07:54:44.000Z

2023-01-08T07:54:44

--- dataset_info: features: - name: ad_id dtype: int64 - name: shop_id dtype: int64 - name: account_id dtype: int64 - name: mean_embedding sequence: float32 - name: cls_embedding sequence: float32 splits: - name: test num_bytes: 5725152 num_examples: 927 - name: train num_bytes: 43769312 num_examples: 7087 - name: val num_bytes: 7726176 num_examples: 1251 download_size: 69324552 dataset_size: 57220640 --- # Dataset Card for "embedding_dataset_distilbert_base_uncased_ad_subwords" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.5727691650390625, -0.5441336631774902, 0.10706023126840591, 0.1042877808213234, -0.5628413558006287, 0.31765782833099365, 0.051310066133737564, 0.3212378919124603, 0.8960884213447571, 0.24476073682308197, -0.6493831276893616, -1.0216084718704224, -0.9090348482131958, -0.2815321385860443...

null

muellerzr/github-pr-history

muellerzr

2023-01-08T15:29:01Z

12

0

null

[ "size_categories:n<1K", "language:en", "license:mit", "region:us" ]

2023-01-08T15:29:01Z

2023-01-08T13:34:38.000Z

2023-01-08T13:34:38

--- license: mit language: - en pretty_name: Github Pull Request History size_categories: - n<1K --- # What is this dataset? This dataset is a collection of Pull Requests **that contain comments** from the [Accelerate](https://github.com/huggingface/accelerate). It contains the full contextual comments as well as code suggestions that exist inside of a code review

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null

Wiebke/newtrain_reddit_gab_bert-base-casedepoch3

Wiebke

2023-01-08T13:40:03Z

12

0

null

[ "region:us" ]

2023-01-08T13:40:03Z

2023-01-08T13:37:45.000Z

2023-01-08T13:37:45

Entry not found

[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

Zappandy/recipe_nlg

Zappandy

2023-01-09T14:26:39Z

12

3

null

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

2023-01-09T14:26:39Z

2023-01-08T13:41:47.000Z

2023-01-08T13:41:47

--- license: apache-2.0 ---

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null

Wiebke/newtrain_reddit_gab_bert-base-casedepoch3_equal

Wiebke

2023-01-08T13:46:53Z

12

0

null

[ "region:us" ]

2023-01-08T13:46:53Z

2023-01-08T13:45:28.000Z

2023-01-08T13:45:28

Entry not found

[ -0.3227647542953491, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965083122253, 0.7915717959403992, 0.07618629932403564, 0.7746022343635559, 0.2563222348690033, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

lvwerra/changeit

lvwerra

2023-01-08T15:43:02Z

12

0

null

[ "region:us" ]

2023-01-08T15:43:02Z

2023-01-08T14:49:08.000Z

2023-01-08T14:49:08

Entry not found

[ -0.3227647542953491, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965083122253, 0.7915717959403992, 0.07618629932403564, 0.7746022343635559, 0.2563222348690033, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

michaelb1225/open-cm

michaelb1225

2023-01-08T15:23:21Z

12

0

null

[ "region:us" ]

2023-01-08T15:23:21Z

2023-01-08T15:09:26.000Z

2023-01-08T15:09:26

--- dataset_info: features: - name: audio dtype: audio splits: - name: train num_bytes: 6129427551.0 num_examples: 671 download_size: 6071742068 dataset_size: 6129427551.0 --- # Dataset Card for "open-cm" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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null

vienduong88/Neyun

vienduong88

2023-01-08T16:54:10Z

12

0

null

[ "license:openrail", "region:us" ]

2023-01-08T16:54:10Z

2023-01-08T16:47:18.000Z

2023-01-08T16:47:18

--- license: openrail ---

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null

vishnun/SpellGram

vishnun

2023-01-09T13:43:11Z

12

0

null

[ "task_categories:text2text-generation", "size_categories:10K<n<100K", "language:en", "license:mit", "NLP", "Text2Text", "region:us" ]

2023-01-09T13:43:11Z

2023-01-09T13:39:23.000Z

2023-01-09T13:39:23

--- license: mit task_categories: - text2text-generation language: - en tags: - NLP - Text2Text pretty_name: Dataset consisting of grammatical and spelling errors size_categories: - 10K<n<100K --- # SpellGram ## Dataset consisting of grammatical and spelling errors - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [train.csv] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

[ -0.031502239406108856, -0.45691996812820435, 0.23213693499565125, 0.3967456519603729, 0.1335676610469818, 0.2275220900774002, -0.22349920868873596, -0.07148230820894241, 0.4629785418510437, 0.7333163619041443, -0.7593204379081726, -1.2123510837554932, -0.8407378792762756, 0.524325966835022...

null

swww/covidw

swww

2023-01-17T12:15:45Z

12

0

null

[ "region:us" ]

2023-01-17T12:15:45Z

2023-01-17T10:18:44.000Z

2023-01-17T10:18:44

Entry not found

[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

paoloitaliani/tms_sentence

paoloitaliani

2023-01-18T15:16:04Z

12

0

null

[ "region:us" ]

2023-01-18T15:16:04Z

2023-01-17T15:13:05.000Z

2023-01-17T15:13:05

Entry not found

[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

marianna13/random_dataset

marianna13

2023-02-25T14:35:53Z

12

1

null

[ "region:us" ]

2023-02-25T14:35:53Z

2023-01-17T15:44:41.000Z

2023-01-17T15:44:41

Entry not found

[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

abelc/italo-diffusion-256

abelc

2023-01-17T17:56:00Z

12

0

null

[ "region:us" ]

2023-01-17T17:56:00Z

2023-01-17T17:54:34.000Z

2023-01-17T17:54:34

--- dataset_info: features: - name: image dtype: image - name: audio_file dtype: string - name: slice dtype: int16 splits: - name: train num_bytes: 29319809.0 num_examples: 658 download_size: 29297971 dataset_size: 29319809.0 --- # Dataset Card for "italo-diffusion-256" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.7658651471138, -0.15019221603870392, 0.3280940651893616, 0.5150654315948486, -0.2189117968082428, 0.07714685797691345, 0.2775276303291321, 0.08862278610467911, 0.8335604071617126, 0.30025607347488403, -0.7314122319221497, -0.8444968461990356, -0.6289349794387817, -0.5386227965354919, ...

null

baffo32/somegitdata

baffo32

2023-01-17T23:01:33Z

12

0

null

[ "region:us" ]

2023-01-17T23:01:33Z

2023-01-17T20:21:56.000Z

2023-01-17T20:21:56

Entry not found

[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

carexl8/telegram_de_ru

carexl8

2023-04-25T22:04:20Z

12

0

null

[ "region:us" ]

2023-04-25T22:04:20Z

2023-01-17T20:29:31.000Z

2023-01-17T20:29:31

--- dataset_info: features: - name: id dtype: string - name: name dtype: string - name: time dtype: string - name: text dtype: string - name: tokens sequence: string - name: language tags sequence: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 5938949 num_examples: 10191 download_size: 1869587 dataset_size: 5938949 --- # Dataset Card for "telegram_de_ru" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.31594952940940857, -0.4754468500614166, 0.04658455401659012, 0.5260713696479797, -0.46412530541419983, 0.019661791622638702, 0.15459884703159332, -0.25580501556396484, 0.9447314143180847, 0.4289696514606476, -0.8484136462211609, -0.8726778626441956, -0.7160326242446899, -0.1985431164503...

null

Tristan/olm-wikipedia-20221220-1-percent

Tristan

2023-01-17T20:47:18Z

12

0

null

[ "region:us" ]

2023-01-17T20:47:18Z

2023-01-17T20:47:06.000Z

2023-01-17T20:47:06

--- dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 209366020.9708762 num_examples: 65879 download_size: 123017868 dataset_size: 209366020.9708762 --- # Dataset Card for "olm-wikipedia-20221220-1-percent" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.8340270519256592, -0.43116384744644165, 0.271524578332901, 0.10575804114341736, -0.06329254060983658, -0.38436540961265564, 0.22831933200359344, 0.09554003179073334, 0.7661848068237305, 0.5482386946678162, -0.9379180073738098, -0.922636866569519, -0.3649454414844513, -0.3293671309947967...

null

yuvalkirstain/beautiful_interesting_spectacular_photo_dog_25000

yuvalkirstain

2023-01-18T06:37:24Z

12

0

null

[ "region:us" ]

2023-01-18T06:37:24Z

2023-01-18T06:36:25.000Z

2023-01-18T06:36:25

--- dataset_info: features: - name: image dtype: image - name: text dtype: string - name: width dtype: int64 - name: height dtype: int64 - name: pclean dtype: float64 splits: - name: train num_bytes: 361773346.0 num_examples: 504 download_size: 361776700 dataset_size: 361773346.0 --- # Dataset Card for "beautiful_interesting_spectacular_photo_dog_25000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.8284481167793274, -0.2143736332654953, 0.1834106147289276, 0.38587138056755066, -0.43337753415107727, -0.19882304966449738, 0.3403880298137665, -0.28982123732566833, 0.680835485458374, 0.28281107544898987, -0.6531713604927063, -0.8225438594818115, -0.5930798053741455, -0.288455009460449...

null

Joe02/obui

Joe02

2023-03-25T00:32:14Z

12

0

null

[ "license:other", "region:us" ]

2023-03-25T00:32:14Z

2023-01-18T07:35:14.000Z

2023-01-18T07:35:14

--- license: other ---

[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...

null

BeardedJohn/ubb-endava-conll-assistant-ner-only-misc-v2

BeardedJohn

2023-01-18T08:53:56Z

12

0

null

[ "region:us" ]

2023-01-18T08:53:56Z

2023-01-18T08:22:55.000Z

2023-01-18T08:22:55

Entry not found

[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

reshinthadith/dfg_augmented_mbpp

reshinthadith

2023-01-18T09:27:02Z

12

0

null

[ "region:us" ]

2023-01-18T09:27:02Z

2023-01-18T09:26:49.000Z

2023-01-18T09:26:49

--- dataset_info: features: - name: prompt dtype: string - name: output dtype: string - name: code dtype: string splits: - name: train num_bytes: 32138 num_examples: 95 download_size: 17897 dataset_size: 32138 --- # Dataset Card for "dfg_augmented_mbpp" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.7902991771697998, -0.390556663274765, 0.21485590934753418, 0.42076602578163147, -0.12265515327453613, 0.2076321542263031, 0.39486163854599, -0.3015674650669098, 0.8437708020210266, 0.5149201154708862, -0.6605716347694397, -0.5967594981193542, -0.7949752807617188, -0.20612865686416626, ...

null